After reading chapter-6 from the attached text book,  type a two page paper regarding what you thought was the most important concepts or methods or terms that you felt was worthy of your understanding.  

Define and describe what you thought was worthy of your understanding in half a page, and then explain why you felt it was important, how you will use it, and/or how important it is in project management processes and methodologies. 

no cheatingNo PlagiarismAPA format must

Project Management
Processes, Methodologies, and Economics
Third Edition
Avraham Shtub
Faculty of Industrial Engineering and Management
The Technion–Israel Institute of Technology
Moshe Rosenwein
Department of Industrial Engineering and Operations Research
Columbia University
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Library of Congress Cataloging-in-Publication Data
Names: Shtub, Avraham, author. | Rosenwein, Moshe, author.
Title: Project management : processes, methodologies, and economics /
Avraham Shtub, Faculty of Industrial Engineering and Management, The
Technion-Israel Institute of Technology, Moshe Rosenwein, Department of
Industrial Engineering and Operations Research, Columbia University.
Other titles: Project management (Boston, Mass.)
Description: 3E. | Pearson | Includes bibliographical references and index.
Identifiers: LCCN 2016030485 | ISBN 9780134478661 (pbk.)
Subjects: LCSH: Engineering—Management. | Project management.
Classification: LCC TA190 .S583 2017 | DDC 658.4/04—dc23 LC record
available at https://lccn.loc.gov/2016030485
10 9 8 7 6 5 4 3 2 1

http://www.pearsoned.com/permissions/

https://lccn.loc.gov/2016030485

ISBN-10: 0-13-447866-5
ISBN-13: 978-0-13-447866-1

This book is dedicated to my grandchildren Zoey, Danielle, Adam, and Noam
Shtub.
This book is dedicated to my wife, Debbie; my three children, David,
Hannah, and Benjamin; my late parents, Zvi and Blanche Rosenwein; and my
in-laws, Dr. Herman and Irma Kaplan.

Contents
1. Nomenclature xv
2. Preface xvii
3. What’s New in this Edition xxi
4. About the Authors xxiii
1. 1  Introduction 1
1. 1.1 Nature of Project Management 1
2. 1.2 Relationship Between Projects and Other Production Systems 2
3. 1.3 Characteristics of Projects 4
1. 1.3.1 Definitions and Issues 5
2. 1.3.2 Risk and Uncertainty 7
3. 1.3.3 Phases of a Project 9
4. 1.3.4 Organizing for a Project 11
4. 1.4 Project Manager 14
1. 1.4.1 Basic Functions 15
2. 1.4.2 Characteristics of Effective Project Managers 16
5. 1.5 Components, Concepts, and Terminology 16
6. 1.6 Movement to Project-Based Work 24
7. 1.7 Life Cycle of a Project: Strategic and Tactical Issues 26

8. 1.8 Factors that Affect the Success of a Project 29
9. 1.9 About the book: Purpose and Structure 31
1. Team Project 35
2. Discussion Questions 38
3. Exercises 39
4. Bibliography 41
5. Appendix 1A: Engineering Versus Management 43
6. 1A.1 Nature of Management 43
7. 1A.2 Differences between Engineering and Management 43
8. 1A.3 Transition from Engineer to Manager 45
9. Additional References 45
2. 2  Process Approach to Project Management 47
1. 2.1 Introduction 47
1. 2.1.1 Life-Cycle Models 48
2. 2.1.2 Example of a Project Life Cycle 51
3. 2.1.3 Application of the Waterfall Model for Software
Development 51
2. 2.2 Project Management Processes 53
1. 2.2.1  Process Design 53
2. 2.2.2 PMBOK and Processes in the Project Life Cycle 54
3. 2.3 Project Integration Management 54

1. 2.3.1  Accompanying Processes 54
2. 2.3.2  Description 56
4. 2.4 Project Scope Management 60
1. 2.4.1  Accompanying Processes 60
2. 2.4.2  Description 60
5. 2.5 Project Time Management 61
1. 2.5.1  Accompanying Processes 61
2. 2.5.2  Description 62
6. 2.6 Project Cost Management 63
1. 2.6.1  Accompanying Processes 63
2. 2.6.2  Description 64
7. 2.7 Project Quality Management 64
1. 2.7.1  Accompanying Processes 64
2. 2.7.2  Description 65
8. 2.8 Project Human Resource Management 66
1. 2.8.1  Accompanying Processes 66
2. 2.8.2  Description 66
9. 2.9 Project Communications Management 67
1. 2.9.1  Accompanying Processes 67
2. 2.9.2  Description 68

10. 2.10 Project Risk Management 69
1. 2.10.1  Accompanying Processes 69
2. 2.10.2  Description 70
11. 2.11 Project Procurement Management 71
1. 2.11.1  Accompanying Processes 71
2. 2.11.2  Description 72
12. 2.12 Project Stakeholders Management 74
1. 2.12.1  Accompanying Processes 74
2. 2.12.2  Description 75
13. 2.13 The Learning Organization and Continuous Improvement 76
1. 2.13.1  Individual and Organizational Learning 76
2. 2.13.2  Workflow and Process Design as the Basis of
Learning 76
1. Team Project 77
2. Discussion Questions 77
3. Exercises 78
4. Bibliography 78
3. 3 Engineering Economic Analysis 81
1. 3.1 Introduction 81
1. 3.1.1 Need for Economic Analysis 82
2. 3.1.2 Time Value of Money 82

3. 3.1.3 Discount Rate, Interest Rate, and Minimum Acceptable
Rate of Return 83
2. 3.2 Compound Interest Formulas 84
1. 3.2.1 Present Worth, Future Worth, Uniform Series, and
Gradient Series 86
2. 3.2.2 Nominal and Effective Interest Rates 89
3. 3.2.3 Inflation 90
4. 3.2.4 Treatment of Risk 92
3. 3.3 Comparison of Alternatives 92
1. 3.3.1 Defining Investment Alternatives 94
2. 3.3.2 Steps in the Analysis 96
4. 3.4 Equivalent Worth Methods 97
1. 3.4.1 Present Worth Method 97
2. 3.4.2 Annual Worth Method 98
3. 3.4.3 Future Worth Method 99
4. 3.4.4 Discussion of Present Worth, Annual Worth and Future
Worth Methods 101
5. 3.4.5 Internal Rate of Return Method 102
6. 3.4.6 Payback Period Method 109
5. 3.5 Sensitivity and Breakeven Analysis 111
6. 3.6 Effect of Tax and Depreciation on Investment Decisions 114

1. 3.6.1 Capital Expansion Decision 116
2. 3.6.2 Replacement Decision 118
3. 3.6.3 Make-or-Buy Decision 123
4. 3.6.4 Lease-or-Buy Decision 124
7. 3.7 Utility Theory 125
1. 3.7.1 Expected Utility Maximization 126
2. 3.7.2 Bernoulli’s Principle 128
3. 3.7.3 Constructing the Utility Function 129
4. 3.7.4 Evaluating Alternatives 133
5. 3.7.5 Characteristics of the Utility Function 135
1. Team Project 137
2. Discussion Questions 141
3. Exercises 142
4. Bibliography 152
4. 4 Life-Cycle Costing 155
1. 4.1 Need for Life-Cycle Cost Analysis 155
2. 4.2 Uncertainties in Life-Cycle Cost Models 158
3. 4.3 Classification of Cost Components 161
4. 4.4 Developing the LCC Model 168
5. 4.5 Using the Life-Cycle Cost Model 175

1. Team Project 176
2. Discussion Questions 176
3. Exercises 177
4. Bibliography 179
5. 5 Portfolio Management—Project Screening and Selection 181
1. 5.1 Components of the Evaluation Process 181
2. 5.2 Dynamics of Project Selection 183
3. 5.3 Checklists and Scoring Models 184
4. 5.4 Benefit-Cost Analysis 187
1. 5.4.1 Step-By-Step Approach 193
2. 5.4.2 Using the Methodology 193
3. 5.4.3 Classes of Benefits and Costs 193
4. 5.4.4 Shortcomings of the Benefit-Cost Methodology 194
5. 5.5 Cost-Effectiveness Analysis 195
6. 5.6 Issues Related to Risk 198
1. 5.6.1 Accepting and Managing Risk 200
2. 5.6.2 Coping with Uncertainty 201
3. 5.6.3 Non-Probabilistic Evaluation Methods when
Uncertainty Is Present 202
4. 5.6.4 Risk-Benefit Analysis 207
5. 5.6.5 Limits of Risk Analysis 210

7. 5.7 Decision Trees 210
1. 5.7.1 Decision Tree Steps 217
2. 5.7.2 Basic Principles of Diagramming 218
3. 5.7.3 Use of Statistics to Determine the Value of More
Information 219
4. 5.7.4 Discussion and Assessment 222
8. 5.8 Real Options 223
1. 5.8.1 Drivers of Value 223
2. 5.8.2 Relationship to Portfolio Management 224
1. Team Project 225
2. Discussion Questions 228
3. Exercises 229
4. Bibliography 237
5. Appendix 5A: Bayes’ Theorem for Discrete Outcomes 239
6. 6 Multiple-Criteria Methods for Evaluation and Group Decision
Making 241
1. 6.1 Introduction 241
2. 6.2 Framework for Evaluation and Selection 242
1. 6.2.1 Objectives and Attributes 242
2. 6.2.2 Aggregating Objectives Into a Value Model 244
3. 6.3 Multiattribute Utility Theory 244

1. 6.3.1 Violations of Multiattribute Utility Theory 249
4. 6.4 Analytic Hierarchy Process 254
1. 6.4.1 Determining Local Priorities 255
2. 6.4.2 Checking for Consistency 260
3. 6.4.3 Determining Global Priorities 261
5. 6.5 Group Decision Making 262
1. 6.5.1  Group Composition 263
2. 6.5.2  Running the Decision-Making Session 264
3. 6.5.3  Implementing the Results 265
4. 6.5.4  Group Decision Support Systems 265
1. Team Project 267
2. Discussion Questions 267
3. Exercises 268
4. Bibliography 271
5. Appendix 6A: Comparison of Multiattribute Utility Theory with
the AHP: Case Study 275
6. 6A.1 Introduction and Background 275
7. 6A.2 The Cargo Handling Problem 276
1. 6A.2.1 System Objectives 276
2. 6A.2.2 Possibility of Commercial Procurement 277
3. 6A.2.3 Alternative Approaches 277

8. 6A.3 Analytic Hierarchy Process 279
1. 6A.3.1 Definition of Attributes 280
2. 6A.3.2 Analytic Hierarchy Process Computations 281
3. 6A.3.3 Data Collection and Results for AHP 283
4. 6A.3.4 Discussion of Analytic Hierarchy Process and Results
284
9. 6A.4 Multiattribute Utility Theory 286
1. 6A.4.1 Data Collection and Results for Multiattribute Utility
Theory 286
2. 6A.4.2 Discussion of Multiattribute Utility Theory and
Results 290
10. 6A.5 Additional Observations 290
11. 6A.6 Conclusions for the Case Study 291
12. References 291
7. 7 Scope and Organizational Structure of a Project 293
1. 7.1 Introduction 293
2. 7.2 Organizational Structures 294
1. 7.2.1 Functional Organization 295
2. 7.2.2 Project Organization 297
3. 7.2.3 Product Organization 298
4. 7.2.4 Customer Organization 298

5. 7.2.5 Territorial Organization 299
6. 7.2.6 The Matrix Organization 299
7. 7.2.7 Criteria for Selecting an Organizational Structure 302
3. 7.3 Organizational Breakdown Structure of Projects 303
1. 7.3.1 Factors in Selecting a Structure 304
2. 7.3.2 The Project Manager 305
3. 7.3.3 Project Office 309
4. 7.4 Project Scope 312
1. 7.4.1 Work Breakdown Structure 313
2. 7.4.2 Work Package Design 320
5. 7.5 Combining the Organizational and Work Breakdown Structures
322
1. 7.5.1 Linear Responsibility Chart 323
6. 7.6 Management of Human Resources 324
1. 7.6.1 Developing and Managing the Team 325
2. 7.6.2 Encouraging Creativity and Innovation 329
3. 7.6.3 Leadership, Authority, and Responsibility 331
4. 7.6.4 Ethical and Legal Aspects of Project Management 334
1. Team Project 335
2. Discussion Questions 336
3. Exercises 336

4. Bibliography 338
8. 8 Management of Product, Process, and Support Design 341
1. 8.1 Design of Products, Services, and Systems 341
1. 8.1.1 Principles of Good Design 342
2. 8.1.2 Management of Technology and Design in Projects 344
2. 8.2 Project Manager’s Role 345
3. 8.3 Importance of Time and the Use of Teams 346
1. 8.3.1 Concurrent Engineering and Time-Based Competition
347
2. 8.3.2 Time Management 349
3. 8.3.3 Guideposts for Success 352
4. 8.3.4 Industrial Experience 354
5. 8.3.5 Unresolved Issues 355
4. 8.4 Supporting Tools 355
1. 8.4.1 Quality Function Deployment 355
2. 8.4.2 Configuration Selection 358
3. 8.4.3 Configuration Management 361
4. 8.4.4 Risk Management 365
5. 8.5 Quality Management 370
1. 8.5.1 Philosophy and Methods 371
2. 8.5.2 Importance of Quality in Design 382

3. 8.5.3 Quality Planning 383
4. 8.5.4 Quality Assurance 383
5. 8.5.5 Quality Control 384
6. 8.5.6 Cost of Quality 385
1. Team Project 387
2. Discussion Questions 388
3. Exercises 389
4. Bibliography 389
9. 9 Project Scheduling 395
1. 9.1 Introduction 395
1. 9.1.1 Key Milestones 398
2. 9.1.2 Network Techniques 399
2. 9.2 Estimating the Duration of Project Activities 401
1. 9.2.1 Stochastic Approach 402
2. 9.2.2 Deterministic Approach 406
3. 9.2.3 Modular Technique 406
4. 9.2.4 Benchmark Job Technique 407
5. 9.2.5 Parametric Technique 407
3. 9.3 Effect of Learning 412
4. 9.4 Precedence Relations Among Activities 414

5. 9.5 Gantt Chart 416
6. 9.6 Activity-On-Arrow Network Approach for CPM Analysis 420
1. 9.6.1 Calculating Event Times and Critical Path 428
2. 9.6.2 Calculating Activity Start and Finish Times 431
3. 9.6.3 Calculating Slacks 432
7. 9.7 Activity-On-Node Network Approach for CPM Analysis 433
1. 9.7.1 Calculating Early Start and Early Finish Times of
Activities 434
2. 9.7.2 Calculating Late Start and Late Finish Times of
Activities 434
8. 9.8 Precedence Diagramming with Lead–Lag Relationships 436
9. 9.9 Linear Programming Approach for CPM Analysis 442
10. 9.10 Aggregating Activities in the Network 443
1. 9.10.1 Hammock Activities 443
2. 9.10.2 Milestones 444
11. 9.11 Dealing with Uncertainty 445
1. 9.11.1 Simulation Approach 445
2. 9.11.2 Pert and Extensions 447
12. 9.12 Critique of Pert and CPM Assumptions 454
13. 9.13 Critical Chain Process 455
14. 9.14 Scheduling Conflicts 457

1. Team Project 458
2. Discussion Questions 459
3. Exercises 460
4. Bibliography 467
5. Appendix 9A: Least-Squares Regression Analysis 471
6. Appendix 9B: Learning Curve Tables 473
7. Appendix 9C: Normal Distribution Function 476
10. 10 Resource Management 477
1. 10.1 Effect of Resources on Project Planning 477
2. 10.2 Classification of Resources Used in Projects 478
3. 10.3 Resource Leveling Subject to Project Due-Date Constraints
481
4. 10.4 Resource Allocation Subject to Resource Availability
Constraints 487
5. 10.5 Priority Rules for Resource Allocation 491
6. 10.6 Critical Chain: Project Management by Constraints 496
7. 10.7 Mathematical Models for Resource Allocation 496
8. 10.8 Projects Performed in Parallel 499
1. Team Project 500
2. Discussion Questions 500
3. Exercises 501

4. Bibliography 506
11. 11 Project Budget 509
1. 11.1 Introduction 509
2. 11.2 Project Budget and Organizational Goals 511
3. 11.3 Preparing the Budget 513
1. 11.3.1 Top-Down Budgeting 514
2. 11.3.2 Bottom-Up Budgeting 514
3. 11.3.3 Iterative Budgeting 515
4. 11.4 Techniques for Managing the Project Budget 516
1. 11.4.1 Slack Management 516
2. 11.4.2 Crashing 520
5. 11.5 Presenting the Budget 527
6. 11.6 Project Execution: Consuming the Budget 529
7. 11.7 The Budgeting Process: Concluding Remarks 530
1. Team Project 531
2. Discussion Questions 531
3. Exercises 532
4. Bibliography 537
5. Appendix 11A: Time–Cost Tradeoff with Excel 539
12. 12 Project Control 545

1. 12.1 Introduction 545
2. 12.2 Common Forms of Project Control 548
3. 12.3 Integrating the OBS and WBS with Cost and Schedule Control
551
1. 12.3.1 Hierarchical Structures 552
2. 12.3.2 Earned Value Approach 556
4. 12.4 Reporting Progress 565
5. 12.5 Updating Cost and Schedule Estimates 566
6. 12.6 Technological Control: Quality and Configuration 569
7. 12.7 Line of Balance 569
8. 12.8 Overhead Control 574
1. Team Project 576
2. Discussion Questions 577
3. Exercises 577
4. Bibliography 580
13. Appendix 12A: Example of a Work Breakdown Structure 581
14. Appendix 12B:  Department of Energy Cost/Schedule Control Systems
Criteria 583
15. 13 Research and Development Projects 587
1. 13.1 Introduction 587
2. 13.2 New Product Development 589

1. 13.2.1 Evaluation and Assessment of Innovations 589
2. 13.2.2 Changing Expectations 593
3. 13.2.3 Technology Leapfrogging 593
4. 13.2.4 Standards 594
5. 13.2.5 Cost and Time Overruns 595
3. 13.3 Managing Technology 595
1. 13.3.1 Classification of Technologies 596
2. 13.3.2 Exploiting Mature Technologies 597
3. 13.3.3 Relationship Between Technology and Projects 598
4. 13.4 Strategic R&D Planning 600
1. 13.4.1 Role of R&D Manager 600
2. 13.4.2 Planning Team 601
5. 13.5 Parallel Funding: Dealing with Uncertainty 603
1. 13.5.1 Categorizing Strategies 604
2. 13.5.2 Analytic Framework 605
3. 13.5.3 Q-Gert 606
6. 13.6 Managing the R&D Portfolio 607
1. 13.6.1 Evaluating an Ongoing Project 609
2. 13.6.2 Analytic Methodology 612
1. Team Project 617

2. Discussion Questions 618
3. Exercises 619
4. Bibliography 619
5. Appendix 13A: Portfolio Management Case Study 622
16. 14 Computer Support for Project Management 627
1. 14.1 Introduction 627
2. 14.2 Use of Computers in Project Management 628
1. 14.2.1 Supporting the Project Management Process Approach
629
2. 14.2.2 Tools and Techniques for Project Management 629
3. 14.3 Criteria for Software Selection 643
4. 14.4 Software Selection Process 648
5. 14.5 Software Implementation 650
6. 14.6 Project Management Software Vendors 656
1. Team Project 657
2. Discussion Questions 657
3. Exercises 658
4. Bibliography 659
5. Appendix 14A: PMI Software Evaluation Checklist 660
6. 14A.1 Category 1: Suites 660
7. 14A.2 Category 2: Process Management 660

8. 14A.3 Category 3: Schedule Management 661
9. 14A.4 Category 4: Cost Management 661
10. 14A.5 Category 5: Resource Management 661
11. 14A.6 Category 6: Communications Management 661
12. 14A.7 Category 7: Risk Management 662
13. 14A.8 General (Common) Criteria 662
14. 14A.9 Category-Specific Criteria Category 1: Suites 663
15. 14A.10 Category 2: Process Management 663
16. 14A.11 Category 3: Schedule Management 664
17. 14A.12 Category 4: Cost Management 665
18. 14A.13 Category 5: Resource Management 666
19. 14A.14 Category 6: Communications Management 666
20. 14A.15 Category 7: Risk Management 668
17. 15 Project Termination 671
1. 15.1 Introduction 671
2. 15.2 When to Terminate a Project 672
3. 15.3 Planning for Project Termination 677
4. 15.4 Implementing Project Termination 681
5. 15.5 Final Report 682
1. Team Project 683

2. Discussion Questions 683
3. Exercises 684
4. Bibliography 685
18. 16 New Frontiers in Teaching Project Management in MBA and
Engineering Programs 687
1. 16.1 Introduction 687
2. 16.2 Motivation for Simulation-Based Training 687
3. 16.3 Specific Example—The Project Team Builder (PTB) 691
4. 16.4 The Global Network for Advanced Management (GNAM)
MBA New Product Development (NPD) Course 692
5. 16.5 Project Management for Engineers at Columbia University
693
6. 16.6 Experiments and Results 694
7. 16.7 The Use of Simulation-Based Training for Teaching Project
Management in Europe 695
8. 16.8 Summary 696
1. Bibliography 697
1. Index 699

Nomenclature
AC annual cost
ACWP actual cost of work performed
AHP analytic hierarchy process
AOA activity on arrow
AON activity on node
AW annual worth
BAC budget at completion
B/C benefit/cost
BCWP budgeted cost of work performed
BCWS budgeted cost of work scheduled
CBS cost breakdown structure
CCB change control board
CCBM critical chain buffer management
CDR critical design review
CE certainty equivalent, concurrent engineering
C-E cost-effectiveness
CER cost estimating relationship
CI cost index; consistency index;

criticality index
CM configuration management
COO chief operating officer
CPIF cost plus incentive fee
CPM critical path method
CR capital recovery, consistency ratio
C/SCSC cost/schedule control systems criteria
CV cost variance
DOD Department of Defense
DOE Department of Energy
DOH direct overhead costs
DSS decision support system
EAC estimate at completion
ECO engineering change order
ECR engineering change request
EMV expected monetary value
EOM end of month
EOY end of year
ERP enterprise resource planning
ETC estimate to complete

ETMS early termination monitoring system
EUAC equivalent uniform annual cost
EV earned value
EVPI expected value of perfect information
EVSI expected value of sample information
FFP firm fixed price
FMS flexible manufacturing system
FPIF fixed price incentive fee
FW future worth
GAO General Accounting Office
GDSS group decision support system
GERT graphical evaluation and review technique
HR human resources
IPT integraded product team
IRR internal rate of return
IRS Internal Revenue Service
ISO International Standards Organization
IT information technology
LCC life-cycle cost
LOB line of balance

LOE level of effort
LP linear program
LRC linear responsibility chart
MACRS modified accelerated cost recovery system
MARR minimum acceptable (attractive) rate of return
MAUT multiattribute utility theory
MBO management by objectives
MIS management information system
MIT Massachusetts Institute of Technology
MPS master production schedule
MTBF mean time between failures
MTTR mean time to repair
NAC net annual cost
NASA National Aeronautics and Space Administration
NBC nuclear, biological, chemical
NPV net present value
OBS organizational breakdown structure
O&M operations and maintenance
PDMS product data management system
PDR preliminary design review

PERT program evaluation and review technique
PMBOK project management body of knowledge
PMI Project Management Institute
PMP project management professional
PO project office
PT project team
PV planned value
PW present worth
QA quality assurance
QFD quality function deployment
RAM reliability, availability, and maintainability; random access
memory
R&D research and development
RDT&E research, development, testing, and evaluation
RFP request for proposal
ROR rate of return
SI schedule index
SOW statement of work
SOYD sum-of-the-years digits
SV schedule variance
TQM total quality management

WBS work breakdown structure
WP work package
WR work remaining

Preface
We all deal with projects in our daily lives. In most cases, organization and
management simply amount to constructing a list of tasks and executing them
in sequence, but when the information is limited or imprecise and when
cause-and-effect relationships are uncertain, a more considered approach is
called for. This is especially true when the stakes are high and time is
pressing. Getting the job done right the first time is essential. This means
doing the upfront work thoroughly, even at the cost of lengthening the initial
phases of the project. Shaving expenses in the early stages with the intent of
leaving time and money for revisions later might seem like a good idea but
could have consequences of painful proportions. Seasoned managers will tell
you that it is more cost-effective in the long run to add five extra engineers at
the beginning of a project than to have to add 50 toward the end.
The quality revolution in manufacturing has brought this point home.
Companies in all areas of technology have come to learn that quality cannot
be inspected into a product; it must be built in. Recalling the 1980s, the
global competitive battles of that time were won by companies that could
achieve cost and quality advantages in existing, well-defined markets. In the
1990s, these battles were won by companies that could build and dominate
new markets. Today, the emphasis is partnering and better coordination of the
supply chain. Planning is a critical component of this process and is the
foundation of project management.
Projects may involve dozens of firms and hundreds of people who need to be
managed and coordinated. They need to know what has to be done, who is to
do it, when it should be done, how it will be done, and what resources will be
used. Proper planning is the first step in communicating these intentions. The
problem is made difficult by what can be characterized as an atmosphere of
uncertainty, chaos, and conflicting goals. To ensure teamwork, all major
participants and stakeholders should be involved at each stage of the process.
How is this achieved efficiently, within budget, and on schedule? The
primary objective in writing our first book was to answer this question from

the perspective of the project manager. We did this by identifying the
components of modern project management and showing how they relate to
the basic phases of a project, starting with conceptual design and advanced
development, and continuing through detailed design, production, and
termination. Taking a practical approach, we drew on our collective
experience in the electronics, information services, and aerospace industries.
The purpose of the second edition was to update the developments in the field
over the last 10 years and to expand on some of the concerns that are
foremost in the minds of practitioners. In doing so, we have incorporated new
material in many of the chapters specifically related to the Project
Management Body of Knowledge (PMBOK) published by the Project
Management Institute. This material reflects the tools, techniques, and
processes that have gained widespread acceptance by the profession because
of their proven value and usefulness.
Over the years, numerous books have been written with similar objectives in
mind. We acknowledge their contribution and have endeavored to build on
their strengths. As such in the third edition of the book, we have focused on
integrative concepts rather than isolated methodologies. We have relied on
simple models to convey ideas and have intentionally avoided detailed
mathematical formulations and solution algorithms––aspects of the field
better left to other parts of the curriculum. Nevertheless, we do present some
models of a more technical nature and provide references for readers who
wish to gain a deeper understanding of their use. The availability of powerful,
commercial codes brings model solutions within reach of the project team.
To ensure that project participants work toward the same end and hold the
same expectations, short- and long-term goals must be identified and
communicated continually. The project plan is the vehicle by which this is
accomplished and, once approved, becomes the basis for monitoring,
controlling, and evaluating progress at each phase of the project’s life cycle.
To help the project manager in this effort, various software packages have
been developed; the most common run interactively on microcomputers and
have full functional and report-generating capabilities. In our experience,
even the most timid users are able to take advantage of their main features
after only a few hours of hands-on instruction.

A second objective in writing this book has been to fill a void between texts
aimed at low- to mid-level managers and those aimed at technical personnel
with strong analytic skills but little training in or exposure to organizational
issues. Those who teach engineering or business students at both the late
undergraduate and early graduate levels should find it suitable. In addition,
the book is intended to serve as a reference for the practitioner who is new to
the field or who would like to gain a surer footing in project management
concepts and techniques.
The core material, including most of the underlying theory, can be covered in
a one-semester course. At the end of Chapter 1, we outline the book’s
contents. Chapter 3 deals with economic issues, such as cash flow, time value
of money, and depreciation, as they relate to projects. With this material and
some supplementary notes, coupled with the evaluation methods and multiple
criteria decision-making techniques discussed in Chapters 5 and 6,
respectively, it should be possible to teach a combined course in project
management and engineering economy. This is the direction in which many
undergraduate engineering programs are now headed after many years of
industry prodding. Young engineers are often thrust into leadership roles
without adequate preparation or training in project management skills.
Among the enhancements in the Third Edition is a section on Lean project
management, discussed in Chapter 8, and a new Chapter 16 on simulation-
based training for project management.
Lean project management is a Quality Management initiative that focuses on
maximizing the value that a project generates for its stakeholders while
minimizing waste. Lean project management is based on the Toyota
production system philosophy originally developed for a repetitive
environment and modified to a nonrepetitive environment to support project
managers and project teams in launching, planning, executing, and
terminating projects. Lean project management is all about people—selecting
the right project team members, teaching them the art and science of project
management, and developing a highly motivated team that works together to
achieve project goals.
Simulation-based training is a great tool for training project team members
and for team development. Chapter 16 discusses the principles of simulation-

based training and its application to project management. The chapter reports
on the authors’ experience in using simulation-based training in leading
business schools, such as members of the Global Network for Advanced
Management (GNAM), and in leading engineering schools, such as the
Columbia University School of Engineering and the Technion. The authors
also incorporated feedback received from European universities such as
Technische Universität München (TUM) School of Management and
Katholieke Universiteit Leuven that used the Project Team Builder (PTB)
simulation-based training environment. Adopters of this book are encouraged
to try the PTB—it is available from http://www.sandboxmodel.com/—and to
integrate it into their courses.
Writing a textbook is a collaborative effort involving many people whose
names do not always appear on the cover. In particular, we thank all faculty
who adopted the first and second editions of the book and provided us with
their constructive and informative comments over the years. With regard to
production, much appreciation goes to Lillian Bluestein for her thorough job
in proofreading and editing the manuscript. We would also like to thank Chen
Gretz-Shmueli for her contribution to the discussion in the human resources
section. Finally, we are forever grateful to the phalanx of students who have
studied project management at our universities and who have made the
painstaking efforts of gathering and writing new material all worthwhile.
Avraham Shtub
Moshe Rosenwein

http://www.sandboxmodel.com/

What’s New in this Edition
The purpose of the new, third edition of this book is to update developments
in the project management field over the last 10 years and to more broadly
address some of the concerns that have increased in prominence in the minds
of practitioners. We incorporated new material in many of the chapters
specifically related to the Project Management Body of Knowledge
(PMBOK) published by the Project Management Institute. This material
reflects the tools, techniques, and processes that have gained widespread
acceptance by the profession because of their proven value and usefulness.
Noteworthy enhancements in the third edition include:
An expanded section regarding Lean project management in Chapter 8;
A new chapter, Chapter 16, discussing the use of simulation and the
Project Team Builder software;
A detailed discussion on activity splitting and its advantages and
disadvantages in project management;
Descriptions, with examples, of resource-scheduling heuristics such as
the longest-duration first heuristic and the Activity Time (ACTIM)
algorithm;
Examples that demonstrate the use of Excel Solver to model project
management problems such as the time–cost tradeoff;
A description of project management courses at Columbia University
and the Global Network of Advanced Management.

About the Authors
Professor Avraham Shtub holds the Stephen and Sharon Seiden Chair in
Project Management. He has a B.Sc. in Electrical Engineering from the
Technion–Israel Institute of Technology (1974), an MBA from Tel Aviv
University (1978), and a Ph.D. in Management Science and Industrial
Engineering from the University of Washington (1982).
He is a certified Project Management Professional (PMP) and a member of
the Project Management Institute (PMI-USA). He is the recipient of the
Institute of Industrial Engineering 1995 Book of the Year Award for his book
Project Management: Engineering, Technology, and Implementation
(coauthored with Jonathan Bard and Shlomo Globerson), Prentice Hall, 1994.
He is the recipient of the Production Operations Management Society Wick
Skinner Teaching Innovation Achievements Award for his book Enterprise
Resource Planning (ERP): The Dynamics of Operations Management. His
books on Project Management were published in English, Hebrew, Greek,
and Chinese.
He is the recipient of the 2008 Project Management Institute Professional
Development Product of the Year Award for the training simulator “Project
Team Builder – PTB.”
Professor Shtub was a Department Editor for IIE Transactions, he was on the
Editorial Boards of the Project Management Journal, The International
Journal of Project Management, IIE Transactions, and the International
Journal of Production Research. He was a faculty member of the department
of Industrial Engineering at Tel Aviv University from 1984 to 1998, where he
also served as a chairman of the department (1993–1996). He joined the
Technion in 1998 and was the Associate Dean and head of the MBA
program.
He has been a consultant to industry in the areas of project management,
training by simulators, and the design of production—operation systems. He
was invited to speak at special seminars on Project Management and

Operations in Europe, the Far East, North America, South America, and
Australia.
Professor Shtub visited and taught at Vanderbilt University, The University
of Pennsylvania, Korean Institute of Technology, Bilkent University in
Turkey, Otego University in New Zealand, Yale University, Universitat
Politécnica de Valencia, and the University of Bergamo in Italy.
Dr. Moshe Rosenwein has a B.S.E. from Princeton University and a Ph.D. in
Decision Sciences from the University of Pennsylvania. He has worked in the
industry throughout his professional career, applying management science
modeling and methodologies to business problems in supply chain
optimization, network design, customer relationship management, and
scheduling. He has served as an adjunct professor at Columbia University on
multiple occasions over the past 20 years and developed a project
management course for the School of Engineering that has been taught since
2009. He has also taught at Seton Hall University and Rutgers University.
Dr. Rosenwein has published over 20 refereed papers and has delivered
numerous talks at universities and conferences. In 2001, he led an industry
team that was awarded a semi-finalist in the Franz Edelman competition for
the practice of management science.

Chapter 1 Introduction
1.1 Nature of Project Management
Many of the most difficult engineering and business challenges of recent
decades have been to design, develop, and implement new systems of a type
and complexity never before attempted. Examples include the construction of
oil drilling platforms in the North Sea off the coast of Great Britain, the
development of the manned space program in both the United States and the
former Soviet Union, and the worldwide installation of fiber optic lines for
broadband telecommunications. The creation of these systems with
performance capabilities not previously available and within acceptable
schedules and budgets has required the development of new methods of
planning, organizing, and controlling events. This is the essence of project
management.
A project is an organized endeavor aimed at accomplishing a specific
nonroutine or low-volume task. Although projects are not repetitive, they
may take significant amounts of time and, for our purposes, are sufficiently
large or complex to be recognized and managed as separate undertakings.
Teams have emerged as the way of supplying the needed talents. The use of
teams complicates the flow of information and places additional burdens on
management to communicate with and coordinate the activities of the
participants.
The amount of time in which an individual or an organizational unit is
involved in a project may vary considerably. Someone in operations may
work only with other operations personnel on a project or may work with a
team composed of specialists from various functional areas to study and solve
a specific problem or to perform a secondary task.
Management of a project differs in several ways from management of a
typical organization. The objective of a project team is to accomplish its
prescribed mission and disband. Few firms are in business to perform just one

job and then disappear. Because a project is intended to have a finite life,
employees are seldom hired with the intent of building a career with the
project. Instead, a team is pulled together on an ad-hoc basis from among
people who normally have assignments in other parts of the organization.
They may be asked to work full time on the project until its completion; or
they may be asked to work only part time, such as two days a week, on the
project and spend the rest of the time at their usual assignments. A project
may involve a short-term task that lasts only a matter of days, or it may run
for years. After completion, the team normally disperses and its members
return to their original jobs.
The need to manage large, complex projects, constrained by tight schedules
and budgets, motivated the development of methodologies different from
those used to manage a typical enterprise. The increasingly complex task of
managing large-scale, enterprise-wide projects has led to the rise in
importance of the project management function and the role of the project
manager or project management office. Project management is increasingly
viewed in both industry and government as a critical role on a project team
and has led to the development of project management as a profession (much
like finance, marketing, or information technology, for example). The Project
Management Institute (PMI), a nonprofit organization, is in the forefront of
developing project management methodologies and of providing educational
services in the form of workshops, training, and professional literature.

1.2 Relationship Between Projects
and Other Production Systems
Operations and production management contains three major classes of
systems: (1) those designed for mass production, (2) those designed for batch
(or lot) production, and (3) those designed for undertaking nonrepetitive
projects common to construction and new product development. Each of
these classes may be found in both the manufacturing and service sectors.
Mass production systems are typically designed around the specific processes
used to assemble a product or perform a service. Their orientation is fixed
and their applications are limited. Resources and facilities are composed of
special-purpose equipment designed to perform the operations required by
the product or the service in an efficient way. By laying out the equipment to
parallel the natural routings, material handling and information processing are
greatly simplified. Frequently, material handling is automated and the use of
conveyors and monorails is extensive. The resulting system is capital
intensive and very efficient in the processing of large quantities of specific
products or services for which relatively little management and control are
necessary. However, these systems are very difficult to alter should a need
arise to produce new or modified products or to provide new services. As a
result, they are most appropriate for operations that experience a high rate of
demand (e.g., several hundred thousand units annually) as well as high
aggregate demand (e.g., several million units throughout the life cycle of the
system).
Batch-oriented systems are used when several products or services are
processed in the same facility. When the demand rate is not high enough or
when long-run expectations do not justify the investment in special-purpose
equipment, an effort is made to design a more flexible system on which a
variety of products or services can be processed. Because the resources used
in such systems have to be adjusted (set up) when production switches from
one product to another, jobs are typically scheduled in batches to save setup
time. Flexibility is achieved by using general-purpose resources that can be

adjusted to handle different processes. The complexity of operations
planning, scheduling, and control is greater than in mass production systems
as each product has its own routing (sequence of operations). To simplify
planning, resources are frequently grouped together based on the type of
processes that they perform. Thus, batch-oriented systems contain
organizational units that specialize in a function or a process, as opposed to
product lines that are found in mass production systems. Departments such as
metal cutting, painting, testing, and packaging/shipping are typical examples
from the batch-oriented manufacturing sector, whereas word processing
centers and diagnostic laboratories are examples from the service sector.
In the batch-oriented system, it is particularly important to pay attention to
material handling needs because each product has its specific set of
operations and routings. Material handling equipment, such as forklifts, is
used to move in-process inventory between departments and work centers.
The flexibility of batch-oriented systems makes them attractive for many
organizations.
In recent years, flexible manufacturing systems have been quick to gain
acceptance in some industrial settings. With the help of microelectronics and
computer technology, these systems are designed to achieve mass production
efficiencies in low-demand environments. They work by reducing setup
times and automating material handling operations but are extremely capital
intensive. Hence they cannot always be justified when product demand is low
or when labor costs are minimal. Another approach is to take advantage of
local economies of scale. Group technology cells, which are based on
clustering similar products or components into families processed by
dedicated resources of the facility, are one way to implement this approach.
Higher utilization rates and greater throughput can be achieved by processing
similar components on dedicated machines.
By way of contrast, systems that are subject to very low demand (no more
than a few units) are substantially different from the first two mentioned.
Because of the nonrepetitive nature of these systems, past experience may be
of limited value so little learning takes place. In this environment, extensive
management effort is required to plan, monitor, and control the activities of
the organization. Project management is a direct outgrowth of these efforts.

It is possible to classify organizations based on their production orientation as
a function of volume and batch size. This is illustrated in Figure 1.1.
Figure 1.1
Classification of production systems.
Figure 1.1 Full Alternative Text
The borderlines between mass production, batch-oriented, and project-
oriented systems are hard to define. In some organizations where the project
approach has been adopted, several units of the same product (a batch) are
produced, whereas other organizations use a batch-oriented system that
produces small lots (the just-in-time approach) of very large volumes of
products. To better understand the transition between the three types of
systems, consider an electronics firm that assembles printed circuit boards in
small batches in a job shop. As demand for the boards picks up, a decision is
made to develop a flow line for assembly. The design and implementation of
this new line is a project.

1.3 Characteristics of Projects
Although the Manhattan project—the development of the first atomic bomb
—is considered by many to be the first instance when modern project
management techniques were used, ancient history is replete with examples.
Some of the better known ones include the construction of the Egyptian
pyramids, the conquest of the Persian Empire by Alexander the Great, and
the building of the Temple in Jerusalem. In the 1960s, formal project
management methods received their greatest impetus with the Apollo
program and a cluster of large, formidable construction projects.
Today, activities such as the transport of American forces in Operations in
Iraq and Afghanistan, the pursuit of new treatments for AIDS and Ebola, and
the development of the joint U.S.–Russian space station and the manned
space mission to Mars are examples of three projects with which most of us
are familiar. Additional examples of a more routine nature include:
Selecting a software package
Developing a new office plan or layout
Implementing a new decision support system
Introducing a new product to the market
Designing an airplane, supercomputer, or work center
Opening a new store
Constructing a bridge, dam, highway, or building
Relocating an office or a factory
Performing major maintenance or repair
Starting up a new manufacturing or service facility

Producing and directing a movie
1.3.1 Definitions and Issues
As the list above suggests, a project may be viewed or defined in several
different ways: for example, as “the entire process required to produce a new
product, new plant, new system, or other specified results” (Archibald 2003)
or as “a narrowly defined activity which is planned for a finite duration with a
specific goal to be achieved” (General Electric Corporation 1983). Generally
speaking, project management occurs when emphasis and special attention
are given to the performance of nonrepetitive activities for the purpose of
meeting a single set of goals, typically under a set of constraints such as time
and budget constraints.
By implication, project management deals with a one-time effort to achieve a
focused objective. How progress and outcomes are measured, though,
depends on a number of critical factors. Typical among these are technology
(specifications, performance, quality), time (due dates, milestones), and cost
(total investment, required cash flow), as well as profits, resource utilization,
market share, and market acceptance.
These factors and their relative importance are major issues in project
management. These factors are based on the needs and expectations of the
stakeholders. Stakeholders are individuals and parties interested in the
problem the project is designed to solve or in the solution selected. With a
well-defined set of goals, it is possible to develop appropriate performance
measures and to select the right technology, the organizational structure,
required resources, and people who will team up to achieve these goals.
Figure 1.2 summarizes the underlying processes. As illustrated, most projects
are initiated by a need. A new need may be identified by stakeholders such as
a customer, the marketing department, or any member of an organization.
When management is convinced that the need is genuine, goals may be
defined, and the first steps may be taken toward putting together a project
team. Most projects have several goals covering such aspects as technical and
operational requirements, delivery dates, and cost. A set of potential projects
to undertake should be ranked by stakeholders based on the relative

importance of the goals and the perceived probability of each potential
project to achieve each of the individual goals.
Figure 1.2
Major processes in project management.
Figure 1.2 Full Alternative Text
On the basis of these rankings and a derived set of performance measures for
each goal, the technological alternatives are evaluated and a concept (or
initial design) is developed along with a schedule and a budget for the
project. This early phase of the project life cycle is known as the initiation
phase, the front end of the project, or the conceptual phase. The next step is

to integrate the design, the schedule, and the budget into a project plan
specifying what should be done, by whom, at what cost, and when. As the
plan is implemented, the actual accomplishments are monitored and recorded.
Adjustments, aimed at keeping the project on track, are made when
deviations or overruns appear. When the project terminates, its success is
evaluated based on the predetermined goals and performance measures.
Figure 1.3 compares two projects with these points in mind. In project 1, a
“design to cost” approach is taken. Here, the budget is fixed and the
technological goals are clearly specified. Cost, performance, and schedule are
all given equal weight. In project 2, the technological goals are paramount
and must be achieved, even if it means compromising the schedule and the
budget in the process.
Figure 1.3
Relative importance of goals.
Figure 1.3 Full Alternative Text
The first situation is typical of standard construction and manufacturing
projects, whereby a contractor agrees to supply a system or a product in
accordance with a given schedule and budget. The second situation is typical
of “cost plus fixed fee” projects where the technological uncertainties argue
against a contractor’s committing to a fixed cost and schedule. This
arrangement is most common in a research and development (R&D)
environment.

A well-designed organizational structure is required to handle projects as a
result of their uniqueness, variety, and limited life span. In addition, special
skills are required to manage them successfully. Taken together, these skills
and organizational structures have been the catalyst for the development of
the project management discipline. Some of the accompanying tools and
techniques, though, are equally applicable in the manufacturing and service
sectors.
Because projects are characterized by a “one-time only” effort, learning is
limited and most operations never become routine. This results in a need for
extensive management involvement throughout the life cycle of the project.
In addition, the lack of continuity leads to a high degree of uncertainty.
1.3.2 Risk and Uncertainty
In project management, it is common to refer to very high levels of
uncertainty as sources of risk. Risk is present in most projects, especially in
the R&D environment. Without trying to sound too pessimistic, it is prudent
to assume that what can go wrong will go wrong. Principal sources of
uncertainty include random variations in component and subsystem
performance, inaccurate or inadequate data, and the inability to forecast
satisfactorily as a result of lack of experience. Specifically, there may be
1. Uncertainty in scheduling. Changes in the environment that are
impossible to forecast accurately at the outset of a project are likely to
have a critical impact on the length of certain activities. For example,
subcontractor performance or the time it takes to obtain a long-term loan
is bound to influence the length of various subtasks. The availability of
scarce resources may also add to uncertainty in scheduling. Methods are
needed to deal with problematic or unstable time estimates. Probability
theory and simulation both have been used successfully for this purpose,
as discussed in Chapter 9.
2. Uncertainty in cost. Limited information on the duration of activities
makes it difficult to predict the amount of resources needed to complete
them on schedule. This translates directly into an uncertainty in cost. In

addition, the expected hourly rate of resources and the cost of materials
used to carry out project tasks may possess a high degree of variability.
3. Technological uncertainty. This form of uncertainty is typically present
in R&D projects in which new (not thoroughly tested and approved)
technologies, methods, equipment, and systems are developed or used.
Technological uncertainty may affect the schedule, the cost, and the
ultimate success of the project. The integration of familiar technologies
into one system or product may cause technological uncertainty as well.
The same applies to the development of software and its integration with
hardware.
There are other sources of uncertainty, including those of an organizational
and political nature. New regulations might affect the market for a project,
whereas the turnover of personnel and changes in the policies of one or more
of the participating organizations may disrupt the flow of work.
To gain a better understanding of the effects of uncertainty, consider the three
projects mentioned earlier. The transport of American armed forces in
Operation Iraqi Freedom faced extreme political and logistical uncertainties.
In the initial stages, none of the planners had a clear idea of how many troops
would be needed or how much time was available to put the troops in place.
Also, it was unknown whether permission would be granted to use NATO air
bases or even to fly over European and Middle Eastern countries, or how
much tactical support would be forthcoming from U.S. allies.
The development of a treatment for AIDS is an ongoing project fraught with
technological uncertainty. Hundreds of millions of dollars have already been
spent with little progress toward a cure. As expected, researchers have taken
many false steps, and many promising paths have turned out to be dead ends.
Lengthy trial procedures and duplicative efforts have produced additional
frustration. If success finally comes, it is unlikely that the original plans or
schemes will have predicted its form.
The design of the U.S.–Russian space station is an example in which virtually
every form of uncertainty is present. Politicians continue to play havoc with
the budget, while other stakeholders like special interest groups (both friendly
and hostile) push their individual agendas; schedules get altered and

rearranged; software fails to perform correctly; and the needed resources
never seem to be available in adequate supply. Inflation, high turnover rates,
and scaled-down expectations take their toll on the internal workforce, as
well as on the legion of subcontractors.
The American Production and Inventory Control Society has, tongue-in-
cheek, fashioned the following laws in an attempt to explain the
consequences of uncertainty on project management.
Laws of Project Management
1. No major project is ever installed on time, within budget or with the
same staff that started it. Yours will not be the first.
2. Projects progress quickly until they become 90% complete, then they
remain at 90% complete forever.
3. One advantage of fuzzy project objectives is that they let you avoid the
embarrassment of estimating the corresponding costs.
4. When things are going well, something will go wrong.
When things just cannot get any worse, they will.
When things seem to be going better, you have overlooked
something.
5. If project content is allowed to change freely, then the rate of change
will exceed the rate of progress.
6. No system is ever completely debugged. Attempts to debug a system
inevitably introduce new bugs that are even harder to find.
7. A carelessly planned project will take three times longer to complete
than expected; a carefully planned project will take only twice as long.
8. Project teams detest progress reporting because it vividly manifests their

lack of progress.
1.3.3 Phases of a Project
A project passes through a life cycle that may vary with size and complexity
and with the style established by the organization. The names of the various
phases may differ but typically include those shown in Figure 1.4. To begin,
there is an initiation or a conceptual design phase during which the
organization realizes that a project may be needed or receives a request from
a customer to propose a plan to perform a project; at this phase alternative
technologies and operational solutions are evaluated and the most promising
are selected based on performances, cost, risk, and schedule considerations.
Next there is an advanced development or preliminary system design phase in
which the project manager (and perhaps a staff if the project is complex)
plans the project to a level of detail sufficient for initial scheduling and
budgeting. If the project is approved, it then will enter a more detailed design
phase, a production phase, and a termination phase.

Figure 1.4
Relationship between project life cycle and cost.
Figure 1.4 Full Alternative Text
In Figure 1.4, the five phases in the life cycle of a project are presented as a
function of time. The cost during each phase depends on the specifics, but
usually the majority of the budget is spent during the production phase.
However, most of this budget is committed during the advanced development

phase and the detailed design phase before the actual work takes place.
Management plays a vital role during the conceptual design phase, the
advanced development phase, and the detailed design phase. The importance
of this involvement in defining goals, selecting performance measures,
evaluating alternatives (including the no-go or not to do the project),
selecting the most promising alternative and planning the project cannot be
overemphasized. Pressures to start the “real work” on the project, that is, to
begin the production (or execution) phase as early as possible, may lead to
the selection of the wrong technological or operational alternatives and
consequently to high cost and schedule risks as a result of the commitment of
resources without adequate planning.
In most cases, a work breakdown structure (WBS) is developed during the
conceptual design phase. The WBS is a document that divides the project
work into major hardware, software, data, and service elements. These
elements are further divided and a list is produced identifying all tasks that
must be accomplished to complete the project. The WBS helps to define the
work to be performed and provides a framework for planning, budgeting,
monitoring, and control. Therefore, as the project advances, schedule and
cost performance can be compared with plans and budgets. Table 1.1 shows
an abbreviated WBS for an orbital space laboratory vehicle.
TABLE 1.1 Partial WBS for
Space Laboratory
Index Work element
1.0 Command module
2.0 Laboratory module
3.0 Main propulsion system
3.1 Fuel supply system
3.1.1 Fuel tank assembly
3.1.1.1 Fuel tank casing
3.1.1.2 Fuel tank insulation

4.0 Guidance system
5.0 Habitat module
6.0 Training system
7.0 Logistic support system
The detailed project definition, as reflected in the WBS, is examined during
the advanced development phase to determine the skills necessary to achieve
the project’s goals. Depending on the planning horizon, personnel from other
parts of the organization may be used temporarily to accomplish the project.
However, previous commitments may limit the availability of these
resources. Other strategies might include hiring new personnel or
subcontracting various work elements, as well as leasing equipment and
facilities.
1.3.4 Organizing for a Project
A variety of structures are used by organizations to perform project work.
The actual arrangement may depend on the proportion of the company’s
business that is project oriented, the scope and duration of the underlying
tasks, the capabilities of the available personnel, preferences of the decision
makers, and so on. The following five possibilities range from no special
structure to a totally separate project organization.
1. Functional organization. Many companies are organized as a hierarchy
with functional departments that specialize in a particular type of work,
such as engineering or sales (see Figure 1.5). These departments are
often broken down into smaller units that focus on special areas within
the function. Upper management may divide a project into work tasks
and assign them to the appropriate functional units. The project is then
budgeted and managed through the normal management hierarchy.

Figure 1.5
Portion of a typical functional organization.
Figure 1.5 Full Alternative Text
2. Project coordinator. A project may be handled through the organization
as described above, but with a special appointee to coordinate it. The
project is still funded through the normal channels and the functional
managers retain responsibility and authority for their portion of the
work. The coordinator meets with the functional managers and provides

direction and impetus for the project and may report its status to higher
management.
3. Matrix organization. In a matrix organization, a project manager is
responsible for completion of the project and is often assigned a budget.
The project manager essentially contracts with the functional managers
for completion of specific tasks and coordinates project efforts across
the functional units. The functional managers assign work to employees
and coordinate work within their areas. These arrangements are depicted
schematically in Figure 1.6.
4. Project team. A particularly significant project (development of a new
product or business venture) that will have a long duration and requires
the full-time efforts of a group may be supervised by a project team.
Full-time personnel are assigned to the project and are physically
located with other team members. The project has its own management
structure and budget as though it were a separate division of the
company.
5. Projectized organization. When the project is of strategic importance,
extremely complex and of long duration, and involves a number of
disparate organizations, it is advisable to give one person complete
control of all the elements necessary to accomplish the stated goals. For
example, when Rockwell International was awarded two multimillion-
dollar contracts (the Apollo command and service modules, and the
second stage of the Saturn launch vehicle) by NASA, two separate
programs were set up in different locations of the organization. Each
program was under a division vice president and had its own
manufacturing plant and staff of specialists. Such an arrangement takes
the idea of a self-sufficient project team to an extreme and is known as a
projectized organization.
Table 1.2 enumerates some advantages and disadvantages of the two
extremes—the functional and projectized organizations. Companies that are
frequently involved in a series of projects and occasionally shift around
personnel often elect to use a matrix organization. This type of organization
provides the flexibility to assign employees to one or more projects. In this
arrangement, project personnel maintain a permanent reporting relationship

that connects vertically to a supervisor in a functional area, who directs the
scope of their work. At the same time, each person is assigned to one or more
projects and has a horizontal reporting relationship to the manager of a
particular project, who coordinates his or her participation in that project. Pay
and career advancement are developed within a particular discipline even
though a person may be assigned to different projects. At times, this dual
reporting relationship can give rise to a host of personnel problems and
creates conflicts.
Figure 1.6

Typical matrix organization.
Figure 1.6 Full Alternative Text
TABLE 1.2 Advantages and
Disadvantages of Two
Organizational Structures
Functional organization Projectized organization
Advantages
Efficient use of technical
personnel
Career continuity and growth for
technical personnel
Good technology transfer between
projects
Good stability, security, and
morale
Good project schedule and cost
control
Single point for customer
contact
Rapid reaction time possible
Simpler project communication
Training ground for general
management
Disadvantages
Weak customer interface Uncertain technical direction
Weak project authority Inefficient use of specialists
Poor horizontal communications Insecurity regarding future jobassignments
Discipline (technology) oriented
rather than program oriented
Poor crossfeed of technical
information between projects
Slower work flow

1.4 Project Manager
The presence of uncertainty coupled with limited experience and hard-to-find
data makes project management a combination of art, science, and, most of
all, logical thinking. A good project manager must be familiar with a large
number of disciplines and techniques. Breadth of knowledge is particularly
important because most projects have technical, financial, marketing, and
organizational aspects that inevitably conspire to derail the best of plans.
The role of the project manager may start at different points in the life cycle
of a project. Some managers are involved from the beginning, helping to
select the best technological and operational alternatives for the project, form
the team, and negotiate the contracts. Others may begin at a later stage and be
asked to execute plans that they did not have a hand in developing. At some
point, though, most project managers deal with the basic issues: scheduling,
budgeting, resource allocation, resource management, stakeholder
management (e.g., human relations and negotiations).
It is an essential and perhaps the most difficult part of the project manager’s
job to pay close attention to the big picture without losing sight of critical
details, no matter how slight. In order to efficiently and effectively achieve
high-level project goals, project managers must prioritize concerns key
stakeholders while managing change that inevitably arises during a project’s
life cycle. A project manager is an integrator and needs to trade off different
aspects of the project each time a decision is called for. Questions such as,
“How important is the budget relative to the schedule?” and “Should more
resources be acquired to avoid delays at the expense of a budget overrun, or
should a slight deviation in performance standards be tolerated as long as the
project is kept on schedule and on budget?” are common.
Some skills can be taught, other skills are acquired only with time and
experience, and yet other skills are very hard to learn or to acquire, such as
the ability to lead a team without formal authority and the ability to deal with
high levels of uncertainty without panic. We will not dwell on these but
simply point them out, as we define fundamental principles and procedures.

Nevertheless, one of our basic aims is to highlight the practical aspects of
project management and to show how modern organizations can function
more effectively by adopting them. In so doing, we hope to provide all
members of the project team with a comprehensive view of the field.
1.4.1 Basic Functions
The PMI (2012) identifies ten knowledge areas that the discipline must
address:
1. Integration management
2. Scope management
3. Time management
4. Cost management
5. Quality management
6. Human resource management
7. Communication management
8. Risk management
9. Procurement management
10. Stakeholders management
Managing a project is a complex and challenging assignment. Because
projects are one-of-a-kind endeavors, there is little in the way of experience,
normal working relationships, or established procedures to guide participants.
A project manager may have to coordinate many diverse efforts and activities
to achieve project goals. People from various disciplines and from various
parts of the organization who have never worked together may be assigned to
a project for different spans of time. Subcontractors who are unfamiliar with

the organization may be brought in to carry out major tasks. A project may
involve thousands of interrelated activities performed by people who are
employed by any one of several different subcontractors or by the sponsoring
organization.
Project leaders must have an effective means of identifying and
communicating the planned activities and their interrelationships. A
computer-based scheduling and monitoring system is usually essential.
Network techniques such as CPM (critical path method) and PERT (program
evaluation and review technique) are likely to figure prominently in such
systems. CPM was developed in 1957 by J.E. Kelly of Remington-Rand and
M.R. Walker of Dupont to aid in scheduling maintenance shutdowns of
chemical plants. PERT was developed in 1958 under the sponsorship of the
U.S. Navy Special Projects Office, as a management tool for scheduling and
controlling the Polaris missile program. Collectively, their value has been
demonstrated time and again during both the planning and the execution
phases of projects.
1.4.2 Characteristics of Effective
Project Managers
The project manager is responsible for ensuring that tasks are completed on
time and within budget, but often has no formal authority over those who
actually perform the work. He or she, therefore, must have a firm
understanding of the overall job and rely on negotiation and persuasion skills
to influence the array of contractors, functionaries, and specialists assigned to
the project. The skills that a typical project manager needs are summarized in
Figure 1.7; the complexity of the situation is depicted in Figure 1.8, which
shows the interactions between some of the stakeholders: client,
subcontractor, and top management.
The project manager is a lightning rod, frequently under a storm of pressure
and stress. He or she must deal effectively with the changing priorities of the
client, the anxieties of his or her own management ever fearful of cost and
schedule overruns or technological failures, and the divided loyalties of the

personnel assigned to the team. The ability to trade off conflicting goals and
to find the optimal balance between conflicting positions is probably the most
important skill of the job.
In general, project managers require enthusiasm, stamina, and an appetite for
hard work to withstand the onslaught of technical and political concerns.
Where possible, they should have seniority and position in the organization
commensurate with that of the functional managers with whom they must
deal. Regardless of whether they are coordinators within a functional
structure or managers in a matrix structure, they will frequently find their
formal authority incomplete. Therefore, they must have the blend of
technical, administrative, and interpersonal skills as illustrated in Figure 1.7
to furnish effective leadership.

1.5 Components, Concepts, and
Terminology
Although each project has a unique set of goals, there is enough commonality
at a generic level to permit the development of a unified framework for
planning and control. Project management techniques are designed to handle
the common processes and problems that arise during a project’s life cycle.
This does not mean, however, that one versed in such techniques will be a
successful manager. Experts are needed to collect and interpret data,
negotiate contracts, arrange for resources, manage stakeholders, and deal with
a wide range of technical and organizational issues that impinge on both the
cost and the schedule.
The following list contains the major components of a “typical” project.
Project initiation, selection, and definition
Identification of needs
Mapping of stakeholders (who are they, what are their needs and
expectations, how much influence and power they have, will they
be engaged and by how much and will they be involved in the
project and by how much)

Figure 1.7
Important skills for the project manager.
Figure 1.7 Full Alternative Text

Figure 1.8
Major interactions of project stakeholders.
Development of (technological and operational) alternatives
Evaluation of alternatives based on performances, cost, duration,
and risk
Selection of the “most promising” alternatives
Estimation of the life cycle cost (LCC) of the promising alternatives
Assessment of risk of the promising alternatives
Development of a configuration baseline
“Selling” the configuration and getting approval
Project organization
Selection of participating organizations
Structuring the work content of the project into smaller work

packages using a WBS
Allocation of WBS elements to participating organizations and
assigning managers to the work packages
Development of the project organizational structure and associated
communication and reporting facilities
Analysis of activities
Definition of the project’s major tasks
Development of a list of activities required to complete the
project’s tasks
Development of precedence relations among activities
Development of a network model
Development of higher level network elements (hammock
activities, subnetworks)
Selection of milestones
Updating the network and its elements
Project scheduling
Development of a calendar
Assigning resources to activities and estimation of activity
durations
Estimation of activity performance dates
Monitoring actual progress and milestones
Updating the schedule

Resource management
Definition of resource requirements
Acquisition of resources
Allocation of resources among projects/activities
Monitoring resource use and cost
Technological management
Development of a configuration management plan
Identification of technological risks
Configuration control
Risk management and control
Total quality management (TQM)
Project budgeting
Estimation of direct and indirect costs
Development of a cash flow forecast
Development of a budget
Monitoring actual cost
Project execution and control
Development of data collection systems
Development of data analysis systems
Execution of activities

Data collection and analysis
Detection of deviations in cost, configuration, schedule, and quality
Development of corrective plans
Implementation of corrective plans
Forecasting of project cost at completion
Project termination
Evaluation of project success
Recommendation for improvements in project management
practices
Analysis and storage of information on actual cost, actual duration,
actual performance, and configuration
Each of these activities is discussed in detail in subsequent chapters. Here, we
give an overview with the intention of introducing important concepts and the
relationships among them. We also mention some of the tools developed to
support the management of each activity.
1. Project initiation, selection, and definition. This process starts with
identifying a need for a new service, product, or system. The trigger can
come from any number of sources, including a current client, line
personnel, or a proposed request from an outside organization. The
trigger can come from one or more stakeholders who may have similar
or conflicting needs and expectations. If the need is considered
important and feasible solutions exist, then the need is translated into
technical specifications. Next, a study of alternative solution approaches
is initiated. Each alternative is evaluated based on a predetermined set of
performance measures, and the most promising compose the “efficient
frontier” of possible solutions. An effort is made to estimate the
performances, duration, costs, and risks associated with the efficient
alternatives. Cost estimates for development, production (or

purchasing), maintenance, and operations form the basis of a Life Cycle
Cost (LCC) model used for selecting the “optimal” alternative.
Because of uncertainty, most of the estimates are likely to be
problematic. A risk assessment may be required if high levels of
uncertainty are present. The risk associated with an unfavorable
outcome is defined as the probability of that outcome multiplied by the
cost associated with it. A proactive risk management approach means
that major risk drivers should be identified early in the process, and
contingency plans should be prepared to handle unfavorable events if
and when they occur.
Once an alternative is chosen, design details are fleshed out during the
concept formulation and definition phase of the project. Preliminary
design efforts end with a configuration baseline. This configuration (the
principal alternative) has to satisfy the needs and expectations of the
most important stakeholders and be accepted and approved by
management. A well-structured selection and evaluation process, in
which all relevant parties are involved, increases the probability of
management approval. A generic flow diagram for the processes of
project initiation selection and definition is presented in Figure 1.9.

Figure 1.9
Major activities in the conceptual design phase.
Figure 1.9 Full Alternative Text
2. Project organization. Many stakeholders, ranging from private firms and
research laboratories to public utilities and government agencies, may
participate in a particular project. In the advanced development phase, it

is common to define the work content [statement of work (SOW)] as a
set of tasks, and to array them hierarchically in a treelike form known as
the WBS. The relationship between participating organizations, known
as the organizational breakdown structure (OBS) is similarly
represented.
In the OBS, the lines of communication between and within
organizations are defined, and procedures for work authorization and
report preparation and distribution are established. Finally, lower-level
WBS elements are assigned to lower-level OBS elements to form work
packages and a responsibility matrix is constructed, indicating which
organizational unit is responsible for which WBS element.
At the end of the advanced development phase, a more detailed cost
estimate and a long-range budget proposal are prepared and submitted
for management approval. A positive response signals the go-ahead for
detailed planning and organizational design. This includes the next five
functions.
3. Analysis of activities. To assess the need for resources and to prepare a
detailed schedule, it is necessary to develop a detailed list of activities
that are to be performed. These activities should be aimed at
accomplishing the WBS tasks in a logical, economically sound, and
technically feasible manner. Each task defined in the initial planning
phase may consist of one or more activities. Feasibility is ensured by
introducing precedence relations among activities. These relations can
be represented graphically in the form of a network model.
Completion of an important activity may define a milestone and is
represented in the network model. Milestones provide feedback in
support of project control and form the basis for budgeting, scheduling,
and resource management. As progress is made, the model has to be
updated to account for the inclusion of new activities in the WBS, the
successful completion of tasks, and any changes in design, organization,
and schedule as a result of uncertainty, new needs, or new technological
and political developments.
4. Project scheduling. The expected execution dates of activities are

important from both a financial (acquisition of the required funds) and
an operational (acquisition of the required resources) point of view.
Scheduling of project activities starts with the definition of a calendar
specifying the working hours per day, working days per week, holidays,
and so on. The expected duration of each activity is estimated, and a
project schedule is developed based on the calendar, precedence
relations among activities, and the expected duration of each activity.
The schedule specifies the starting and ending dates of each activity and
the accompanying slack or leeway. This information is used in
budgeting and resource management. The schedule is used as a basis for
work authorization and as a baseline against which actual progress is
measured. It is updated throughout the life cycle of the project to reflect
actual progress.
5. Resource management. Activities are performed by resources so that
before any concrete steps can be taken, requirements have to be
identified. This means defining one or more alternatives for meeting the
estimated needs of each activity (the duration of an activity may be a
function of the resources assigned to perform it). Based on the results,
and in light of the project schedule, total resource requirements are
estimated. These requirements are the basis of resource management and
resource acquisition planning.
When requirements exceed expected availability, schedule delays may
occur unless the difference is made up by acquiring additional resources
or by subcontracting. Alternatively, it may be possible to reschedule
activities (especially those with slack) so as not to exceed expected
resource availability. Other considerations, such as minimizing
fluctuations in resource usage and maximizing resource utilization, may
be applicable as well.
During the execution phase, resources are allocated periodically to
projects and activities in accordance with a predetermined timetable.
However, because actual and planned use may differ, it is important to
monitor and compare progress to plans. Low utilization as well as
higher-than-planned costs or consumption rates indicate problems and
should be brought to the immediate attention of management. Large

discrepancies may call for significant alterations in the schedule.
6. Technological management. Once the technological alternatives are
evaluated and a consensus forms, the approved configuration is adopted
as a baseline. From the baseline, plans for project execution are
developed, tests to validate operational and technical requirements are
designed, and contingency plans for risky areas are formulated. Changes
in needs or in the environment may trigger modifications to the
configuration. Technological management deals with execution of the
project to achieve the approved baseline. Principal functions include the
evaluation of proposed changes, the introduction of approved changes
into the configuration baseline, and development of a total quality
management (TQM) program. TQM involves the continuous effort to
prevent defects, to improve processes, and to guarantee a final result that
fits the specifications of the project and the expectations of the client.
7. Project budgeting. Money is the most common resource used in a
project. Equipment and labor have to be acquired, and suppliers have to
be paid. Overhead costs have to be assigned, and subcontractors have to
be put on the payroll. Preparation of a budget is an important
management activity that results in a time-phased plan summarizing
expected expenditures, income, and milestones.
The budget is derived by estimating the cost of activities and resources.
Because the schedule of the project relates activities and resource use to
the calendar, the budget is also related to the same calendar. With this
information, a cash flow analysis can be performed, and the feasibility
of the predicted outlays can be tested. If the resulting cash flow or the
resulting budget is not acceptable, then the schedule should be modified.
This is frequently done by delaying activities that have slack.
Once an acceptable budget is developed, it serves as the basic financial
tool for the project. Credit lines and loans can be arranged, and the cost
of financing the project can be assessed. As work progresses,
information on actual cost is accumulated and compared with the
budget. This comparison forms the basis for controlling costs. The
sequence of activities performed during the detailed design phase is
summarized in Figure 1.10.

Figure 1.10
Major activities in the detailed design phase.
Figure 1.10 Full Alternative Text
8. Project execution and control. The activities described so far compose
the necessary steps in initializing and preparing a project for execution.
A feasible schedule that integrates task deadlines, budget considerations,
resource availability, and technological requirements, while satisfying
the precedence relations among activities, provides a good starting point
for a project.

It is important, however, to remember that successful implementation of
the initial schedule is subject to unexpected or random effects that are
difficult (or impossible) to predict. In situations in which all resources
are under the direct control of management and activated according to
plan, unexpected circumstances or events may sharply divert progress
from the original plan. For resources that are not under complete
management control, much higher levels of uncertainty may exist, for
example, a downturn in the economy, labor unrest, technology
breakthroughs or failures, and new environmental regulations.
Project control systems are designed with three purposes in mind: (1) to
detect current deviations and to forecast future deviations between actual
progress and the project plans; (2) to trace the source of these
deviations; and (3) to support management decisions aimed at putting
the project back on the desired course.
Project control is based on the collection and analysis of the most recent
performance data. Actual progress, actual cost, resource use, and
technological achievements should be monitored continually. The
information gleaned from this process is compared with updated plans
across all aspects of the project. Deviations in one area (e.g., schedule
overrun) may affect the performance and deviations in other areas (e.g.,
cost overrun).
In general, all operational data collected by the control system are
analyzed, and, if deviations are detected, a scheme is devised to put the
project back on course. The existing plan is modified accordingly, and
steps are taken to monitor its implementation.
During the life cycle of the project, a continuous effort is made to update
original estimates of completion dates and costs. These updates are used
by management to evaluate the progress of the project and the efficiency
of the participating organizations. These evaluations form the basis of
management forecasts regarding the expected success of the project at
each stage of its life cycle.
Schedule deviations might have implications on a project’s finances or
Profit and Loss (P and L), if payments are based on actual progress. If a

schedule overrun occurs and payments are delayed, then cash flow
difficulties might result. Schedule overruns might also cause excess load
on resources as a result of the accumulation of work content. A well-
designed control system in the hands of a well-trained project manager
is the best way to counteract the negative effects of uncertainty.
9. Project termination. A project does not necessarily terminate as soon as
its technical objectives are met. Management should strive to learn from
past experience to improve the handling of future projects. A detailed
analysis of the original plan, the modifications made over time, the
actual progress, and the relative success of the project should be
conducted. The underlying goal is to identify procedures and techniques
that were not effective and to recommend ways to improve operations.
An effort aimed at identifying missing or redundant managerial tools
should also be initiated; new techniques for project management should
be adopted when necessary, and obsolete procedures and tools should be
discarded.
Information on the actual cost and duration of activities and the cost and
utilization of resources should be stored in well-organized databases to
support the planning effort in future projects. Only by striving for
continuous improvement and organizational learning through programs
based on past experience is competitiveness likely to persist in an
organization. Policies, procedures, and tools must be updated on a
regular basis.

1.6 Movement to Project-Based
Work
Increased reliance on the use of project management techniques, especially
for research and development, stems from the changing circumstances in
which modern businesses must compete. Pinto (2002) pointed out that among
the most important influences promoting a project orientation in recent years
have been the following:
1. Shortened product life cycles. Products become obsolete at an
increasingly rapid rate, requiring companies to invest ever-higher
amounts in R&D and new product development.
2. Narrow product launch windows. When a delay of months or even
weeks can cost a firm its competitive advantage, new products are often
scheduled for launch within a narrow time band.
3. Huge influx of global markets. New global opportunities raise new
global challenges, such as the increasing difficulty of being first to
market with superior products.
4. Increasingly complex and technical problems. As technical advances are
diffused into organizations and technical complexity grows, the
challenge of R&D becomes increasingly difficult.
5. Low inflation. Corporate profits must now come less from raising prices
year after year and more from streamlining internal operations to
become ever more efficient.
Durney and Donnelly investigated the effects of rapid technological change
on complex information technology projects (2013). The impact of these and
other economic factors has created conditions under which companies that
use project management are flourishing. Their success has encouraged
increasingly more organizations to give the discipline a serious look as they

contemplate how to become “project savvy.” At the same time, they
recognize that, for all the interest in developing a project-based outlook, there
is a severe shortage of trained project managers needed to convert market
opportunities into profits. Historically, lack of training, poor career ladders,
strong political resistance from line managers, unclear reward structures, and
almost nonexistent documentation and operating protocols made the decision
to become a project manager a risky move at best and downright career
suicide at worst. Increasingly, however, management writers such as Tom
Peters and insightful corporate executives such as Jack Welch have become
strong advocates of the project management role. Between their sponsorship
and the business pressures for enhancing the project management function,
there is no doubt that we are witnessing a groundswell of support that is
likely to continue into the foreseeable future.
Recent Trends in Project
Management
Like any robust field, project management is continuously growing and
reorienting itself. Some of the more pronounced shifts and advances can be
classified as follows:
1. Risk management. Developing more sophisticated up-front
methodologies to better assess risk before significant commitment to the
project.
2. Scheduling. New approaches to project scheduling, such as critical chain
project management, that offer some visible improvements over
traditional techniques.
3. Structure. Two important movements in organizational structure are the
rise of the heavyweight project organization and the increasing use of
project management offices.
4. Project team coordination. Two powerful advances in the area of project
team development are the emphasis on cross-functional cooperation and

the model of punctuated equilibrium as it pertains to intra-team
dynamics. Punctuated equilibrium proposes that rather than evolution
occurring gradually in small steps, real natural change comes about
through long periods of status quo interrupted by some seismic event.
5. Control. Important new methods for tracking project costs relative to
performance are best exemplified by earned value analysis. Although
the technique has been around for some time, its wider diffusion and use
are growing.
6. Impact of new technologies. Internet and web technologies have given
rise to greater use of distributed and virtual project teams, groups that
may never physically interact but must work in close collaboration for
project success.
7. Lean project management. The work of teams of experts from academia
and industry led to the development of the guide to lean enablers for
managing engineering programs (2012). The list of these enablers and
the way they should be implemented is an important step in the
development and application of lean project management
methodologies.
8. Process-based project management. The PMBOK (PMI Standards
Committee 2012) views project management as a combination of the ten
knowledge areas listed in Section 1.14.1. Each area is composed of a set
of processes whose proper execution defines the essence of the field.

1.7 Life Cycle of a Project: Strategic
and Tactical Issues
Because of the degree to which projects differ in their principal attributes,
such as duration, cost, type of technology used, and sources of uncertainty, it
is difficult to generalize the operational and technical issues they each face. It
is possible, however, to discuss some strategic and tactical issues that are
relevant to many types of projects. The framework for the discussion is the
project life cycle or the major phases through which a “typical” project
progresses. An outline of these phases is depicted in Figure 1.11 and
elaborated on by Cleland and Ireland (2006), who identify the long-range
(strategic) and medium-range (tactical) issues that management must
consider. A synopsis follows.
Figure 1.11

Project life cycle.
Figure 1.11 Full Alternative Text
1. Conceptual design phase. In this phase, a stakeholder (client, contractor,
or subcontractor) initiates the project and evaluates potential
alternatives. A client organization may start by identifying a need or a
deficiency in existing operations and issuing a request for proposal
(RFP).
The selection of projects at the conceptual design phase is a strategic
decision based on the established goals of the organization, needs,
ongoing projects, and long-term commitments and objectives. In this
phase, expected benefits from alternative projects, assessment of cost
and risks, and estimates of required resources are some of the factors
weighed. Important action items include the initial “go/no go” decision
for the entire project and “make or buy” decisions for components and
equipment, development of contingency plans for high-risk areas, and
the preliminary selection of subcontractors and other team members who
will participate in the project.
In addition, upper management must consider the technological aspects,
such as availability and maturity of the required technology, its
performance, and expected usage in subsequent projects. Environmental
factors related to government regulations, potential markets, and
competition also must be analyzed.
The selection of projects is based on a variety of goals and performance
measures, including expected cost, profitability, risk, and potential for
follow-on assignments. Once a project is selected and its conceptual
design is approved, work begins on the second phase where many of the
details are ironed out.
2. Advanced development phase. In this phase, the organizational structure
of the project is formed by weighing the tactical advantages and
disadvantages of each possible arrangement mentioned in Section 1.3.4.
Once a decision is made, lines of communication and procedures for
work authorization and performance reporting are established. This

leads to the framework in which the project is executed.
3. Detailed design phase. This is the phase in a project’s life cycle in which
comprehensive plans are prepared. These plans consist of:
Product and process design
Final performance requirements
Detailed breakdown of the work structure
Scheduling information
Blueprints for cost and resource management
Detailed contingency plans for high-risk activities
Budgets
Expected cash flows
In addition—and most importantly—procedures and tools for executing,
controlling, and correcting the project are developed. When this phase is
completed, implementation can begin since the various plans should
cover all aspects of the project in sufficient detail to support work
authorization and execution.
The success of a project is highly correlated with the quality and the
depth of the plans prepared during this phase. A detailed design review
of each plan and each aspect of the project is, therefore, conducted
before approval. A sensitivity analysis of environmental factors that
contribute to uncertainty also may be needed. This analysis is typically
performed as part of “what-if” studies using expert opinions and
simulation as supporting mechanisms.
In most situations, the resources committed to the project are defined
during the initial phases of its life cycle. Although these resources are
used later, the strategic issues of how much to spend and at what rate are
addressed here.

4. Production or execution phase. The fourth life-cycle phase involves the
execution of plans and in most projects dominates the others in effort
and duration. The critical strategic issue here relates to maintaining top
management support, while the critical tactical issues center on the flow
of communications within and among the participating organizations. At
this level, the focus is on actual performance and changes in the original
plans. Modifications can take different forms—in the extreme case, a
project may be canceled. More likely, though, the scope of work,
schedule, and budget will be adjusted as the situation dictates.
Throughout this phase, management’s task is to assign work to the
participating parties, to monitor actual progress and compare it with the
baseline plans. The establishment and operation of a well-designed
communications and control system therefore are necessary.
Support of the product or system throughout its entire life (logistic
support) requires management attention in most engineering projects for
which an operational phase is scheduled to follow implementation. The
preparation for logistic support includes documentation, personnel
training, maintenance, and initial acquisition of spare parts. Neglecting
this activity or giving it only cursory attention can doom an otherwise
successful venture.
5. Termination phase. In this phase, management’s goal is to consolidate
what it has learned and translate this knowledge into ongoing
improvements in the process. Current lessons and experience serve as
the basis for improved practice. Although successful projects can
provide valuable insights, failures can teach us even more. Databases
that store and support the retrieval of project management information
related to project cost, schedules, resource utilization, and so on are
assets of an organization. Readily available, accurate information is a
key factor in the success of future projects.
6. Operational phase. The operational phase is frequently outside the scope
of a project and may be carried out by organizations other than those
involved in the earlier life-cycle stages. If, for example, the project is to
design and build an assembly line for a new model of automobile, then
the operation of the line (i.e., the production of the new cars) will not be

part of the project because running a mass production system requires a
different type of management approach. Alternatively, consider the
design and testing of a prototype electric vehicle. Here, the operational
phase, which involves operating and testing the prototype, will be part of
the project because it is a one-time effort aimed at a very specific goal.
In any case, from the project manager’s point of view, the operational
phase is the most crucial because it is here that a judgment is made as to
whether the project has achieved its technical and operational goals.
Strategic issues such as long-term relationships with customers, as well
as customer service and satisfaction, have a strong influence on upper
management’s attitudes and decisions. Therefore, the project manager
should be particularly aware of the need to open and maintain lines of
communication between all parties, especially during this phase.
Other life cycle models are used including the Spiral model (Boehm,
1986), which emphasizes prototyping and Agile Project Management
(2001), which emphasizes collaboration and communication, with
particular application to software development.

1.8 Factors that Affect the Success
of a Project
A study by Pinto and Slevin (1987) sought to find those factors that
contribute most to a project’s success and to measure their significance over
the life cycle. They found the following ten factors to be of primary
importance. Additional insights are provided by Balachandra and Friar
(1997) regarding new product development and by the Standish Group that
focused on Information Technology (IT) projects since 1994 (The CHAOS
reports 1995–2013).
1. Project mission and goals. Well-defined and intelligible understanding
of the project goals are the basis of planning and executing the project.
Understanding the goals and performance measures used in the
evaluation is important for good coordination of efforts and building
organizational support. Therefore, starting at the project initiation or the
conceptual design phase of the project life cycle, the overall mission
should be defined and explained to team members, contractors, and
other participants.
2. Top management support. The competition for resources, coupled with
the high levels of uncertainty typically found in the project environment,
often leads to conflict and crisis. The continuous involvement of top
management throughout the life cycle of the project increases their
understanding of its mission and importance. This awareness, if
translated into support, may prove invaluable in resolving problems
when crises and conflicts arise or when uncertainty strikes. Therefore,
continued, solid communication between the project manager and top
management is a catalyst for the project to be a success.
3. Project planning. The translation of the project mission, goals, and
performance measures into a workable (feasible) plan is the link
between the initiation phase and the execution or production phase. A
detailed plan that covers all aspects of the project—technical, financial,

organizational, scheduling, communication, and control—is the basis of
implementation. Planning does not end when execution starts because
deviations from the original plans during implementation may
call for replanning and updating from one period to the next. Thus,
planning is a dynamic and continuous process that links changing goals
and performance to the final results.
4. Client consultation. The ultimate user of the project is the final judge of
its success. A project that was completed on time according to the
technical specifications and within budget but was never (or rarely) used
can certainly be classified as a failure. In the conceptual design phase of
the project life cycle, client input is the basis for setting the mission and
establishing goals. In subsequent phases, continual consultation with the
client can help in correcting errors previously made in translating goals
into performance measures. In many projects, the client is a group of
project stakeholders, each having needs and expectations from the
project. However, as a result of changing needs and conditions, a
mission statement that represented accurately the client’s needs in the
conceptual design phase may no longer be valid in the planning or
implementation phases. As discussed in Chapter 6, the configuration
management system provides the link between existing plans and
change requests issued by the client, as well as the project team.
5. Personnel issues. Satisfactory achievement of technical goals without
violating schedule and budgetary constraints does not necessarily
constitute a complete success, even if the stakeholders are satisfied. If
relations among team members, between team members and the client,
or between team members and other personnel in the organization are
poor and morale problems are frequent, then project success is doubtful.
Well-motivated teams with a sufficient level of commitment to the
project and a good relationship with the other stakeholders are the key
determinants of project success.
6. Technical issues. Understanding the technical aspects of the project and
ensuring that members of the project team possess the necessary skills
are important responsibilities of the project manager. Inappropriate
technologies or technical incompatibility may affect all aspects of the

project, including cost, schedule, system performance, and morale.
7. Client acceptance. Ongoing client consultation (as well as consultation
with other important stakeholders) during the project life cycle increases
the probability of success regarding user acceptance. In the final stages
of implementation, the stakeholders evaluate the resulting project and
decide whether it is acceptable. A project that is rejected at this point
must be viewed as a failure.
8. Project control. The continuous flow of information regarding actual
progress is a feedback mechanism that allows the project manager to
cope with uncertainty. By comparing actual progress with current plans,
the project manager can identify deviations, anticipate problems, and
initiate corrective actions. Lower-than-planned achievements in
technical areas as well as schedule and cost deviations detected early in
the life cycle can help the project manager focus on the important issues.
Plans can be updated or partially adjusted to keep the project on
schedule, on budget, and on target with respect to its mission.
9. Communication. The successful transition between the phases of a
project’s life cycle and good coordination between participants during
each phase requires a continuous exchange of information. In general,
communication within the project team, with other parts of the
organization, and between the project manager and the client is made
easier when lines of authority are well defined. The organizational
structure of the project should specify the communication channels and
the information that should flow through each one. In addition, it should
specify the frequency at which this information should be generated and
transmitted. The formal communication lines and a positive working
environment that enhances informal communication within the project
team contribute to the success of a project.
10. Troubleshooting. The control system is designed to identify problem
areas and, if possible, to trace their source through the organization.
Because uncertainty is always a likely culprit, the development of
contingency plans is a valuable preventive step. The availability of
prepared plans and procedures for handling problems can reduce the
effort required for dealing with them should they actually occur.

1.9 About the Book: Purpose and
Structure
This book is designed to bridge the gap between theory and practice by
presenting the tools and techniques most suited for modern project
management. A principal goal is to give managers, engineers, and technology
experts a larger appreciation of their roles by defining a common terminology
and by explaining the interfaces between the underlying disciplines.
Theoretical aspects are covered at a level appropriate for a senior
undergraduate course or a first-year graduate course in either an Engineering
or an MBA program. Special attention is paid to the use and evaluation of
specific tools with respect to their real-world applicability. Whether the book
is adopted for a course or is read by practitioners who want to learn the “tools
of the trade,” we tried to present the subject matter in a concise and fully
integrated manner.
A simulation tool, called the Project Team Builder (PTB), can be used to
integrate the different aspects of project management and to provide hands-on
experience of using these tools in a dynamic, uncertain environment. The
PTB software is available from Sandboxmodel http://
www.sandboxmodel.com/.
The book is structured along functional lines and offers an in-depth treatment
of basic processes, the economic aspects of project selection and evaluation,
the technological aspects of configuration management, and the various
issues related to budgeting, scheduling, and control. By examining these
functions and their organizational links, a comprehensive picture emerges of
the relationship that exists between project planning and implementation.
The end of each chapter contains a series of discussion questions and
exercises designed to stimulate thought and to test the readers’ grasp of the
material. In some cases, the intent is to explore supplementary issues in a
more open-ended manner. Also included at the end of each chapter is a team

http://www.sandboxmodel.com/

project centering on the design and construction of a solid waste disposal
facility known as a thermal transfer plant. As the readers go from one
chapter to the next, they are asked to address a particular aspect of project
management as it relates to the planning of this facility.
Each of the remaining chapters deals with a specific component of project
management or a specific phase in the project life cycle. A short description
of Chapters 2 through 16 follows.
Chapter 2 focuses on process-based project management; it begins with a
discussion of life-cycle models and their importance in planning,
coordination, and control. We then introduce the concept of a process, which
is a group of activities designed to transform a set of inputs consisting of
data, technology, and resources into the desired outputs. The remainder of the
chapter is devoted to the processes underlying the ten project management
knowledge areas contained in the PMBOK. As we explain, these processes,
along with an appropriate information system, constitute the cornerstones of
process-based project management.
In Chapter 3, we address the economic aspects of projects and the
quantitative techniques developed for analyzing a specific alternative. The
long-term perspective is presented first by focusing on the time value of
money. Investment evaluation criteria based on net present value, internal
rate of return, and the payback period are discussed. Next, the short-term
perspective is given by considering the role that cash flow analysis plays in
evaluating projects and comparing alternatives. Ideas surrounding risk and
uncertainty are introduced, followed by some concepts common to decision
making, such as expected monetary value, utility theory, breakeven analysis,
and diminishing returns. Specific decisions such as buy, make, rent, or lease
are also elaborated.
The integration of LCC analysis into the project management system is
covered in Chapter 4. LCC concepts and the treatment of uncertainty in the
analysis are discussed, as well as classification schemes for cost components.
The steps required in building LCC models are outlined and explained to
facilitate their implementation. The idea that the cost of new product
development is only a fraction of the total cost of ownership is a central
theme of the chapter. The total LCC is determined largely in the early phases

of a project when decisions regarding product design and process selection
are being made. Some of the issues discussed in this context include cost
estimation and risk evaluation. The concept of the cost breakdown structure
and how it is used in planning is also presented.
The selection of a project from a list of available candidates and the selection
of a particular configuration for a specific project are two key management
decisions. The purpose of Chapter 5 is to present several basic techniques that
can be used to support this process. Checklists and scoring models are the
simplest and first to be introduced. This is followed by a presentation of the
formal aspects of benefit-cost and cost-effectiveness analysis. Issues related
to risk, and how to deal with them, tie all the material together. The chapter
closes with a comprehensive treatment of decision trees. The strengths and
weakness of each methodology are highlighted and examples are given to
demonstrate the computations.
It is rare that any decision is made on the basis of one criterion alone. To deal
more thoroughly with situations in which many objectives, often in conflict
with one another, must be juggled simultaneously, a value model that goes
beyond simple checklists is needed. In Chapter 6, we introduce two of the
most popular such models for combining multiple, possibly conflicting
objectives into a single measure of performance. Multiattribute utility theory
(MAUT) is the first presented. Basic theory is discussed along with the
guiding axioms. Next, the concepts and assumptions behind the analytical
hierarchy process (AHP) are detailed. A case study contained in the appendix
documents the results of a project aimed at comparing the two approaches
and points out the relative advantages of each.
The OBS and the WBS are introduced in Chapter 7. The former combines
several organizational units that reside in one or more organizations by
defining communication channels for work authorization, performance
reports, and assigning general responsibility for tasks. Questions related to
the selection of the most appropriate organizational structure are addressed,
and the advantages and disadvantages of each are presented. Next, the WBS
of projects is discussed. This structure combines hardware, software, data,
and services performed in a project into a hierarchical framework. It further
facilitates identification of the critical relationships that exist among various

project components. Subsequently, the combined OBS-WBS matrix is
introduced, whereby each element in the lowest WBS level is assigned to an
organizational unit at the lowest level of the OBS. This type of integration is
the basis for detailed planning and control, as explained in subsequent
chapters. We close with a discussion of human resources, focusing on a
project manager’s responsibilities in this area.
In Chapter 8, the process by which the technological configuration of projects
is developed and maintained is discussed. The first topic relates to the
importance of time-based competition, the use of teams, and the role of QFD
in engineering. We then show how tools such as benefit-cost analysis and
MAUT can be used to select the best technological alternative from a set of
potential candidates. Procedures used to handle engineering change requests
via configuration management and configuration control are presented.
Finally, the integration of quality management into the project and its
relationship to configuration test and audit are highlighted.
Network analysis has played an important role in project scheduling over the
past 50 years. In Chapter 9, we introduce the notions of activities, precedence
relations, and task times, and show how they can be combined in an analytic
framework to provide a mechanism for planning and control. The idea of a
calendar and the relationship between activities and time are presented, first
by Gantt charts and then by network models of the activity-on-arrow/activity-
on-node type. This is followed by a discussion of precedence relations,
feasibility issues, and the concepts of milestones, hammock activities, and
subnetworks. Finally, uncertainty is introduced along with the PERT
approach to estimating the critical path and the use of Monte Carlo
simulation to gain a deeper understanding of a project’s dynamics.
Chapter 10 opens with a discussion of the type of resources used in projects.
A classification scheme is developed according to resource availability, and
performance measures are suggested for assessing efficiency and
effectiveness. Some general guidelines are presented as to how resources
should be used to achieve better performance levels. The relationship
between resources and their cost and the project schedule is analyzed, and
mathematical models for resources allocation and leveling are described.
In Chapter 11, we deal with the budget as a tool by which organizational

strategies, goals, policies, and constraints are transformed into an executable
plan that relates task completions and capital expenditures to time.
Techniques commonly used for budget development, presentation, and
execution are discussed. Issues also examined are the relationship between
the duration and timing of activities and the budget of a project, cash flow
constraints and liabilities, and the interrelationship among several projects
performed by a single organizational unit.
The execution of a project is frequently subject to unforeseen difficulties that
cause deviation from the original plans. The focus of Chapter 12 is on project
monitoring and control—a function that depends heavily on early detection
of such deviations. The integration of OBS and WBS elements serves as a
basis for the control system. Complementary components include a
mechanism for tracing the source of each deviation and a forecasting
procedure for assessing their implications if no corrective action is taken.
Cost and schedule control techniques such as the earned value approach are
presented and discussed.
Engineering projects where new technologies are developed and implemented
are subject to high levels of uncertainty. In Chapter 13, we define R&D
projects and highlight their unique characteristics. The typical goals of such
projects are discussed, and measures of success are suggested. Techniques for
handling risk, including the idea of parallel funding, are presented. The need
for rework or repetition of some activities is discussed, and techniques for
scheduling R&D projects are outlined. The idea of a portfolio is introduced,
and tools used for portfolio management are discussed. A case study that
involves screening criteria, project selection and termination criteria, and the
allocation of limited resources is contained in the appendix.
A wide variety of software has been developed to assist the project manager.
In Chapter 14, we discuss the basic functions and range of capabilities
associated with these packages. A classification system is devised, and a
process by which the most appropriate package can be selected for a project
or an organization is outlined.
In Chapter 15, the need to terminate a project in a planned, orderly manner is
discussed. The process by which information gathered in past projects can be
stored, retrieved, and analyzed is presented. Post-mortem analysis is

suggested as a vehicle by which continuous improvement can be achieved in
an organization. The goal is to show how projects can be terminated so that
the collective experience and knowledge can be transferred to future
endeavors.
In Chapter 16, we present new developments in teaching Project
Management in MBA and Engineering programs. First we discuss the need to
improve the way we teach project management. Next the idea of Simulation
Based Training (SBT) as a way to gain “hands-on” experience in a
controlled, safe environment where the cost of errors is minimized and
learning by doing is implemented. The “Project Team Builder (PTB)”
simulator is described next with a focus on the main features of this SBT tool.
This is followed by two specific examples based on our experience using
Simulation Based Training and PTB in the Global Network for Advanced
Management (GNAM) New Product Development (NPD) Course and in a
Project Management course at Columbia University, School of Engineering.
It goes without saying that the huge body of knowledge in the area of project
management cannot be condensed into a single book. Over the past 25 years
alone, much has been written on the subject in technical journals, textbooks,
company reports, and trade magazines. In an effort to cover some of this
material, a bibliography of important works is provided at the end of each
chapter. The interested reader can further his or her understanding of a
particular topic by consulting these references.
TEAM PROJECT* Thermal
Transfer Plant
* The authors thank Warren Sharp and Ian St. Maurice for their help in
writing this case study.
Introduction

To exercise the techniques used for project planning and control, the reader is
encouraged to work out each aspect of the thermal transfer plant case study.
At the end of each chapter, a short description of the relevant components of
the thermal transfer plant is provided along with an assignment. If possible,
the assignment should be done in groups of three or four to develop the
interpersonal and organizational skills necessary for teamwork.
Not all of the information required for each assignment is given. Before
proceeding, it may be necessary for the group to research a particular topic
and to make some logical assumptions. Accordingly, there is no “correct
solution” to compare recommendations and conclusions. Each assignment
should be judged with respect to the availability of information and the force
of the underlying assumptions.
Total Manufacturing Solutions, Inc.
Total Manufacturing Solutions, Inc. (TMS) designs and integrates
manufacturing and assembly plants. Their line of products and services
includes the selection of manufacturing and assembly processes for new or
existing products, the design and selection of manufacturing equipment,
facilities design and layout, the integration of manufacturing and assembly
systems, and the training of personnel and startup management teams. The
broad range of services that TMS provides to its customers makes it a unique
and successful organization. Its headquarters are in Nashville, Tennessee,
with branches in New York and Los Angeles.
TMS began operations in 1980 as a consulting firm in the areas of industrial
engineering and operations management. In the late 1990s, the company
started its design and integration business. Recently it began promoting just-
in-time systems and group technology-based manufacturing facilities. The
organization structure of TMS is depicted in Figure 1.12; financial data are
presented in Tables 1.3 and 1.4.

Figure 1.12
Simplified organization chart.
Figure 1.12 Full Alternative Text
TABLE 1.3 TMS Financial
Data: Income Statement
Income Statement ($1,000)
Year ending December 31, 2004

Net sales $47,350
 Cost of goods sold
 Direct labor 26,600
 Overhead  6,000
32,600
Gross profit 14,750
General and administrative 5,350
Marketing  4,900
10,250
Profit before taxes 4,500
Income tax (32%)  1,440
Net profit $3,060
TABLE 1.4 TMS Financial
Data: Balance Sheet
Balance Sheet ($1,000)
Year ending December 31, 2004
Assets
Current assets
 Cash $1,100
 Accounts receivables 1,500
 Inventory 12
 Other    3
  Total current assets 2,615
Net fixed assets   325
  Total assets 2,940

Liabilities
Current Liabilities
 Notes payable 35
 Accounts payable 137
 Accruals    90
  Total current liabilities 262
Long-term debt 50
Capital stock and surplus 1,300
Earned surplus  1,328
 Net worth  2,628
  Total liabilities $2,940
TMS employs approximately 500 people, 300 of whom are in the Nashville
area, 100 in New York, and 100 in Los Angeles. Approximately 50% of these
are industrial, mechanical, and electrical engineers, and approximately 10%
also have MBA degrees, mostly with operations management concentrations.
The other employees are technicians, support personnel, and managers. Some
information on labor costs follows.
Engineers $50,000/year
Technicians $25/hour
Administrators $35,000/year
Other $10/hour
These rates do not include fringe benefits or overhead. Moreover, bear in
mind that individual salaries are a function of experience, position, and
seniority within the company.
In the past 10 years, TMS averaged 20 major projects annually. Each project
consisted of the design of a new manufacturing facility, the selection,
installation, and integration of equipment, and the supervision of startup
activities. In addition, TMS experts are consultants to more than 100 clients,
many of whom own TMS-designed facilities.
The broad technical basis of TMS in the areas of mechanical, electrical, and

industrial engineering and its wide-ranging experience are its most important
assets. Management believes that the company is an industry leader in
automatic assembly, material handling, industrial robots, command and
control, and computer-integrated manufacturing. TMS is using subcontractors
mainly in software development and, when necessary, for fabrication,
because it does not have any shops or manufacturing facilities.
Recently, management has decided to expand its line of operations and
services into the area of recycling and waste management. New regulations in
many states are forcing the designers of manufacturing plants to analyze and
solve problems related to waste generation and disposal.
Your team has been selected by TMS-Nashville to work on this new line of
business. Your first assignment is to analyze the needs and opportunities in
your geographical area. On the bases of a literature search and conversations
with local manufacturers, environmentalists, and politicians, making
whatever assumptions you believe are necessary, write a report and prepare a
presentation that answers the following questions:
1. How well does this new line of business fit into TMS operations? What
are the existing or potential opportunities?
2. How should a waste management project be integrated into TMS’s
current organizational structure?
3. What are the problems that TMS might encounter should it embark on
this project? How might these problems affect the project? How might
they affect TMS’s other business activities?
4. If a project is approved in waste management, then what would its major
life-cycle phases be?
Any assumptions regarding TMS’s financial position and borrowing power,
personnel, previous experience, and technological capabilities relating, for
example, to computer-aided design, should be stated explicitly.

Discussion Questions
1. Explain the difference between a project and a batch-oriented production
system.
2. Describe three projects, one whose emphasis is on technology, one with
emphasis on cost, and one with emphasis on scheduling.
3. Identify a project that is “risk free.” Explain why this project is not
subject to risk (low probability of undesired results, low cost of
undesired results, or both).
4. In the text, it is stated that a project manager needs a blend of technical,
administrative, and interpersonal skills. What attributes do you believe
are desirable in an engineering specialist working on a project in a
matrix organization?
5. Write a job description for a project manager.
6. Identify a project with which you are familiar, and describe its life-cycle
phases and between 5 and 10 of the most important activities in each
phase of its life cycle.
7. Find a recent news article on an ongoing project, evaluate the
management’s performance, and explain how the project could be better
organized and managed.
8. Analyze the factors that affect the success of projects as a function of the
project’s life cycle. Explain in which phase of the life cycle each factor
is most important, and why.
9. In a matrix management structure, the person responsible for a specific
activity on a specific project has two bosses. What considerations in a
well-run matrix organization reduce the resulting potential for conflict?
10. Outline a strategy for effective communication between project

personnel and the customer (client).
11. Select a project and discuss what you think are the interfaces between
the engineers and managers assigned to the project.
12. The project plan is the basis for monitoring, controlling, and evaluating
the project’s success once it has started. List the principal components or
contents of a project plan.

Exercises
1. 1.1 What type of production system would be associated with the
following processes?
1. A production line for window assemblies
2. A special order of 150 window assemblies
3. Supplying 1,000 window assemblies per month throughout the year
2. 1.2 You decided to start a self-service restaurant. Identify the stages of
this project and the type of production system involved in each stage,
from startup until the restaurant is running well enough to sell.
3. 1.3 Select two products and two services and describe the needs that
generated them. Give examples of other products and services that could
satisfy those needs equally well.
4. 1.4 You have placed an emergency order for materials from a company
that is located 2,000 miles away. You were told that it will be shipped
by truck and will arrive within 48 hours, the time at which the materials
are needed. Discuss the issues surrounding the probability that the
shipment will reach you within the 48 hours. How would things change
if shipment were by rail?
5. 1.5 Your plumber recommends that you replace your cast iron pipes
with copper pipes. He claims that although the price for the job is
$7,000, he has to add $2,000 for unforeseen expenses. Discuss his
proposal.
6. 1.6 In statistical analysis, the coefficient of variation is considered to be
a measure of uncertainty. It is defined as the ratio of the standard
deviation to the mean. Select an activity, say driving from your home to
school, generate a frequency distribution for that activity, and calculate
its mean and the standard deviation. Analyze the uncertainty.

7. 1.7 Specify the type of uncertainties involved in completing each of the
following activities successfully.
1. Writing a term paper on a subject that does not fall within your
field of study
2. Undertaking an anthropological expedition in an unknown area
3. Driving to the airport to pick up a friend
4. Buying an item at an auction
8. 1.8 Your professor told you that the different departments in the school
of business are organized in a matrix structure. Functional areas include
organizational behavior, mathematics (operations research and
statistics), and computer science. Develop an organization chart that
depicts these functions along with the management, marketing,
accounting, and finance departments. What is the product of a business
school? Who is the customer?
9. 1.9 Provide an organizational structure for a school of business
administration that reflects either a functional orientation or a product
orientation.
10. 1.10 Assume that a recreational park is to be built in your community
and that the city council has given you the responsibility of selecting a
project manager to lead the effort. Write a job description for the
position. Generate a list of relevant criteria that can be used in the
selection process, and evaluate three fictitious candidates (think about
three of your friends).
11. 1.11 Write an RFP soliciting proposals for preparing your master’s
thesis. The RFP should take into account the need for tables, figures, and
multiple revisions. Make sure that it adequately describes the nature of
the work and what you expect so that there will be no surprises once a
contract is signed.
12. 1.12 Explain how you would select the best proposal submitted in

Exercise 1.11 . That is, what measures would you use, and how would
you evaluate and aggregate them with respect to each proposal?
13. 1.13 The following list of activities is relevant to almost any project.
Identify the phase in which each is typically performed, and order them
in the correct sequence.
1. Developing the network
2. Selecting participating organizations
3. Developing a calendar
4. Developing corrective plans
5. Executing activities
6. Developing a budget
7. Designing a project
8. Recommending improvement steps
9. Monitoring actual performance
10. Managing the configuration
11. Allocating resources to activities
12. Developing the WBS
13. Estimating the LCC
14. Getting the customer’s approval for the design
15. Establishing milestones
16. Estimating the activity duration

14. 1.14 Drawing from your personal experience, give two examples for
each of the following situations.
1. The original idea was attractive but not sufficiently important to
invest in.
2. The idea was compelling but was not technically feasible.
3. The idea got past the selection process but was too expensive to
implement.
4. The idea was successfully transformed into a completed project.
15. 1.15 List two projects with which either you or your organization is
involved that are currently in each of the various life-cycle phases.
16. 1.16 Select three national, state, or local projects (e.g., construction of a
new airport) that were completed successfully and identify the factors
that affect their success. Discuss the attending risks, uncertainty,
schedule, cost, technology, and resources usage.
17. 1.17 Identify three projects that have failed, and discuss the reasons for
their failure.

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Scheduling and Control, Seventh Edition, John Wiley & Sons, New
York, 2000.
Kezsbom, D. S. and K. A. Edward, The New Dynamic Project
Management: Winning Through the Competitive Advantage, John Wiley
& Sons, New York, 2001.
Meredith, J. R. and S. J. Mantel, Jr., Project Management: A
Managerial Approach, Fourth Edition, John Wiley & Sons, New York,
1999.

Oehmen, J., Oppenheim, B. W., Secor, D., Norman, E., Rebentisch, E.,
Sopko, J. A., . . . and Driessnack, J., The Guide to Lean Enablers for
Managing Engineering Programs, 2012.
PMI Standards Committee, A Guide to the Project Management Body of
Knowledge (PMBOK), Project Management Institute, Newtown Square,
PA, 2012 (http://www.PMI.org).
Randolph, W.A. and Z.B. Posner, Checkered Flag Projects: Ten Rules
for Creating and Managing Projects that Win! Second Edition, Prentice
Hall, Upper Saddle River, NJ, 2002.

http://www.PMI.org

Appendix 1A Engineering Versus
Management
1A.1 Nature of Management
Practically everyone has some conception of the meaning of the word
management and to some extent understands that it requires talents that are
distinct from those needed to perform the work being managed. Thus, a
person may be a first-class engineer but unable to manage a high-tech
company successfully. Similarly, a superior journeyman may make an
inferior foreman. We all have read about cases in which an enterprise failed
not because the owner did not know the field, but because he was a poor
manager. To cite just one example, Thomas Edison was perhaps the foremost
inventor of the last century, but he lost control of the many businesses that
grew from his inventions because of his inability to plan and to direct and
supervise others.
So what exactly is management, and what does a good manager have to
know? Although there is no simple answer to this question, there is general
agreement that, to a large extent, management is an art grounded in
application, judgment, and common sense. To be more precise, it is the art of
getting things done through other people. To work effectively through others,
a manager must be able to perform competently the seven functions listed in
Table 1A.1. Of those, planning, organizing, staffing, directing, and
controlling are fundamental. If any of these five functions is lacking, then the
management process will not be effective. Note that these are necessary but
not sufficient functions for success. Getting things done through people
requires the manager also to be effective at motivating and leading others.
The relative importance of the seven functions listed in Table 1A.1 may vary
with the level of management. Top management success requires an
emphasis on planning, organizing, and controlling. Middle-level management
activities are more often concerned with staffing, directing, and leading.

Lower-level managers must excel at motivating and leading others.
1A.2 Differences between
Engineering and Management
Many people start out as engineers and, over time, work their way up the
management ladder. As Table 1A.2 shows, the skills required by a manager
are very different from those normally associated with engineering (Badawy
and Trystram 1995, Eisner 2002).
TABLE 1A.1 Functions of
Management
Functions Description
Planning
The manager first must decide what must be done.
This means setting short- and long-term goals for the
organization and determining how they will be met.
Planning is a process of anticipating problems,
analyzing them, estimating their likely impacts, and
determining actions that will lead to the desired
outcomes, objectives, or goals.
Organizing
Establishing interrelationships between people and
things in such a way that human and material
resources are effectively focused toward achieving the
goals of the enterprise. Organizing involves grouping
activities and people, defining jobs, delegating the
appropriate authority to each job, specifying the
reporting structure and interrelationships between
these jobs, and providing the policies or other means
for coordinating these jobs with each other.
In organizing, the manager establishes positions and

Staffing
decides which duties and responsibilities properly
belong to each. Staffing involves appraising and
selecting candidates, setting the compensation and
reward structure for each job, training personnel,
conducting performance appraisals, and performing
salary administration. Turnover in the workforce and
changes in the organization make it an ongoing
function.
Directing
Because no one can predict with certainty the
problems or opportunities that will arise, duties must
naturally be expressed in general terms. Managers
must guide and direct subordinates and resources
toward the goals of the enterprise. This involves
explaining, providing instructions, pointing out proper
directions for the future, clarifying assignments,
orienting personnel in the most effective directions,
and channeling resources.
Motivating
A principal function of lower management is to instill
in the workforce a commitment and enthusiasm for
pursuing the goals of the organization. Motivating
refers to the interpersonal skills to encourage
outstanding human performance in others and to instill
in them an inner drive and a zeal to pursue the goals
and objectives of the various tasks that may be
assigned to them.
Leading
This means encouraging others to follow the example
set for them, with great commitment and conviction.
Leading involves setting examples for others,
establishing a sense of group pride and spirit, and
instilling allegiance.
Controlling
Actual performance will normally differ from the
original plan, so checking for deviations and taking
corrective actions is a continuing responsibility of
management. Controlling involves monitoring
achievements and progress against the plans,
measuring the degree of compliance with the plans,

deciding when a deviation is significant, and taking
actions to realign operations with the plans.
TABLE 1A.2 Engineering
Versus Management
What engineers do What managers do
Minimize risks, emphasize
accuracy and mathematical
precision
Take calculated risks, rely heavily
on intuition, take educated guesses,
and try to be “about right”
Exercise care in applying
sound scientific methods, on
the basis of reproducible data
Exercise leadership in making
decisions under widely varying
conditions, based on sketchy
information
Solve technical problems on
the basis of their own
individual skills
Solve techno-people problems on
the basis of skills in integrating the
talents and behaviors of others
Work largely through their
own abilities to get things
done
Work through others to get things
done
Engineering involves hands-on contact with the work. Managers are always
one or more steps removed from the shop floor and can influence output and
performance only through others. An engineer can derive personal
satisfaction and gratification in his or her own physical creations, and from
the work itself. Managers must learn to be fulfilled through the achievements
of those whom they supervise. Engineering is a science. It is characterized by
precision, reproducibility, proven theories, and experimentally verifiable
results. Management is an art. It is characterized by intuition, studied
judgments, unique events, and one-time occurrences. Engineering is a world
of things; management is a world of people. People have feelings, sentiments,
and motives that may cause them to behave in unpredictable or unanticipated
ways. Engineering is based on physical laws, so that most events occur in an
orderly, predictable manner.

1A.3 Transition from Engineer to
Manager
Engineers are often propelled into management out of economic
considerations or a desire to take on more responsibility. Some organizations
have a dual career ladder that permits good technical people to remain in the
laboratory and receive the same financial rewards that attend supervisory
promotions. This type of program has been most successful in research-
intensive environments such as those found at the IBM Research Center in
Yorktown Heights and the Department of Energy research laboratories
around the United States.
Nevertheless, when an engineer enters management, new perspectives must
be acquired and new motivations must be found. He or she must learn to
enjoy leadership challenges, detailed planning, helping others, taking risks,
making decisions, working through others, and using the organization. In
contrast to the engineer, the manager achieves satisfaction from directing the
work of others (not things), exercising authority (not technical knowledge),
and conceptualizing new ways to do things (not doing them). Nevertheless,
experience indicates that the following three critical skills are the ones that
engineers find most troublesome to acquire: (1) learning to trust others, (2)
learning how to work through others, and (3) learning how to take
satisfaction in the work of others.
The step from engineering to management is a big one. To become successful
managers, engineers usually must develop new talents, acquire new values,
and broaden their point of view. This takes time, on-the-job and off-the-job
training, and careful planning. In short, engineers can become good managers
only through effective career planning.
Additional References
Badawy, M. K. and D. Trystram, Developing Managerial Skills in
Engineers and Scientists, John Wiley & Sons, New York, 1995.

Eisner, H., Essentials of Project and Systems Engineering Management,
Second Edition, John Wiley & Sons, New York, 2002.
Jones, G. R. and J. M. George, Essentials of Contemporary
Management, McGraw-Hill, New York, 2003.
Moore, D. C. and D. S. Davies, “The Dual Ladder: Establishing and
Operating It,” Research Management, Vol. 20, No. 4, pp. 21–27, 1977.

Chapter 2 Process Approach to
Project Management
2.1 Introduction
A project is an organized set of activities aimed at accomplishing a specific,
non-routine, or low-volume task such as designing an e-commerce website or
building a hypersonic transport. Projects are aimed at meeting the objectives
and expectations of their stakeholders. Because of the need for specialization,
as well as the number of hours usually required, most projects are undertaken
by multidisciplinary teams. In some cases, the team members belong to the
same organization, but often, at least a portion of the work is assigned to
subcontractors, consultants, or partner firms. Leading the effort is the project
manager, who is responsible for the successful completion of all activities.
Coordination between the individuals and organizations involved in a project
is a complex task and a major component of the project manager’s job. To
ensure success, integration of deliverables produced at different geographical
locations, at different times, by different people, in different organizations is
required.
Projects are typically performed under time pressure, limited budgets, tight
cash flows, and uncertainty using shared resources. The triple constraint of
time, cost, and scope (i.e., project deliverables that are required by the end-
customers or end-users) requires the project manager to repeatedly make
tradeoffs between these factors with the implicit goal of balancing risks and
benefits. Moreover, disagreements among stakeholders on the best course of
action to follow can lead to conflicting direction and poor resource allocation
decisions. Thus, a methodology is required to support the management of
projects. Early efforts in developing such a methodology focused on specific
tools for different aspects of the problem. Tools for project scheduling, such
as the Gantt chart and the critical path method, were developed along with
tools for resource allocation, project budgeting, and project control. Each is

covered in considerable detail in the chapters that follow.
Nevertheless, although it is important to gain an appreciation of these tools,
each is limited in the view that it provides the project manager. For example,
tools for scheduling rarely address problems related to configuration
management, and tools for budgeting typically do not address problems
associated with quality. The integration of these tools in a way that supports
decision making at each stage in a project’s life cycle is essential for
understanding the dynamics of the project environment. This chapter
identifies the relevant management processes and outlines a framework for
applying them to both single and multiple projects.
A project management process is a collection of tools and techniques that are
used on a predefined set of inputs to produce a predefined set of outputs. The
processes are interconnected and interdependent. The full collection forms a
methodology that supports all of the aspects of project management
throughout a project’s life cycle—from the initiation of a new project to its
(successful) completion and termination.
The framework that we propose to organize and study the relevant processes
is based on the ten knowledge areas identified by the Project Management
Institute (PMI) and published as the Project Management Body of
Knowledge (PMBOK). PMI also conducts a certification program based on
the PMBOK. A Project Management Professional certificate can be earned
by passing an exam and accumulating relevant experience in the project
management discipline.
The benefit gained from implementing the full set of project management
processes has been evident in many organizations. Although each project is a
one-time effort, process-oriented management promotes learning and
teamwork through the use of a common set of tools and techniques. A
detailed description of their use is provided in the remainder of the book.
Each chapter deals with a specific knowledge area and highlights the tools
and techniques in the form of mathematical models, templates, charts, and
checklists used in the processes developed for that area.

2.1.1 Life-Cycle Models
Because a project is a transitory effort designed to achieve a specific set of
goals, it is convenient to identify the phases that accompany the
transformation of an idea or a concept into a product or system. The
collection of such phases is defined as the project life cycle.
A life-cycle model is a set of stages or phases through which a family of
projects goes, in which each phase may be performed sequentially or
concurrently. The project life cycle defines the steps required to achieve the
project goals as well as the contents of each step. The end of each phase often
serves as a checkpoint or milestone for assessing progress, as the actual status
of the project is compared with the original plan in an effort to identify
deviations in cost, schedule, and performance so that any necessary
corrective action can be taken.
For software projects, the spiral life-cycle model proposed by Boehm (1988)
and further refined by Muench (1994) has gained widespread popularity. The
model, shown in Figure 2.1, is very useful for repetitive development in
which a project goes through the same phases several times, each time
becoming more complete; that is, closer to the final product. It has two main
distinguishing features. The first is a cyclic approach for incrementally
expanding a system’s definition and degree of implementation while
decreasing its level of risk. The other is a set of anchor point milestones for
ensuring stakeholder commitment to feasible and mutually satisfactory
solutions. The general idea is to ensure that the riskier aspects of the project
are completed first to avoid failures in an advanced phase.

Figure 2.1
Spiral life-cycle model.
Figure 2.1 Full Alternative Text
Construction projects also have their own set of life-cycle models, such as the
one proposed by Morris (1988). In this model, a project is divided into four
stages to be performed in sequence.
Stage
(Feasibility) This stage terminates with a go/no go decision for the
project. It includes a statement of goals and objectives, conceptual

I design, high-level feasibility studies, the formulation of strategy, and
the approval of both the design and the strategy by upper management.
Stage
II
(Planning and Design) This stage terminates with the awarding of
major contracts. It includes detailed design, cost and schedule
planning, contract definitions, and the development of the road map
for execution.
Stage
III
(Production) This stage terminates with the completion of the facility.
It includes construction, installation of utilities, equipment acquisition
and setup, landscaping, roadwork, interior appointments, and
operational testing.
Stage
IV
(Turnover and Startup) This stage terminates with full operation of the
facility. It includes final testing and the development of a maintenance
plan.
Clearly this model does not fit research and development (R&D) projects or
software projects because of the sequential nature in which the work is
performed. In R&D projects, for example, it is often necessary to undertake
several activities in parallel with the hope that at least one will turn out to
meet technological and cost objectives.
Other life-cycle models include:
Waterfall model. Each phase is completed before the initiation of the
following phase. This model is most relevant for information technology
projects.
Incremental release model. In the early phases, an imperfect version of
the project is developed with the goal of maximizing market share.
Toward the later phases, a final version of the product emerges. This is a
special case of the spiral model.
Prototype model. In the early phases, the rudimentary functions
associated with the user interface are developed before the product itself
is finalized. This model is most appropriate for information technology
projects.
By integrating the ideas of project processes and the project life cycle, a

methodology for project management emerges. The methodology is a
collection of processes whereby each process is associated with a phase of
the project life cycle. The project manager is responsible for identifying
individuals who have the necessary skills and experience and for assigning
them to the appropriate processes. A project’s likelihood of success increases
when the definition of inputs and outputs of each process is clear and when
team members are clear about the lines of authority, individual
responsibilities, and overall project objectives. Clear communication of the
overall project’s objectives as well as clear delineation of major work streams
is necessary to ensure a well-coordinated flow of information and good
communications between project participants.
Life-cycle models are indispensable project management tools. They
provide a simple, yet effective, means of monitoring and controlling a project
at each stage of its development. As each phase comes to an end, all results
are documented and all deliverables are certified with respect to quality and
performance standards.
2.1.2 Example of a Project Life
Cycle
The DOD uses a simple life-cycle model for systems acquisition (US DOD
5000.2 1993). Its components are shown in Figure 2.2. The project starts only
after the determination of mission needs and approval is given. At the end of
stage IV the system is taken out of service. This is the end of the life cycle.

Figure 2.2
DOD life-cycle model.
Figure 2.2 Full Alternative Text
2.1.3 Application of the Waterfall
Model for Software Development
A waterfall model captures the relevant phases of software development
effort through a series of stages. There are specific objectives to be
accomplished in each stage, and each activity must be deemed successful for
work to proceed to the subsequent phase. The process is usually considered
non-iterative. Each phase requires the delivery of particular documentation
(contract data requirements list). In addition, many of the phases require
successful completion of a government review process. Critics of the
waterfall model, in fact, find that the model is geared to recognize documents
as a measure of progress rather than actual results.
The nine major activities are as follows:
1. Systems concept/system requirements analysis
2. Software requirements analysis
3. Software parametric cost estimating
4. Preliminary design
5. Detailed design
6. Coding and computer software unit testing
7. Computer software component integration and testing

8. Computer software configuration item testing
9. System integration and operational testing
A schematic of the process, representing concurrent hardware and software
development, is given in Figure 2.3.

Figure 2.3
Waterfall model.

Figure 2.3 Full Alternative Text
An alternative approach to software development involves the use of
incremental builds. With this approach, software development begins with
the design of certain core functions to meet critical requirements. Each
successive software build (iteration on product development) provides
additional functions or enhances performance. Once system requirements are
defined and preliminary system design is complete, each build may follow
the waterfall pattern for subsequent development phases. Each successive
build will usually have to be integrated with previous builds.

2.2 Project Management Processes
A process is a group of activities designed to transform a set of inputs into the
desired outputs. The transformation consists of the following three elements:
1. Data and information
2. Decision making
3. Implementation and action
A well-defined set of processes, supported by an appropriate information
system (composed of a database and a model base) and implemented by a
team trained in performing the processes, is a cornerstone in modern project
management.
The following discussion is based on the work of Shtub (2001).
2.2.1 Process Design
The design of a process must address the following issues.
1. Data required to support decisions, including:
data sources
how the data should be collected
how the data should be stored
how the data should be retrieved
how the data should be presented as information to decision makers
2. Models required to support decisions. A model is a simplified

representation of reality that is used in part to transform data into useful
information. When a problem is too complicated to solve or some
information is missing, simplifying assumptions are made and a model
is developed. There are many types of models including mathematical,
physical, and statistical. The model—the simplified presentation of
reality—is analyzed and a solution is obtained. Sensitivity analysis is
then used to evaluate the applicability of the solution found to the real
problem and its sensitivity to the simplifying assumptions. Consider, for
example, a simple way of estimating the time required to travel a given
distance. Assuming a constant speed and movement in a straight line,
one possibility would be: time = distance/speed. This simple algebraic
model is frequently used, although most vehicles do not travel at a
constant speed or in a straight line. In a similar way, a variety of models
are used in project management, including:
models that support routine decisions
models that support ad-hoc decisions
models used for project control
Their value depends on how useful their estimates are in practice.
3. Data and models integration:
How data from the database are analyzed by the models
How information generated by the models is transferred and
presented to decision makers
2.2.2 PMBOK and Processes in the
Project Life Cycle
A well-defined set of processes that apply to a large number of projects is
discussed in the PMBOK published by the PMI. Although some of the

PMBOK processes may not apply to all projects, and others may need to be
modified before they can be applied, the PMBOK is a widely accepted,
widely known source of information. The processes are classified in two
ways.
1. By project phase:
initiating processes
planning processes
executing processes
monitoring and controlling processes
closing processes
2. By knowledge areas or management functions:
Knowledge areas are:
Project Integration Management
Project Scope Management
Project Time Management
Project Cost Management
Project Quality Management
Project Human Resource Management
Project Communications Management
Project Risk Management
Project Procurement Management

Project Stakeholder Management

2.3 Project Integration Management
2.3.1 Accompanying Processes
Project integration management involves six processes:
1. Project charter development—This process involves some sort of cost-
benefit analysis that leads to a go/no go decision regarding a proposed
project. A project charter is created at the conclusion of this phase, and a
project manager is selected. The charter defines the business or societal
need that the project addresses, the project timeline, and the budget.
Considerations like the fit of the proposed project to the organization
strategy, stakeholders’ needs and expectations, competition,
technological and economic feasibility are important in this process.
2. Project plan development—gathering results of various planning
processes and integrating it all into an acceptable plan.
3. Management and directing project execution—implementation of the
project plan during the project execution.
4. Monitoring and controlling the project work during execution—an effort
to identify deviations from the project plan in order to take corrective
actions when needed.
5. Integrated change control—coordination of changes in scope, schedule,
budget, and other parts of the plans for the entire project.
6. Project closing—the last process in the project life cycle ensuring that
the project work was done, deliverables are accepted, and all contracts
with different stakeholders are terminated.
The purpose of these processes is to ensure coordination across the various
work streams of the project.

Integration management is concerned with the identification, monitoring, and
control of all interfaces between the various components of a project,
including:
1. Human interface—the personnel associated with the various aspects of
the project such as the project team members, subcontractors,
consultants, stakeholders, and customers.
2. Scope interface—if the scope is not defined properly, then some
required work may not be performed or work that is not required may be
done.
3. Time interface—adequate resources must be provided to avoid delays
and late deliverables.
4. Communication interface—Timely transfer of the right information to
the right stakeholders at the right time is critical to project success.
5. Technological interface—since in most projects the work content is
divided among project participants, the interfaces between the
deliverables supplied by the participants must be managed throughout
the project to ensure smooth integration of the parts into the final
deliverables as specified.
Proper integration management requires proper communication between
members of the project’s stakeholders; indeed, one of the knowledge areas is
communication management. The life-cycle model plays an important role.
The project plan is developed in the early phases of the project, whereas
execution of the plan and change control occurs during the later phases.
2.3.2 Description
Project charter development
Many alternative project proposals may exist. On the basis of an appropriate

set of evaluation criteria and a selection methodology, the best alternative is
chosen, a project charter is issued, and a project manager is selected.
Projects are initiated in response to a need typically arising at a level in the
organization that is responsible for setting strategic goals. Research has
shown that the most important criterion guiding organizations in choosing
projects is financial. Projects are selected for implementation when they
support clear business goals and have an attractive rate of return or net
present value. A second factor that is likely to trigger a new project is an
advance in technology. In the electronics industry, for example, the steady
reduction in cost and increase in performance of integrated circuits and
memory chips has forced firms to offer new products on a semiannual basis,
just to remain competitive.
In summary, projects are initiated when:
1. a defined need arises
2. there is strategic support and a willingness to undertake the project
3. the technology is compelling
4. there are available resources
Potential projects can be classified in several ways:
1. External versus internal projects; that is, projects performed for
customers outside the organization versus customers within the
organization
2. Projects that are initiated to:
1. address a business opportunity
2. solve a problem
3. follow a directed order
3. Due date and completion time

4. Organizational priority
The project plan
The project plan and its execution are the major outputs of this process. The
plan is based on inputs from other processes such as scope planning, schedule
development, resource planning, and cost estimating, along with historical
information and organizational policies. It is updated continuously on the
basis of corrective actions triggered by approved change requests and
analysis of performance measures. As a tool for coordination, the documents
that define the plan must address:
1. The time dimension—when is each stage performed
2. The scope dimension—what should be achieved
3. The human dimension—who does what
4. The risk dimension—how to deal with uncertainty
5. The resource dimension—the plan must ensure availability of resources
6. The information and communication dimension—the way data is
collected, analyzed, stored, and communicated to stakeholders must be
addressed as part of the project plan
The primary purpose of the plan is to guide the execution of the project. It
assists the project manager in leading the project team and in achieving the
project’s goals. Critical characteristics are fluidity and flexibility, allowing
changes to be incorporated easily as they occur. The corresponding document
typically consists of the following parts:
1. Preface, including a general review, goals, outputs, scope of work to be
done, and technical specifications
2. Project organization description—interfaces, organizational structure,
responsibilities

3. Management processes—for example, procurement, reporting, and
monitoring
4. Technical processes—for example, design and verification
5. Execution—the way work will be done, scheduling (i.e., timeline) and
budget information, resource allocation, and information flow
A project plan should reflect the needs and expectations of stakeholders.
Therefore, a project manager should perform an analysis, prior to formally
proposing a project idea, to determine stakeholders’ principal concerns and
perspectives and understand the organization’s underlying unmet needs.
This information can be used to develop guidelines for managing the
relationship between project personnel and the stakeholders. The level of
influence and the needs and expectations of any particular stakeholder may
have a significant impact on the success or failure of the project. Moving in a
direction that is at crossroads with an influential stakeholder can spell doom.
Execution of the plan
Execution of the project plan produces the deliverables. For integration
management to be successful, a project manager must be skilled in the three
areas listed below. Some of these skills are innate, whereas others can be
learned.
1. The technology that is used by the project is referred to as the product
scope. Often the project manager can delegate responsibilities for
technological issues to a team member with detailed expertise. Most of
the effort of the project manager, then, is related to integration—seeing
that the pieces come together properly.
2. The organizational factor—the project manager must understand the
nature of the organization, the human interrelations, the common types
of interactions, and so on. Organizational understanding can be
expressed as follows.

1. Human resources (HR) framework—the focus is on creating
harmony among the organizational needs, needs of the project
participants, and the project requirements.
2. Cultural framework—the focus is on understanding the
organizational culture; that is, the values of the organization.
3. Symbolic framework—the focus is on positions and
responsibilities, coordination, and monitoring. The organizational
breakdown structure (OBS) and the work breakdown structure
(WBS) aid in defining this framework.
The project manager’s authority is invested through the WBS but
also through the political, HR, and cultural frameworks.
4. Political framework—begins with the assumption that the project
organization is a coalition of different stakeholders. Key points to
bear in mind are internal struggle and governing power. Because of
the transitory nature of a project, the project manager must use the
stakeholders’ power to advance project goals. Stakeholders can,
typically, be divided into two groups.
1. Stakeholders with an interest in the failure of the project
2. Stakeholders with an interest in the success of the project
The project manager must identify all of the stakeholders and their
political influence, their objectives, and their ability to affect the
project. Once again, a project manager should spend some time to
determine the significant needs and requirements of the chief
stakeholders.
3. The business factor—the project manager must understand all aspects of
the business associated with the project.
In terms of personal characteristics, the most successful project managers are:
1. Efficient

2. Decisive
3. Supportive of team members’ decisions
4. Confident
5. Articulate communicators
6. Highly motivated
7. Technologically oriented
8. Able to deal with high levels of uncertainty
Project execution involves the management and administration of the work
described in the project plan. Usually most of the budget, time, and resources
are spent during the execution phase. When the focus of the project is on new
product development, success is often determined by the depth and details of
the plan. As the saying goes, “measure twice, cut once.” Vital tools and
techniques for project implementation are as follows:
1. Authorization management system—enables the project manager to
verify that an authorized team member is performing a specific task at
the correct point in time.
2. Status review meetings—prescribed meetings for information exchange
regarding the project.
3. Project management software—decision support software (including a
database and a model base) to help the project manager plan, implement,
and control all aspects of the project, including budgets, personnel,
schedule, and other resources.
4. Monitoring system—software, spreadsheets, or other mechanisms for
comparing budget outlays, work performed, and resources consumed
over time with the original plan.

Integrated change control
Once a project launches, changes to the original project plan are inevitable. A
procedure must be put in place to identify, quantify, and manage the changes
throughout the project life cycle. The main targets of change control are:
1. Evaluation of the change requests to determine whether the benefits of
the change will be sufficient to justify the corresponding disruption and
expense;
2. Determining that a change has occurred;
3. Managing the actual changes when and as they occur.
The original project scope must be maintained by continuously managing
changes to the baseline. This is accomplished either by rejecting new change
proposals or by approving changes and incorporating them into a revised
project baseline.
As described in greater detail in Chapter 8, change control makes use of the
following modules in the configuration management system.
1. Configuration identification. Conceptually, each configuration item
should be coded in a way that facilitates reference to its accompanying
documents. Any changes approved in the configuration item should
trigger a corresponding change in the documents, thus ensuring the
correct description of the element.
2. Change management. A change is initiated via an engineering change
request (ECR). The ECR contains the basis of the change along with a
statement of the effect that it will have on activity times, schedules, and
resource usage, as well as any new risks that may result.
To guarantee that each type of change is handled by the proper authority, a
change classification system should be put in place. The most important
changes are handled by the change control board (CCB) that represents all of
the stakeholders. After a review, a change request can be accepted or rejected

by the board. Once a request is accepted, an engineering change order (ECO)
is issued. The ECO contains all relevant information, such as the nature of the
change, the party responsible for its execution, and the time when the change
is to take place.

2.4 Project Scope Management
2.4.1 Accompanying Processes
Project scope management consists of the following six processes:
1. Plan scope management. The scope management plan is part of the
project plan. This process focuses on the preparation of the scope
management plan and the requirement management plan, as both are
part of the project plan.
2. Requirements Gathering. The driving force of any project is the needs
and expectations of the stakeholders that are translated into
requirements.
3. Define Scope. The project scope is the work content of the project. This
work content and the way it should be performed are described in a
document that defines a project’s scope.
4. Create WBS. The work content is broken into work packages. Each
work package is assigned to a work package manager who can provide
information on the time and effort required to perform the work for
planning purposes and is also responsible for the execution of the work.
5. Validate Scope. To ensure that the project work was performed as
required and the deliverables satisfy the requirements, inspection and
testing are conducted as part of the validation process.
6. Control Scope. The actual work performed and the project deliverables
are monitored throughout the project life cycle to ensure stakeholders’
satisfaction. When needed, corrective actions are taken to update the
project plan or the requirements.
The purpose of these processes is to ensure that the project includes all work

(and only that work) required for its successful completion. Scope
management relates to:
the product scope—defined as the features and functions to be included
in the product or service that translate into specific project scope
the project scope—encompasses the project management processes
defined as the work that must be done in order to deliver the product
scope
Management of a project’s scope is similar for many projects, although the
product scope is context-specific.
2.4.2 Description
Scope management encompasses the effort required to perform the work
associated with a project, as well as the processes required to produce the
intended products or services.
The scope management processes address the statement of work (SOW), and
work breakdown structure (WBS), respectively. An outline of what is
included in each follows.
SOW. The SOW gives information on:
1. Scope of work—what work should be completed and how;
2. Where will the work take place (at what physical location);
3. Duration of execution—initial schedule along with milestones for every
product;
4. Applicable standards;
5. Product allocation;
6. Acceptance criteria;

7. Additional requirements—transportation needs, special documentation,
insurance requirements, safety and security.
WBS. The WBS decomposes the project into subprojects. Each subproject
should be described with full detail of owner, schedule, activities, how each
is to be performed and when, and so on. It is advisable to have a WBS
template, especially for organizations with many similar projects. The
template specifies how to divide the project into the work packages.
A disconcerting issue related to scope management is “scope creep,” in
which new features and performance requirements are added to the project
without a proper change management process. By adhering to the
management processes described in this chapter, scope creep can be
minimized.

2.5 Project Time Management
2.5.1 Accompanying Processes
Time management establishes the schedule for tasks and activities defined in
the work packages. The following seven processes are included:
1. Plan Schedule Management. This process, which is part of the project
plan, focuses on the preparation of a schedule management plan.
2. Define Activities. This process focuses on the preparation of a list of
activities required to complete the project along with the attribute of
each activity and, when applicable, specific dates or milestones of the
project.
3. Sequence Activities. This process focuses on the precedence relationship
among activities, including technological precedence relationships and
managerial precedence relationships. In some cases, a lead or a lag is
part of the precedence relationship.
4. Estimate Activity Resources. This process focuses on the resources
required to perform the project activities, including human resources,
material, machine, equipment, etc.
5. Estimate Activity Durations. This process deals with the estimate of the
duration of the activities. In many projects, activity duration is a
function of the resources assigned to perform the activity, and it is
possible to reduce the duration of some activities by adding resources (a
process known as activity crashing).
6. Develop Schedule. Various tools and techniques are used to integrate the
information on activities, their duration, precedence relations, and
resources into a schedule that specifies the dates resources will perform
each activity. Network-based models are widely used to perform this

process, including the Critical Path Method and the Critical Chain.
7. Control Schedule. The actual duration of activities as well as their start
and finish dates are monitored throughout the project life cycle to ensure
timely completion of the project and its milestones. When needed,
corrective actions are taken to update the project plan or the schedule.
The purpose of time management is to ensure the timely completion of the
project. The schedule defines what is to be done, when it is to be done, and
by what resources. The schedule is used throughout the project to
synchronize people, resources, and organizations involved and as a basis for
control. When activities slip beyond their due dates, at least two major
problems may arise:
1. Time and money are often interchangeable. As projects are pushed
beyond their due date, time-related costs are incurred.
2. Most contracts specify rigid due dates, possibly with penalties for late
deliveries.
Alternatively, early deliveries may have incentives associated with them.
Scheduling issues can create conflicts in some organizations, especially
during the implementation phase and specifically in organizations that have a
matrix structure. By implementing proper processes for project management,
conflicts can be minimized.
2.5.2 Description
Project work content is defined in the SOW and then translated into the
WBS. Each work package in the WBS is decomposed into a set of activities
that reflect its predefined scope. Estimating the duration of each activity is a
major issue in time management. Activity durations are rarely known with
certainty and are estimated by either point estimates or probability
distributions. The work package manager is the best source of these estimates
because he or she knows the technology. Sometimes an estimate can be

derived from a database of similar activities. A problem is created when
organizations do not maintain time-related records or do not associate
parameters with an activity. The absence of parameterized data often
precludes its use in deriving time estimates.
In developing the schedule, precedence relations among activities are
defined, and a model, such as a Gantt chart or network, is constructed. Both
technological and managerial precedence relations may be present. The
former are drawn from the physical attributes of the product or system being
developed. The latter emerge from procedures dictated by the organization;
for example, issuing a purchase order usually requires that a low-ranking
manager give his or her approval before the senior officer signs the final
forms. Whereas managerial precedence relations can be sidestepped in some
instances, say, if the project is late, technological precedence relations are
invariant.
An initial schedule is the basis for estimating costs and resource
requirements. After a blueprint is developed, constraints imposed by due
dates, cash flows, resource availability, and resource requirements of other
projects can be added. Further tuning of the schedule may be possible by
changing the combination of resources (these combinations are known as
modes) assigned to activities. In constructing a graph of cost versus duration,
the modes correspond to the data points. Such graphs have two endpoints: (1)
minimum cost (at maximum duration) and (2) maximum cost (at minimum
duration). Implicit in this statement is the rule that the shorter the activity
duration, the higher the cost.
As a first cut, the project manager normally uses the minimum cost–
maximum duration point for each activity to determine the earliest finish time
of the project. If the result is not satisfactory, then different modes for one or
more activities may be examined. If the result still is not satisfactory, then
more sophisticated methods can be applied to determine the optimal
combination of costs and resources for each activity. Fast-tracking some
activities is also possible by repositioning them in parallel or overlapping
them to a certain degree. In any case, the schedule is implemented by
performing the activities in accordance with their precedence relations.
Uncertainty, though, calls for a control mechanism to detect deviations and to

decide how to react to change requests. The schedule control system is based
on performance measures such as actual completion of deliverables
(milestones), actual starting times of activities, and actual finishing times of
activities. Changes to the baseline schedule are required whenever a change
in the project scope is implemented.

2.6 Project Cost Management
2.6.1 Accompanying Processes
Project cost management involves four processes:
1. Plan Cost Management. The cost management plan is part of the project
plan. This process focuses on the preparation of a cost management
plan.
2. Estimate Costs. This process requires information about activities, the
project schedule, and resources assigned to perform project activities to
estimate the cost of the project.
3. Determine Budget. Funding for the estimated costs is crucial. This
process is based on aggregation of costs of individual activities and
work packages into a cost baseline and matching the available funds to
the estimated costs based on the policies of the organization and its
ability to provide the needed funds.
4. Control Costs. The actual cost of activities as well as the project and
product scope may change during the life cycle of the project and,
therefore, they are monitored to ensure that the project budget is realistic
and satisfies stakeholders’ needs and expectations. When needed,
corrective actions are taken to update the project plan or the budget.
These processes are designed to provide an estimate of the cost required
to complete the project scope, to develop a budget based on availability
of funds, management policies, and strategy, and to ensure that the
project is completed within the approved budget and approved changes
to the budget.
2.6.2 Description

To complete the project activities, different resources are required depending
on whether the work is to be done internally or by outside contractors. Labor,
equipment, and information, for example, are required for in-house activities,
whereas money is required for outsourcing. The work packages derived from
the SOW contain plans for using resources and suggest different operational
modes for each activity.
There are various methods of estimating activity costs, from detailed
accounting procedures to guesswork. Formal accounting procedures can be
tedious and time consuming and perhaps a waste of time in case the project is
discarded. Thus, early in the project life cycle, rough order-of-magnitude
estimates are best, although they are not likely to be accurate.
Estimates of the amount of resources required for each activity, as well as the
timing of their use, are based on the activity list and the schedule. Resource
allocation is performed at the lowest level of the WBS—the work package
level—and requirements are rolled up to the project level and then to the
organizational level. A comparison of resource requirements and resource
availability along with corporate strategies and priorities forms the basis of
the allocation decisions at the organizational level. Resource planning results
in a detailed plan specifying which resources are required for each work
package. By applying the resource cost rates to the resource plan and adding
overhead and outsourcing expenses, a cost estimate of the project is
developed. This provides a basis for budgeting. As determined by the
schedule, cost estimates are time-phased to allow for cash flow analysis.
Additional allocations may also be made in the form of, say, a management
reserve, to buffer against uncertainty. The resulting budget is the baseline for
project cost control.
Because of uncertainty, cost control is required to detect deviations and to
decide how to react to get the project back on track and within budget.
Change requests require a similar response. The cost control system is based
on performance measures, such as actual cost of activities or deliverables
(milestones), and actual cash flows. Changes to the baseline budget are
required whenever a change in the project scope is implemented.

2.7 Project Quality Management
2.7.1 Accompanying Processes
Project quality management consists of three processes:
1. Plan Quality Management. The quality management plan is part of the
project plan. This process focuses on the preparation of a quality
management plan.
2. Perform Quality assurance. This process is focusing on analyzing the
quality requirements and building the processes, tools, and techniques
that guarantee that the project and its deliverables will satisfy these
requirements.
3. Control Quality. This process is based on a comparison between quality
requirements and results of tests and audits to verify that quality
requirements are met and to recommend corrective actions in case the
results of quality testing show substandard results.
The purpose of these processes is to ensure that the finished product satisfies
the needs for which it was undertaken. Garvin (1987) suggested the following
eight dimensions for measuring quality.
1. Performance. This dimension refers to the product or service’s primary
characteristics, such as the acceleration, cruising speed, and comfort of
an automobile or the sound and the picture clarity of a TV set.
Understanding of the stakeholder’s performance requirements and the
design of the product or service to meet those requirements are key
factors in quality-based competition.
2. Features. This is a secondary aspect of performance that supplements the
basic functions of the product or service. Features could be considered
“bells and whistles.” The flexibility afforded a customer to select desired

options from a long list of possibilities contributes to the quality of the
product or service.
3. Reliability. This performance measure reflects the probability of a
product’s malfunctioning or failing within a specified period of time. It
affects both the cost of maintenance and downtime of the product.
4. Conformance. This is the degree to which the design and operating
characteristics of the product or service meet established standards.
5. Durability. This is a measure of the economic and technical service
duration of a product. It relates to the amount of use that one can get
from a product before it has to be replaced due to technical or
economical considerations.
6. Serviceability. This measure reflects the competence and courtesy of the
agent performing the repair work, as well as the speed and ease with
which it is done. The reliability of a product and its serviceability
complement each other. A product that rarely fails and—on those
occasions when it does—can be repaired quickly and inexpensively has
a lower downtime and better serves its owner.
7. Aesthetics. This is a subjective performance measure related to how the
product feels, tastes, looks, or smells and reflects individual preferences.
8. Perceived quality. This is another subjective measure related to the
reputation of the product or service. Reputation may be based on past
experience and partial information, but, in many cases, the customers’
opinions are based on perceived quality as a result of the lack of
accurate information on the other performance measures.
2.7.2 Description
Until the mid-1980s, quality was defined as meeting or exceeding a specific
set of performance measures. Since then, the need to understand user
requirements and application requirements has been on the rise. Quality starts

with understanding stakeholders’ requirements. Stakeholders require products
that carry different grades at maximum achievable quality. Quality is the
proper match for the desired requirements at the expected grade. The product
should have specific characteristics.
Quality management starts with the definition of standards or performance
levels for each dimension of quality. On the basis of the scope of the project,
quality policy, standards, and regulations, a quality management plan is
developed. The plan describes the organizational structure, responsibilities,
procedures, processes, and resources needed to implement quality
management; that is, how the project management team will implement its
quality policy to achieve the required quality levels. Checklists and metrics or
operational definitions are also developed for each performance measure so
that actual results and performance can be evaluated against
stated requirements.
To provide confidence that the project will achieve the required quality level,
a quality assurance process is implemented. By continuously reviewing (or
auditing) the actual implementation of the plan developed, quality assurance
systematically seeks to increase the effectiveness and efficiency of the project
and its results. Actual results are monitored and controlled. The quality
control process forms the basis of acceptance (or rejection) decisions at
various stages of development.

2.8 Project Human Resource
Management
2.8.1 Accompanying Processes
HR management during the life cycle of a project is primarily concerned with
the following four processes:
1. Plan Human Resource Management. The human resource management
plan is part of the project plan. This process focuses on the preparation
of a human resource management plan.
2. Acquire Project Team. The process of obtaining the project team
members from inside or outside the performing organization.
3. Develop Project Team. The process of developing shared understanding
among project team members regarding project goals and the way to
achieve those goals together.
4. Manage Project Team. The process of leading the project team during
the project life cycle to achieve project goals by working together
resolving conflicts and creating synergy among team members.
Collectively, these processes are aimed at making the most effective use of
people associated with the project. The temporary nature of the project
structure and organization, the frequent need for multi-disciplinary teams,
and the participation of people from different organizations transform into a
need for team building, motivation, and leadership if goals are to be met
successfully.
2.8.2 Description

The work content of the project is allocated among the performing
organizations by integrating the project’s WBS with its OBS (Organizational
Breakdown Structure). As mentioned, work packages—specific work content
assigned to specific organizational units—are defined at the lowest level of
these two hierarchical structures. Each work package is a building block; that
is, an elementary project with a specific scope, schedule, budget, and quality
objectives. Organizational planning activities are required to ensure that the
total work content of the project is assigned and performed at the work
package level, and that the integration of the deliverables produced by the
work packages into the final product is possible according to the project plan.
The organizational plan defines roles and responsibilities, as well as staffing
requirements and the OBS of the project.
On the basis of the organizational plan, manpower assessments are made
along with staff assignments. The availability of staff is compared with
project requirements, and gaps are identified. These gaps are filled by the
project manager working in conjunction with the HR department of the firm
or agency. The assignment of available staff to the project and the acquisition
of new staff result in the creation of a project team that may be a combination
of full-time employees assigned full time to the project, full-timers assigned
part time, and part-timers. Subcontractors, consultants, and other outside
resources may be part of the team also.
The assignment of staff to the project is the first step in the team development
process. To succeed in achieving project goals, teamwork and a team spirit
are essential ingredients. The transformation of disparate individuals who are
assigned to a project into a high-performance team requires leadership,
communication skills, and negotiation skills, as well as the ability to motivate
people, to coach and to mentor them, and to deal with conflicts in a
professional, yet effective manner.

2.9 Project Communications
Management
2.9.1 Accompanying Processes
The three processes associated with project communications management
are:
1. Plan Communication Management. The Communication Management
plan is part of the project plan. This process focuses on the preparation
of a communication management plan to satisfy the needs of
stakeholders for information.
2. Manage Communication. The process of collecting data, storing and
retrieving the data, and processing it to create useful information that is
distributed according to the Communication Management plan.
3. Control Communication. The process of monitoring the information
distributed to stakeholders throughout the project life cycle and
comparing it to the needs for information of the stakeholders to identify
gaps and to take corrective actions when needed.
These processes are required to ensure “timely and appropriate generation,
collection, dissemination, storage, and ultimate disposition of project
information” (PMBOK). Each is tightly linked with organizational planning.
Communication between team members, with stakeholders, and with external
parties and systems can take many forms. For example, it can be formal or
informal, written or verbal, and planned or ad hoc. The decisions regarding
communication channels, the information that should be distributed, and the
best form of communication for each type of information are crucial in
supporting teamwork and coordination.

2.9.2 Description
Communications planning is the process of selecting the communication
channels, the modes of communication, and the contents of the
communication between project participants, stakeholders, and the
environment. Taking into account information needs, available technology,
and constraints on the availability and distribution of information, the
communications management plan specifies the frequency and methods by
which information is collected, stored, retrieved, transmitted, and presented
to the parties involved in the project. On the basis of the plan, data collection
as well as data storage and retrieval systems can be implemented and used
throughout the project life cycle. The project communication system that
supports the transmission and presentation of information should be designed
and established early to facilitate the transfer of information.
Information distribution is based on the communication management plan
and occurs throughout the project life cycle. As one can imagine,
documentation of ongoing performance with respect to costs, schedule, and
resource usage is important for several reasons. In general, performance
reporting provides stakeholders with information on the actual status of the
project, current accomplishments, and forecasts of future project status and
progress. It is also essential for project control because deviations between
plans and actual progress trigger corrective actions. In addition to the timely
distribution of information, historical records are kept to enable post-project
analysis in support of organizational and individual learning.
To facilitate an orderly closure of each phase of the project, information on
actual performance levels of all activities is collected and compared with the
project plan. If a product is the end result, then performance information is
similarly collected and compared with the product specifications at each
phase of the project. This verification process ensures an ordered, formal
acceptance of the project’s deliverables by the stakeholders and provides a
means for record keeping that supports organizational learning.
Communications planning should answer the following questions:

1. What information is to be provided?
2. Who will be the correspondent?
3. When and in what form is the information to be provided?
4. What templates are to be used?
5. What are the methods for gathering the information to be provided?
6. With what frequency will the information be passed?
7. What form will the communication take—formal, informal, handwritten,
oral, hard copy, email?
Information distribution is the implementation of the communication
program. If the program is lacking appropriate definition, then it is possible
to create a situation of information overload in which too much irrelevant
information is passed to project participants at too great a frequency. When
this happens, essential information may be overlooked, ignored, or lost. To be
more precise regarding the appropriateness of various communication
channels, we have:
Informal communication. This is the result of an immediate need for
information that was not addressed by the communication plan.
Verbal communication. This is vital in a project setting. The project
manager must make sure that team meetings are held on a scheduled
basis.
Performance reporting is an important part of communication. It enables the
project manager to compare the actual status of each activity with the
baseline. This provides the foundations for the change control process and
allows for the collection and aggregation of knowledge.

2.10 Project Risk Management
2.10.1 Accompanying Processes
Risk is an unwelcome but inevitable part of any project or new undertaking.
Risk management includes six processes:
1. Plan Risk Management. The risk management plan is part of the project
plan. This process focuses on the preparation of the risk management
plan.
2. Identify Risks. The process of determining risk events that might impact
the project success.
3. Perform Qualitative Risk Analysis. The process of assessing the
likelihood and impact of identified risk events in order to prioritize and
focus on the most significant risks.
4. Perform Quantitative Risk Analysis. The process of estimating the
probability and impact of identified risk events and applying numerical
analysis in order to assess overall project risk.
5. Plan Risks Response. The process of selecting risk events for mitigation
and deciding the best way to mitigate those risks as well as developing
contingency and risk response plans and setting reserves for residual
risks and risks that are not mitigated.
6. Control risk. The process of monitoring identified risks and
identification of new risks throughout the project life cycle as a trigger
for activation of contingency plans and a basis for corrective actions and
changes.
These processes are designed to identify and evaluate possible events that
could have a negative impact on the project. Tactics are developed to handle

each type of disruption identified, as well as any uncertainty that could affect
project planning, monitoring, and control.
2.10.2 Description
All projects have some inherent risk as a result of the uncertainty that
accompanies any new nonrepetitive endeavor. In many industries, the riskier
the project, the higher the payoff. Thus, risk is at times beneficial because it
has the potential to increase profits (i.e., “upside”). Risk management is not
risk avoidance, but a method to control risks so that, in the long run, projects
provide a net benefit to the organization.
A decision maker’s attitude toward risk may be described as either risk
averse, risk prone, or risk natural. For different circumstances and payoffs,
the same decision maker can fall into any of these categories. In Chapter 3,
we discuss how to construct individual utility functions that capture risk
attitudes in specific situations. In project management, these utility functions
should reflect the inclination of the organization. Risks can affect the scope,
quality, schedule, cost, and other goals of the project such as client
satisfaction.
Major risks should be handled by performing a Pareto analysis to assess their
magnitude. As a historical footnote, Villefredo Pareto studied the distribution
of wealth in the 18th century in Milan and found that 20% of the city’s
families controlled approximately 80% of its wealth. His findings proved to
be more general than the initial purpose of his study. In many populations, it
turns out that a small percentage of the population (say, 15%–25%) accounts
for a significant portion of a measured factor (say, 75%–85%). This
phenomenon is known as the Pareto rule. Using this rule it is possible to
focus one’s attention on the most important items in a population. In risk
management, by focusing on the 10%–20% of the risks with the highest
magnitude, it is possible to take care of approximately 80% of the total risk
impact on the project.
In a Pareto analysis, events that might have the most severe effect on the
project are identified first, for example, by examining the history of similar

projects. A risk checklist is then created with the help of team members and
outside experts. Next, the magnitude of each item on the list is assessed in
terms of impact and probability. Multiplying these terms together gives the
expected loss for that risk. When probability estimates are not readily
available, methods such as simulations and expert judgments can be used.
A risk event is a discrete random occurrence that cannot be factored into the
project plan explicitly. Risk events are identified on the basis of the potential
difficulty that they impose on (1) achieving the project’s objectives (the
characteristics of the product or service), (2) meeting the schedule and
budget, and (3) satisfying resource requirements. The environment in which
the project is performed is also a potential source of risk. Historical
information is an important input in the risk identification process. In high-
tech projects, for example, knowledge gaps are a common source of risk.
Efforts to develop, use, or integrate new technologies necessarily involve
uncertainty and, hence, risk. External sources of risk include new laws,
transportation delays, raw material shortages, and labor union problems.
Internal difficulties or disagreements may also generate risks.
The probability of risk events and their magnitude and effect on project
success are estimated during the risk quantification process. The goal of this
process is to rank risks in order of the probability of occurrence and the level
of impact on the project. Thus, a high risk is an event that is highly probable
and may cause substantial damage. On the basis of the magnitude of risk
associated with each risk event, a risk response is developed. Several
responses are used in project management, including:
Risk elimination—in some projects it is possible to eliminate some risks
altogether by using, for example, a different technology or a different
supplier.
Risk reduction—if risk elimination is too expensive or impossible, then
it may be possible to reduce the probability of a risk event or its impact
or both. A typical example is redundancy in R&D projects when two
mutually exclusive technologies are developed in parallel to reduce the
risk that a failure in development will harm the project. Although only
one of the alternative technologies will be used, the parallel effort
reduces the probability of a failure.

Risk sharing—it is possible in some projects to share risks (and benefits)
with some stakeholders such as suppliers, subcontractors, partners, or
even the client. Buying insurance is another form of risk sharing.
Risk absorption—if a decision is made to absorb the risk, then buffers in
the form of management reserve or extra time in the schedule can be
used. In addition, it may be appropriate to develop contingency plans to
help cope with the consequences of any disruptions.
Because information is collected throughout the life cycle of a project, new
information is used to update the risk management plan continuously. A
continuous effort is required to identify new sources of risk, to update the
estimates regarding probabilities and impacts of risk events, and to activate
the risk management plan when needed. By constantly monitoring progress
and updating the risk management plan, the impact of uncertainty can be
reduced and the probability of project success can be increased. Being on the
lookout for symptoms of risk is the first step in warding off trouble before it
begins. One way to do this is to formulate a list of the most prominent risks to
be checked periodically. Because risks change with time, the list must be
updated continuously and new estimates of their impact and probability of
occurrence must be derived.

2.11 Project Procurement
Management
2.11.1 Accompanying Processes
Procurement management for projects consists of the following four
processes:
1. Plan Procurement Management. The procurement management plan is
part of the project plan. This process focuses on the preparation of the
procurement management plan.
2. Conduct Procurement. The process of selecting the sellers and signing
contracts with them.
3. Control Procurement. The process of managing the relationship with the
seller throughout the procurement process after signing the contract.
Includes the management of changes and the monitoring of the contract
performances.
4. Close Procurement. The process of completing the procurement process.
These processes accompany the acquisition of goods and services from
outside sources, such as consultants, subcontractors, and third-party
suppliers. The decision to procure goods and services from the outside (the
“make or buy” decision) has a short-term or tactical-level (project-related)
impact as well as a long-term or strategic-level (organization-related) impact.
At the strategic level, core competencies should rarely be outsourced, even
when such action can reduce the project cost, shorten its duration, reduce its
risk, or improve quality. At the tactical level, outsourcing can alleviate
resource shortages, help in closing knowledge gaps, off-load certain financial
risks, and increase the probability of project success. Management of the
outsourcing process from supplier selection to contract closeout is another

important part of the project manager’s job.
2.11.2 Description
The decision on which parts of a project to purchase from outside sources,
and how and when to do it, is critical to the success of most projects. This is
because significant parts of many projects are candidates for outsourcing, and
the level of uncertainty and consequent risk is different from the
corresponding measures associated with activities performed in-house. To
gain a competitive advantage from outsourcing, the planning, execution, and
control of outsourcing procedures must be well-defined and supported by
data and models.
The first step in the process is to consider which parts of the project scope
and product scope to outsource. This decision is related to capacity and
know-how and can be crucial in achieving project goals; however, a conflict
may exist between project goals and the goals of the stakeholders. For
example, subcontracting may help a firm in a related industry develop the
skills and capabilities that would give it a competitive advantage at some
future time. This was the case with IBM, which outsourced the development
of the Disk Operating System to Microsoft and the development of
the central processing unit to Intel. The underlying analysis should take into
account the cost, quality, speed, risk, and flexibility of in-house development
versus the use of subcontractors or suppliers to deliver the same goods and
services. The decisions should also take into account the long-term or
strategic factors discussed earlier. Some additional considerations are:
the prospect of ultimately producing a less-expensive product with
higher quality
the lack of in-house skills or qualifications as defined by prevailing laws
and regulations
the ability to shift risks to the supplier
Once the decision to outsource is made, the following questions must be

addressed:
Should the purchase be made from a single supplier, or should a bid be
issued?
Should the purchase be for a single project or for a group of projects?
Should finished products or parts be purchased or just the labor hours
and have the work done in-house?
How much should be purchased if, for example, quantity discounts are
available?
When should the purchase be made? There is a tradeoff between time at
which a spending commitment is made and the risk associated with
delaying the purchase.
Should the idea of shared purchases be considered whereby joint orders
are placed with (competing) organizations to receive quantity discounts
or better contractual terms?
Once a decision is made to outsource, the solicitation process begins. This
step requires an exact definition of the goods or services to be purchased, the
development of due dates and cost estimates, and the preparation of a list of
potential sources. Various types of models can be used to support the process
by arraying the alternatives and their attributes against one another and
allowing the decision maker to input preferences for each attribute. The use
of simple scoring models, such as those described in Chapter 5, or more
sophisticated methods, such as those described in Chapter 6, can help
stakeholders reach a consensus by making the selection process more
objective.
In conjunction with selecting a vendor, a contractual agreement is drawn up
that is based on the following items:
1. Memorandum of understanding. This is a non-obligatory legal document
that provides the foundations for the contract. It is preliminary to the
contract.

2. SOW—description of required work to be purchased. The SOW offers
the vendor a better understanding of the customer’s expectations.
3. Product technical specifications.
4. Acceptance test procedure.
5. Terms and conditions—defines the contractual terms.
The contract is a legal binding document that should specify the following:
1. What—scope of work (deliverables)
2. Where—location of work
3. When—period of performance
4. Schedule for deliverables
5. Applicable standards
6. Acceptance criteria—the criteria that must be satisfied for the project to
be accepted
7. Special requirements related to testing, documentation, standards, safety,
and so on
Solicitation can take many forms. One extreme is a request for proposal
(RFP) advertised and open to all potential sources; a direct approach to a
single preferred (or only) source is another extreme. There are many options
in between, such as requests for letters of inquiry, qualification statements,
and pre-proposals. The main output of the solicitation process is to generate
one or more proposals—from the outside—for the goods or services required.
A well-planned solicitation planning process followed by a well-managed
solicitation process is required for the next step—source selection—to be
successful. Source selection is required whenever more than one acceptable
vendor is available. If a proper selection model is developed during the
solicitation planning process and all the data required for the model are

collected from the potential vendors during the solicitation process, the rest is
easy. On the basis of the evaluation criteria and organizational policies,
proposals are evaluated and ranked to identify the top candidates.
Negotiations with a handful of them follow to get their best and final offer.
The process is terminated when a contract is signed. If, however, solicitation
planning and the solicitation process do not yield a clear set of criteria and a
manageable selection model, then source selection may become a difficult
and time-consuming process; it may not end with the best vendor selected or
the best possible contract signed. It is difficult to compare proposals that are
not structured according to clear RFP requirements; in many cases, important
information may be missing.
Throughout the life cycle of a project, contracts are managed as part of the
execution and change control efforts. Deliverables, such as test results,
prototype models, subassemblies, documentation, hardware and software are
submitted and evaluated, payments are made; and, when necessary, change
requests are issued. When these are approved, changes are made to the
contract. Contract management is equivalent to the management of a work
package performed in-house; therefore, similar tools are required during the
contract administration process.
Contract closeout is the final process that signals formal acceptance and
closure. On the basis of the original contract and all of the approved changes,
the goods or services provided are evaluated and, if accepted, payment is
made and the contract is closed. Information collected during this process is
important for future projects and vendor selection.

2.12 Project Stakeholders
Management
2.12.1 Accompanying Processes
Stakeholders management for projects consists of the following four
processes:
1. Identify Stakeholders. This process identifies and maps the individuals
and parties that may impact the project or may be impacted by the
project. The needs and interests of important and influential stakeholders
early on in the project life cycle are the basis for setting project
objectives, goals, and constraints.
2. Plan Stakeholders Management. Based on the analysis of needs and
interests of important and influential stakeholders, a stakeholders
management plan is developed specifying how each stakeholder should
be engaged throughout the project life cycle.
3. Manage Stakeholders Engagement. Throughout the life cycle of the
project the stakeholders management plan is executed by
communicating and working with the stakeholders according to the plan.
Information is distributed to the stakeholders and collected from them,
their concerns, needs, and expectations are analyzed, and appropriate
actions are taken.
4. Control Stakeholders Engagement. Due to uncertainty, stakeholders
needs and expectations may change as well as their interests and level of
influence on the project. Throughout the life cycle of the project
important stakeholders are monitored, and the stakeholders management
plan is updated and adjusted based on new information that becomes
available.

These processes are key to setting project objectives, goals, and constraints
early on in the project life cycle and developing/updating project plans to
achieve those objectives, goals, and constraints. Stakeholders may be part of
the performing organization; they may come from outside the performing
organization, may support the project, or may oppose the project and try to
stop it or to limit its success. Therefore, specific attention to developing plans
to manage the stakeholders is crucial to improving the probability of project
success.
2.12.2 Description
Projects are performed to satisfy the needs and expectations of some
stakeholders. Stakeholders management is therefore an important and, yet, a
very difficult task. Frequently, the needs and expectations of different
stakeholders are in conflict and, sometimes, satisfying one group of
stakeholders means that another group will not be satisfied or, even worse,
will oppose the project.
Mapping of stakeholders is the first step—an effort to understand who they
are, what are their needs, expectations, and interests, their power to influence
the project, and their desire to be involved in the project and their expected
level of engagement in the project. Based on the mapping, a strategy for
managing each stakeholder is developed. Some influential stakeholders who
are very interested in the project may be partners and take part in the
decision-making process, while other stakeholders will be satisfied if they get
specific information during the project life cycle to guarantee their support.
The stakeholders management plan should translate this strategy into specific
actions like setting regular meetings with some stakeholders and providing
some specific information by email or phone in specific points of time to
other stakeholders.
The stakeholders management plan is an important part of the project plan,
and it should specify who is responsible for the ongoing relationship with
each of the stakeholders, what should be done, and when.
An important aspect of a stakeholders management plan is the ongoing effort

to monitor and control the stakeholders already identified and to update the
list of stakeholders when new stakeholders are identified. This activity is
required because the needs and expectations of stakeholders may change
throughout the project life cycle, as well as their level of interest in the
project and their ability to influence it. Changes in the market, the economic
and political environment, and technological changes may all introduce new
stakeholders to the project. The earlier these new players are identified and
managed, the better it is.

2.13 The Learning Organization
and Continuous Improvement
2.13.1 Individual and
Organizational Learning
To excel as a project manager, an individual must have expertise in a number
of arenas—planning, initiation, execution, supervision—and an ability to
recognize when each phase of a project has been completed successfully and
the next phase is ready to begin. If such an individual has facility with all
aspects of the managerial process, then he or she will be in a prime position
to educate, challenge, stimulate, direct, and inspire those whose work he or
she is overseeing. A good project manager will be able to serve as a
powerfully effective role model and as a source of knowledge and inspiration
for those less experienced. In essence, organizational growth and
development can be enhanced by way of this “trickle-down” effect from a
project manager who enjoys his or her work and takes pride in doing it well;
is reliable, committed, and disciplined; can foster development of a strong
work ethic and a sense of prideful accomplishment in those whom he or she
is managing; and is a font of knowledge, a master strategist, and a visionary
who never loses sight of the long-term goal.
The ability of groups to improve performance by learning parallels the same
abilities found in individuals. Katzenbach and Smith (1993) explained how to
combine individual learning with team building, a key component of any
collective endeavor. Just as it is important for each person to learn and master
his or her assignment in a project, it is equally important for the group to
learn how to work as a team. By establishing clear processes with well-
defined inputs and outputs and by ensuring that those responsible for each
process master the tools and techniques necessary to produce the desired
output, excellence in project management can be achieved.

2.13.2 Workflow and Process Design
as the Basis of Learning
The one-time, nonrepetitive nature of projects implies that uncertainty is a
major factor that affects a project’s success. In addition, the ability to learn by
repetition is limited because of the uniqueness of most projects. A key to
project management success is the exploitation of the repetitive parts of the
project scope. By identifying repetitive processes (both within and between
projects) and by building an environment that supports learning and data
collection, limited resources can be more effectively allocated. Reuse of
products and procedures is also a key to project success. For example, in
software projects, the reuse of modules and subroutines reduces development
time and cost.
A valuable step in the creation of an environment that supports learning is the
design and implementation of a workflow management system—a system
that embodies the decision-making processes associated with each aspect of
the project. Each process, discussed in this chapter, should be studied,
defined, and implemented within a workflow management system.
Definitional elements include the trigger or initiation mechanism of the
process, inputs and outputs, skills and resource requirements, activities
performed, data required, models used, relative order of execution,
termination conditions, and, finally, an enumeration of results or deliverables.
The workflow management system uses a workflow enactment system or
workflow process engine that can create, manage, and execute multiple
process instances.
By identifying processes that are common to more than one project within an
organization, it is possible to implement a workflow system that supports and
even automates those processes. Automation means that the routing of each
process is defined along with the input information, processing tools and
techniques, and output information. Although the product scope may vary
substantially from project to project, when the execution of the project scope
is supported by an automatic workflow system, the benefits are twofold: (1)
the level of uncertainty is reduced because processes are clearly defined and

the flow of information required to support those processes is automatic, and
(2) learning is enabled. In general, a well-structured process can be taught
easily to new employees or learned by repetition. For the organization that
deals with many similar projects, efficiency is greatly enhanced when the
same processes are repeated, the same formats are used to present
information, and the same models are used to support decision making. The
workflow management system provides the structure for realizing this
efficiency.
TEAM PROJECT Thermal
Transfer Plant
Develop two project life-cycle models for the plant. Focus on the phases in
the model and answer the following questions.
1. What should be done in each phase?
2. What are the deliverables?
3. How should the output of each phase be verified?
Discuss the pros and cons of each life-cycle model and select the one that you
believe is best. Explain your choice.

Discussion Questions
1. Explain what a project life cycle is.
2. Draw a diagram showing the spiral life-cycle model for a particular
project.
3. Draw a diagram showing the waterfall life-cycle model for a particular
project.
4. Discuss the pros and cons of the spiral project life-cycle model and the
waterfall project life-cycle model.
5. How are the processes in the PMBOK related to each other? Give a
specific example.
6. How are the processes in the PMBOK related to the project life cycle?
Give a specific example.
7. If time to market is the most important competitive advantage for an
organization, then what life-cycle model should it use for its projects?
Explain.
8. What are the main deliverables of project integration?
9. What are the relationships between a learning organization and the
project management processes?
10. What are the characteristics of a good project manager?

Exercises
1. 2.1 Find an article describing a national project in detail. On the basis of
the article and on your understanding of the project, answer the
questions below. State any assumptions that you feel are necessary to
provide answers.
1. Who were the stakeholders?
2. Was it an internal or external project?
3. What were the most important resources used in the project?
Explain.
4. What were the needs and expectations of each stakeholder?
5. What are the alternative approaches for this project?
6. Was the approach selected for the project the best, in your opinion?
Explain.
7. What were the risks in the project?
8. Rank the risks according to severity.
9. What was done or could have been done to mitigate those risks?
10. Was the project a success? Why?
11. Was there enough outsourcing in the project? Explain.
12. What lessons can be learned from this project?
2. 2.2 Find an article that discusses workflow management systems (e.g.,
Stohr and Zhao 2001) and explain the following:

1. What are the advantages of workflow systems?
2. Under what conditions is a workflow system useful in a project
environment?
3. Which of the processes described in the PMBOK are most suitable
for workflow systems?
4. What are the disadvantages of using a workflow system in a project
environment?
3. 2.3 On the basis of the material in this chapter and any outside sources
you can find, answer the following.
1. Define what is meant by a “learning organization.”
2. What are the building blocks of a learning organization?
3. What are the advantages of a learning organization?
4. What should be done to promote a learning organization in the
project environment?

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Chapter 3 Engineering Economic
Analysis
3.1 Introduction
The design of a system represents a decision about how resources will be
transformed to achieve a given set of objectives. The final design is a choice
of a particular combination of resources and a blueprint for using them; it is
selected from among other combinations that would accomplish the same
objectives but perhaps with different cost and performance consequences. For
example, the design of a commercial aircraft represents a choice of structural
materials, size and location of engines, spacing of seats, and so on; the same
result could be achieved in any number of ways.
A design must satisfy a host of technical considerations and constraints
because only some things are possible. In general, it must conform to the
laws of natural science. To continue with the aircraft example, there are limits
to the strength of metal alloys or composites and to the thrust attainable from
jet engines. The creation of a good design for a system requires solid
technical knowledge and competence. Engineers may take this to be self-
evident, but it often needs to be stressed to upper management and political
leaders, who may be motivated by what a proposed system might accomplish
rather than by costs and the limitations of technology.
Economics and value must also be taken into account in the choice of design;
the best configuration cannot be determined from technical qualities alone.
Moreover, value per dollar spent tends to dominate the final choice of a
system. As a general rule, the engineer must pick from among many possible
configurations, each of which may seem equally effective from a technical
point of view. The selection of the best configuration is determined by
comparing the costs and relative values associated with each. The choice
between constructing an aircraft of aluminum or titanium is generally a
question of cost, as both can meet the required standards. For more complex

systems, political or other values may be more important than costs. In
planning an airport for a city, for instance, it is usually the case that several
sites will be judged suitable. The final choice hinges on societal decisions
regarding the relative importance of accessibility, congestion, and other
environmental and political impacts, in addition to cost.
As engineers have become increasingly involved with interoperability and
integration of systems, they must deal with new issues and incorporate new
methods into their analyses. Traditionally, engineering education and practice
have been concerned with detailed design. At that level, technical problems
dominate, with economics taking a back seat. In designing an engine, for
example, the immediate task––and the trademark of the engineer––is to make
the device work properly. At the systems level, however, economic
considerations are likely to be critical. Thus the design of a transportation
system generally assumes that engines to power vehicles will be available
and focuses attention on such issues as whether service can be provided at a
price low enough to generate sufficient traffic to make the enterprise
worthwhile.
3.1.1 Need for Economic Analysis
The purpose of an economic evaluation is to determine whether any project
or investment is financially desirable. Specifically, an evaluation addresses
two sorts of questions:
Is an individual project worthwhile? That is, does it meet our minimum
standards?
Given a list of projects, which is the best? How does each project rank
or compare with the others on the list?
This chapter shows how both of these questions should be answered when
dealing strictly with cash flows. Chapters 5 and 6 add qualitative
considerations to the discussion.
In practice, economic evaluations are difficult to perform correctly. This is in

great part because of the fact that those who are responsible for carrying out
the analyses––middle-level managers or staff––necessarily have a limited
view of their organization’s activities and cannot realistically take into
account all potential opportunities and risks. The result is that most
evaluations are done on the basis of incomplete and/or inaccurate
information, leading to erroneous assumptions.
Project proposals are evaluated using financial criteria such as net present
value (NPV), rate of return (ROR), and payback period. Each method is
discussed in detail and then compared with the others. Each criterion requires
assumptions on the part of decision makers that can lead to biases in
evaluating project proposals. The chapter concludes with a discussion of
utility theory that can be used to explain how decision makers deal with
uncertain outcomes.
3.1.2 Time Value of Money
Many projects, particularly large systems, evolve over long periods. Costs
incurred in one period may generate benefits for many years to come. The
evaluation of whether these projects are worthwhile therefore must compare
benefits and costs that occur at quite different times.
The essential problem in evaluating projects over time is that money has a
time value. A dollar now is worth less than a dollar a year from now. The
money represents the same nominal quantity, to be sure, but a dollar later
does not have the same usefulness or buying power that it has today. The
problem is one of compatibility. Because of this value differential, we cannot
estimate total benefits (or costs) simply by adding dollar amounts that are
realized in different periods. To make a valid comparison, we need to
translate all cash flows into comparable quantities.
From a mathematical point of view, the solution to the evaluation problem is
simple. It consists of using a handful of formulas that depend on only two
parameters: the duration, or “life,” of the project, n, and the discount rate, i.
These formulas are built into many pocket calculators and are routinely
embedded in spreadsheet programs available on personnel computers. In the

next three sections, we present these essential formulas and examine their
use.
From a practical point of view, the analytic solutions are delicate and must be
interpreted with care. Values generated by the formulas are sensitive to their
two parameters, which are rarely known with certainty. Results, therefore, are
somewhat arbitrary, implying that the problem of evaluating projects over
time is a mixture of art and science.
3.1.3 Discount Rate, Interest Rate,
and Minimum Acceptable Rate of
Return
A dollar today is worth more than a dollar in the future because it can be used
productively between now and then. For example, you can place money in a
savings account and get a greater amount back after some period. In the
economy at large, businesses and governments can use money to build plants,
manufacture products, grow food, educate people, and undertake other
worthwhile activities.
Moreover, any given amount of money now is typically worth more than the
same amount in the future because of inflation. As prices go up as a result of
inflation, the current buying power of the dollar erodes. The discount rate is
one way of translating cash flows in the future to the present. It is used to
determine by how much any future receipt or expenditure is discounted; that
is, reduced to make it correspond to an equivalent amount today. The
discount rate thus is the key factor in the evaluation of projects over time. It
is the parameter that permits us to compare costs and benefits incurred at
different instances in time.
The discount rate is generally expressed as an annual percentage. Normally,
this percentage is assumed to be constant for any particular evaluation.
Because we usually have no reason to believe that it would change in any
known way, we take it to be constant over time when looking at any project.

It may, however, be different for various individuals, companies, or
governments, and may also vary among people or groups as circumstances
change. Baumol (1968) discussed the effect of the discount rate on social
choice, and De Neufville (1990) indicated how to select an appropriate value
for both public- and private-sector investments.
The discount rate is similar to what we think of as the prevailing interest rate
but is actually a different concept. It is similar in that both can be stated as a
percentage per period, and both can indicate a connection between money
now and money later. The difference is that the discount rate represents real
change in value to a person or a group, as determined by their possibilities for
productive use of the money and the effects of inflation. By contrast, the
interest rate narrowly defines a contractual arrangement between a borrower
and a lender. This distinction implies a general rule:
discount rate>interest rate. Indeed, if people were not getting more value from
the money that they borrow than the interest that they pay for it, then they
would be silly to go to the trouble of incurring the debt.
When an organization launches a project, it is inherently taking on some risk.
As we know from real-world applications, certain projects will fail altogether
while others will under-deliver and/or be delayed. In order to protect itself
against risk, an organization will seek a financial return on a project that is
greater than the prevailing interest rate that can be obtained in a bank. The
discount rate that an organization uses to assess project opportunities can
reflect some of the inherent risk associated with proposed projects. Different
projects may use different discount factors, depending on their respective
level of risk.
It is common in the engineering economic literature to use the terms discount
rate and interest rate interchangeably. A third term, minimum acceptable rate
of return (MARR), also has the same meaning. In the remainder of the book,
we follow convention and take all three terms to be synonymous unless
otherwise indicated.

3.2 Compound Interest Formulas
Whenever the interest charge for any period is based on the remaining
principal to be repaid plus any accumulated interest charges up to the
beginning of that period, the interest is said to be compound. Basic
compound interest formulas and factors that assume discrete (lump-sum)
payments and discrete interest periods are discussed in this section. The
notation used to present the concepts is summarized below:
 
i=interest rate per interest period, sometimes referred to as the discount
rate or MARR; given as a decimal number in the formulas below (e.g.,
12% is equivalent to 0.12)
n=number of compounding periods
P=present sum of money (equivalent worth of one or more cash flows at
a point in time called the present)
F=future sum of money (equivalent worth of one or more cash flows at a
point in time called the future)
A n =discrete payment or receipt occurring at the end of some interest
period n
A=end-of-period cash flow (or equivalent end-of-period value) in a
uniform series continuing for n periods (sometimes called “annuity”);
special case in which A 1 = A 2 =…= A n =A
G=gradient or amount by which end-of-period cash flows increase or
decrease linearly (arithmetic gradient); A n = A 1 +( n−1 )G
g=gradient or amount by which end-of-period cash flows increase or
decrease geometrically; A n = A 1 ( 1+g ) n−1

The compound interest formulas follow:
Single payment compound amount factor
( F/P, i, n )= ( 1+i ) n
Single payment present worth factor
( P/F, i, n )= 1 ( 1+i ) n = 1 ( F/P, i, n )
Uniform series compound amount factor
( F/A, i, n )= ( 1+i ) n −1 i
Uniform series sinking fund factor
( A/F, i, n )= i ( 1+i ) n −1 = 1 ( F/A, i, n )
Uniform series present worth factor
( P/A, i, n )= ( 1+i ) n −1 i ( 1+i ) n
Uniform series capital recovery factor
( A/P, i, n )= i ( 1+i ) n ( 1+i ) n −1 = 1 ( P/A, i, n )
Arithmetic gradient present worth factor
( P/G, i, n )= ( 1+i ) n −in−1 i 2 ( 1+i ) n
Arithmetic gradient uniform series factor
( A/G, i, n )= ( 1+i ) n −in−1 i ( 1+i ) n −i
Geometric gradient present worth factor
( P/ A 1 , g, i, n )= 1− ( 1+g ) n ( 1+i ) − n g−i  for i≠g = n ( 1+i )
   for i=g
Limiting cases:

As n→∞: ( F/P, i, n )→∞, ( P/F, i, n )→0, ( P/A, i, n )→1/i, ( A/P, i, n
)→i,  ( F/A, i, n )→∞, ( A/F, i, n )→0, ( P/G, i, n )→1/ i 2 , ( A/G, i, n
)→1/i For i=0: ( F/P, i, n )=1, ( P/F, i, n )=1, ( P/A, i, n )=n, ( A/P, i, n
)=1/n, ( F/A, i, n )=n, ( A/F, i, n )=1/n, ( P/G, i, n )=n( n−1 )/2, ( A/G, i, n
)=( n−1 )/2
In using the compound interest formulas to solve a problem, it is useful to
note that the chain rule is applicable. For example, if you want to find P
given F, instead of calculating P with the expression P=F( P/F, i, n ), you can
make use of the relationship P=F( A/F, i, n )( P/A, i, n ) should it be more
convenient to do so.
3.2.1 Present Worth, Future Worth,
Uniform Series, and Gradient Series
Figure 3.1 is a diagram that shows typical placements of P, F, A, and G over
time for n periods with interest at i% per period. Upward pointing arrows
usually indicate payments or disbursements, and downward pointing arrows
indicate receipts or savings. As depicted in the figure, the following
conventions apply in using the discrete compound interest formulas and
corresponding tables:

Figure 3.1
Standard cash flow diagram indicating points in time for P, F, A,
and G.
Figure 3.1 Full Alternative Text
1. A occurs at the end of the interest period.
2. P occurs one interest period before the first A.
3. F occurs at the same point in time as the last A, and n periods after P.
4. There is no G cash flow at the end of period 1; hence, the total gradient
cash flow at the end of period n is ( n−1 )G.
Most economic analyses involve conversion of estimated or given cash flows
to some point or points in time, such as the present, per annum, or the future.
The specific calculations are best illustrated with the help of examples.
Example 3-1
Suppose that a $20,000 piece of equipment is expected to last 5 years and
then result in a $4,000 salvage value; that is, can be sold for $4,000. If the
minimum acceptable rate of return (interest rate) is 15%, what are the
following values?
1. Annual equivalent (cost)
2. Present equivalent (cost)
Solution
Figure 3.2 shows all the cash flows.

Figure 3.2
Cash flow diagram for Example 3-1.
Figure 3.2 Full Alternative Text
1. A=−$20,000( A/P, 15%, 5 )+$4,000( A/F, 15%, 5 ) =−$20,000( 0.2983
)+$4,000( 0.1483 )=−$5,373
[Note: $5,373 is sometimes called the annual cost (AC) or equivalent
uniform annual cost (EUAC).]
2. P=−$20,000+$4,000( P/F, 15%, 5 ) =−$20,000+$4,000( 0.4972 )=
−$18,011
Alternatively, it is possible to solve part (b) by exploiting the results obtained
from part (a) as follows:
P=A( P/A, 15%, 5 ) =−$5,373( 3.3522 )=−$18,011
Example 3-2
(Deferred Uniform Series and Gradient Series)
Suppose that a certain savings is expected to be $10M at the end of year 3
and to increase $1M each year until the end of year 7. If the MARR is 20%,

then what are the following values?
1. Present equivalent (at beginning of year 1)
2. Future equivalent (at end of year 7)
Solution
Once again, the first step is to draw the cash flow diagram. Figure 3.3 shows
the gradient beginning at the end of year 3 and the unknowns to be calculated
(dashed arrows). In the solution, subscripts are used to indicate a point or
points in time.
Figure 3.3
Cash flow diagram for Example 3-2 showing deferred uniform and
gradient series.
Figure 3.3 Full Alternative Text
1. A 3−7 =$10M+$1M( A/G, 20%, 5 ) =$10M+$1M( 1.6405 )=$11.64M P
2 = A 3−7 ( P/A, 20%, 5 ) =11.64M( 2.9906 )=$34.81M P 0 = F 2 (
P/F, 20%, 2 ) =$34.81M( 0.6944 )=$24.17M
Notice that in the last calculation, the value of P 2 is substituted for F 2 .

2. (Skipping intermediate calculations):
F 7 =[ $10M+$1M( A/G, 20%, 5 ) ]( F/A, 20%, 5 ) =[ $10M+$1M(
1.6405 ) ]( 7.4416 )=$86.62M
Alternatively, one can use part (a) results to obtain F 7 as follows:
F 7 = P 0 ( F/P, 20%, 7 )=$24.17M(3.5832)=$86.62M
Example 3-3
 
(Repeating Cycle of Payments)
Suppose that the equipment in Example 3-1 is expected to be replaced three
times with identical equipment, making four life cycles of 5 years each. To
compare this investment correctly with another alternative that can serve 20
years, what are the following values when MARR=15%?
1. Annual equivalent (cost)
2. Present equivalent (cost)
Solution
Figure 3.4 shows the costs involved. The key to this type of problem is to
recognize that if the cash flows repeat each cycle, then the annual equivalent
for one cycle will be the same for all other cycles.

Figure 3.4
Cash flow diagram for Example 3-3.
1. We demonstrate a slightly different way to get the same answer as in
Example 3-1.
A=[ −$20M+$4M( P/F, 15%, 5 ) ]( A/P, 15%, 5 ) =[− $20M+$4M(
0.4972 ) ]( 0.2983 )=−$5,373K
2. P=−$5,373K( P/A, 15%, 20 ) =−$5,373K( 6.2593 )=−$33,629K
3.2.2 Nominal and Effective Interest
Rates
Interest rates are often quoted many different ways. In standard terminology,
we have
Nominal interest rate, r, is the annual interest rate without considering
the effects of compounding.
Effective interest rate, i eff , is the annual interest rate taking into
account the effects of compounding during the year.

To work with these rates, it is necessary to know the number of compounding
periods per year, denoted by p. The nominal interest rate is typically stated as
a percentage compounded p times per year.
Example 3-4
The nominal rate is 16%/year compounded quarterly. What is the effective
rate?
Solution
r=16%/year divided by 4=4%/quarter. On an annual basis, this equals
16.99%/year. The general formula is
i eff = ( 1+r/p ) p −1 i eff = ( 1+0.16/4 ) 4 −1=( 1.04
)4−1=1.1699−1=0.1699→16.99%
Example 3-5
 
(Nominal vs. Effective Rates)
A credit card company advertises a nominal rate of 16% on unpaid balances
compounded daily. What is the effective interest rate per year being charged?
Solution
 
r=16%/year, p=365 days/year i eff = ( 1+0.16/365 ) 365 −1=0.1735→17.35%
In the beginning of this section, i was defined simply as the interest rate per

interest period. A more precise definition, we now know, is that i is the
effective interest rate per interest period. When compounding is continuous,
we have the special case in which i eff = e r −1.
3.2.3 Inflation
Inflation is a condition in the economy characterized by rising prices for
goods and services. An inflationary trend makes future dollars have less
purchasing power than current dollars. This helps long-term borrowers at the
expense of lenders because a loan negotiated today will be repaid in the
future with dollars of lesser value.
In an economic analysis, one approach used to compensate for inflation is
first to convert all cash flows from year-n, or actual dollars, into year-0, or
real dollars. If the inflation rate is, say f, then this can be done by discounting
or deflating future dollars to the present as follows:
year-0 dollars=[ ( 1+f ) −n ] ( year-n dollars )
We would now proceed as before with the analysis. Alternatively, one may
compute an interest rate, i′ with inflation
i′=i+f+i×f
and use it in conjunction with the present worth factors to compute the
present value of future cash flows. Either approach should give the same
results. The important thing to remember is that all cash flows must be
expressed in the same units.
Example 3-6
1. Tuition at Big State University is $2,000 today. We expect college costs
to increase at a 6% annual rate. What will tuition be in 10 years?
Future tuition=$2,500 ( 1+0.06 ) 10 =$4,477

2. If the cost of a hamburger is $3 today, then what did it cost 40 years
ago? Assume the average rate of inflation during that time was 5%.
Former price of hamburger=3/ ( 1+0.05 ) 40 =$0.43
When all receipts and expenses escalate at the same rate as inflation, we can
ignore inflation and do the analysis in real dollars using i. In practice,
however, cash flows may be given in both real and actual dollars so we must
select a constant frame of reference in which to perform the analysis.
Example 3-7
You are considering a $10,000 investment that has a life of 10 years and no
salvage. On the basis of today’s economic environment, it is estimated that
operating costs will be $500 per year and revenue $2,000 per year
the general inflation rate will be 5% ( f=0.05 )
operating costs will escalate at the same rate as general inflation
revenues will not increase with time
For a 4% MARR without inflation ( i=0.04 ), what is the NPV of the
investment?
Solution
The components of the cash flow increase at different rates than general
inflation, so we must either convert all of them to actual dollars and use the
MARR with inflation i′ or convert all of them to real dollars and use the
MARR without inflation (i).  The analysis for both approaches is presented.
1. Analysis in terms of actual dollars: We first must find the appropriate
interest rate.

i′=0.04+0.05−0.04×0.05=0.092 or 9.2%
The revenues are already expressed in actual dollars, so it is necessary
only to convert the costs to actual dollars. The data in the last column of
the first table below represent the present worth (PW) of the cash flow at
the end of year n using an MARR of 9.2%.
Time Costs (actual $) Revenues(actual $) Net cash flow (actual $) PW(9.2%) (actual $)
0          10,000
           
−10,000
        
−10,000
1 525 2,000 1,475           1,351
2 551 2,000 1,449           1,215
3 579 2,000 1,421           1,091
4 608 2,000 1,392
5 638 2,000 1,362
6 670 2,000 1,330
7 704 2,000 1,296
8 739 2,000 1,261
9 776 2,000 1,224
10 814 2,000 1,186
            NPV
2. Analysis in terms of real dollars: For this case, we use i=0.04 to compute
PW. To get the net cash flows in each year, it is first necessary to
convert revenues to real dollars using the formula
Revenue in real $ ( in year n )=$2,000/ ( 1.05 ) n
Time Costs (real $) Revenues (real $) Net cash flow (real $)
0 10,000                     

1 500         1,905         1,405            
2 500         1,814         1,314            
3 500         1,728         1,228            
4 500         1,645         1,145            
5 500         1,567         1,067            
6 500         1,492         992            
7 500         1,421         921            
8 500         1,354         854            
9 500         1,289         789            
10 500         1,228         728            
As expected, both sets of computations give the same NPV of −$1,332.
3.2.4 Treatment of Risk
Risk comes in many forms. If a new product is being developed, then the
probability of commercial success is a major consideration. If a new
technology is being pursued, then we must constantly reevaluate the
probability of technical success and the availability of critical personnel and
resources. Once a product is ready for the market, such factors as financing,
contractual obligations, reliability of suppliers, and strength of competition
must be brought into the equation.
In the private sector, projects that are riskier than others are forced to pay
higher interest rates to attract capital. A speculative new company will have
to pay the banks several percentage points more for its borrowing than will
established, prime customers. Private companies, which always run the risk
of bankruptcy, have to pay more than the government. This extra amount of
interest is known as the risk premium and, as a practical matter, is already
included in the discount rate.
When a particular project faces uncommon technical or commercial risks, the
evaluation process should address each directly. Decision analysis (Chapter

5), coupled with the use of multiple-criteria methodologies (Chapter 6), is the
preferred way to appraise projects with a high component of risk.

3.3 Comparison of Alternatives
The essence of all economic evaluation is a discounted cash flow analysis.
The first step in every situation is to lay out the estimated cash flows, the
sequence of benefits (returns), and costs (payments) over time. These are then
discounted back to the present, using the methods shown in the previous
section, either directly or indirectly in the case of the rate-of-return and
payback period methods.
The relative merits of the available alternatives are determined by comparing
the discounted cash flows of benefits and costs. In general, a project is
considered to be worthwhile when its benefits exceed its costs. The relative
ranking of the projects is then determined by one of several evaluation
criteria. The methods of evaluation differ from each other principally in the
way in which they handle the results of the discounted cash flow analysis.
The present value method focuses on the difference between the discounted
benefits and costs, the ratio methods involve various comparisons of these
qualities, and the internal rate-of-return method tries to equalize them. The
question of what one does with the results of the discounted cash flows is the
central problem of economic evaluation.
Most methods presume that the discount rate to be used in the cash flow
analysis is known. This is often a reasonable assumption, because many
companies or agencies require that a specific rate be used for all of their
economic evaluations. In many instances, however, the discount rate must be
determined.
In carrying out an evaluation, estimation of the discount rate may be crucial.
Its choice can easily change the ranking of projects, making one or another
seem best depending on the rate used. This is because lower rates make long-
term projects, with benefits in the distant future, seem much more attractive
relative to short-term projects with immediate benefits than they would be if a
higher rate were used.
To see this, suppose that your organization has the choice of two storage and

retrieval systems, one that requires a human operator and one that is fully
automated. Both will last for 10 years. The human-assisted system costs
$10,000 and requires $4,200 per year of labor. The automated system has an
initial cost of $18,000 and consumes an additional $3,000 per year in power.
The decision is a question of whether the benefits of the annual savings (
$4,200−$3,000=$1,200 a year ) justify the additional initial cost of $8,000. Is
the NPV of the upgrade to the more expensive alternative positive?
If the discount rate were zero, implying that future benefits are not
discounted, then the upgrade is clearly worthwhile.
NPV( i=0% )=( $1,200/yr )( 10 years )−$8,000=$4,000
Conversely, if the discount rate were large, then future benefits would be
heavily discounted. For infinite i,
NPV( i=∞ )=$1,200( 0 )−$8,000= −$8,000
so the project is not worthwhile.
The variation of the NPV with the discount rate is summarized as follows:
i% 0 5 10 15 ∞
NPV(i%) $4,000 $1,264 −$632 −$1,976 −$8,000
The critical value of i, below which the more expensive system is preferred,
is approximately 8.5%, as determined by interpolation.
As this example shows, the choice of the discount rate can steer an analysis in
one direction or another. Powerful economic and political forces allied with a
particular technology may encourage this. When the U.S. Federal Highway
Administration promulgated a regulation in the early 1970s that the discount
rate for all federally funded highways would be zero, this was widely
interpreted as a victory for the cement industry over asphalt interests. Roads
that are made of concrete cost significantly more than those that are made of
asphalt but require less maintenance and less frequent replacement.

3.3.1 Defining Investment
Alternatives
Every evaluation deals with two distinct sets of projects or alternatives: the
explicit and the implicit. The explicit set consists of the opportunities that are
to be considered in detail; they are the focus of the analysis. The implicit set,
which can only be defined imprecisely, is important because it provides the
frame of reference for the evaluation and defines the minimum standards.
Explicit set of alternatives
This is a limited list of the potential projects that could actually be chosen.
The list is usually defined by a manager who is concerned with a particular
issue; for example,
an official of the department of highways who is responsible for
maintenance and construction of roads
a manager of a computer center, proposing to acquire new equipment
an investment officer for a bank, presenting a menu of opportunities for
construction loans
The projects suggested by each of the preceding situations illustrate two
characteristics typical of the choices considered in an evaluation. The explicit
set is:
1. Limited in scope, in that it includes only a portion of the projects that
might be in front of the organization as a whole. Thus, the manager of
the computer center is competent only in and considers only various
ways to improve the information systems; whether money should be
spent on developing a new product or replacing the central heating is
literally not his or her department.

2. Limited in number, being only a fraction of all of the projects that could
be defined over the next several years. Usually, the explicit list deals
only with the immediate choices, not the ones that could arise during the
next budget or decision period.
Since the sets of projects that we consider explicitly are limited, any
procedure that analyzes separate sets of projects independently can easily
lead to a list of recommended choices that are not the best ones for the
organization as a whole. For example, consider a company with an
information systems department, a research laboratory, and a manufacturing
plant: If we evaluate the projects proposed by each group, we can determine
the best software, the best instrument, and the best machine tool to buy, but
this plan may not be in the best interests of the company. It is possible that
the second-best machine tool is a better investment than the best instrument
or that none of the software is worthwhile financially.
The issue is: How does an organization ensure that the projects selected by its
components are best for the organization as a whole? In addressing this
question, we must recognize that the obvious answer, considering all possible
projects simultaneously, is neither practical nor even possible. A large
number of analyses could be done, but the level of computation is not the real
obstacle.
An analysis of all alternatives at once is not practical because it would be
extremely difficult for any group in an organization to be sufficiently
knowledgeable both to generate the possible projects for all departments and
to estimate their benefits and costs. They simply would not have sufficient
knowledge of the topic, region, or clients. Furthermore, the analysis of all
alternatives at once is not even conceptually feasible because we are unable
to predict which options will be available in the future. We therefore can
never be sure that the projects that we select from a current list, however
comprehensive it may be, will include all of the opportunities that will be
available over the life of the projects and that might otherwise be selected.
Some degree of sub-optimization is unavoidable.
To reduce the likelihood for sub-optimization, it is necessary to create some
means of evaluating any set of explicit alternatives that do not critically
depend on future developments. This can be done by creating a substitute for

the universe of possibilities. The implicit alternatives fill this role.
Implicit set of alternatives
This set is intended to represent all projects that were available in the past and
that might be available in the near future. Because it refers in part to
unknown prospects, it can never be described in detail. It thus indicates
inexactly what could be done instead of what can be done by opting for one
of the explicit alternatives.
The implicit set of alternatives is of interest because it establishes minimum
standards for deciding whether any explicit project is worthwhile. To
illustrate, consider the situation in which a person has consistently been able
to choose investments that provide yearly profits of 12% or more and has
rejected all others with smaller returns. Faced now with the problem of
evaluating an explicit set of specific proposals, this person will naturally turn
to past experience for guidance. If the investment possibilities have not
changed fundamentally, then the person may assume that there are continued
possibilities—the implicit set of alternatives—for earning at least 12% as
before and should correctly conclude that any explicit choice can be
worthwhile only if its profitability equals or exceeds the 12% implicitly
available elsewhere.
The minimum standards suggested by the implicit alternatives can be stated
in several ways. An obvious and common way is to stipulate a minimum
acceptable rate of return. Minimum standards of profitability can also be
expressed differently, however. In business, they are typically stated in terms
of the highest number of periods that will be required for the benefits to equal
the initial investment (the maximum payback period, see Section 3.4.6).
Minimum standards can also be defined in terms of minimum ratios of
benefits to costs (Section 5.4).
Organizations use minimum standards for the economic acceptability of
projects, as they force each department or group to take into account the
global picture. They cannot, for example, choose projects unless they are at
least as good as others available elsewhere in the organization.

3.3.2 Steps in the Analysis
A systematic procedure for comparing investment alternatives can be
outlined as follows:
1. Define the alternatives.
2. Determine the study period.
3. Provide estimates of the cash flows for each alternative.
4. Specify the interest rate (MARR).
5. Select the measure(s) of effectiveness (i.e., the criteria for judging
success).
6. Compare the alternatives.
7. Perform sensitivity analyses.
8. Select the preferred alternative(s).
The study period defines the planning horizon over which the analysis is to
be performed. It may or may not be the same as the useful lives of the
equipment, facility, or project involved. In general, if the study period is less
than the useful life of an asset, then an estimate of its salvage value should be
provided in the final period; if the study period is longer than the useful life,
then estimates of cash flows are needed for subsequent replacements of the
asset.
Whenever alternatives that have different lives are to be compared, the study
period is usually one of the following:
1. The organization’s traditional planning horizon
2. The life of the shortest-lived alternative
3. The life of the longest-lived alternative

4. The lowest common multiple of the lives of the alternatives
When the study period for the alternatives is forced to be the same by using
measures 1, 2, or 3 above or for any other reason, the so-called co-terminated
assumption is said to apply and whatever cash flows are thought appropriate
are considered within that study period. When the study period is chosen by
measure 4 above, the alternatives normally are assumed to satisfy the
following so-called repeatability assumptions.
1. The period of needed service is either indefinitely long or a common
multiple of the lives.
2. What is estimated to happen in the first life cycle will happen in all
succeeding life cycles, if any, for each alternative.
In the upcoming subsections that illustrate the various analytic methods,
when alternatives have different lives and nothing is indicated to the contrary,
the repeatability assumptions are used. These assumptions are commonly
adopted for computational convenience. The decision maker must decide
whether they are reasonable for the situation.

3.4 Equivalent Worth Methods
For purposes of analysis, equivalent worth methods convert all relevant cash
flows into equivalent (present, annual, or future) amounts using the MARR.
If a single project is under consideration, then it is acceptable (earns at least
the MARR) if its equivalent worth is greater than or equal to zero; otherwise,
it is not acceptable. These methods all assume that recovered funds (net cash
inflows) can be reinvested at the MARR.
If two or more mutually exclusive alternatives are being compared and
receipts or savings (cash inflows) as well as costs (cash outflows) are known,
then the project that has the highest net equivalent worth should be chosen, as
long as that equivalent worth is greater than or equal to zero. If only costs are
known or considered (assuming that all alternatives have the same benefits),
then the project that has the lowest total equivalent of those costs should be
chosen. Because all three equivalent worth methods give completely
consistent results, the choice of which to use is a matter of computational
convenience and preference for the form in which the results are expressed.
3.4.1 Present Worth Method
PW denotes a lump-sum amount at some early point in time (often the
present) that is equivalent to a particular schedule of receipts and/or
disbursements under consideration. If receipts and disbursements are
included in the analysis, PW can best be expressed as the difference between
the present worth of benefits and the present value of costs, otherwise known
as NPV.
Example 3-8
Consider the following two mutually exclusive alternatives and recommend
which one (if either) should be implemented.

Machine
A
Initial cost $20,000                   $30,000                  
Life 5 years                   years                  
Salvage value $4,000                  
Annual
receipts $10,000                   $14,000                  
Annual
disbursements $4,400                   $8,600                  
  Minimum acceptable rate of return=15%
  Assume 10-year study period and repeatability
Solution
(using PW method)
Machine
A
Annual receipts
 $10,000(P/A, 15%, 10) $50,188           
 $14,000(P/A, 15%, 10) $70,263           
Salvage value at end of  
year 10=$4,000(
P/F, 15%, 10 )
   $989                
  Total PW of cash
inflow $51,177            $70,263           
Annual disbursements:
 $4,400(P/A, 15%, 10) −$22,083            
 $8,600(P/A, 15%, 10) −$43,162
Initial cost −$20,000             −$30,000
Replacement:

   ( $20,000−$4,000 )(
P/F, 15%, 5 )
           
−$7,955 _                 
 Total PW of cash
outflow −$50,038             −$73,162
Net PW (NPV) $1,139            −$2,899
Thus project A has the higher NPV and represents the better economic
choice. Since the NPV of project B is negative, a firm would never select
project B in any case.
3.4.2 Annual Worth Method
Annual worth (AW) is merely an “annualized” measure for assessing the
financial desirability of a proposed undertaking. It is a uniform series of
money over a certain period of time that is equivalent in amount to a
particular schedule of receipts and/or disbursements under consideration. Any
“period” can be used in the analysis, such as a month or a week. The word
“annual” is used to represent a generic time period. If only disbursements are
included, then the term is usually expressed as annual cost (AC) or
equivalent uniform annual cost (EUAC). The examples in this section include
both cash inflows and outflows.
Calculation of capital recovery cost
The capital recovery (CR) cost for a project is the equivalent uniform annual
cost of the capital that is invested. It is an annual amount that covers the
following two items.
1. Depreciation (loss in value of the asset)
2. Interest (MARR) on invested capital
Consider an alternative requiring a lump-sum investment P and a salvage

value S at the end of n years. At interest rate i per year, the annual equivalent
cost can be calculated as
CR=P( A/P, i, n )−S( A/F, i, n )
There are several other formulas for calculating the CR cost. Probably the
most common is
CR=( P−S )( A/P, i, n )+Si
One might want to reverse signs so that a cost is negative, as is done in the
following example, which includes CR costs in an AW comparison.
Example 3-9
Given the same machines A and B as used to demonstrate the net PW method
in Example 3-8, we now compare them by the net AW method.
Machine
A
Initial cost $20,000                   $30,000                  
Life 5 years                   years                  
Salvage value $4,000                  
Annual
receipts $10,000                   $14,000                  
Annual
disbursements $4,400                   $8,600                  
  Minimum acceptable rate of return=15%
  Assume repeatability
Solution

(using AW method)
Machine
A
Annual
receipts $10,000                $14,000               
Annual
disbursements
−$4,400
                               
CR amount:
−$20,000(
A/P, 15%, 5 )
−$5,966
               
  +$4,000(
A/F, 15%, 5 ) +$593                
  −$30,000(
A/P, 15%, 10
)
                                  
Net AW $227                −$578                
Thus project A, having the higher net annual worth which also is greater than
$0, is the better economic choice. A shortcut for calculating the net AWs
given the net PWs calculated in the preceding section is
AW( A )=$1,139( A/P, 15%, 10 )=$227 AW( B )=−$2,889( A/P, 15%, 10 )=
−$578
One significant computational shortcut when comparing alternatives with
different lives by the PW method and assuming repeatability is first to
calculate AWs as above and then calculate the PWs for the lowest common
multiple-of-lives study period. Thus,
PW( A )=$227( P/A, 15%, 10 )=$1,139 PW( B )=−$578( P/A, 15%, 10 )=
−$2,899
3.4.3 Future Worth Method

The future worth (FW) measure of merit is a lump-sum amount at the end of
the study period which is equivalent to the cash flows under consideration.
Example 3-10
Given the same machines A and B (Examples 3-8 and 3-9), determine which
is better on the basis of FW at the end of the 10-year study period.
Solution
(using FW method) Rather than calculating FWs of all the types of cash
flows involved (as was done for the PW solution above), shown below are
shortcut solutions based on (a) PWs and (b) AWs calculated previously:
1. FW( A )=$1,139( F/P, 15%, 10 )=$4,608 FW( B )=−$2,899(
F/P, 15%, 10 )=−$11,728
2. FW( A )=$227( F/A, 15%, 10 )=$4,608 FW( B )=−$578( F/A, 15%, 10
)=−$11,735
Not surprisingly, we have once again found that alternative A is preferred.
The ratios of the numbers produced by each of the equivalent worth methods
will always be the same. For machines A and B, FW( A )/FW( B )=PW( A
)/PW( B )=AW( A )/AW( B )=−0.393.
Example 3-11
 
(Different Useful Lives: Fixed-Length Study Period)
Suppose that two measurement instruments are being considered for a certain
industrial laboratory. Following are the principal cost data for one life cycle

of each alternative:
Instrument
M1
Investment $15,000                   $25,000                  
Life 3 years                   5 years                  
Salvage value 0                  
Annual
disbursements $4,400                   $8,600                  
  Minimum acceptable rate of return=20%
  Assume no repeatability
Which instrument is preferred?
Solution
The calculations will be done using the PW method and MARR=20% for the
following two cases:
1. If the study period is taken to be 3 years, then we need a salvage value
for alternative M2 at the end of the third year. Assuming it to be, say,
$6,000, the following results are obtained:
Instrument
M1
Investment $15,000           $25,000                  
Annual
disbursements:
 
$8,000(A/P,
20%, 3)
$16,852          
 
$5,000(A/P,
20%, 3)
$10,533                  

Salvage:
−$6,000(
A/F, 20%, 3 )
                                 
 Net PW
(NPV) $31,852           $32,061                  
Thus the first alternative is slightly better. Note that “+” is used for costs.
2. If the study period is taken to be 5 years, then we need estimates of what
will happen after the first life cycle of alternative M1. Let us assume that
it can be replaced at the beginning of the fourth year for $18,000 and
that the annual disbursements will be $9,000 for years 4 and 5.
Furthermore, it will have a $7,000 salvage value at the end of year 5. In
this case, we obtain
Instrument
    M1
Investment $15,000                $25,000          
Annual
disbursements
  
$8,000(A/P,
20%, 3)
$16,852               
  
$9,000(P/A,
20%, 2)(P/F,
20%, 3)
$7,975               
  
$5,000(A/P,
20%, 5)
$14,953          
Additional
investment:
$18,000(P/F,
20%, 3)
$10,147               
Salvage: −$2,813 _

−$7,000(
P/F, 20%, 5 )
               
  Net PW
(NPV) $47,413                $39,953          
Thus, alternative M2 has a slightly lower net PW and hence is better
with the new assumption.
3.4.4 Discussion of Present Worth,
Annual Worth, and Future Worth
Methods
Some academics and accountants assert that the net PW methods—and in
particular, the NPV criterion—should be used in all economic analyses. This
prescription should be resisted. NPV (and its equivalents) provides a good
comparison between projects only when they are strictly comparable in terms
of level of investment or total budget. This condition is rarely met in the real
world. The practical consequence is that NPVs are used primarily for the
analysis of investments, particularly of specific sums of money, rather than
for the evaluation of projects, which come in many different sizes.
The advantage of the net PW criteria is that they focus attention on quantity
of money, which is what the evaluation is ultimately concerned with. Net
PW, AW, and FW differ in this respect from the other criteria of evaluation,
which rank projects by ratios and hence do not directly address the bottom-
line question of maximizing profit.
One disadvantage of NPV is that its precise meaning is difficult to explain.
NPV does not measure profit in any usual sense of the term. In ordinary
language, profit is the difference between what we receive and what we pay
out. As an example, consider an investment now for a lump sum of revenue
later. In crude terms,
profit = money received−money invested

More precisely, if we had to borrow money to make the original investment,
then the profit would be net of interest paid for n periods:
profit = money received−( money invested )( F/P, i, n )
Where i is the interest rate. This profit can also be placed in present value
terms using the appropriate MARR for the organization concerned. Note that
it is now important to make the distinction between the MARR and the
interest rate.
present value of profit=( money received )( P/F, MARR, n ) −(
money invested )( F/P, i, n )( P/F, MARR, n )
In the last calculation, it turns out that because the MARR is not, in general,
equal to the interest rate, NPV≠present value of profit. Thus even when NPV
equals zero, a project may be profitable, as understood in common language.
A project with NPV=0 is simply not advantageous compared with other
alternatives available to the organization. NPV thus indicates “extra
profitability” beyond the minimum.
Another difficulty with the net PW criteria is that they give no indication of
the scale of effort required to achieve the result. To see this, consider the
problem of evaluating projects P1 and P2 below.
Project Benefit Cost
P1 $2,002,000           $2,000,000          
P2 $2,000           $1,000          
If one considers only NPV, then project P1 seems better. Most investors
would consider that an absurd choice, however, because of the difference in
scale between the projects. Taking scale into account, P2 presumably gives a
much better return than P1: the money saved by investing in the former rather
than the latter can be invested elsewhere for a return greater than that offered
by P1. In any case, NPV by itself is not a good criterion for ranking projects.
Formally, the essential conditions for net worth to be an appropriate criterion
for the evaluation and ranking of projects are that:

we have a fixed budget to invest
projects require the same investment
These conditions do not hold with any regularity. On the contrary, it is most
often the case that the list of projects consists of a variety of possibilities with
varying costs. A central problem in the evaluation and choice of systems is to
delimit their size and budget. Analysis of net worth is not particularly helpful
in those contexts.
3.4.5 Internal Rate of Return
Method
The internal rate of return (IRR) method involves the calculation of an
interest rate that is compared against a minimum threshold (i.e., the MARR).
As we will see, it is the interest rate for which the NPV of a project is zero.
The concept is that the IRR expresses the real return on any investment (i.e.,
return on investment). For evaluation, the idea is that projects should be
ranked from the highest IRR down.
The IRR is now used increasingly by sophisticated business analysts. The
advantage of this criterion is that it overcomes two difficulties inherent in the
calculation of both NPV and benefit-cost ratios. That is:
1. It eliminates the need to determine the appropriate MARR.
2. Its rankings cannot be manipulated by the choice of a MARR.
It also focuses attention directly on the rate of return of each project, an
attribute that cannot be understood from either the net present value or the
benefit-cost ratio.
The IRR is known by other names, such as investor’s rate of return,
discounted cash flow return, and so on. We will demonstrate its use for a
single project and then for the comparison of mutually exclusive projects.

IRR method for single project
The most common method of calculation of the IRR for a single project
involves finding the interest rate, i, at which the PW of the cash inflow
(receipts or cash savings) equals the PW of the cash outflow (disbursements
or cash savings foregone). That is, one finds the interest rate at which PW of
cash inflow equals PW of cash outflow; or at which PW of cash inflow minus
PW of cash outflow equals 0; or at which PW of net cash flow equals 0. The
IRR could also be calculated by using the same procedures applied to either
AW or FW.
The calculations normally involve trial and error until the correct interest rate
is found or can be interpolated. Closed-form solutions are not available
because the equivalent worth factors are a nonlinear function of the interest
rate. The procedure is described below for several situations. (When both
cash inflows and outflows are involved, the convention of using a “+” sign for
inflows and a “−” sign for outflows will be followed.)
Example 3-12
Given the same machine A as in Section 3.4.1, find the IRR and compare it
with a MARR of 15%.
Machine A
Initial cost $20,000
Life 5 years
Salvage value $4,000
Annual receipts $10,000
Annual disbursements $4,400
Solution

Expressing the NPV of cash flow and setting it equal to zero results in the
following:
NPV( i )=−$20,000+( $10,000−$4,400 )( P/A, i, 5 )+$4,000( P/F, i, 5 )=0
Try i=10%
NPV( 10% )=−$20,000+$5,600( P/A, 10%, 5 )+$4,000( P/F, 10%, 5 )
=$3,713>0
Try i=15%
NPV( 15% )=−$20,000+$5,600( P/A, 15%, 5 )+$4,000( P/F, 15%, 5 )
=$730>0
Try i=20%
NPV( 20% )=−$20,000+$5,600( P/A, 20% 5 )+$4,000( P/F, 20%, 5 ) =
−$1,196<0 Because we have both a positive and a negative NPV, the desired answer is bracketed. Linear interpolation can be used to approximate the unknown interest rate, i, as follows: i−15% 20%−15% = $730−0 $730−( −$1,196 ) so i=15%+ $730 $730+$1,196 ( 20%−15% ) Solving gives i=16.9%. 1 Now, because 16.9% is greater than the MARR of 15%, the project is justified. A plot of NPV versus interest rate is given in Figure 3.5. 1 A more exact calculation gives i%=16.47%, but we use 16.9% for the remainder of the chapter. Figure 3.5 Relationship between NPV and IRR for Example 3-12. Because the P/A and P/F factors are nonlinear functions of the interest rate, the linear interpolation (above) causes an error, but the error is usually inconsequential in economic analyses. The narrower the range of rates over which the interpolation is done, the more accurate are the results. Finally note that as the trial interest rate is increased, the corresponding NPV decreases. IRR Method for Comparing Mutually Exclusive Alternatives When comparing alternatives by any rate of return (ROR) method when at most one alternative will be chosen, there are three main principles to keep in mind: 1. Any alternative whose IRR is less than the MARR can be discarded immediately. 2. Each increment of investment capital must justify itself (by sufficient ROR on that increment). 3. Compare a higher investment alternative against a lower investment alternative only if that lower investment alternative is justified. The usual approach when using a ROR method is to choose the alternative that requires the highest investment for which each increment of investment capital is justified. This choice assumes that the organization wants to invest any capital needed as long as the capital is justified by earning a sufficient ROR on each increment of capital. In general, a sufficient ROR is any value greater than or equal to the MARR. The IRR on the incremental investment for any two alternatives can be found by: 1. finding the rate at which the PW (or AW or FW) of the net cash flow for the difference between the two alternatives is equal to zero or 2. finding the rate at which the PWs (or AWs or FWs) of the two alternatives are equal. Example 3-13 Suppose that we have the same machines, A and B, as considered in Section 3.4.1. In addition, machines C and D are mutually exclusive alternatives also to be included in the comparison by the IRR method. Relevant data and the solution are presented below. Repeatability of the alternatives is assumed. Machine A B Initial cost $20,000        $30,000        $35,000        Life 5 years        10years        5 years        Salvage value $4,000        0        $4,000        Annual receipts $10,000        $14,000        $20,000        Annual disbursements $4,400        $8,600        $9,390  Net annual receipts– disbursements $5,600        $5,400        $10,610        IRR 16.9%        12.4%        17.9%        Solution As a first step, it is best to arrange the alternatives in order of increasing initial investment because this is the order in which the increments will be considered. The symbol Δ means “increment,” and A→B means “the increment in going from alternative A to alternative B.” Recall that an increment of investment is justified if the IRR on that increment (i.e., ΔIRR ) is ≥15%. The least expensive alternative is always compared with the “do nothing” option. A A→B † A→C C→D ΔInvestment $20,000 $10,000 $15,000 $8,000 ΔSalvage $4,000 −$4,000 $0 $1,000 Δ (annual receipts— disbursements) $5,600 −$200 $5,010 $2,140 ΔIRR 16.9% 0% 20% 13.3% Is ΔInvestment justified? Yes No Yes No †Analysis must include $16,000 replacement cost for alternative A at end of year 5. The analysis indicates that alternative C would be chosen because it is associated with the largest investment for which each increment of investment capital is justified. The analysis was performed without considering the IRR on the total investment for each alternative. However, when we look at the individual IRRs, we see that IRR( B )=12.4% <15%= MARR, so alternative B could have been discarded. In choosing alternative C, one increment of investment was justified as follows: Increment Incrementalinvestment IRR on increment, ΔIRR( % ) A $20,000 16.9 A→C $15,000 20.0 Total investment $35,000 Coincidentally, alternative C had the largest IRR, which seems intuitive but is not always the case. If the MARR were, say, 12%, then alternative D would have been selected. As a general rule, if the most expensive alternative has the highest IRR, it will always turn out to be preferred. In Example 3-13, because the useful lives of A and B are different and repeatability is assumed, one should closely examine the cash flows for A→B (B minus A) for the lowest common multiple of lives. For the 10-year period, ∑ ( positive cash flows )=$16,000=(replacement cost)=∑(negative cash flows)=$10,000+$4,000+10($200). Thus ΔIRR=0%; any i>0 would
produce a negative NPV.
Occasionally, situations arise in which a single positive interest rate cannot be
determined from the cash flow; that is, solving for NPV=0 yields more than
one solution. Descartes’s rule of signs indicates that multiple solutions can
occur whenever the cash flow series reverses sign (from net outflow to net
inflow, or vice versa) more than once over the study period. This is
demonstrated in the following example.
Example 3-14

 
(No Single IRR Solution)
The Converse Aircraft Company has an opportunity to supply a wide-body
airplane to Banzai Airlines. Banzai will pay $19 million when the contract is
signed and $10 million one year later. Converse estimates its second- and
third-year net cash flows at $50 million each during production. Banzai will
take delivery of the plane during year 4 and agrees to pay $20 million at the
end of that year and the $60 million balance at the end of year 5. Compute the
ROR on this project.
Solution
Computation of NPV at various interest rates, using single payment PW
factors (for year 2 and i=10%, PW=−50( P/F, 10%, 2 )=−50( 0.826 )=−41.3 )
is presented:
Year Cash flow 0% 10% 20% 40% 50%
0 +19 +19 +19 +19 +19 +19
1 +10 +10 +9.1 +8.3 +7.1 +6.7
2 −50 −50 −41.3 −34.7 −25.5 −22.2
3 −50 −50 −37.6 −28.9 −18.2 −14.8
4 +20 +20 +13.7 +9.6 +5.2 +4.0
5 +60 +60 _ +37.3 _ +24.1 _ +11.2 _ +7.9 _
NPV= +9 +0.2 −2.6 −1.2 +0.6
The NPV plot for these data is depicted in Figure 3.6. We see that the cash
flow produces two points at which NPV=0; one at approximately 10.1% and
the other at approximately 47%. Whenever multiple answers such as these
exist, it is likely that neither is correct.

Figure 3.6
NPV plot for more than one change in sign.
An effective way to overcome this difficulty and obtain a “correct” answer is
to manipulate cash flows as little as necessary so that there is only one sign
reversal in the net cash flow stream. This can be done by using an appropriate
interest rate to move lump sums either forward or backward, and then solve
in the usual manner. To demonstrate, let us assume that all money held
outside the project earns 6%. (This value could be considered the external
interest rate that Converse faces. If it had to borrow money, the interest rate
might be different.) At both year 0 and year 1, there is an inflow of cash
resulting from the advance payments by Banzai. The money will be needed
later to help pay the production costs. Given an external interest rate of 6%,
the $19 million will be invested for 2 years and the $10 million for 1 year.
Their compounded amount at the end of year 2 will be
FW at end of year 2=19( F/P, 6%, 2 )+10( F/P, 6%, 1 ) =19( 1.124 )+10( 1.06

) =32
When this amount is returned to the project, the net cash flow for year 2
becomes −50+32=−18. The resulting cash flow for the 5 years is:
Year Cash flow 0% 8% 10%
0 0 0 0 0
1 0 0 0 0
2 −18 −18 −15.4 −14.9
3 −50 −50 −39.7 −37.6
4 +20 +20 +14.7 +13.7
5 +60 +60 _ +40.8 _ +37.3 _
NPV= +12 +0.4 −1.5
This cash flow stream has one sign change, indicating that there is either zero
or one positive interest rate. By interpolation, we can find the point where
NPV=0:
i=8%+2% 0.4 1.5+0.4 =8%+2%( 0.21 )=8.42%
Thus, assuming an external interest rate of 6%, the internal rate of return for
the Banzai plane contract is 8.42%.
In many situations, we are asked to compare and rank independent
investment opportunities rather than a set of mutually exclusive alternatives
designed to meet the same need. Portfolio analysis is such an example in
which the firm is considering a number of different R&D projects and must
evaluate the costs and benefits of each. Here, the IRR method will always
give results that are consistent (regarding project acceptance or rejection)
with those obtained from the PW, AW, or FW method. However, the IRR
method may give a different ranking regarding the order of desirability when
comparing independent investment opportunities.
As an example, consider Figure 3.7, depicting the relation of IRR to NPV for
two projects, X and Y. The IRR for each project is the interest rate at which
the NPV for that project is zero. This is shown for a nominal MARR. For the

hypothetical but quite feasible relationship shown in Figure 3.7, project Y has
the higher IRR, whereas project X has the higher NPV of all IRRs except for
the rate at which the net present values are equal. This illustrates the case in
which the IRR method does result in a different ranking of alternatives
compared with the PW (AW or FW) method. Nevertheless, because both
projects have an NPV greater than zero, the IRR for either is greater than the
MARR. The determination of acceptance of both projects is shown
consistently by either method. It should be noted that if X and Y had been
mutually exclusive alternatives, then there would have been no inconsistency
regarding which to choose provided an incremental IRR analysis was
performed.
Figure 3.7

Relationship between NPV and IRR for independent investment.
3.4.6 Payback Period Method
In its simplest form, the payback period is the number of periods, usually
measured in years, required for the accruing net undiscounted benefits from
an investment to equal its cost. If we assume that the benefits are equal in
each future year and that depreciation and income taxes are not included into
the calculations, the formula is
payback period= initial investment annual net undiscounted benefits
When the benefits differ from year to year, it is necessary to find the smallest
value of n such that
∑ j=1 n B j ≥P
where P is the initial investment and B j is the annual net benefit in year i.
Example 3-15
The cash flows for two alternatives are as follows:
Year
Alternative 0 1 2 3 4 5
A −$2,700 +1,200 +1,200 +1,200 +1,200 +1,200
B −$1,000 +200 +200 +1,200 +1,200 +1,200
On the basis of the payback period, which alternative is best?
Solution
Alternative A: Because the annual benefits are uniform, the payback period

can be computed from the first formula in this section; that is,
$2,700 $1,200/yr =2.25 years
Alternative B: The payback period is the length of time required for profits or
other benefits of an investment to equal the cost of the investment. In the first
2 years, only $400 of the $1,000 cost is recovered. The remaining $600 is
recovered in the first half of the third year. Thus the answer is 2.5 years.
Therefore, to minimize the payback period, choose alternative A.
The great advantage of the payback period is that it is simple. It thus is an
excellent mechanism for allowing middle managers and technical staff to
choose among proposals without going through a detailed analysis or to sort
through many possibilities before resorting to a more sophisticated approach.
Situations that are suitable for the use of the payback period are often found
in industry. These are projects in which a constant benefit is expected to
accrue for an extended period as a result of a particular investment. A typical
case would be the purchase of a new robot that would reduce operating
expenses each year by a fixed amount, or some insulation or control that
would regularly save on energy bills.
The weakness of this criterion is that it is crude; it does not clearly
distinguish between projects with different useful lives. For any projects with
identical useful lives, for which the capital recovery factor will be identical,
the payback period gives as good a measure of economic desirability as the
NPV or IRR. When the useful lives of projects are different, the capital
recovery factors are not the same and the results can be highly misleading, as
the following analysis shows:
P1
Investment $2,000              
Useful
live               years              
Annual $1,000              

receipts              
Payback
period               years              
NPV at
10%               $487              
IRR               23.4%              
In this example, project P1 has a shorter payback period than the alternative
P2 and would seem better by this criterion, yet project P2 is, in fact, more
economically desirable for a wide range of discount rates. This is because P2
provides substantial benefits over a much longer period. Thus over a 6-year
cycle, P1 would have to be repeated twice for a total cost of $4,000 and
benefits of $6,000, whereas P2 would cost only $2,000 and yield returns of
$4,800—greater net benefits and a higher NPV for any number of discount
factors.

3.5 Sensitivity and Breakeven
Analysis
Much of the data collected in solving a business or engineering problem
represent projections of future consequences and hence may possess a high
degree of uncertainty. As the desired result of the analysis is decision
making, an appropriate question is: “To what extent do the variations in the
data affect the decision?” When small variations in a particular estimate
would change the alternative selected, the decision is said to be sensitive to
the estimate. To better evaluate the impact of any parameter, one should
determine the amount of variation necessary in it to effect a change in
outcome. This is called sensitivity analysis.
This type of analysis highlights the important and significant aspects of a
problem. For example, one might be concerned that the estimates for annual
maintenance and future salvage value in a facility modernization project vary
substantially, depending on the assumptions used. Sensitivity analysis might
indicate, however, that the decision is insensitive to the salvage value
estimates over the full range of possibilities. At the same time it might show
that small changes in annual maintenance expenditures strongly influence the
choice of equipment. Under these circumstances, one should place greater
emphasis on pinning down the true maintenance costs than on worrying about
salvage value estimates.
Succinctly, sensitivity analysis describes the relative magnitude of a
particular variation in one or more elements of a problem that is sufficient to
alter a particular decision. Closely related is breakeven analysis, which
determines the conditions under which two alternatives are equivalent. These
two evaluation techniques frequently are useful in engineering problems
called stage construction. That is, should a facility be constructed now to
meet its future full-scale requirements, or should it be constructed in stages as
the need for the increased capacity arises? Three examples of this situation
are as follows:

Should we install a cable with 400 circuits now, or a 200-circuit cable
now and another 200-circuit cable later?
A 10-cm water main is needed to serve a new area of homes. Should the
10-cm main be installed now, or should a 15-cm main be installed to
provide an adequate water supply later for adjoining areas when other
homes are built?
An industrial firm currently needs a 10,000 -m 2 warehouse and
estimates that it will need an additional 10,000 m 2 in 4 years. The firm
could have a warehouse built now and later enlarged, or have a 20,000
m 2 warehouse built today.
Examples 3-16 and 3-17, adapted from Newnan et al. (2000), illustrate the
principles and calculations behind sensitivity and breakeven analysis.
Example 3-16
Consider the following situation in which a project may be constructed to full
capacity now or may be undertaken in two stages.
Construction costs
Two-stage construction
 Construct first stage now $100,000
 Construct second stage n years from now $120,000
Full-capacity construction $140,000
Other factors
1. All facilities will last until 40 years from now regardless of when they
are installed; at that time, they will have zero salvage value.

2. The annual cost of operation and maintenance is the same for both
alternatives.
3. Assume that the MARR is 8%.
Plot a graph showing “age when second stage is constructed” versus “costs
for both alternatives.” Mark the breakeven point. What is the sensitivity of
the decision to second-stage construction 16 or more years in the future?
Solution
Because we are dealing with a common analysis period, the calculations may
be either AC or PW. PW calculations seem simpler and are used here:
Construct full capacity now
PW of cost=$140,000
Two-stage construction
In this alternative, the first stage is constructed now with the second stage to
be constructed n years hence. To begin, compute the PW of cost for several
values of n (years).
PW of cost=$100,000+$120,000( P/F, 8%, n )
n=5:    PW=$100,000+$120,000( 0.6806 )=$181,700
n=10:  PW=$100,000+$120,000( 0.4632 )=$155,600
n=20:  PW=$100,000+$120,000( 0.2145 )=$125,700
n=30:  PW=$100,000+$120,000( 0.0994 )=$111,900
These data are plotted in Figure 3.8 in the form of a breakeven chart. The
horizontal axis is the time when the second stage is constructed; the vertical
axis represents PW. We see that the PW of cost for two-stage construction

naturally decreases as the time for the second stage is deferred. The one-stage
construction (full capacity now) option is unaffected by the time variable and
hence is a horizontal line on the graph.
Figure 3.8
Breakeven chart diagram for Example 3-16.
Figure 3.8 Full Alternative Text
The breakeven point on the graph is the point at which both alternatives have
equivalent costs. We see that, if in two-stage construction, the second stage is
deferred for 15 years, the PW of that alternative is equal to the PW of the
first, which is approximately $137,800. Thus, year 15 is the breakeven point.

The plot also shows that if the second stage were needed before year 15, then
one-stage construction, with its smaller PW of cost, would be preferred. If the
second stage were not needed until after year 15, then the opposite is true.
The decision as to how to construct a project is sensitive to the age at which
the second stage is needed only if the range of estimates includes 15 years.
For example, if one estimated that the second-stage capacity would be needed
sometime over the next 5 to 10 years, then the decision is insensitive to that
estimate. The more economical thing to do is to build the full capacity now,
but if demand for the second-stage capacity were between, say, years 12 and
18, then the decision would depend on the estimate of when full capacity
would actually be needed.
One question posed by Example 3-16 is how sensitive the decision is to the
need for the second stage at or beyond 16 years. The graph shows that the
decision is insensitive. In all cases for construction on or after 16 years, two-
stage construction has a lower PW of cost.
Example 3-17
In this example, we have three mutually exclusive alternatives, each with a
20-year life and no salvage value. Assume that the MARR is 6% and
A B C
Initial cost $2,000 $4,000 $5,000
Uniform annual benefit $410 $639 $700
Calculating the NPV of each alternative gives
NPV=A( P/A, 6%, 20 ) NPV( A )=$410( 11.470 )−$2,000=$2,703 NPV( B
)=$639( 11.470 )−$4,000=$3,329 NPV( C )=$700( 11.470 )−$5,000=$3,029
so alternative B is preferred. Now we would like to know how sensitive the
decision is to the estimate of the initial cost of B. If B is preferred at an initial
cost of $4,000, then it will continue to be preferred for any smaller values,
but how much higher than $4,000 can the initial cost go up and still have B as

the preferred alternative?
Solution
 
The computations may be performed in several different ways. The first thing
to note is that for the three alternatives, B will maximize NPV only as long as
its NPV is greater than $3,029. Let X=initial cost of B. Thus, we have
NPV( B )=$639( 11.470 )−X>$3,029
or
X<$7,329−$3,029=$4,300 implying that B is the best alternative if its initial cost does not exceed $4,300. The breakeven chart for the problem is displayed in Figure 3.9. Because we are maximizing NPV, we see that B is preferred if its initial cost is less than $4,300. At an initial cost above this value, C is preferred. At the breakeven point, B and C are equally desirable. For the data given, alternative A is always inferior to alternative C. Figure 3.9 Breakeven chart diagram for Example 3-17. Figure 3.9 Full Alternative Text Sensitivity analysis and breakeven point calculations can be very useful in identifying how different estimates affect the decision. It must be recognized, however, that these calculations assume that all parameters except one are held constant and that the sensitivity of the decision to that parameter is what is being evaluated. 3.6 Effect of Tax and Depreciation on Investment Decisions The discussion thus far referred to investment earnings as cash flows implicitly net of tax consequences. The reason for this is that only the actual cash flow produced by an investment is relevant to the decision process. Earnings before depreciation and taxes do not represent the actual benefits realized by a firm. Consequently, the expected income from an investment must be adjusted to represent the true cash inflow before ranking can take place. Note that depreciation can be viewed as an expense and thus reduces gross income for tax purposes. The procedures and schedules used to compute depreciation in any year are promulgated by the Internal Revenue Service (IRS). Assume that a machine that costs $10,000 has a useful life of 5 years and is expected to produce gross earnings of $4,000 each year. With straight-line depreciation [ amount per year=( initial cost−salvage value )/( useful life ) ], no salvage value, and a 40% tax rate, the annual cash flow in each of the 5 years will be A. Gross earnings $4,000 B. Depreciation expense $2,000 C. Taxable income ( A−B ) $2,000 D. Taxes (40% of C) −$800 _ E. Cash flow ( A−D ) $3,200 Now, if the MARR for the firm is 10%, then the NPV of the investment is $3,200( P/A, 10%, 5 )−$10,000=$3,200( 3.708 )−$10,000=$2,131 which makes it worthwhile. Income tax rates are specified differently for individuals and corporations, and depend on the level of income. Most countries have what is called a progress tax system in which the more money you make, the higher your tax rate is on the additional income. In such a system, income brackets and corresponding tax rates are defined. Each dollar earned within a bracket after accounting for deductions is taxed at the corresponding rate. In 2004, in the United States, all individual income over $297,374 was taxed at the rate of 39.1%, the highest bracket. For corporations, the situation is a bit more complicated, but all income over $15M was taxed at 38%. The rationale for a progress tax system is based on what economists call the marginal utility of the last dollar earned. If someone is poor and struggling to pay for basic necessities such as food and housing, then an extra dollar or an extra $100 probably mean a lot to him or her. For a wealthy person, an extra $100 might be the equivalent of pocket change. Therefore, “removing” $39 of the $100 from someone who makes $300,000 per year should have much less of an impact on that person than on someone who makes only $25,000 per year. In fact, one could argue, as do the proponents of the system, that the amount that should be removed from the lower wage earner to achieve an equivalent impact is roughly $15, or 15%, the current tax bracket for $25,000. As the argument goes, the wealthier you are, the less you should miss the additional dollars earned so taxing them at a progressively higher rate is reasonable. At some point, though, this argument breaks down because the system becomes confiscatory. This was realized in the U.S. in the mid- 1960s, when the highest marginal rate peaked at 90%. Since then, the U.S. Congress has been steadily lowering all brackets for both economic and political reasons. It should be mentioned that profits that are realized on the sale of assets such as stocks, homes, antiques, businesses, and equipment are not taxed as income, but as capital gains. The capital gains tax rate is flat so everyone pays the same percentage on their net profits. Losses can be balanced against gains in any given year so only the net counts in computing your taxes. When determining a depreciation allowance on an asset, it is necessary to use the method prescribed by the IRS. In the past, straight-line, sum-of-the-years digits (SOYD), and declining balance were the common methods. For all assets put into productive service in recent years, the modified accelerated cost recovery system (MACRS) must be used. This system assigns all property to a handful of classes distinguished by their tax life. For example, computers are given a 3-year life, whereas nonresidential real property is given a 31.5-year life. Depreciation is calculated as a percentage of the initial cost. The MACRS percentages for the 3-year class are 33.33%, 44.45%, 14.81%, and 7.41%; that is, a 3-year asset must be depreciated over four years according to this schedule. For the 5-year class, the percentages are 20%, 32%, 19.2%, 11.52%*, 11.52%, and 5.76%. The 3-, 5-, 7-, and 10-year classes are based on double declining balance depreciation with conversion to the straight-line method in the appropriate year (*) to maximize the deduction. 3.6.1 Capital Expansion Decision Example 3-18 The Leeds Corporation leases plant facilities in which expendable thermocouples are manufactured. Because of rising demand, Leeds could increase sales by investing in new equipment to expand output. The selling price of $10 per thermocouple will remain unchanged if output and sales increase. On the basis of engineering and cost estimates, the accounting department provides management with the following cost estimates based on an annual increased output of 100,000 units. Cost of new equipment having an expected life of 5 years $500,000 Equipment installation cost $20,000 Expected salvage value 0 New operation’s share of annual lease expense $10,000 Annual increase in utility expenses $40,000 Annual increase in labor costs $160,000 Annual additional cost for raw materials $400,000 The SOYD method of depreciation will be used, and taxes are paid at a rate of 40%. Mr. Leeds’s policy is not to invest capital in projects that earn less than a 20% ROR. Should the proposed expansion be undertaken? Solution Compute cost of investment: Acquisition cost of equipment $500,000 Equipment installation costs $20,000 Total cost of investment $520,000 Determine yearly cash flows throughout the life of the investment. The lease expense is a sunk cost. It will be incurred regardless of whether the investment is made and therefore is irrelevant to the decision and should be disregarded. Annual production expenses to be considered are utility, labor, and raw materials. These total $600,000 per year. Annual sales revenue is $10×100,000 units of output, or $1,000,000. Yearly income before depreciation and taxes thus is $1,000,000 gross revenue less $600,000 expenses, or $400,000. Determine the depreciation charges to be deducted from the $400,000 income each year using the SOYD method ( ∑ =1+2+3+4+5=15 ). With SOYD, the depreciation in year j is: ( initial cost−salvage value )×( N−j+1 )/ ∑ for j=1, …, N. Year Proportion of $500,000 to bedepreciated Depreciation charge 1 5/15×$500,000 =$166,667 2 4/15×$500,000 =$133,333 3 3/15×$500,000 =$100,000 4 2/15×$500,000 = $66,667 5 1/15×$500,000 = $33 333 _ Accumulated depreciation =$500,000 Find each year’s cash flow when taxes are 40%. Cash flow for only the first year is illustrated: Earnings before depreciation and taxes $400,000  Depreciation expense $166,667  Taxable income $233,333 Taxes ( 0.4×$233,333 ) −$93,332 _ Cash flow (first year) $306,668 Determine present value of the cash flows. Because Leeds demands at least a 20% ROR on investments, multiply the cash flows by the 20% present value factor (P/F, 20%, j) for each year j. Year Present-value factor Cash flow Present value 1 0.833 × $306,667 = $255,454 2 0.694 × $293,333 = $203,573 3 0.579 × $280,000 = $162,120 4 0.482 × $266,667 = $128,533 5 0.402 × $253,334 = $101,840 Total present value of cash flows   (discounted at 20%) = $851,520 Find whether NPV is positive or negative: Total present value of cash flows $851,520 Total cost of investment $520,000 NPV $331,520 Decision Net present value is positive when returns are discounted at 20%. Therefore, the proposed expansion should be undertaken. 3.6.2 Replacement Decision We now consider the case of fixed assets, such as equipment or buildings, and ask whether they should be replaced. The normal means of monitoring expenditures in industry as well as in government are by annual budgets. One important factor in budgeting is the allocation of money for new capital expenditures, either new facilities or replacement and upgrading of current facilities. Existing assets are replaced for many reasons, including deterioration, reduced performance, new requirements, increasing operations and maintenance (O&M) costs, reduced reliability, obsolescence, or more attractive leasing options. In each of these cases, the ability of a current asset to produce a desired output for the lowest cost is challenged. This adversarial situation has given rise to the terms defender—the existing asset, and the challenger—the potential replacement. Example 3-19 For 5 years Emetic Pharmaceuticals has been using a machine that attaches labels to bottles. The machine was purchased for $4,000 and is being depreciated over 10 years to a zero salvage value using the straight-line method. The machine can be sold now for $2,000. Emetic can buy a new labeling machine for $6,000 that will have a useful life of five years and cut labor costs by $1,200 annually. The old machine will require a major overhaul in the next few months. The cost of the overhaul is expected to be $300. If purchased, the new machine will be depreciated over five years to a $500 salvage value using the straight-line method. The company will invest in any project that earns more than the 12% cost of capital. Its tax rate is 40%. Should Emetic invest in the new machine? Solution Determine the cost of investment: Price of the new machine $6,000   Less: Sale of old machine $2,000     Avoidable overhaul costs $300 Total deductions −$2,300 _ Effective cost of investment $3,700 Determine the increase in cash flow resulting from investment in the new machine: Yearly cost savings =$1,200. Differential depreciation: Annual depreciation on old machine: cost−salvage useful life = $4,000−$0 10 =$400 Annual depreciation on new machine: cost−salvage useful life = $6,000−$500 5 =$1,100 Differential depreciation=$1,100−$400=$700 Yearly net increase in cash flow into the firm: Cost savings $1,200    Deduct: Taxes at 40% $480    Add: Advantage of increase in depreciation ( 0.4×$700 ) $280 Net deductions −$200_ Yearly increase in cash flow $1,000 Determine the total present value of the investment: The 5-year cash flow of $1,000 per year is an annuity. Discounted at 12%, the cost of capital, the present value is $1,000×3.605=$3,605. The present value of the new machine, if sold at its salvage value of $500 at the end of the fifth year is $500×0.567=$284. Total present value of the expected cash flows: $3,605+$284=$3,889 Determine whether the NPV is positive: Total present value $3,889 Cost of investment 3,700 NPV $189 Decision Emetic Pharmaceuticals should make the purchase because the investment will return slightly more than the cost of capital. Note The importance of depreciation has been shown in this example. The present value of the yearly cash flow resulting from operations is only ( cost savings−taxes ) ( PV factor ) ( $1,200−$480 ) × 3.605 =$2,596 This figure is $1,104 less than the $3,700 cost of the investment. Only a very large depreciation advantage makes this investment worthwhile. The total present value of the advantage is $1,009; that is, ( tax rate×differential depreciation ) ( PV factor ) ( 0.4×$700 ) × 3.605 =$1,009 In this problem, we did a 5-year analysis based on the useful life of the new asset. In most situations, it is more appropriate first to determine the “life” of the competing assets. Types of asset lives include: 1. The physical life is the period until the asset is salvaged, scrapped, or torn down. 2. The accounting life or tax life is the time over which the asset is depreciated. It may or may not reflect the physical life. 3. Useful life is the time over which the asset will provide useful service. 4. Economic life is the number of years at which the equivalent uniform annual cost (EUAC) or net annual cost (NAC) of ownership is minimized. It is often the case that the economic life is shorter than the physical or useful life of an asset as a result of increasing O&M costs in the later years of ownership. In a traditional replacement analysis, the economic lives of the defender and challenger along with the accompanying costs are used to make the decision. To conduct an analysis, let N=useful life P=investment at time 0 A( n )=net cost in year n (i.e., O&M–revenue) S( n )=salvage value in year n PA( n )=present worth of annual costs for n years NAC( n )=net annual cost for n years where PA( n )=A( 1 )( P/F, i, 1 )+A( 2 )( P/F, i, 2 )+…+A( n )( P/F, i, n ) NAC( n )=[ P+PA( n ) ]( A/P, i, n )−S( n )( A/F, i, n ) The economic life is then argmin { NAC( n ) : n=1,…, N }; that is, the value of n that minimizes NAC(n). Example 3-20 You have purchased a router for $15,000. The machine has a useful life of 10 years and will be depreciated to zero using the SOYD method over that period of time. Assume that the salvage in year n is equal to the book value. In the first year, operating costs are expected to be $500, increasing by 40% in each subsequent year. If your MARR is 18%, then what is the economic life of the router? Solution The following table lists the relevant data. The last column indicates the equivalent annual cost if the router is keep for n years, n=1,…, 10. The minimum NAC(n) occurs at year 6, which is economic life. Time, n Operating cost, A(n) Salvage value, S(n) 0        $15,000         1        $500         $12,273         2        $700         $9,818         3        $980         $7,636         4        $1,372         $5,727         5        $1,921         $4,091         6        $2,689         $2,727         7        $3,765         $1,636         8        $5,271         $818         9        $7,379         $273         10        $10,331         0         Although in this example the initial investment cost at time 0 was given, it is usually not so straightforward to figure out what value should be used for an existing asset. In general, the investment cost that should be used for the defender is the money that you give up by not disposing of it; that is, the opportunity cost. You must also add any costs at time 0 to make it equivalent to the challenger. In summary, the investment consists of the: current market value for the defender, less costs necessary for its disposal, less taxes on the capital gain (when taxes are considered), plus any real costs at time 0 necessary to keep it. Example 3-21 Eight years ago you bought a used car for $4,500 that has a trade-in value of $500. You are now considering a replacement for $8,250 and want to know whether to go ahead with the deal. Your minimum acceptable rate of return is 12%. Some additional cost data are given below. Old car (defender) Maintenance costs next year will be $800, and are expected to go up by $400 a year in the coming years ($800, $1200, $1600, . . .). The car is now a death trap, not worth more than $250. To trade it in, you’re going to have to clean it up for a cost of $50. At any time in the future, net salvage value is also expected to be $200. New car (challenger) This car is supposed to last for 10 years with a trade-in value of $750 at the end of that time. If you sell it before the end of its useful life, you expect the trade-in value to be the same as the book value computed with straight-line depreciation. Maintenance will be $100 per year for the first three years and $300 per year thereafter. Solution For the defender, we have Investment: P D =$200 Operating cost: A D ( n )=$800+$400( n−1 ) Salvage value: S D ( n )=$200 NAC D ( n )= P D ( A/P, i, n )+$800+$400( A/G, i, n )−200( A/F, i, n ) For the challenger, Investment: P C =$8,250 Operating cost: A C ( n )=$100, $100, $100, $300, $300, $300, . . . Salvage value: S C ( n )=$8,250−$750n NAC C ( n )= P C ( A/P, i, n )+100− S C ( n )( A/F, i, n ) for n=1, 2, 3 NAC C ( n )= P C ( A/P, i, n )+300−200( P/A, i, 3 )( A/P, i, n )− S C ( n )( A/F, i, n ) for n=4,…, 10 The investment cost specified for the defender is simply the opportunity cost of not trading it in plus the $50 to get it working satisfactorily. It is not the book value or the actual trade-in value. The investment cost specified for the challenger is the purchase cost which does not include the trade-in. Generally speaking, we do not use any challenger characteristics to compute the defender investment, costs, or salvage, and vice versa. The following table lists the data used in the analysis. As can be seen, the economic life is 1 year for the defender and the corresponding cost is $824. The economic life of the challenger is 10 years with an annual cost of $1,632. Thus, it is optimal to keep the defender one more year at least. Defender Age, n A D ( n ) S D ( n ) NAC D ( n ) Age, 0    $200      0    1    $800      $200      $824      1    2    $1,200      $200      $1,013      2    3    $1,600      $200      $1,194      3    4    5    6    7    8    9    10    This example illustrates a common situation: namely, that the economic life of the defender is often one year, and the economic life of the challenger is often its useful life. Finally, we mention that when considering taxes, an after-tax cash flow analysis should be used. In such cases, there will be a tax consequence if the book value of the defender does not equal its net market value. 3.6.3 Make-or-Buy Decision Example 3-22 The GIGO Corporation manufactures and sells computers. It makes some of the parts and purchases others. The engineering department believes that it might be possible to cut costs by manufacturing one of the parts that is currently being purchased for $8.25 each. The firm uses 100,000 of these parts each year, and the accounting department compiles the following list of annual costs based on engineering estimates: Fixed costs will increase by $50,000. Labor costs will increase by $125,000. Factory overhead, currently running $500,000 per year, is expected to increase 12%. Raw materials used to make the part will cost $600,000. Given the estimates above, should GIGO make the part or continue to buy it? Solution Find the total cost per year incurred if the part were manufactured: Additional fixed costs $50,000 Additional labor costs $125,000 Raw materials cost $600,000 Additional overhead costs=0.12×$500,000 $60,000  Total cost to manufacturer $835,000 Find cost per unit to manufacture: $835,000 100,000 =$8.35 per unit Decision   GIGO should continue to buy the part. Manufacturing costs exceed the present cost to purchase by $0.10 per unit. Perspective   The decision to make or buy is arguably the most fundamental component of manufacturing strategy. Should a firm be highly integrated, such as Henry Ford’s River Rouge plant, with raw iron ore and coal flowing in one end and a finished Model A rolling out the other? Or should they simply purchase components from capable suppliers and then perform an assembly role much like today’s PC manufacturers such as Compaq and Dell? Henry Ford’s model of vertical integration slipped from favor in the early 1960s, when outsourcing became increasingly attractive. Businesses found that outsourcing had certain advantages, potentially allowing them to: Convert fixed costs to variables costs, thereby providing flexibility in an economic downturn Balance workforce requirements Reduce capital investment requirements Reduce costs via suppliers’ economies of scale and lower wage structures Accelerate new product development Gain access to invention and innovation from suppliers Focus resources on high-value-added activities Nevertheless, recent studies have shown that many make-or-buy decisions have historically been taken with a disproportionate weight placed on unit cost and an insufficient regard for strategic or technical issues (e.g., see Dertouzos et al. 1989). This cost-focused approach has led to competitive disaster for many firms, indeed, entire industries in the United States. The list of those affected by this phenomenon is well known. Some of the most notable include consumer electronics, machine tools, semiconductors, and office equipment. As recently as 2004, General Motors reported more than 8,000 suppliers for direct material alone. 3.6.4 Lease-or-Buy Decision Example 3-23 Jeremy Sitzer is a small businessman who has need for a pickup truck in his everyday work. He is considering buying a used truck for $3,000. If he goes ahead, he believes that he will be able to sell it for $1,000 at the end of 4 years, so he will depreciate $2,000 of the truck’s value on a straight-line basis. Sitzer can borrow $3,000 from the bank and repay it in four equal annual installments at 6% interest. However, a friend advises him that he may be better off to lease a truck if he can get the same terms from the leasing company that he receives at the bank. Assuming that this is so, should Sitzer buy or lease the truck? He is in the 40% tax rate bracket. Solution Find the cost to buy: The bank loan is an installment loan at 6% interest, so the payments constitute a 4-year annuity. Divide the amount of the loan by the present value factor for a 4-year annuity at 6% [ ( P/A, 6%, 4 )=3.465 ] to find the annual payment. Multiply the annual payments by 4 to find the total payment. $3,000 3.465 =$866 annual payment 4×$866=$3,464total payment Next, find the present value of the cost of the loans: (1) Year (2) Yearly Payment (3) Interest at 6% (4) Payment onprincipal 1     $866        $180        $686        2     $866        $139        $727        3     $866        $95        $771        4     $866        $50        $816        (7) Tax deductible expense ( 3 )+( 6 ) (8) Tax saving 0.4×( 7 ) (9) Cost of owning 8 ) $680            $272        $594          $639            $256        $610          $595            $238        $628          $550            $220        $646          Total present value of payments Present value of salvage =$1,000×0.792=$792 Present value of cost of loan =$2,127−$792=$1,335 Find the cost to lease: (1) Year (2) Lease payment (3) Tax savings 0.4×866 (4) Lease cost after taxes ( 2 )−( 3 ) (5) Present value factor at 6% (6) Present value ( 4 )×( 5 ) 1 $866 $346 $520 0.943 $490 2 $866 $346 $520 0.890 $463 3 $866 $346 $520 0.840 $437 4 $866 $346 $520 0.792 $411 Total present value of lease payments =$1,801          Compare present values of cost to buy and cost to lease: Present value of cost to lease $1,801 Present value of cost to buy $1,335 Advantage of buying $ 466 Decision   Mr. Sitzer should buy the truck. Note   Again, the importance of depreciation should be mentioned. When Sitzer purchases the truck, he gains the accompanying tax advantages of ownership. If the truck were leased, then the leaser would depreciate it and thereby gain advantage. Sitzer was also aided by being able to reduce the cost of buying by the present value of the salvage (or disposal) value of the truck. In general, depreciation and salvage value reduce the cost of buying. Nevertheless, if an asset is subject to rapid obsolescence, then it may be less expensive to lease. 3.7 Utility Theory Decision theory is concerned with giving structure and rationale to the various conditions under which decisions are made. In general, one must choose from among an array of alternatives. These are referred to as actions (or strategies), and each results in a payoff or outcome. If decision makers knew the payoff associated with each action, then they would be able to choose the action with the largest payoff. Most situations, however, are characterized by incomplete information, so for a given action, it is necessary to enumerate all probable outcomes together with their consequences and probabilities. The degree of information and understanding that the decision maker has about a particular situation determines how the underlying problem can be approached. Two people, faced with the same set of alternatives and conditions, are likely to arrive at very different decisions regarding the most appropriate course of action for them. What is optimal for one may not even be an attractive alternative for the other. Judgment, risk, and experience work together to influence attitudes and choices. Implicit in any decision-making process is the need to construct, either formally or informally, a preference order so that alternatives can be ranked and a final choice is made. For some problems this may be easy to accomplish, as we saw in the preceding sections, where the decision was based on a profit-maximization or cost-minimization rule. There, the preference order is adequately represented by the natural order of real numbers. In more complex situations, where factors other than profit maximization or cost minimization apply, it may be desirable to explore the decision maker’s preference structure in an explicit manner and to attempt to construct a preference ordering directly. An important class of techniques that works by eliciting preference information from the decision maker is predicated on what is known as utility theory. This, in turn, is based on the premise that the preference structure can be represented by a real-valued function called a utility function.2 Once such a function is constructed, selection of the final alternative should be relatively simple. In the absence of uncertainty, an alternative with the highest utility would represent the preferred solution. For the case in which outcomes are subject to uncertainty, the appropriate choice would correspond to the one that attains the highest expected utility. Thus, the decision maker is faced with two basic problems involving judgment: 2 Technically speaking, the term utility function is reserved for the case in which uncertainty is present. When each alternative has only one possible outcome, the term value function is used. In either case, the construction procedure is the same. 1. How to quantify (or measure) utility for various payoffs 2. How to quantify judgments concerning the probability of the occurrence of each possible outcome or event In this section, we focus on the first question––of quantifying and exploiting personal preference; the second, subjective probability estimation, falls more appropriately in the realm of elementary statistics and so is not treated here. 3.7.1 Expected Utility Maximization Assuming the presence of uncertainty, when a decision maker is repeatedly faced with the same problem, experience often leads to a strategy that provides, on average, the best results over the long run. In technical terms, such a strategy is one that maximizes expected monetary value (EMV). Let A be a particular action with possible outcomes j=1,…, n. Also, let p j be the probability of realizing outcome j with corresponding payoff or return x j . The expected monetary value of A is calculated as follows: EMV( A )= ∑ j=1 n p j x j (3.1) For the case in which the decision maker is faced with a unique problem, using the EMV criterion might not be such a good idea. In fact, a large body of empirical evidence suggests that it is rarely the criterion selected. To see this, assume that you must select one of the two alternatives in each of the following five situations: Situation 1: a 1  : The certainty of receiving $1 or a 2  : on the flip of a fair coin, $10 if it comes up heads or −$1 if it comes up tails. Situation 2: b 1  : The certainty of receiving $100 or b 2  : on the flip of a fair coin, $1,000 if it comes up heads or −$100 if it comes up tails. Situation 3: c 1  : The certainty of receiving $1,000 or c 2  : on the flip of a fair coin, $10,000 if it comes up heads or −$1,000 if it comes up tails. Situation 4: d 1  : The certainty of receiving $10,000 or d 2  : on the flip of a fair coin, $100,000 if it comes up heads or −$10,000 if it comes up tails. Situation 5: e 1  : The certainty of receiving $10,000 or a payment of $2 n , where n is the number of times e 2  : that a fair coin is flipped until heads comes up. If heads appears on the first toss, you receive $2; if the coin shows tails on the first toss and heads on the second, then you receive $4 and so forth. However, you are given only one chance; the game stops with the first showing of heads. Most people would probably choose a 2 , b 2 , c 1 , d 1 , and e 1 . The choices a 2 and b 2 would be those derived from an EMV maximization criterion because EMV( a 2 )= 1 2 $10+12(−$1)=$4.5 is greater than the return from the certain choice a 1 =$1, and EMV( b 2 )=$450 is greater than $100. Nevertheless, in situations 3 and 4, c 1 would probably be preferred to c 2 , even though EMV( c 2 )=$4,500 is greater than $1,000, and d 1 would be preferred to d 2 even though EMV( d 2 )=$45,000 is greater than $10,000. In situation 5, the EMV of e 2 is infinite; that is, EMV( e 2 )= 1 2 ( $2 )+ 1 4 ( $4 )+ 1 8 +… =$1+$1+$1+… =∞ yet e 1 would be preferred to e 2 practically by everyone. In the first four situations, most people would tend to change their decision criterion away from maximizing EMV as soon as the thought of losing a large sum of money (say $1,000) became too painful despite the pleasure to be gained from possibly obtaining a large sum (say, $10,000). At this point, the person faced with such a choice would not be considering EMV but would instead be thinking solely of utility. In this sense, utility refers to the pleasure (utility) or displeasure (disutility) that one would derive from certain outcomes. In essence, we are saying that the person’s displeasure from losing $1,000 is greater that the pleasure of winning many times that amount. In situation 5, no prudent person would choose the gamble e2 over the certainty of a relatively modest amount obtained from e1. This problem, known as the St. Petersburg paradox, led Daniel Bernoulli to the first investigations of utility, rather than EMV, as the basis of decision making. 3.7.2 Bernoulli’s Principle Logic, observed behavior, and introspection all indicate that any adequate procedure for handling choice under uncertainty must involve two components: personal valuation of consequences and personal strengths of belief about the occurrence of uncertain events. Bernoulli’s principle, as refined by von Neumann and Morgenstern (1947), has the normative justification of being a logical deduction from a small number of axioms that most people find reasonable. The relevant axioms differ slightly depending on whether the decision maker (a) has a single goal, (b) has multiple goals between which he or she can establish acceptable trade-off relations, or (c) has multiple goals that are not substitutable. The first two cases lead to a one- dimensional utility measure (i.e., real number) for each alternative action; the last to a lexicographically ordered utility vector.3 We consider only the single-goal case here; multiple goals are taken up in subsequent chapters. 3 Given two n-dimensional vectors x and y, if xi=yi, for i=1,…,r−1, and xr>yr, then x is said to be lexicographically greater than y.
Axioms:
1. Ordering. For the two alternatives A 1 and A 2 , one of the following
must be true: the person either prefers A 1 to A 2 or A 2 to A 1 , or is
indifferent between them.
2. Transitivity. The person’s evaluation of alternatives is transitive: if he or
she prefers A 1 to A 2 , and A 2 to A 3 , then he or she prefers A 1 to A 3
.
3. Continuity. If A 1 is preferred to A 2 , and A 2 to A 3 , then there exists a
unique probability p, 0 A 2 ), then
the utility index of A 1 will be greater than that of A 2 [i.e., U( A 1 )>U(
A 2 ) ]. Conversely, U( A 1 )>U( A 2 ) implies that A 1 is preferred to A
2 .
2. If A is the risky prospect with a set of outcomes { θ } distributed
according to the probability density function p( θ ), then the utility of A is
equal to the statistically expected utility of A; that is,
U( A )=E[ U( A ) ] (3.2)
If p( θ ) is discrete,
E[ U( A ) ]= ∑ θ U( θ )p( θ ) (3.3a)
and if p( θ ) is continuous,
E[ U( A ) ]= ∫ −∞ ∞ U( θ )p( θ )dθ (3.3b)
As these equations indicate, only the first moment (i.e., the mean or

expected value) of utility is relevant to the choice. For a person who
accepts the axioms underlying Bernoulli’s principle, the variance or
other higher moments of utility are irrelevant; the expected value takes
full account of all of the moments (mean, variance, skewness, etc.) of
the probability distribution p( θ ) of outcomes.
3. Uniqueness of the function is defined only up to a positive linear
transformation. Given a utility function U, any other function U* such
that
U*=aU+b, a>0, (3.4)
for scalars a and b, will serve as well as the original function. Thus,
utility is measured on an arbitrary scale and is a relative measure
analogous, for example, to the various scales used for measuring
temperature. Because there is no absolute scale for utility and because a
person’s utility function reflects his or her own personal valuations, it is
not possible to compare one person’s utility indices with another’s (for
further discussion of numbers and scales, see Gass 2001).
Bernoulli’s principle thus provides a mechanism for ranking risky prospects
in order of preference, the most preferred prospect being the one with the
highest utility. Hence, Bernoullian or statistical decision theory implies the
maximization of utility, which, by the expected utility theorem, is equivalent
to maximization of expected utility. Equations (3.3a) and (3.3b) provide the
empirical basis of application of the theory. Two concepts are involved:
degree of preference (or utility) and degree of belief (or probability).
3.7.3 Constructing the Utility
Function
Utility functions must be assessed separately for each decision maker. To be
of use, utility values (i.e., subjective preferences) must be assigned to all
possible outcomes for the problem at hand. Usually, we define a frame of
reference whose lower and upper bounds represent the worst and best

possible outcomes, respectively. In many circumstances, outcomes are
nonmonetary in nature. For example, in selecting a portable computer, one
weighs such factors as speed, memory, display quality, and weight. It is
possible to assign utility values to these outcomes; however, in most
business-related problems, a monetary consequence is of major importance.
Hence, we illustrate how to evaluate one’s utility function for money,
although the same procedure applies to nonmonetary outcomes.
The assessment of a person’s utility function involves pinning down, in
quantitative terms, subjective feelings that may not have been thought of
before in such a precise way. At least four approaches for doing this have
been distinguished (Keeney and Raiffa 1993): (1) direct measurement; (2) the
von Neumann-Morgenstern (NM) method or standard reference contract; (3)
the modified NM method; and (4) the Ramsey method.
The first approach involves asking a series of questions of the type: “Suppose
that I were to give you an outright gift of $100. How much money would you
need to make you twice as happy as the $100 would make you feel?” The
answers to a sequence of such questions enable the plotting of a utility curve
against whatever arbitrarily chosen utility (value) scale is desired. The
drawbacks of this approach are that it is not concerned with uncertainty, and
for many people, it cannot be expected to be as precise as the other methods.
The other three approaches deal with the question of risk attitude directly and
ask the decision maker to compare certain gambles to sure sums of money, or
gambles to gambles. For example, in a new product development problem, a
question might be to have the project manager choose between receiving
$200,000 for certain versus a gamble (lottery), with equal chances of winning
$1,000,000 and losing $500,000. Such a situation might arise if the project
manager were faced with selecting one of two technologies: the first being a
sure thing, the second being much more risky. Through this type of
questioning, one can find some riskless value that would make the project
manager indifferent (Axiom 3). This value is called the certainty equivalent
(CE) of the gamble. When the CE is less than the expected monetary value (
CEA3>A1.
Most people have utility functions whose slopes do not change very much for
small changes in money, suggesting risk-neutral attitudes. In considering
courses of action, however, in which one of the consequences is very adverse
or in which one of the payoffs is very favorable, people can be expected to
depart from the maximization of EMV criterion. In fact, most people are risk
seekers for small gains and losses and risk avoiders when the stakes are high,
in either direction. This explains why most of us buy insurance and stay with
secure but often unexciting jobs rather than seek out risky opportunities that
have the probability of making us wildly rich.
For many business decisions, for which the monetary consequences may
represent only a small fraction of the total assets of the organization,
maximization of EMV constitutes a reasonable approximation to the
decision-making criterion of maximization of expected utility. In such cases,
the utility function may be considered linear over the range of possible
monetary outcomes. Moreover, Shoemaker (1982) summarizes many
examples of experiments that have shown decision makers to make
“irrational” decisions, violating the axioms of utility theory. In general, utility
curves are challenging to construct, as they are not intuitive to business

decision makers. Moreover, many decision makers have difficulty in
assessing probabilities and attaching outcomes to a particular probability. For
these reasons, utility theory is not generally used in practice. Decision
making, with respect to project selection, for example, is based on either
EMV (straightforward and easy to apply) or heuristic judgment.
Nevertheless, it is useful to understand utility theory, as it is a normative
approach to decision making. Moreover, gaining insight into a decision
maker’s attitude toward risk (averse, neutral, seeking) is important for a
project manager in positioning project proposals in front of senior
management decision makers.
TEAM PROJECT Thermal
Transfer Plant
On the basis of your excellent report and presentation, Total Manufacturing
Solutions (TMS) has decided to approve a prototype project in the area of
waste management and recycling. Because there is a need for a rotary
combustor in one of the company’s new plant designs, a decision was made
to select this project as a prototype.
Rotary combustors are designed to burn a variety of solid combustible
wastes, including municipal, commercial, industrial, and agricultural wastes.
The basic component of the combustor is the rotating barrel, made out of
alternating carbon steel water tubes and perforated steel bars (Figure 3.13).
The barrel assembly is set at a slope of −6° and is rotated slowly
[approximately 10 revolutions per hour (rph)]. Solid waste is charged from
the higher end of the barrel, and the combustion air comes into the barrel
through the perforated holes. As the material burns, it tumbles through the
barrel and eventually comes out of the lower end as residue (Figure 3.14). In
the process, heated forced air promotes drying and burning.

Figure 3.13
Arrangement of rotary combustor.
Figure 3.13 Full Alternative Text

Figure 3.14
Details of rotary combustor.
Figure 3.14 Full Alternative Text
Hot gases created inside the barrel convert the boiler water into steam, which
is used in the generation of electricity. High thermal efficiency of up to 80%
provides maximum energy recovery through heat transfer from all hot
surfaces of the combustor/boiler. Simplified moving parts assure ease of
operation, maintenance, and servicing, as well as minimal repair costs.
The combustor capacity is targeted for 14 tons/day. Estimated costs are as
follows:
Cost of material:

 Combustor barrel $10,000
 Tires and trunnions $50,000
 Chutes $10,000
 Drive gears and chain $20,000
 Pushers (2) $2,000
 Enclosure and insulation $20,000
 Rotary water joint $5,000
 Hydraulic drive system (includes power unit,
cylinders, and combustor drive components) $90,000
 Welding materials $30,000
Cost of labor:
 Combustor barrel fabrication
  10 workers, 8 weeks at $50/hr $160,000
 Tire and trunnion installation
  5 workers, 1 week $10,000
 Chute fabrication
  4 workers, 2 weeks $16,000
 Gear installation
  2 workers, 2 days $1,600
 Pusher fabrication
  2 workers, 2 weeks $8,000
 Enclosure fabrication
  10 workers, 4 weeks $80,000
 Water joint installation
  2 workers, 1 day $800
Other:
 Design $15,000
 Instrumentation $13,000
 Pressure testing $2,500
 Preassembly $7,000
 Break down and loading for shipment $3,000
 Overhead 25%

The following factors contribute to the risk of the project:
Schedule risks
NIMBY (not in my back yard). The construction of thermal transfer
plants may prove to be a long, drawn-out affair. In addition to
Environmental Protection Agency requirements, local opposition must
be considered.
One option for the combustor drive is to use a single large hydraulic
motor. There are two manufacturers of this type of motor, one in
Sweden and one in Germany.
Cost risks
Costs due to delays (see first item above).
Price increases—an entire plant is being built—estimated duration is 2
years.
Design time—difficult to estimate.
Fabrication time.
Overhead is very difficult to estimate and control.
Technological risks
Hazardous location—a rotary combustor is a furnace and because of fire
hazards, mineral-based hydraulic fluid cannot be used. Fire-resistant
fluids are an alternative but require that certain hydraulic components be
down-rated.
Speed control—the accuracy and degree of variability of speed control

for the rams are yet to be determined.
No satisfactory design exists yet for a rotary water joint.
Satisfactory seals around the combustor are yet to be developed.
Hydraulic leaks at other installations have been a problem, particularly
at the rams and at the power unit.
Instrumentation—there is disagreement as to the sophistication of
instrumentation required, particularly on the rams.
TMS’s main business is design and consulting; however, it is believed that
this new area of operation may present an opportunity for the company to
develop manufacturing capabilities. Management has three alternatives under
consideration:
1. Design the rotary combustor at TMS based on customer needs, but
subcontract all manufacturing and assembly.
2. Design the rotary combustor at TMS, subcontracting all manufacturing
of parts but assembling the system at TMS facilities.
3. Design, manufacture, and assemble the combustor at TMS.
Your assignment is to compare the economic aspects, including risks.
1. For each alternative, list the risks involved and their associated costs.
2. Analyze TMS’s overall financial position under each alternative.
3. Include projected differences in total expenditures and investments.
In evaluating these alternatives, you can make any assumptions necessary.
Each should be stated explicitly.

Discussion Questions
1. What are the shortcomings of engineering economic analysis? What
difficulties and uncertainties might one face when performing such an
analysis?
2. American businesses have often been criticized for short-term thinking
that places too much emphasis on payback period and ROR. When
Honda started making cars in the early 1970s, for example, the chief
executive officer stated that the firm would be “willing to accept an
ROR no greater than 2% or 3% for as long as it took to be recognized as
the best car maker in the world.” In light of the success of many
Japanese firms, is the criticism of American business justified?
3. If a firm is short of capital, then what action might it take to conserve
the capital it has and to obtain more?
4. Explain why the marginal cost for borrowing money increases. Why
might the cost also be high for borrowing small amounts?
5. Are there any reasons for using present value analysis rather than future
value analysis?
6. Why might a decision maker like to see the payback analysis as well as
the ROR and the NPV?
7. In the 1960s, the top marginal tax rate for individuals in the United
States was 90%; that is, for each dollar that a person earned above
roughly $100,000, he or she had to pay 90 cents in taxes. It was argued
by many economists at the time that this rate was much too high. What
do you think are the negative economic and social consequences of such
“confiscatory” tax rates?
8. Discuss why the comparison of alternative investment decisions is
especially difficult when the investment choices have different useful
lives.

9. Breakeven analysis is typically simplified by using constant-unit
variable costs and revenues. What would you expect realistic costs and
revenues to be, and what would a corresponding breakeven chart look
like?
10. Identify a situation and set of alternatives whose outcomes are not
measured on a monetary scale. Assess your utility function for this
situation.
11. Give some examples for which the axioms underlying Bernoulli’s
principle are violated.
12. Most countries have a progressive income tax system whereby each
dollar earned in incrementally higher tax brackets is taxed at an
increasingly higher rate. Do you think that a flat tax system would be
more fair? How about a proportional tax system? Explain your answer.
13. If you just assessed a corporate executive’s utility function for a problem
concerning the purchase of a supercomputer, then could you use the
same utility function for a problem of buying an automobile? A personal
computer? Explain.
14. It has been argued that comparable interpersonal utility scales may be
established on the basis of equating people’s best conceivable situations
at the top end and their worst conceivable situations at the bottom end.
What’s wrong, if anything, with this approach?
15. In situations in which wealthy employers bargain over wages and
benefits with needy employees on an individual basis, the employer
usually gives away much less than he actually might have been
pressured into or could have afforded. Can you explain this consequence
in terms of utility theory?

Exercises
1. 3.1 Construct a diagram illustrating the cash flows involved in the
following transactions from the borrower’s viewpoint. The amount
borrowed is $2,000 at 10% for 5 years.
1. Year-end payment of interest only; repayment of principal at the
end of the 5 years
2. Year-end repayment of one fifth of the principal ($400) plus
interest on the unpaid balance
3. Lump-sum repayment at the end of year 5 of principal plus accrued
interest compounded annually
4. Year-end payments of equal-sized installments, as in a standard
installment loan contract
2. 3.2 A firm wants to lease some land from you for 20 years and build a
warehouse on it. As your payment for the lease, you will own the
warehouse at the end of the 20 years, estimated to be worth $20,000 at
that time.
1. If i=8%, then what is the PW of the deal to you?
2. If i=2% per quarter, then what is the PW of the deal to you?
3. 3.3 In payment for engineering services, a client offers you a choice
between (1) $10,000 now and (2) a share in the project, which you are
fairly certain you can cash in for $15,000 five years from now. With
i=10%, which is the most profitable choice?
4. 3.4 Assume that a medium-size town now has a peak electrical demand
of 105 megawatts, increasing at an annually compounded rate of 15%.
The current generating capacity is 240 megawatts.

1. How soon will additional generating capacity be needed on-line?
2. If the new generator is designed to take care of needs 5 years past
the on-line date, then what size should it be? Assume that the
present generators continue in service.
5. 3.5 A local government agency has asked you to consult regarding
acquisition of land for recreation needs for the urban area. The following
data are provided:
Urban population 10 years ago 49,050
Urban area population now 89,920
Desired ratio of recreation land in acres per 1,000
population
10
acres/1,000
Actual acres of land now held by local
government for recreational purposes 803 acres
1. Find the annual growth rate in the urban area by assuming that the
population grew at a compounded annual rate over the past 10
years.
2. How many years ago was the desired ratio of recreation land per
1,000 population exceeded if no more land was acquired and the
population continued to grow at the indicated rate?
3. The local government is planning to purchase more land to supply
the recreational needs for 10 years past the point in time found in
part (b). How many acres of land should they purchase to maintain
the desired ratio, assuming that the population growth continues at
the same rate?
6. 3.6 A young engineer decides to save $240 per year toward retirement in
40 years.
1. If he invests this sum at the end of every year at 9%, then how
much will be accumulated by retirement time?

2. If by astute investing the interest rate could be raised to 12%, then
what sum could be saved?
3. If he deposits one fourth of this annual amount each quarter ($60
per quarter) in an interest bearing account earning a nominal annual
interest rate of 12%, compounded quarterly, how much could be
saved by retirement time?
4. In part (c), then what annual effective interest rate is being earned?
7. 3.7 A lump sum of $100,000 is borrowed now to be repaid in one lump
sum at end of month (EOM) 120. The loan bears a nominal interest rate
of 12% compounded monthly. No partial repayments will be accepted
on the loan. To accumulate the repayment lump sum due, monthly
deposits are made into an interest-bearing account that bears interest at
0.75% per month from EOM 1 until EOM 48. From EOM 48 until EOM
120 the interest rate changes to 0.5%. Monthly deposits of amount A
begin with the first deposit at EOM 1 and continue until EOM 48.
Beginning with EOM 49, the deposits are doubled at amount 2A and
continued at this level until the final deposit at EOM 120. Draw the cash
flow diagram and find the initial monthly deposit amount A.
8. 3.8 A backhoe is purchased for $20,000. The terms are 10% down and
2% per month on the unpaid balance for 60 months.
1. How much are the monthly payments?
2. What annual effective interest rate is being charged?
9. 3.9 Your firm owns a large earth-moving machine and has contracts to
move earth for $1 per cubic yard. For $100,000, this machine may be
modified to increase its production output by an extra 10 yd3 per hour,
with no increase in operating costs. The earth-moving machine is
expected to last another 8 years, with zero salvage value at the end of
that time. Determine whether this investment meets the company
objective of earning at least 15% return. Assume that the equipment
works 2,000 hours per year.

10. 3.10 Your firm wants to purchase a $50,000 computer, no money down.
The $50,000 will be paid off in 10 equal end-of-year payments with
interest at 8% on the unpaid balance.
1. What are the annual end-of-year payments?
2. What hourly charge should be included to pay off the computer,
assuming 2,000 hours of work per year, credited at the end of the
year?
3. Assume that 5 years from now you would like to trade in the
computer and purchase a new one. You expect a 5% increase in
price each year. What would the new computer cost at the end of
year 5?
4. What is the unpaid balance on the current computer after 5 years?
11. 3.11 A transportation authority asks you to check on the feasibility of
financing for a toll bridge that will cost $2,000,000. The authority can
borrow this amount now and repay it from tolls. It will take 2 years to
construct and be open for traffic at end of year (EOY) 2. Tolls will be
accumulated throughout the third year and will be available for the
initial annual repayment at EOY 3. In subsequent years, the tolls are
deposited at the end of the year. Draw the cash flow diagram assuming a
flow rate of 10,000 cars/day. How much must be charged to each car to
repay the borrowed funds in 20 equal annual installments (first
installment due at EOY 3), with 8% compound interest on the unpaid
balance?
12. 3.12 A firm invested $15,000 in a project that seemed to have excellent
potential. Unfortunately, a lengthy labor dispute in year 3 resulted in
costs that exceeded benefits by $8,000. The cash flow for the project is
as follows:
Year 0 1 2 3 4 5 6
Cash
flow
($)
−15,000 +10,000 +6,000 −8,000 +4,000 +4,000 +4,000

Compute the ROR for the project. Assume a 12% interest rate on
external investments for purposes of moving money from one period to
another.
13. 3.13 An oil company plans to purchase for $70,000 a piece of vacant
land on the corner of two busy streets. The company has four different
types of businesses that it installs on properties of this type.
Plan Cost ofimprovements† Description
A $75,000
Conventional gas station with service
facilities for lubrication, oil changes,
etc.
B $230,000 Automatic car wash facility withgasoline pump island in front
C $30,000 Discount gas station (no service bays)
D $130,000 Gas station with low-cost, quick-car-wash facility
†Cost of improvements does not include the $70,000 cost of
land.
In each case, the estimated useful life of the improvements is 15 years.
The salvage value for each is estimated to be the $70,000 cost of the
land. The net annual income, after paying all operating expenses, is
projected as follows:
Plan Net annual income
A $23,300
B $44,300
C $10,000
D $27,500
If the oil company expects a 10% ROR on its investments, then which
plan (if any) should be selected?

14. 3.14 A firm is considering three mutually exclusive alternatives as part
of a production improvement program. The relevant data are:
A   B   C  
Installation cost $10,000 $15,000 $20,000
Uniform annual benefit $1,625 $1,625 $1,890
Useful life (years) 10 20 20
For each alternative, the salvage value at the end of useful life is zero. At
the end of 10 years, alternative A could be replaced by a copy of itself
that has identical cost and benefits. The MARR is 6%. If the analysis
period is 20 years, then which alternative should be selected?
15. 3.15 Consider four mutually exclusive alternatives that each has an 8-
year useful life. The costs and benefits of each are given in the following
table.
A B C D
Initial cost $1,000 $800 $600 $500
Uniform annual benefit $122 $120 $97 $122
Salvage value $750 $500 $500 0
If the minimum acceptable ROR is 8%, then which alternative should be
selected?
16. 3.16 A project has the following costs and benefits. What is the payback
period?
Year Costs  Benefits
0 $1,400
1 $500
2 $300 $400
3–10 $300 per year
17. 3.17 A motor with a 200-horsepower output is needed for intermittent

use in a factory. A Teledyne motor costs $7,000 and has an electrical
efficiency of 89%. An Allison motor costs $6,000 and has an 85%
efficiency. Neither motor would have any salvage value after 20 years of
use because the cost to remove them would equal their scrap value. The
maintenance cost for either motor is estimated at $300 per year. Electric
power costs $0.072/kilowatt hour (1hp=0.746 KW). If a 10% annual
interest rate is used in the calculations, then what is the minimum
number of hours that the higher-initial-cost Teledyne motor must be
used each year to justify its purchase? Use a 20-year planning horizon.
18. 3.18 Lu Hodler planned to buy a rental property as an investment. After
looking for several months, she found an attractive duplex that could be
purchased for $93,000 cash. The total expected income from renting out
both sides of the duplex would be $800 per month. The total annual
expenses for property taxes, repairs, gardening, and so on are estimated
at $600 per year. For tax purposes, Lu plans to depreciate the building
by the SOYD method, assuming that the building has a 20-year
remaining life and no salvage value. Of the total $93,000 cost of the
property, $84,000 represents the value of the building and $9,000 is the
value of the lot (only the former can be depreciated). Assume that Lu is
in the 38% incremental income tax bracket (combined state and federal
taxes) throughout the 20 years.
In this analysis Lu estimates that the income and expenses will remain
constant at their present levels. If she buys and holds the property for 20
years, then what after-tax ROR can she expect to receive on her
investment, using the assumptions noted below?
1. The building and lot can be sold at the end of 20 years for the
$9,000 estimated value of the lot.
2. A more optimistic estimate of the future value of the property is
that it can be sold for $100,000 at the end of the 20 years.
19. 3.19 The effective combined tax rate in an owner-managed corporation
is 40%. An outlay of $20,000 for certain new assets is under
consideration. It is estimated that for the next 8 years, these assets will
be responsible for annual receipts of $9,000 and annual disbursements

(other than for income taxes) of $4,000. After this time, they will be
used only for standby purposes, and no future excess of receipts over
disbursements is expected.
1. What is the prospective ROR before income taxes?
2. What is the prospective ROR after taxes if these assets can be
written off for tax purposes in 8 years using straight-line
depreciation?
3. What is the prospective ROR after taxes if it is assumed that these
assets must be written off over the next 20 years using straight-line
depreciation?
20. 3.20 The Coma Chemical Company needs a large insulated stainless
steel tank for the expansion of its plant. Coma has located one at a
brewery that has just been closed. The brewery offers to sell the tank for
$15,000, including delivery. The price is so low that Coma believes that
it can sell the tank at any future time and recover its $15,000 investment.
The outside of the tank is lined with heavy insulation that requires
considerable maintenance. Estimated costs are as follows:
Year 0 1 2 3 4 5
Maintenance cost $2,000 $500 $1,000 $1,500 $2,000 $2,500
1. On the basis of a 15% before-tax MARR, what is the economic life
of the insulated tank; that is, how long should it be kept?
2. Is it likely that the insulated tank will be replaced by another tank at
the end of its computed economic life? Explain.
21. 3.21 The Gonzo Manufacturing Company is considering the
replacement of one of its machine fixtures with a more flexible variety.
The new fixture would cost $3,700, have a 4-year useful and depreciable
life, and have no salvage value. For tax purposes, SOYD depreciation
would be used. The existing fixture was purchased 4 years ago at a cost
of $4,000 and has been depreciated by straight-line depreciation
assuming an 8-year life and no salvage value. It could be sold now to a

used equipment dealer for $1,000 or be kept in service for another 4
years. It would then have no salvage value. The new fixture would save
approximately $900 per year in operating costs compared with the
existing one. Assume a 40% combined state and federal tax rate and that
capital gains (and losses) are taxed at 40% as well.
Hint: For the existing fixture, the “investment” cost is the opportunity
cost of not selling it.
1. Compute the before-tax ROR on the replacement proposal of
installing the new fixture rather than keeping the old one.
2. Compute the after-tax ROR on the proposal.
22. 3.22 The following estimates have been made for two mutually
exclusive alternatives; one must be chosen. The before-tax ROR
required is 20%.
A B
Installed cost $120,000 $150,000
Estimated useful life 10 years 10 years
Salvage at retirement $20,000 $30,000
Annual operating costs $20,000 $15,000
Try to minimize your computations as you determine which course of
action to recommend.
23. 3.23 The following cost estimates apply to independent equipment
alternatives A and B. The before-tax ROR required is 20%.
A B
Installed cost $100,000 $40,000
Operating
costs
$5,000 at the end of
year 1 and increasing
by $1,000 per year for
20 years
$10,000 at the end of
year 1 and increasing
by $2,000 per year for
10 years

Overhaul
costs every 5
years
$10,000 None required
Economic life 20 years 10 years
Salvage value
at end of life
(just
overhauled)
$20,000 $10,000
1. Compare the NPV of each using a study period of 20 years.
2. Compare the annual equivalent costs.
24. 3.24 An investor requires a MARR of 12% before inflation (not
considering the effect of inflation on future costs and benefits).
1. If an inflation rate of 8% is expected, then what MARR should the
investor require for an analysis that includes the effect of inflation?
2. If the labor cost is $15 an hour today and the inflation rate is 6%,
then how much would you expect the labor cost to be in three
years?
25. 3.25 Martha is considering the purchase of the piece of land and some
new equipment adjacent to her day care center to use as a play area.
Maintenance costs (e.g., mowing the lawn, repairs) are expected to be
$500 a year for every year of the project. She expects that the additional
lure of the play area will bring in extra business, increasing her income
by $1,000 in the first year, and then by an additional $600/year
thereafter ($1,600 in year 2, and so on). She plans to keep the land for
five years, then donate it to the town (meaning no salvage value). All of
these costs and revenues are estimated in today’s dollars. The cash flows
are expected to inflate by 7% per year. This is the same as the general
rate of inflation.
How much should she pay for the land to get a 12% ROR? The 12%
includes the effect of the 7% inflation rate.

26. 3.26 An investment of $2,000 results in the cash flow below. The
amounts are expressed in constant dollars.
1. The general rate of inflation is 6%, and future cash flows are
expected to increase with inflation. Show the amounts in actual
(year-n) dollars in the following table.
Year 0 1 2 3 4 5
Cash flow
2. Your minimum acceptable ROR without considering inflation is
10%. Should you accept this investment opportunity? Show your
work.
27. 3.27 For the cash flow given in the figure of the previous exercise, say
that you must pay taxes on the incomes shown. The investment for the
project is to be depreciated with the SOYD method. The future incomes
are expected to increase with an inflation rate of 6%. The general rate of
inflation is also 6%. The tax rate is 40%, the tax life is 5 years, and
salvage is zero.
Show in the table below the after-tax cash flows for the 5 years
associated with this project. Also show the interest rate that you should
use that is appropriate for these cash flows. The after-tax MARR

without considering inflation is 10%.
Year 0 1 2 3 4 5
ATCF
Should you accept or reject this project? Show your work.
28. 3.28 Your brother needs a $5,000 loan to go to college. Because of his
poverty, he will pay nothing for the next four years. Five years from
today he will begin paying you $2,500 a year for the next 4 years. The
first payment occurs 5 years from today, and the total of the four
payments will be $10,000.
1. If your minimum ROR is 8%, then is this an acceptable
investment? Explain.
2. For the same payment schedule but with a 5% rate of inflation, is
this an acceptable investment? Note that your brother pays you
$2,500 a year regardless of the inflation rate. Provide quantitative
justification for your decision.
29. 3.29 You are to do an analysis of an investment with and without taxes
and with and without considering inflation. The initial investment (at
time 0) is $10,000. The projected benefits of the investment are $1,000
per year. After 5 years the project will be sold for $8,000. All of these
amounts are estimated in real (year-0) dollars. The MARR for the
project is 20% and does not include an allowance for inflation. This
MARR is to be used for both the before-tax and after-tax analyses. In
each case, you are to write the formula for the NPV of the investment.
Be sure to show the appropriate interest rate. It is not necessary to
evaluate the formula.
1. Consider the investment without taxes and without inflation. Write
the formula for the NPV of the investment.
2. Consider the investment with taxes but without inflation. Write the
formula for the NPV of the investment. Use straight-line
depreciation with a salvage of 0. All income and capital gains are

taxed at 40%.
3. Consider the investment without taxes but with inflation. The
original information given about the problem was in real dollars.
The inflation rate is 10% per year for the benefits. The salvage
value is also expected to be affected by inflation, growing at a rate
of 10% per year. The general inflation rate is also 10% per year.
4. Consider both taxes and inflation in this part. The general inflation
rate is 10% per year, affecting both the annual benefits and the
salvage value. Use straight-line depreciation with a salvage value
of 0. Assume that all income and capital gains are taxed at 40%.
Find the NPV of the investment.
30. 3.30 The tables below show the operating cost and salvage value for a
machine that was purchased for $50,000 and has a useful life of 3 years.
Find its economic life using an MARR of 10%.
1.
Year Operating cost Salvage value
1 $10,000 0
2 $40,000 0
3 $70,000 0
2.
Year Operating cost Salvage value
1 $10,000 $30,000
2 $10,000 $20,000
3 $10,000 0
31. 3.31 Your company purchased a machine for $14,000 with a 6-year tax
life. The SOYD method is used for depreciation, and the tax salvage
value is zero.
1. After the third year of use, the machine is sold for $10,000. How

much does the company get from the sale after taxes, assuming that
the tax rate on capital gains is 40%?
2. Neglect taxes in this part. After the third year of life, the company
is thinking about replacing the machine with a new one. It can be
sold now for $10,000. Next year it will be worth only $6,000 and in
two years, only $4,000. Three years from now the machine will
have no resale value. The operating cost of the machine is expected
to be constant for the next three years at $1,000 per annum. The
new machine has a life of 10 years with a NAC of $5,000. Should
the old machine be replaced with the new one if the company’s
MARR is 10%? Explain.
32. 3.32 A milling machine (machine A) in your company’s shop has a
current market value of $30,000. It was bought nine years ago for
$54,000 and has since been depreciated by the straight-line method
assuming a 12-year tax life. If the decision is made to keep the machine
at this point in time, then it can be expected to last another 12 years
(measured from today). At the end of the 12 years, it will be worthless.
The operating costs of this machine are $7,500 per year and are not
expected to change for its remaining life.
Alternatively, machine A can be replaced by a smaller machine B,
which costs $42,000 and is expected to last 12 years. Its operating costs
are $5,000 per year and would be depreciated by the straight-line
method over the 12-year period with no salvage value expected.
Both income and capital gains are taxed at 40%. Compare the after-tax
EUACs of the two machines and decide whether machine A should be
retained or replaced by machine B. Use a 10% after-tax MARR in your
calculations.
33. 3.33 What is the argument for using assessment procedures based on 50-
50 gambles as opposed to assessment procedures based on using
reference gambles?
34. 3.34 Explain why identification of special attitudes toward risk can
simplify the utility assessment process.

35. 3.35 Given the following information, plot four points on the person’s
preference curve. The maximum payoff is $1,000. The minimum payoff
is $0. The CE for a 50-50 gamble between $1,000 and $0 is $400. The
CE for a 50-50 gamble between $400 and $0 is $100.
36. 3.36 As part of a decision analysis, Archie Leach provided the following
information:
He was indifferent between a 50-50 chance at +$10 million and
−$10 million and −$5 million for certain.
His CE for a lottery offering a 0.5 chance at −$5 million and a 0.5
chance at +$10 million was $0.
He was indifferent between a lottery with a 0.7 chance at
+$10 million and a 0.3 chance at $0, and +$5 million for certain.
Sketch a preference curve for Leach on the basis of this information.
37. 3.37 Refer to Figure 3.15 .

Figure 3.15
Preference curve for risk-averse decision maker.
1. Specify a reference gamble that is equivalent (based on this curve)
to the certain amount $30,000.
2. Specify a 50-50 gamble that is equivalent (based on this curve) to
the certain amount $30,000.
38. 3.38 Beverly Silverman had long been promised a graduation present of
$10,000 by her father, to be received on graduation day 3 months hence.
Her father had recently offered an alternative gift of 1,000 shares of
stock in Opera Systems, Inc., a consulting firm with which Beverly was

slightly acquainted. He requested that she choose between the two gifts
by the following day. On the day she was trying to decide, the stock was
selling for $12 per share. Thus it looked like it would be wise to take the
stock because its present value was $12,000. She recognized, however,
that she would not receive the stock until graduation day and that the
stock price 3 months in the future was uncertain. She also recognized
that her utility for money was not linear and that her risk aversion would
play a major role in her decision. With these facts in mind, Beverly
reached the following conclusions:
1. She believed that the stock price was more likely to rise than to fall
in the intervening 3 months, and that it was as likely to be above
$14 per share as below that figure when she was to receive the
stock.
2. She believed that there was only 1 chance in 100 that the stock
price would drop to less than $6 per share and an equal chance that
the price would be more than twice its current value on graduation
day.
3. She also thought that there was only 1 chance in 5 that the price
would be below $10 and that there was 1 chance in 4 that it would
be above $16 when she received it.
In considering her preferences, Beverly decided the following:
1. That her CE for a lottery offering a 50-50 chance at $0 and $25,000
was $9,000.
2. That her CE for a lottery offering a 0.2 chance at $25,000 and a 0.8
chance at 0 was $3,000.
3. That her CE for a lottery offering a 50-50 chance at $3,000 or
$25,000 was $12,000.
4. That her CE for a lottery offering a 50-50 chance at $12,000 or
$25,000 was $17,000.

Determine the cumulative probability distribution that Ms. Silverman
has assigned to the stock price. Calculate her CE for the gift of the stock.
39. 3.39 A manager expresses indifference between a certain profit of
$5,000 and a venture with a 70% chance of making $10,000 and a 30%
chance of making nothing. If the manager’s utility scale is set at 1 utile
for $0, and 100 utiles for $10,000, then what is the utility index for
$5,000?
40. 3.40 The manager in Exercise 3.39 is indifferent between a venture that
has a 60% chance of making $10,000 and a 40% chance of making
$1,000, and a sure investment that yields $5,000. Find the value of
$1,000 in utiles for this manager.
41. 3.41 Below are the results of a preference test given to an executive:
1. She is indifferent between an investment that will yield a certain
$10,000 and a risky venture with a 50% chance of $30,000 profit
and a 50% chance of a loss of $1,000.
2. Her utility function for money has the following shape:
Money ($) −1,000 0 5,000 20,000 30,000
Utility −2 0 10 20 30
A new risky venture is proposed. The possible payoffs are either $0
or $20,000. The probabilities of the gain cannot be determined.
Find the probability combination of $0 and $20,000 that would
make the executive indifferent to the certain $10,000.
42. 3.42 Frances Gumm has an opportunity to invest $3,000 in a venture
that has a 0.2 chance of making nothing, a 0.3 chance of making $2,000,
a 0.2 chance of making $4,000, and a 0.3 chance of making $6,000. Her
utilities for each of the outcomes are 0 for $2,000, 35 for $4,000, and 40
for making $6,000. Draw Frances’s utility curve and advise her on
making the investment.
43. 3.43 A plant manager has a utility of 10 for $20,000, 6 for $11,000, 0 for

$0, and −10 for a loss of $5,000.
1. The plant manager is indifferent between receiving $11,000 for
certain and a lottery with a 0.6 chance of winning $5,000 and a 0.4
chance of winning $20,000. What is the utility of $5,000 for the
manager? Construct the manager’s utility curve.
2. Using this curve, find the CE for the following gamble (i.e., the
amount of cash that will make the manager indifferent to the
gamble):
Payoff Probability
−$2,000 0.2
0 0.3
$3,000 0.4
$10,000 0.1
3. What probability combination of $0 and $20,000 would make the
manager indifferent to the certain $11,000? Show your work.
4. The manager is facing a decision about buying a new production
machine that can bring a net profit of $15,000 (80% chance) or a
loss of $1,000 (20% chance); alternatively, the manager can use the
old machine and make a $10,000 profit. Use the utility curve to
find which alternative the manager should select. Specify all
necessary assumptions.

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Chapter 4 Life-Cycle Costing
4.1 Need for Life-Cycle Cost
Analysis
The total cost of ownership of a product, structure, or system over its useful
life defines its life-cycle cost (LCC). For products purchased off the shelf, the
major factors are the cost of acquisition, operations, service, and disposal. For
products or systems that are not available for immediate purchase, it may be
necessary to include the costs associated with conceptual analysis, feasibility
studies, development and design, logistics support analysis, manufacturing,
and testing.
In discussing the LCC of a system or a product versus a project, a distinction
is often made between the various phases of the two. The main difference is
that the project usually terminates when the system or product enters its
operational life. The life cycle of the system or product, however, continues
far beyond that point. In Chapter 1, we introduced the five life-cycle phases
of a project. Here we introduce the five life-cycle phases of a system or
product:
1. Conceptual design phase
2. Advanced development and detailed design phase
3. Production phase
4. System operations and maintenance phase
5. System divestment/disposal phase
The need for life-cycle costing arises because decisions made during the early
phases of a project inevitably have an impact on future outlays as the design

evolves and the product matures. This need was recognized in the mid-1960s
by the Logistics Management Institute, which issued a report stating that “the
use of predicted logistics costs, despite their uncertainty, is preferable to the
traditional practice of ignoring logistics’ costs because the absolute accuracy
of their quantitative values cannot be assured in advance.”
An LCC analysis is intended to help managers identify and evaluate the
economic consequences of their decisions. In 1978, the Massachusetts
Institute of Technology (MIT) Center for Policy Alternatives published one
of the first studies on LCC estimates. The focus was on appliances; some of
the estimates are summarized in Table 4.1. As can be seen, the cost of
acquisition was between 40.9% and 60.2%; the rest was spent after the
acquisition on operations, maintenance, and disposal. Nevertheless, the
decisions made at the acquisition stage affect 100% of the LCC. Because the
product’s design dictates its LCC, it is of utmost importance to consider
different options and their overall impact. A design that increases the
production costs may be justified if it reduces the system’s operational costs
over its useful life.
TABLE 4.1 LCC Estimates
for Appliances
Air conditioners Refrigerators
Useful life: 10 years 15 years
Cost
element
Acquisition $204(58.7%)        
$295
(40.9%)         (60.2%)        
Operations $131(37.8%)        
 $92
(54.3%)         (26.8%)        
Service   $4   (1.2%)        
 $19 
(2.6%)         (11.9%)        
Disposal     $8     $16 

(2.3%)         (2.2%)         (1.1%)
$347
(100%)        
$722
(100%)         (100%)        
The MIT research demonstrated the importance of considering costs that are
incurred during the operational stage of a system or product. This led the
principal investigators to propose the establishment of consumer LCC data
banks. Today, information on the operational costs of appliances such as
energy consumption of refrigerators is posted on the units in the retail outlets.
Similarly, the Environmental Protection Agency makes data on gasoline
mileage of passenger cars readily available to the public.
A parallel situation exists for purchased commodities, as well as for research,
development, and construction projects, in which decisions made in the early
stages have a significant impact on the entire LCC. Engineering projects in
which a new system or product is being designed, developed, manufactured,
and tested may span years, as in the case of a new automobile, or decades in
the case of a nuclear power plant. New product development takes anywhere
from several months to several years. In lengthy processes of this type,
decisions made at the outset may have substantial, long-term effects that are
frequently difficult to analyze. The tradeoff between current objectives and
long-term consequences of each decision is therefore a strategic aspect of
project management that should be integrated into the project management
system.
A typical example of a decision that has a long-term effect deals with the
selection of components and parts for a new system at the advanced
development and detailed design phase. Often, manufacturing costs can be
reduced by selecting less expensive components and parts at the expense of a
higher probability of failures during the operational life of the system.
Another example is the decision regarding inspection and testing of
components and subassemblies. Time and money can be saved at the early
stages of a project by minimizing these efforts, but design errors and faulty
components that surface during the operational phase may have severe cost
consequences.
A third example relates to the need for logistics support. In this regard,

consider the maintenance costs during the operational phase of a system.
These costs can be reduced by including in the design built-in test equipment
that identifies problems, locates their source, and recommends a corrective
course of action. Systems of this type that combine sensors with automated
checklists and expert systems logic are expensive to develop, but in the long
run decrease maintenance costs and increase availability.
LCC models track the costs of development, design, manufacturing,
operations, maintenance, and disposal of a system over its useful life. They
relate estimates of these cost components to independent (or explanatory)
decision variables. By developing a functional representation [known as a
cost estimating relationship (CER)] of the cost components in terms of the
decision variables, the expected effect of changing any of the decision
variables on one or more of the cost components can be analyzed.
A typical example of a CER is the effect of work design on the cost of labor.
One aspect of this effect is the learning phenomenon discussed in Chapter 9.
Because the slope of the learning curve depends on the type of manufacturing
technology used, a CER can help the design engineers select the most
appropriate technology. This situation is depicted in Figure 4.1, where two
manufacturing technologies are considered. Technology 1 requires lower
labor cost for the first unit produced but has a slower learning rate than that
of technology 2. The decision to adopt either technology depends on the
number of units required and the cost of capital (assuming that everything
else is equal). For a small number of units, technology 1 is better, as labor
costs are lower in the early stages of the corresponding learning curve. Also,
if the cost of money is high, then technology 1 might be preferred because it
displaces a substantial portion of the labor cost into the future. Finally, for a
large number of units, technology 2 is preferred. In Figure 4.1, the point
where the two technologies yield the same total cost is called the breakeven
point.

Figure 4.1
Learning curves for two technologies.
In this example (as in many others) the importance of the LCC model
increases when the proportion of manufacturing, operations, and maintenance
costs is greater than the proportion of design and development costs over the
lifetime of the product or system.
The development and widespread use of LCC models is particularly justified
when a number of alternatives exist in the early stages of a project’s life cycle
and the selection of an alternative has a noticeable influence on the total
LCC. At the outset of a project, they provide a means of evaluating
alternative designs; as work progresses, they may be called on to evaluate
proposed engineering changes. These models are also used in logistics
planning, where it is necessary, for example, to compare different
maintenance concepts, training approaches, and replenishment policies. At a
higher level, model results support decisions regarding logistic and
configuration issues, the selection of manufacturing processes, and the
formulation of maintenance procedures. By proper use, engineers and
managers can choose alternatives so that the LCC is minimized while the
required system effectiveness is maintained. The development and
application of LCC models therefore is an essential part of most engineering
projects.

As another example, let us consider a project involving the construction of an
office building in which the windows can be either single- or double-pane
glass. Material and installation costs make the initial investment in the second
option greater than in the first; however, if an LCC analysis is conducted,
then the cash flow over the useful life of the windows should be evaluated.
The aim would be to consider not only the initial investment but also the
intermittent and recurrent costs resulting from the decision, such as the loss
of energy as a result of differences in isolation abilities. Taking qualitative
factors into account, though, presents a problem. Although double-pane
windows have technical advantages, such as better noise isolation, it is
difficult if not impossible to translate these types of advantages in monetary
terms. If this is the case, then the multi-criteria methods for project evaluation
discussed in Chapters 5 and 6 should be used.

4.2 Uncertainties in Life-Cycle Cost
Models
In the conceptual design phase where LCC models are usually developed,
little may be known about the system, the activities required to design and
manufacture it, its modes of operation, and the maintenance policies to be
employed. Consequently, LCC models are subject to the highest degrees of
uncertainty at the beginning of a project. This uncertainty declines as
progress is made and additional information becomes available.
Because decisions taken in the early stages of a project’s life cycle have the
potential to affect the overall costs more than decisions taken later, the
project team faces a situation in which the most critical decisions are made
when uncertainty is highest. This is illustrated in Figure 4.2 and 4.3 where the
potential effect of decisions on cost and the corresponding level of
uncertainty are plotted as a function of time. From these graphs, the
importance of a good LCC model in the early phases of a system’s life cycle
is evident.
There are two principal types of uncertainty that LCC model builders should
consider: (1) uncertainty regarding the cost-generating activities during the
system’s life cycle, and (2) uncertainty regarding the expected cost of each of
these activities. The first type of uncertainty is typically present when a new
system is being developed and few historical data points exist. The
equipment used on board several of the early

Figure 4.2
Percentage of budget affected by decision made is life-cycle phase
of a system.
Figure 4.2 Full Alternative Text

Figure 4.3
Cost estimate errors over time.
Figure 4.3 Full Alternative Text
earth-orbiting satellites and the first space shuttle, Columbia, fall into this
category. There was a high level of uncertainty with respect to maintenance
requirements for this equipment as well as the procedures for operating and
maintaining the launch vehicles and supporting facilities. Maintenance
practices were finalized only after sufficient operational experience was
accumulated. The reliability and dependability of these systems were studied
carefully to determine the required frequency of scheduled maintenance.
Nevertheless, the accuracy of LCC models in which this type of uncertainty
is present is relatively low, implying that their benefits may be somewhat
limited to providing a framework for enumerating all possible cost drivers
and promoting consistent data collection efforts throughout the life of the
system. But even if this were the only use of the model, benefits would
accrue from the available data when the time came to upgrade or build a
second-generation system.
The second type of uncertainty, estimating the magnitude of a specific cost-
generating activity, is common to all LCC models. There are multiple sources
of this type of uncertainty, such as future inflation rates, the expected
efficiency and utilization of resources, and the failure rate of system
components. Each affects the accuracy of the cost estimates. To obtain better
results, sophisticated forecasting techniques are often used, fueled by a wide
array of data sources. Analysts who build LCC models should always trade
off the desired level of accuracy with the cost of achieving that level. Most
engineering projects are associated with improving current systems or
developing new generations of existing systems. For such projects it is
frequently possible to increase the accuracy of cost estimates by investing
more effort in collecting and analyzing the underlying data. Therefore, it is
important to determine when the point of diminishing returns has been
reached. More sophisticated models may pose an increasingly problematic

challenge to their intended users and may become more expensive or
complicated than the quality of the input data can justify.
The accuracy of cost estimates changes over the life cycle of the system.
During the conceptual design phase, a tolerance of −30% to +50% may be
acceptable for some factors. By the end of the advanced development and
detailed design phase, more reliable estimates are expected to be available.
Further improvement is realized during the production and system operations
phases when field data are collected.

4.3 Classification of Cost
Components
The selection of a specific design alternative, the adoption of a maintenance
or training policy, or the analysis of the impact of a proposed engineering
change is based on the tradeoff between the expected costs and the expected
benefits of each candidate. To ensure that the economic analysis is complete,
the LCC model should include all significant costs that are likely to arise over
the system’s life cycle. In this effort it is essential for the model builder to
consider the type of system being developed. On the basis of the logical
design of the project, common management concerns, and supporting data
requirements, the cost classifications and structures can be defined.
Many ways of classifying costs are possible in an LCC analysis. Some are
generic, whereas others are tailored to meet individual circumstances. In the
following discussion, we present several commonly used schemes. Each can
be modified to fit a specific situation, but a particular application may require
a unique approach.
One way to classify costs is by the five life-cycle phases:
1. Cost of the conceptual design phase. This category highlights the costs
associated with early efforts in the life cycle. These efforts include
feasibility studies, configuration analysis and selection, systems
engineering, initial logistic analysis, and initial design.
The cost of the conceptual design phase usually increases with the
degree of innovation involved. In projects aimed at developing new
technologies, this phase tends to be long and expensive. For example,
consider the development of a new drug for AIDS or the development of
a permanently manned lunar base. In such projects, high levels of
uncertainty motivate in-depth feasibility studies, including the
development of models, laboratory tests, and detailed analyses of
alternatives. When a modification or improvement of an existing system

is being weighed, the level of uncertainty is lower, and consequently, the
cost associated with the conceptual design phase is lower. This is the
case, for example, with many construction projects in which the use of
new techniques or technologies is not the main issue.
The LCC model can be used in this phase to support benefit-cost
analyses. One must proceed with caution, however, because initial LCC
estimates may be subject to large errors. A comparison of alternatives is
appropriate only when the cost difference between them is measurably
larger than the estimation errors and hence can be detected by the LCC
models.
2. Cost of the advanced development and detailed design phases. Here the
cost of planning and detailed design is presented. This includes product
and process design; preparation of final performance requirements;
preparation of the work breakdown structure, schedule, budget, and
resource management plans; and the definition of procedures and
management tools to be used throughout the life cycle of the project.
These phases are labor intensive. Engineers and managers design the
product and plan the project for smooth execution. Attempts to save
time and money by starting implementation prior to a satisfactory
completion of these phases can lead to future failures. The development
of a good product design and a comprehensive project plan are
preconditions for successful implementation. In the advanced
development and detailed design phase of the LCC analysis, accurate
estimates of cost components are required. These estimates are used, in
part, to support decisions regarding the selection of alternative
technologies and the logistic support system for the product.
3. Cost of the production phase. This category consists of the costs
associated with the execution of the design, including the construction of
new facilities or the remodeling of existing facilities for assembly,
testing, production, and repair. Also included are the actual costs of
equipment, labor, and material required for operations, as well as
blueprint reproduction costs for engineering drawings and the costs
associated with documenting production, assembly, and testing
procedures.

In many projects and systems this is the highest cost phase. The quality
of the requirements and design decisions made earlier in the project
determine the actual cost of production. By accumulating and storing the
actual costs in appropriate databases, LCC analysis can be improved for
similar future projects. The LCC model in this phase becomes
increasingly accurate, making detailed cost analysis of alternative
operations and maintenance policies possible.
4. Cost of operating and maintaining the system. This category identifies
the costs surrounding the activities performed during the operational life
of the system. These include the cost of personnel required for
operations and maintenance together with the cost of energy, spare parts,
facilities, transportation, and inventory management. Design changes
and system upgrade costs also fall into this category.
5. Cost of divestment/disposal phase. When the end of the useful life of a
system has been reached, it must be phased out. Parts and subassemblies
must be inventoried, sold for scrap, or discarded. In some cases, it is
necessary to take the system apart and dispose of its components safely.
The phasing out or disposal of a system might have a negative cost (i.e.,
produce revenue) when it is sold at the end of its useful life, or it might
have a positive cost (often high), as in the case of a nuclear reactor that
has to be carefully dismantled and its radioactive components safely
discarded.
The relative importance of each phase in the total LCC model is system
specific. Figure 4.4 presents a comparison for two generic systems by life-
cycle phase. In general, when alternative projects are being considered, the
relative magnitude and timing of the different cost components figure
prominently in the analysis. In Figure 4.4, system A requires substantial
research and development efforts. The conceptual design phase and the
advanced development phase account for 50% of the LCC. In system B, these
two phases account for only 30% of the total cost. Thus, system B can be
thought of more as a production/implementation project, whereas system A
represents more of a design/development project.

Figure 4.4
Cost comparison of two projects by life-cycle phase.
Figure 4.4 Full Alternative Text
A second classification scheme has its origins in manufacturing and is based
on cost type; that is, direct labor versus indirect labor, subcontracting,
overhead allocations, and material (direct and indirect), as illustrated in
Figure 4.5. These categories parallel those traditionally found in cost
accounting, so data should be readily available for many applications.

Figure 4.5
Cost classification for manufacturing.
Figure 4.5 Full Alternative Text
A third means of classification is based on the time period in which each cost
component is realized. To make this scheme operational, it is necessary to
define a minimum time period, such as 1 month or 1 quarter, in the system’s
life cycle. All costs that are incurred in this predetermined time period are

grouped together. This is illustrated in Figure 4.6, where the graphs provide a
12-month history of costs. This type of classification scheme is important
when cash flow constraints are considered. Two projects with the same total
cost may have a different cost distribution over time. In this case, because of
cash flow considerations (the time value of money), the project for which
cost outlays are delayed may be preferred.
Figure 4.6
LCC as a function of time.
Figure 4.6 Full Alternative Text
A fourth classification scheme is by work breakdown structure (WBS). In
this approach, the cost of each element is estimated at the lowest level of the
WBS. If more detail is desired, each element can be disaggregated further by
life-cycle phase (first classification), cost type (second classification), or time
period (third classification).
As the situation dictates, other schemes, perhaps based on the bill of material,
the product structure, or the organization breakdown structure (OBS), might

be used. In particular, classification based on the organizational breakdown
structure has proved useful as a bridge between the LCC model and the
project budget, which traditionally is prepared along organizational lines.
It goes without saying that the scheme chosen should directly support the
kinds of analyses to be undertaken. Thus, if future cash flow analyses are
required, then the timing of each cost component is important. If, however, a
system is developed by one organization (a contractor) for use by another
(the client), and the customer is scheduled to deliver some of the subsystems,
as in the case of government-furnished equipment in government contracts,
then classification of cost based on the organization responsible for each cost
component might be appropriate.
Sophisticated LCC models apply several classification schemes in the cost
breakdown structure (CBS) so that each cost component can be categorized
by the life-cycle phase and time period in which it arises, the WBS element in
which it appears, and the class type from an accounting point of view. The
cost of developing and maintaining such models depends on the desired
resolution (number of subcategories in each classification scheme) and
accuracy of the cost estimates, the updating frequency, and the number of
classification schemes used. LCC model builders should strive to balance
development costs with maintenance and data collection requirements.
An example of an LCC model for a hypothetical system in which a simple
three-dimensional cost structure is used is given next. In this classification
scheme, costs are broken down by (1) the life-cycle phase, (2) the quarter in
which they occur, and (3) labor and material. The data are presented in Table
4.2.
In the example we assume that three different models of the same system are
being developed during the first two years (eight quarters). Production starts
on the first model before detailed design of the other two is finalized. Thus
during quarters 6 through 8, advanced development and detailed design as
well as production costs are present. Similarly, the first model becomes
operational before the completion of the production phase of the other
models, implying overlapping costs in these categories in quarters 9 and 4.
The three models are phased out in quarters 14, 15, and 17 as noted by
divestment costs and reduced operations and maintenance costs in these

periods.
TABLE 4.2 Example of an
LCC Model ($1,000)
System life-cycle phase
Conceptual
design
Advanced
development
& detailed
design
Production
Operations
&
maintenance
Divestment/disposal
Quarter Labor Mat’l Labor Mat’l Labor Mat’l Labor Mat’l Labor
1 2
2 3
3 3
4 1 3
5 4 1
6 5 1 10 3
7 5 1 12 4
8 3 1 15 6
9 10 5 3 1
10 7 3 4 2
11 5 3
12 5 3
13 5 3
14 5 3 1
15 4 2 1
16 4 2
17 3 1 1
18
Total 9 – 20 4 54 21 38 20 3

The LCC data in Table 4.2 can be used to produce several views, each giving
a different perspective and highlighting different aspects of the project. For
example, in Figure 4.7 we plot the cumulative LCC of the system over time,
as well as the cost that is incurred in each quarter. The LCC can also be
presented by life-cycle phase. This is illustrated in Figure 4.8. A third
possibility is labor cost versus material cost, as shown in Figure 4.9.
Although the periodic and total LCCs are the same in Figure 4.8 and 4.9, the
breakdown of these costs is different and can serve different purposes, as
discussed in the next section.
Figure 4.7
Total LCC of the system.

Figure 4.8
LCC by phase.
Figure 4.8 Full Alternative Text

Figure 4.9
Cost breakdown by labor and material.
Figure 4.9 Full Alternative Text
In the example, a fourth classification (or dimension) might correspond to the
WBS and a fifth to the OBS. By using a 5-dimensional grid, questions such
as, “What is the expected cost of software development by the main
contractor for the real-time control system during the third quarter of the
project?” can be answered. The type of questions and scenarios for which the
LCC model is to be exercised is the principal consideration in its design.

4.4 Developing the LCC Model
The first step in the design of an LCC model is to identify the types of
analyses that it is intended to support. The following is a list of several
common applications.
Strategic or long-range budgeting. Because the LCC model covers the
entire life cycle of a system, it can be used to coordinate investment
expenditures over the system’s useful life or to adjust the requirement
for capital for one system or project with capital needed or generated by
other systems or projects. Such long-range budget planning is important
for strategic investment decisions.
Strategic or long-range technical decisions. Strategic decision making as
it relates to such issues as the redesign of a system or the early
termination of a research and development (R&D) project is difficult to
support. The LCC model can be used to monitor changes in cost
estimates as the project evolves. Revised estimates of production,
operations, or maintenance costs that are substantially higher than the
baseline figures may serve as a trigger for unscheduled design reviews,
major changes in system engineering, or even a complete shutdown of
the project. Because LCC estimates improve over time, rough
projections made in the early phases of a project’s life cycle may be
updated later and provide managers with more accurate data to support
the technical decision making process.
Data analysis and processing. LCC models routinely serve as a
framework for the collection, storage, and retrieval of cost data. By
using an appropriate data structure (e.g., LCC breakdown structure), the
cost components of current or retired systems can be analyzed
simultaneously to yield better estimates for future systems.
Logistic support analysis. Logistics is generally concerned with
transportation, inventory and spare parts management, database systems,
maintenance, and training. Questions such as which maintenance

operations should be performed and at what frequency, how much to
invest in spare parts, how to package and ship systems and parts, which
training facilities are required, and which type of courses should be
offered to the operators and maintenance personnel are examples of
decisions supported by LCC analyses.
Once agreement is reached on the types of analyses that will be conducted,
LCC model development can proceed. The following steps should be carried
out:
1. Classification. In this step the classification schemes are developed.
Major activities that generate cost are listed and major cost categories
(labor, material, etc.) are identified. For example, the LCC data
presented in Table 4.2 can be classified by the organizational unit
responsible for each cost component and the activities performed by that
unit.
2. CBS. Next, a coding system is selected to keep track of each cost
component. To gain further insights, the latter may be organized in a
multidimensional hierarchical structure based on the system chosen in
step 1. Each component at each level of the hierarchy is assigned an
identification number. The CBS enables the cost components to be
aggregated based on the classification scheme. Thus, with the proper
scheme the labor cost of a specific activity in a given period or the cost
of a specific subsystem during its operational phase can be determined.
The CBS links cost components to organizational units, to WBS
elements, and to the system’s bill of material.
As an example, consider the CBS of a project aimed at developing a
new radar system. The system is composed of a transmitter, receiver,
antenna, and computer. The plan is to subcontract the computer design
and its software as well as part of the antenna servo, while developing
the rest of the components in-house. The coding scheme for the CBS is
as shown in Table 4.3.
TABLE 4.3 Coding and

Classification Scheme for
LCC
Digit Classification Code assignment
1 Who performs the work Performed in-house 1
Subcontracted 2
2 System part Transmitter 1
Receiver 2
Antenna 3
Computer 4
3 Life-cycle phase Conceptual 1
Detailed design 2
Production 3
Operations & maintenance 4
Divestment 5
4 Type of cost Direct labor 1
Direct material 2
Overhead 3
Using this simple four-digit code, a question such as, “What is the direct
cost of material to be used during the production phase of the receiver?”
can be answered by retrieving all cost components with the following
LCC codes:
first digit 1 or 2
second digit 2
third digit 3
fourth digit 2
Thus, we would search for the LCC codes 1232 and 2232. The
corresponding cost components might represent the cost at different
months of the project, assuming that cost is estimated on a monthly

basis. Other situations are possible.
3. Cost estimates. After the various cost components are identified and
organized within the chosen classification scheme, the final step is to
estimate each cost component. The American Association of Cost
Engineers AACE 1986 has proposed three classifications for this
purpose:
Order of magnitude: accuracy of −30% to +50%. An estimate that is
made without any detailed engineering data.
Budget: accuracy of −15% to +30%. This estimate is based on
preliminary layout design and equipment details, and is performed
by the client to establish a budget for a new project (at the request
for proposal (RFP) stage).
Definitive: accuracy of −5% to +15%. This cost estimate is based on
well-defined engineering data and a complete set of specifications.
The work involved in preparing cost estimates is a function of the required
accuracy and the size and cost of the project. In the process industries, the
typical costs for preparing estimates were estimated by Pikulik and Diaz
(1977):
Order-of-magnitude estimates
Project cost ($ million) Cost of estimate ($ thousand)
Up to 1      7.5 to 20
1 to 5      17.5 to 45
5 to 50      30 to 60
Budget estimates
Project cost ($ million) Cost of estimate ($ thousand)
Up to 1      20 to 50
1 to 5      45 to 85
5 to 50      70 to 130

Definitive estimates
Project cost ($ million) Cost of estimate ($ thousand)
Up to 1      35 to 85
1 to 5      85 to 175
5 to 50      150 to 330
A variety of estimation procedures are used in industry, all of which are
based on the assumption that past experience is a valid predictor of future
performance. Estimation procedures fall into one of two categories: (1)
causal, whereby the aim is to derive CERs; and (2) noncausal, or direct.
Causal estimates follow from an assumed functional relationship between the
cost component and one or more explanatory variables. For example, the cost
of fuel required during the operational life of a car might be estimated as a
function of the distance driven, the weight of the car, the car’s engine size,
and the expected road conditions. An equation, relating the cost of fuel to the
explanatory variables, can be developed by using regression analysis or any
other curve fitting technique (see Section 9.2.5). With the use of CERs, the
expected effect of changing any explanatory variable on the LCC can be
analyzed. To develop CERs, past data on the values of the cost component
under investigation and the explanatory variables are required.
As an example, consider the equipment CER proposed by Fabrycky and
Blanchard (1991),
C= C r × ( Q c Q r ) β (4.1)
where
C=cost for a new design size Q c
C r =cost for existing reference design Q r
Q c =design size—new design
Q r =design size—existing reference design

β=correlation parameter; 0<β≤1 Taking the logarithm of both sides of Eq. (4.1) gives the CER log C−log C r =β ( log Q c −log Q r ) (4.2) where β is to be determined from a regression analysis. Suppose that a cost estimate for a new 750-gallon water desalination system is required and that information on the actual cost of five systems is available. These data are presented below. Reactor Cost Size (gallons) 1 $14,000 200 2 $18,000 300 3 $21,500 400 4 $25,000 500 5 $28,000 600 A pairwise comparison between the five systems yields the following data in the form needed for Eq. (4.2). C r C Q r Q c log C−log C r log Q c −log Q r 14,000 18,000 200 300 0.109 0.176 18,000 21,500 300 400 0.077 0.125 21,500 25,000 400 500 0.066 0.096 25,000 28,000 500 600 0.049 0.079 A regression analysis using the first three systems yields the CER log C−log C r =0.628 ( log Q c −log Q r ) with R 2 =0.983. Now, using the fourth system as a reference ( Q r ), the estimated cost for a new 750-gallon ( Q c ) system (same type) is C=$25,000× ( 750 500 ) 0.628 =$32,249 This type of CER is useful for a company that has to estimate the cost of new systems that differ from existing systems mainly by size. Cost estimates can alternatively be derived using noncausal methods, such as: Judgment and experience, rules of thumb, or the use of organizational standards for similar activities. These techniques are informal, inexpensive, and therefore appropriate when formal LCC models and cost estimates with high levels of accuracy are not essential. Analogy to a similar system or component and an appropriate adjustment of cost components according to the difference between the systems. Technical estimation based on drawings, specifications, time standards, and values of parameters such as mean time to failure and mean time to repair. Value of contracts for similar systems, such as office cleaning contracts and maintenance contracts. It is also possible to estimate costs on the basis of bids from contractors who respond to RFPs. Each technique requires a combination of resources, such as time, data, equipment, and software, and may call on the expertise and experience of people within or external to the organization. From the data and resources available, the required accuracy, and the cost of using each cost estimating technique, the most suitable approach for each application can be selected. For each cost component, one or more cost estimating techniques might be appropriate. In the early stages of the life cycle, technical estimation is usually not feasible as drawings and other information are not available. For new systems, analogy might not be feasible if similar systems have not been developed or previously deployed. Let us demonstrate the derivation of a CER for a project related to the development of a training course. Stark Awareness, Inc. is a company that specializes in developing such courses for its customers and wishes to estimate the labor hours required for putting together a new course. The deliverable is a packet of materials that will include all of the documents and slides required for conducting the class. Dr. Stark, the chief statistician for the company, decided to develop a CER based on expert judgment, in this case a team of instructors who have wide experience in this type of project. The experts identified the relevant parameters and the labor hours associated with each. For example, for the parameter “number of lecture hours” for the course, it was agreed that for every new lecture hour there is a need to spend 15 labor hours on activities such as reading new material, summarizing the main points, and preparing PowerPoint slides. The above process led to the following equation: LH=15L+4E+20T+10P where LH=number of labor hours required to develop new course L=number of lecture hours for new course E=number of exercises that students will be assigned T=number of tests to be given P=number of course projects For example, if there is a need to develop a training program that consists of 12 lecture hours, 3 exercises, and one project, then the estimated number of labor hours required to organize the class is: LH=15×12+4×3+10×1=202 hours LCC models are relatively mature in the areas of software development and maintenance planning. Several exist for estimating labor requirements for different tasks as a function of system characteristics and the level of experience of the project team. One such model, called COCOMO II, is based on the analysis of data collected from approximately 160 projects (Bohem et al. 2000). To estimate the resource requirements (independent variable) for a software project, the authors proposed using the following parameters (dependent variables): Project size, expressed by the number of old and new line codes Technical complexity of the new system Risk level Size of the databases required for the system Experience of the project team Complexity of communication channels Previous experience of the organization on projects of similar nature Organizational ability in the application of project management methodology Availability of advanced programming tools Organizational turnover Obviously, it is impractical to use the same model for every project; however, it is not uncommon for an organization to use similar estimation techniques and models for similar projects The selection of a cost estimating procedure depends on data availability, required accuracy, and cost. The analyst should consider all three aspects in the process of model design and application. To demonstrate further the process of developing an LCC model, consider the problem of estimating energy costs in residential buildings. It is possible to reduce the cost of energy by proper design, the use of insulation and improved ventilation, and the selection of efficient heating and cooling devices. The following is an example of a basic LCC model for such a project. The model has only two classifications: the first centers on the activities that generate cost, and the second is based on time. Table 4.4 depicts levels 1 and 2 of the CBS for the cost-generating activities. TABLE 4.4 Partial CBS for Residential Building Example 1. Cost of engineering 1. 1.1 Structural design 2. 1.2 Interior design 3. 1.3 Drawing preparation 4. 1.4 Supervision 5. 1.5 Management 2. Cost of construction 1. 2.1 Equipment 2. 2.2 Contractors 3. 2.3 Material 4. 2.4 Labor 5. 2.5 Energy 6. 2.6 Inspection 7. 2.7 Management 3. Cost of operations 1. 3.1 Energy 2. 3.2 Maintenance 3. 3.3 Consumables 4. 3.4 Subcontractors A time dimension is added to the model by introducing the timing of each cost component. For example, the structural design (1.1) may take 3 months. Assuming that the cost of the first month is $500, the cost of the second month is $1,100, and the cost of the last month is $400, the total cost of structural design is $500+$1100+$400=$2000 over a 3-month period. By assigning the cost of each cost component in Table 4.4 to a specific month, the time aspect of this LCC model is introduced. If more detail is needed, then the model can be expanded to three or four levels. For example, consider item 2.1, equipment, which can be broken down further by air conditioning system, heater, and so on. Once the lowest level is identified and the data elements are defined, the model can be used to estimate the cost of each component for each design alternative on a periodic basis, if necessary. Alternatives might differ in their total LCC, in the allocation of costs over the life cycle, and in the allocation of costs among different system components. As discussed in Chapters 5 and 6, the selection of the best alternative depends on the evaluation criteria specified. System reliability, maintenance requirements, and safety are common criteria, but LCC usually plays a predominant role. In particular, if the minimum net present cost is the criterion chosen, between two design alternatives with the same total LCC, then the one that delays monetary outlays the longest would be preferred. In the above example, it should not be surprising that this might lead to an energy-inefficient house—one that is less expensive to build but more expensive to maintain. A possible CER for the example might be a linear equation relating the cost of heating to the insulation used and the difference between the desired temperature inside the house and the ambient temperature outside. Additional explanatory variables that might be included are the area of windows and the type of glass used. The CBS can be as detailed as required to capture the impacts of decisions on overall cost and performance. Continuing with item 2.1, equipment can be broken down further to the level of components used in the air-conditioning system if it were thought that the selection of these components would measurably affect the LCC. 4.5 Using the Life-Cycle Cost Model The integration of the CBS with estimates of each component produces the aggregate LCC model for the system. This model (distributed over time) is the basis for several types of analyses and decision making. 1. Design evaluations. In the planning stages of a project, alternative designs for the entire system or its components have to be evaluated. The LCC model combined with a measure of system effectiveness produce a basis for cost-effectiveness analysis during various stages of the development cycle. Methodological details are provided in Chapters 5 and 6, where issues related to risk, benefit estimation, and criteria selection are discussed. 2. Evaluation of engineering change requests (ECRs). As explained in Chapter 8, the process of ECR approval or rejection is based on estimates of cost and effectiveness with and without the proposed change. The LCC model provides the foundation for conducting the analysis. 3. Sensitivity analysis and risk assessment. In the development of CERs, parameters that affect the LCC of the system are used as the explanatory variables. A sensitivity analysis should always be conducted to see how the LCC changes, as each parameter is varied over its feasible range. Depending on the nature of the project and the time horizon, some typical explanatory variables might be the rate of inflation, the cost of energy, and the minimum acceptable rate of return. 4. Logistic support analysis. The evaluation of policies for maintenance, training, stocking of spare parts, inventory management, shipping, and packaging is supported by appropriate LCC models. By estimating the cost of different alternatives for logistic support, decision makers can trade off the cost and benefits of each scenario under consideration. 5. Pareto, or ABC, analysis. This analysis is used to identify the most important cost components of a project. The first step is to sort each component by cost and then to place them into one of the following three groups: Group A: small percentage of the top cost components (10% to 15%), which together account for roughly 60% or more of the total cost Group B: all cost components that are not members of group A or C Group C: large percentage of the bottom cost components (about 50%) which account for 10% or less of the total cost In the sorted list, the first 10 to 15% of the cost components are members of group A and the last 50% are members of group C. The remaining components in the middle range of the list are assigned to group B. This clustering scheme is the basis for management control. The strategy is to monitor closely those items that account for the largest percentage of the total LCC (group A components). Conversely, group C components, which represent a relatively large number of items but account for a relatively small portion of the total cost, require the least amount of attention. 6. Budget and cash flow analysis. Here the concern is staying within budget and cash flow constraints and estimating future capital investment needs. By combining the LCC models of all projects in an organization, the net cash flow for each future period can be forecast. The results then may be used to support feasibility analyses, decisions regarding the acceptance of new projects, and recommendations for rescheduling or abandoning ongoing projects. The LCC model is an important project management tool for strategic financial planning, logistics analysis, and technology-related decision making. Properly designed and maintained LCC models help the project manager in both planning and control by linking together the cost and technological aspects of a project. By using CERs, the impact that different alternatives have on the system’s LCC can be analyzed and used as a basis for technology evaluation and selection, resource acquisition, and configuration management. TEAM PROJECT Thermal Transfer Plant Your plans for the prototype rotary combustor project have been approved. Total Manufacturing Solutions (TMS) management is now weighing the possibility of investing in a plant for manufacturing the combustors. There is a feeling, however, that the degree of subcontracting associated with producing the prototype may not be appropriate for the repetitive manufacturing environment of the new plant. Your team has been requested to perform an LCC analysis to help determine which parts and components of the rotary combustor to manufacture in-house and which to buy or subcontract. Design your models to answer these “make or buy” questions, keeping in mind that the expected life of a rotary combustor is approximately 25 years and TMS would like to support these units throughout their life cycle. State any assumptions that you believe are necessary to estimate costs and risks. Discuss the sensitivity of your results, assumed parameter values, timing of costs, levels of risk, and so on. Discussion Questions 1. Estimate the LCC for a passenger car. In so doing, select an appropriate CBS and explain your cost estimates. 2. Explain how the design of a car affects its LCC. 3. Compare the cost of ownership of a new car with that of a used car of similar type. 4. Explain the design factors that affect the LCC of an elevator in a New York City office building. 5. What are the sources of uncertainty in Question 4? 6. What do you think are the principal cost drivers in designing a permanently manned lunar base? What noncost factors would you want to consider? 7. Identify a potential consumer product that is not yet on the market, such as video telephones, and list the major costs in each phase of its life cycle. How might these costs be estimated? 8. Pick an R&D project of national scope, such as mapping all of the genes on a human chromosome (the human genome project). First, sketch a potential OBS for the project and identify the tasks that might fall within each organizational unit. Then develop a CBS and relate it to the OBS. 9. Develop an LCC model to assist you in selecting the best heating system for your house. Discuss the alternatives and explain the cost structure that you have selected. 10. Discuss the effect of taxes on the LCC of passenger cars. Compare domestic and imported cars. 11. Discuss the effect of LCC on the decision to locate a new warehouse. 12. Discuss a project in which the first phase of the life cycle accounts for more than 50% of the LCC. 13. Discuss a project in which the detailed design phase accounts for more than 50% of the LCC. Exercises 1. 4.1 The cost of a used car is highly correlated with the following variables: t=age of the car 1≤t≤5 (years) V=volume of engine 1000≤V≤2,500 (cubic centimeters) D=number of doors D=2, 3, 4, 5 A=accessories and style A=1, 2, 3, 4, 5, 6 (qualitative) Using regression analysis, the following relationship between the cost of a car and the four independent variables was found: Purchase cost=( 1+ 1 t )×V×( D 2 +A ) 1. Plot the purchase cost as a function of the four variables. 2. Which variable has the greatest effect on cost? 3. You have a total of $5,000. List the different types of cars (combinations of the parameters) that you can afford. 4. Develop a model by which you select the best car for your needs. 5. Operations and maintenance costs for the car are estimated as follows: annual maintenance cost= t 2 ×V× s 1,000 annual operating cost=( D×t+ V 1,000 )× s 250 where s is the number of miles driven annually. What is the best car (combination of parameters) for a person who drives 12,000 miles every year? 2. 4.2 A construction project consists of 10 identical units. The cost of the first unit is $25,000, and a learning curve of 90% applies to the cost and the duration of consecutive units. Assume that the first unit takes 6 months to finish and that the project is financed by a loan taken at the beginning of the project at an annual interest rate of 10%. 1. Should the units be constructed in sequence (to maximize learning) or in parallel (to minimize the cost of the loan)? 2. Find the schedule for the 10 units that minimizes the total cost of the project. 3. 4.3 Develop three cost classifications for the LCC of an office building. 4. 4.4 Develop a cost breakdown structure for the cost of an office building. Estimate the cost of each component. 5. 4.5 Show a cash flow analysis for the LCC of an office building. 6. 4.6 Perform a Pareto (ABC) analysis on the data of the LCC of an office building. 7. 4.7 Develop an estimate for the cost of a 3-week vacation in Europe. 8. 4.8 Develop an LCC model to support the decision to buy or rent a car. 9. 4.9 Natasha Gurdin is debating which of two possible models of a car to buy (A or B), being indifferent with regard to their technical performance. She has been told that the average monthly cost of owning model A, based on an LCC analysis, is $500. 1. Using the following data for model B, calculate its LCC and determine which model is the better choice for Natasha: Purchase price $23,000 Life expectancy 4 years Resale value $13,000 Maintenance $1,100 per year Operational cost (gas, etc.) $90 per month Car insurance $1,400 per year Mean time between failures 14 months Repair cost per failure $650 2. Develop a general model that can be used to calculate the LCC for a car. 10. 4.10 Your company has just taken over an old apartment building and is renovating it. You have been appointed manager and must decide which brand of refrigerator to install in each apartment unit. Your analysis should consider expenses such as purchase price, delivery charges, operational costs, insurance for service, and selling price after 6 years of use. Identify two brands of 18-cubic-foot refrigerators and compare them. 11. 4.11 You have been told that even warehouse location decisions should be based, at least in part, on the results of an LCC analysis. Discuss this issue. 12. 4.12 Maurice Micklewhite has decided to replant his garden. Show him what the cost is of making an erroneous decision at various stages of the project, starting with conceptual design and ending with the ongoing maintenance of the garden. 13. 4.13 The relative cost of each stage in the project life cycle is a function of the nature of the project or product. Generate a list of possible projects and group them by the similarities in their relative cost profile. 14. 4.14 Different organizations and customers look at different aspects of the LCC data. Select five projects and identify the relevant LCC aspects for each organization and customer involved. 15. 4.15 Develop a list of cost components for two projects and estimate their values. Identify the components that represent approximately 80% of the projects’ costs and discuss possible alternatives to reduce the LCC of one particular component. What might be the expected impact of the suggested alternatives? Bibliography Life-Cycle Cost Blanchard, B. S., Design and Manage to Life Cycle Cost, Matrix Press, Chesterland, OH, 1978. Cabeza, L. F., et al. “Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: a review.” Renewable and Sustainable Energy Reviews, Vol. 29, pp. 394–416, 2014. Dhillon, B. S., Life Cycle Costing: Techniques, Models and Applications, Gordon and Breach Science Publishers, New York, 1989. Earls, U. E., Factors, Formulas and Structures for Life Cycle Costing, Second Edition, Eddins-Earles, Concord, MA, 1981. Emblemsvag, J., Life-Cycle Costing: Using Activity-Based Costing and Monte Carlo Methods to Manage Future Costs and Risks, John Wiley & Sons, New York, 2003. Fabrycky, J. W. and B. S. Blanchard, Life Cycle Cost and Economic Analysis, Prentice Hall, Englewood Cliffs, NJ, 1991. Nugent, D.L. and K. S. Benjamin, “Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey.” Energy Policy, Vol. 65, pp. 229–244, 2014. Perera, H., N. Nagarur, and M. Tabucanon, “Component Part Standardization: A Way to Reduce the Life-Cycle Costs of Products,” International Journal of Production Economics, Vol. 60–61, pp. 109– 117, 1999. Riggs, J. L. and D. Jones, “Flowgraph Representation of Life-Cycle Cost Methodology: A New Perspective for Project Managers,” IEEE Transactions on Engineering Management, Vol. 37, No. 2, pp. 147–152, 1990. Spence, G., “Designing for Total Life Cycle Costs,” Printed Circuit Design, Vol. 6, No. 8, pp. 14–17, 1989. Yao, J., “A multi-objective (energy, economic and environmental performance) life cycle analysis for better building design,” Sustainability, Vol. 6, No. 2, pp. 602–614, 2014. Cost Estimation AACE, Standard Cost Engineering Terminology, American Association of Cost Engineers, Morgantown, WV, 1986. Augustine, N. R., Augustine’s Laws, Viking, Penguin, New York, 1997. Bledsoe, J. D., Successful Estimating Methods: From Concept to Bid, RSMeans, Kingston, MA, 1991. Bohem, B. W., E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B. Steece, A. W. Brown, S. Chulani, and C. Abts, Software Cost Estimation with COCOMO II, Prentice Hall, Upper Saddle River, NJ, 2000. Coombs, P., IT Project Estimation: A Practical Guide to the Costing of Software, Cambridge University Press, Cambridge, England, 2003. Emblemsvag, J., Life Cycle Costing, John Wiley & Sons, New York, 2003. Neil, J. M. (Editor), Skills and Knowledge of Cost Engineering, Second Edition, American Association of Cost Engineers, Morgantown, WV, 1988. Ostwald, P., Construction Cost Analysis and Estimating, Prentice Hall, Upper Saddle River, NJ, 2000. Pikulik, A. and H. E. Diaz, “Cost Estimating for Major Process Equipment,” Chemical Engineering, Vol. 84, p. 106, 1977. Puerifoy, R., Estimating Construction Costs, Fifth Edition, McGraw- Hill, New York, 2001. Stewart, R. D. and R. M. Wyskida, Cost Estimator’s Reference Manual, John Wiley & Sons, New York, 1987. Chapter 5 Portfolio Management— Project Screening and Selection 5.1 Components of the Evaluation Process Every new project starts with an idea. Typically, new ideas arrive continuously from a variety of sources, such as customers, suppliers, upper management, and shop floor personnel. Details of the steps involved in processing these ideas and the related analyses are highlighted in Figure 5.1. Depending on the scope and estimated costs, management may simply be interested in determining the merit of the idea or it may want to determine how best to allocate a budget among a portfolio of projects. If the organization is a consulting firm or an outside contractor, then it may want to decide on the most advantageous strategy for responding to requests for proposals (RFPs). Of course, there are many different types of projects, so the evaluation criteria and accompanying methodology should reflect the particular characteristics of the sponsoring or responding organization. The usual divisions are public sector versus private sector, research and development (R&D) versus operations, and internal customer versus external customer. Project size, expected duration, underlying risks, and required resources are some of the factors that must weigh on the decision. Regardless of the source or nature of the customer, screening is usually the first step. A proposed project is analyzed in a preliminary manner in light of the most prominent criteria or prevailing conditions. This should be a quick and inexpensive exercise. The results may suggest, for example, that no further effort is warranted as a result of uncertainty in the technology or the lack of a well-defined market. If some promise exists, then the project may be temporarily backlogged in deference to more attractive contenders. At some time in the future when conditions are more favorable, it may be desirable to re-visit the go/no go decision, or the project may be deemed so urgent or beneficial to the organization that it is placed at the top of the priority list. Alternatively, Figure 5.1 Project evaluation and selection process. Figure 5.1 Full Alternative Text results of the project screening process may indicate that the proposed project possesses some merit and deserves further investigation. If a project passes the organization screening process for evaluating new project ideas, then a more in-depth analysis should be performed with the goal of narrowing uncertainties associated with the project’s costs, benefits, and risks. In contrast to the screening process, the evaluation process usually involves extensive and in-depth data collection, the solicitation of expert opinion, sample computations, and perhaps technological forecasting. As with the screening process, several courses of action might be suggested. The proposal may be rejected or abandoned for lack of merit, it may be backlogged for later retrieval and analysis, or it may be found to be acceptable and placed on a candidate list for a comparative analysis. In some cases, it may be initiated immediately. When the results of the evaluation process indicate that a proposal passes an acceptance threshold but that it is not clearly superior to other candidates, each proposal should be assessed and ranked competitively. The relative strengths and weaknesses of each candidate project are examined carefully, and a weighted ranking is obtained. Ideally, the ranking would indicate not only the most preferred project but also the degree to which it is preferred over the other contenders. A number of assessment methodologies are presented in Sections 5.3 through 5.7 and Chapter 6. If the ranking of a particular proposal is high enough, then resources may tentatively be assigned. However, the decision to fund and initiate work on a proposal involves the full consideration of the available human and financial resources within the organization. The level of available funds and personnel skill types and the commitments to the current portfolio of activities must be factored into the decision. It may be that the new idea is so meritorious that it should replace one or more ongoing projects. If this is the case, then some ongoing project(s) will be terminated or halted temporarily so that resources can be freed up for the new project. Portfolio models have been developed to aid in making these decisions. A portfolio model determines the best way to allocate available resources among competing alternatives, including new candidates and ongoing projects. An example of such a model is presented in Chapter 13. Portfolio models are used only when multiple projects compete for the same resources. In the remainder of this chapter, we discuss methods for screening and prioritizing alternatives when resources limit the size of the portfolio. 5.2 Dynamics of Project Selection As Figure 5.1 suggests, project selection can be a very dynamic process. Screening, evaluation, prioritizing, and portfolio analysis decisions may be made at various points, and new ideas may not even go through these steps in sequence. An idea may be shelved or abandoned at any point in time. New information and changed circumstances may reverse a previous decision to reject or abandon a project. For example, efforts to develop lightweight portable computers were given a new impetus with the dramatic improvement in flat-screen display technology. Alternatively, new information or changed circumstances may cause a previously backlogged project to be rejected. The drastic reduction in the price of imported oil in the early 1980s dealt a death blow to some exotic alternative energy projects, such as coal gasification and shale oil reclamation. The available budget or labor skills within an organization may constrain the project selection process. A meritorious project may be delayed if insufficient budget is available to fund it. Alternatively, a project may be phased, and certain portions initiated while others are postponed until the financial situation becomes more favorable. Customer complaints, competitive threats, or unique opportunities may occasion an urgent need to pursue a particular idea. Depending on the urgency, the project may receive only a cursory screening and evaluation and may go directly into the portfolio. Screening, evaluation, prioritizing, and portfolio decisions may be repeated several times over the life cycle of a project in response to emerging technologies and changing environmental, financial, or commercial circumstances. The advent of a new RFP, a change in competitive pressures, and the appearance of a new technology are some factors that may cause management to reevaluate an ongoing project. Moreover, with each advance that is recorded, new technical information that may influence other efforts and proposed ideas will be forthcoming. As current projects near completion, key personnel and equipment may be released so that they can be used on another project, perhaps one that was previously backlogged for lack of appropriate resources. In general, evaluation and selection of new product ideas and project proposals is a complex process, consisting of many interrelated decisions. The complexities involve the variety of data that must be collected and the difficulty of unequivocally measuring and assessing candidate projects on the basis of information derived from these data. Much of the resultant information is subjective and uncertain in nature. Many ideas and proposals exist only as embryonic thoughts and are propelled forward by the sheer force of the sponsor’s enthusiasm. The presence of various organizational and behavioral factors tends to politicize the decision-making process. In many cases, the potential costs and benefits of a project play only a small role in the final decision. For example, an extensive two-year analysis of LANDSAT, an earth-orbiting satellite with advanced resource monitoring capabilities, concluded that the benefits to the user community would fall significantly short of the expected costs associated with operating and maintaining the system over its 10-year lifetime, even under the most optimistic of scenarios (Bard 1984). Nevertheless, pressure from National Aeronautics and Space Administration (NASA) and its congressional allies, who saw LANDSAT as a high-profile, nonmilitary application of space technology that might actually return some benefits, persuaded the U.S. Department of the Interior to provide funding. The more sophisticated analytical and behavioral tools that have been developed to aid managers in evaluating projects vary in their approach for handling nonquantitative aspects of the decision. 5.3 Checklists and Scoring Models The idea-generation stage of a project, when done properly, will often lead to more proposals than can realistically be pursued. Thus, a screening procedure designed to eliminate those proposals that are clearly infeasible or without merit must be established. Compatibility with the organization’s objectives, existing product and service lines, and resources is a primary concern. It is also important to keep in mind that when comparing alternatives early on, a wide range of criteria should be introduced in the analysis. The fact that these criteria are often measured on differing scales makes the screening and evaluation much more difficult. Of the several techniques available to aid in the screening process, perhaps the most commonly used are rating checklists. They are appropriate for eliminating the most undesirable proposals from further consideration. Because they require a relatively small amount of information, they can be used when the available data are limited or when only rough estimates have been obtained. Such methods should be viewed as expedient; they do not provide a great deal of depth and should be used with this caveat in mind. Table 5.1 presents an illustration of a checklist. In constructing a checklist, it is necessary to identify the criteria or set of requirements that will be used in making the decision. In the next step, a (arbitrary) scoring scale is developed to measure how well a project does with respect to each criterion. Words such as “excellent” and “good” may be associated with the numerical values [see Gass (2001) for a more complete discussion of several issues related to the choice of scales and their effect on rankings]. TABLE 5.1 An Example of a Checklist for Screening Projects Criteria Profitability Time tomarket Development risks Commercial success Score: 3 2 1 3 2 1 3 2 1 3 2 1 Totalscore Project A × × × × 10 Project B × × × ×  6 Project C × × × ×  8 In the example displayed in Table 5.1, the criteria include profitability, time to market, development risks, and commercial success. Each candidate is evaluated subjectively and scored using a 3-point scale. The built-in assumption is that each criterion is weighted equally. Total scores are displayed in the rightmost column. Typically, a cutoff point or threshold is specified below which the project is abandoned. Of those that exceed the threshold, the top contenders are held for further analysis, whereas the remainder are backlogged or shelved temporarily. Here, if 7 is specified as the threshold total score, then only projects A and C would be pursued. An alternative means of displaying the information in Table 5.1 is a multidimensional diagram known as a polar graph (Canada et al. 1996), shown in Figure 5.2. In one sense, this type of representation is more efficient than a table because it allows the analyst quickly to ascertain the presence of dominance. For example, by noting that the performance measure surface of project B is completely within that of project A, we can conclude that B is no better than A on any dimension and thus can be discarded or backlogged. Figure 5.2 Multidimensional diagram for checklist example. Figure 5.2 Full Alternative Text Scoring models extend the logic of checklists by assigning a weight to each criterion that signifies the relative importance of one to the other (Baker 1974, Hobbs 1980, Souder and Mandakovic 1986). A weighted score is then computed for each candidate. In deriving the weights, a team approach should be used to head off disagreement after the assessment. One way of accomplishing this is to list all criteria in descending order of importance. Next, assign the least important (last-listed) criterion a value of 10, and assign a numerical weight to each criterion on the basis of how important it is relative to this one. A criterion considered to be twice as important as the least important criterion would be assigned a weight of 20. If team members cannot agree on specific values, then sensitivity analysis should be performed. An example of the use of a scoring model for screening projects associated with the development of new products is shown in Table 5.2. Here eight criteria are to be rated on a numerical scale of 0 to 30, where 0 means poor and 30 means excellent. Because this scale is arbitrary, no significance should be placed on relative values. For convenience, the weights are scaled between 0 and 1. In general, the factor score for project j, call it T j , is obtained by multiplying the relative weights, w i for criterion i, by the ratings, s ij , and summing. That is, T j = ∑ i w i S ij (5.1) TABLE 5.2  An Example of a Scoring Model for Screening Projects Rating Criteria Relativeweight Excellent 30 Good 20 Fair 10 Poor 0 Factor score Marketability 0.20 ×  6 Risk 0.20 ×  4 Competition 0.15 ×  3 Value added 0.15 ×  0 Technical opportunities 0.10 ×  3 Material availability 0.10 ×  1 Patent protection 0.05 ×  0 Current products  0.05  ×   1  Total 1.00 18 In this example, the project under consideration received a factor score of 18. A variety of other formulas have been proposed for deriving the relative weights. Three of the simplest are presented below. More elaborate schemes are discussed in the next chapter. 1. Uniform or equal weights. Given N criteria, the weight for each is w i = 1 N 2. Rank sum weights. If R i is the rank position of criterion i (with 1 as the highest rank) and there are N criteria, then rank sum weights for each criterion may be calculated as w i = N− R i +1 ∑ k=1 N ( N− R k +1 ) where the denominator is the sum of the first N integers; that is, N( N+1 )/2. 3. Rank reciprocal weights. These weights may be calculated as w i = 1/ R i ∑ k=1 N 1/ R k The advantage of a scoring model is that it takes into account the tradeoffs among the criteria, as defined by the relative weights. The disadvantage is that it lacks precision and relies on an arbitrary scoring system. An environmental scoring form developed by Niagara Mohawk, a New York utility, is depicted in Table 5.3. Note that the procedure for assigning points is specified. 5.4 Benefit-Cost Analysis Evaluation of the merits of alternative investment opportunities begins with technical feasibility. The next step involves a comparison at some minimum attractive rate of return (MARR) of the estimated stream of costs and benefits over the expected TABLE 5.3  Environmental Scoring Form Used by Niagara Mohawk Points, P Environmental attributes Weight, W 0 1 2 3 4 Air emissions  Sulfur oxides (lb/MWh) 7 >6 4.0–6.0 2.5–3.9
1.5-
2.4 0.5–1.4
 Nitrogen
oxides
(lb/MWh)
16 >6 4.0–6.0 2.5–3.9 1.5–2.4 0.5–1.4
 Carbon
dioxide
(lb/MWh)
3 >1500 1050–1500 650–1049
250–
649 100–249
 Particulates
(lb/MWh) 1 >0.3 0.2–0.3 0.1–0.199
0.05–
0.099
0.01–
0.049
Water effects
 Cooling
water flow
(annual intake 1 80–100 60–79 40–59 20–39 5–19

as % of lake
volume)
 Fish
protection 1 None
Operational
restrictions
Fish
protection
 NY State
water quality
classification of
receiving water
1 A orbetter B C+ C+ D
Land effects
 Acreage
required
(acres/MW)
1 0.3–0.5 0.2–0.29 0.1–0.19
0.05–
0.09 0.01–0.05
 Terrestrial 1
Unique
ecological
or
historical
value
Rural or
low-
density
suburban
Industrial
area
 Visual
aesthetics 1
Highly
visible
Within
existing
developed
area
Not
visible
from
public
roads
 Transmission 2 New OH>5 miles
New
OH 1–5
miles
New UG
>5 miles
New
UG
1–5
miles
Use
existing
facilities
 Noise ( L eq
−backgrd L 90
)
2 5–10 0–4.9
 Solid waste
disposal
(lb/MWh)
2 >300 200–300 100–199
50–
99 10–49

 Solid waste
as fuel (% of
total Btu)
1 0 1–30 31–50 51–
80
81–90
 Fuel delivery
method 1
New RR
spur
Truck
and
existing
RR
New
pipeline Barge
Use
existing
pipeline
 Distance
from receptor
area (km)
1 <10 10–39 40–69 70–100 >100
Total score
economic life of each project. Engineering studies must be undertaken to
establish the fundamental data. The estimated benefits and costs are then
compared, usually on a present value basis, using a predetermined discount
rate.
In the private sector, the firm generally pays all of the costs and receives all
of the benefits, both quantitative and qualitative. Replacing an outdated piece
of equipment is an example in which the returns are measurable, whereas
constructing a new company cafeteria illustrates the opposite case. Where the
activities of government are concerned, however, a different situation arises.
Revenues are received through various forms of taxation and are supposed to
be spent “in the public interest.” Thus, the government pays but receives very
few, if any, benefits. This can present all sorts of problems. For one, it means
that the intended beneficiaries of a federal project will be very anxious to get
the project approved and funded. Such situations may induce otherwise
virtuous people to redefine the standards of acceptable ethical behavior. A
second problem concerns the measurement of benefits, which are often
widely disbursed. Other difficulties include the selection of an interest rate
and choosing the correct viewpoint from which the analysis should be made.
Finally, in the benefit-cost (B/C) analysis, where the B/C ratio is used to rank
competing projects, there may be legitimate ambiguity in deciding what goes
in the numerator and what goes in the denominator of the ratio.
At first glance, it would seem to be a simple matter of sorting out the
consequences into benefits (for the numerator) or costs (for the denominator).

This works satisfactorily when applied to projects for a firm or a person. In
government projects it may be considerably more difficult to classify the
various consequences, as shown in Example 5-1.
Example 5-1
On a proposed government project, the following consequences have been
identified:
Initial cost of project to be paid by government is $100K.
Present worth (PW) of future maintenance to be paid by government is
$40K.
PW of benefits to the public is $300K.
PW of additional public users costs is $60K.
Show the various ways of computing the B/C ratio.
Solution
Putting the benefits in the numerator and all costs in the denominator gives
B/C ratio= All benefits All costs = 300 100+40+60 = 300 200 =1.5
An alternative computation is to consider user costs as disbenefits and to
subtract them in the numerator rather than add them in the denominator:
B/C ratio= public benefits−public costs government costs = 300−60 100+40
= 240 140 =1.7
Still another variation would be to consider maintenance costs as disbenefits:
B/C ratio= 300−60−40 100 = 200 100 =2.0

It should be noted that although three different B/C ratios may be computed,
the net present value (NPV) does not change:
NPV=PW of benefits−PW of costs=300−60−40−100=100.
There is no inherently correct way to compute the B/C ratio. Using the
notation of Chapter 3, two commonly used formulations are given below:
1. Conventional B/C
B/C= PW of benefits to user PW of total costs to supplier = PW[ B ]
PW[ CR+( O+M ) ] (5.2a)
or
B/C= Annual worth ( AW ) of benefits to user
AW of total costs to supplier = B CR+( O+M ) (5.2b)
where
B=AW of benefits to user
CR=capital recovery cost (equivalent annual cost of initial
investment, considering any salvage value)
O=equivalent uniform annual operating cost
M=equivalent uniform maintenance cost
2. Modified B/C
B/C= PW[ B−( O+M ) ] PW[ CR ]  or B/C= B−( O+M ) CR
The modified method has become more popular with governmental agencies
and departments over the last decade. Although both methods yield the same
recommendation when comparing mutually exclusive alternatives, they may
yield different rankings for independent investment opportunities. In either
case, using PW, AW, or future worth (FW) should always provide the same
results.

Example 5-2 
(Single-Project Analysis)
An individual investment opportunity is deemed to be worthwhile if its B/C
ratio is greater than or equal to 1. Consider the project of installing a new
inventory control system with the following data:
Initial cost $20,000
Project life 5 years
Salvage value $4,000
Annual savings $10,000
Operating & Maintenance disbursements $4,400
MARR 15%
By interpreting annual savings as benefits, the conventional and modified
B/C ratios based on annual equivalents are computed as follows:
CR= $20,000( A/P, 15%, 5 )−$4,000( A/F, 15%, 5 )
=20,000(0.2983)−4,000(0.1483)=$5,373
conventional B/C= B CR+( O+M ) = $1,0000 $5,373+$4,400 =1.02
modified B/C= B−( O+M ) CR = $10,000−$4,400 $5,373 =1.04
Because either B/C is greater than 1, the investment is worthwhile.
Nevertheless, there is an opportunity cost associated with the investment that
may preclude other possibilities. The fact that the B/C of a project is greater
than 1 does not necessarily mean that it should be pursued.
Example 5-3 
(Comparing Mutually Exclusive Alternatives)
As was true for rate of return (ROR) calculations, when comparing a set of
mutually exclusive alternatives by any B/C method, an incremental approach

is preferred. The principles and criterion of choice as explained in Chapter 3
apply equally to B/C methods, the only difference being that each increment
of cost (the denominator) must be justified by a B/C ratio≥1.
Consider the data in Table 5.4a associated with the four alternative projects
used in Example 3.9 to demonstrate the internal rate of return (IRR) method.
Each is listed in increasing order of investment. The symbol Δ( B/C ) means
that the B/C ratio is being computed on the incremental cost. Once again, a
MARR of 15% is used.
TABLE 5.4  Input Data and
Results for Incremental
Analysis
Project
(a) Input data A B C D
Initial cost $20,000 $30,000 $35,000 $43,000
Useful life 5 years 10years 5 years 5 years
Salvage value $4,000 0 $4,000 $5,000
Annual receipts $10,000 $14,000 $20,000 $18,000
Annual disbursements  $4,400  $8,600  $9,390  $5,250
  Net annual receipts
−disbursements $5,600 $5,400 $10,610 $12,750
(b) Results A A→B A→C C→D
ΔInvestment $20,000 $10,000 $15,000 $8,000
ΔSalvage 4,000 −4,000 0 1,000
ΔCR=ΔC 5,373 605 4,477 2,386
Δ( annual receipts
−disbursements )=ΔB 5,600 −200 5,010 2,140
Δ( B/C )=ΔB/ΔC 1.04 −0.33 1.12 0.90

Is Δinvestment justified? Yes No Yes No
The output data in Table 5.4b confirm the results previously found using the
IRR method. Alternative C would be chosen given that it is the most
expensive project for which each increment of cost is justified (by
B/C ratio≥1 ).
B/C studies within the public sector in particular may be approached from
several points of view. The perspective taken may have a significant impact
on the outcome of the analysis. Possible viewpoints include
1. That of the governmental agency conducting the study
2. That of the local area (e.g., town, municipality)
3. The nation as a whole
4. The targeted industry
Thus, it is essential that the analyst have clearly in mind which group is being
represented before proceeding with the study. If the objective is to promote
the general welfare of the public, then it is necessary to consider the impact
of alternative policies on the entire population, not merely on the income and
expenditures of a selected group. Practically speaking, however, without
regulations, the best that can be hoped for is that the broader interests of the
community will be taken into account. Most would agree, for example, that
without environmental and health regulations and the attendant threat of
prosecution, there would be little incentive for firms to treat their waste
products before discharging them into local waterways.
The national viewpoint would seem to be the correct one for all federally
funded public works projects; however, most such projects provide benefits
only to a local area, making it difficult, if not impossible, to trace and
evaluate quantitatively the national effects. The following example parallels
an actual case history.
Example 5-4

The government wants to decide whether to give a $5,000,000 subsidy to a
chemical manufacturer who is interested in opening a new factory in a
depressed area. The factory is expected to generate jobs for 200 people and
further stimulate the local economy through commercial ventures and tourist
trade. The benefits as a result of jobs created and improved trade in the area
are estimated at $1,000,000 per year. Six percent is considered to be a fair
discount rate. The study period is 20 years. Calculate the B/C ratio to
determine whether the project is worthwhile.
Solution
PW of benefits= $1,000,000( P/A, 6%, 20 )=$11,470,000
B/C ratio= $11,470,000 $5,000,000 =2.3
Outcome
The plant was funded on the basis of the foregoing study, but pollution
control equipment was not installed. During operations, raw by-products
were dumped into the river, causing major environmental problems
downstream. Virtually all of the fish died, and the river became a local health
hazard. The retrofitting of pollution control equipment sometime later made
the entire project uneconomical, and the plant eventually closed.
Conclusion
Because the full costs of the project were not taken into account originally,
the results were overly optimistic and misleading. Had the proper viewpoint
been established at the outset and all of the factors considered, the outcome
might not have been so unfortunate.
5.4.1 Step-by-Step Approach

To conduct a benefit-cost (B/C) analysis for an investment project, it is
important to complete the following steps:
1. Identify the problem clearly.
2. Explicitly define the set of objectives to be accomplished.
3. Generate alternatives that satisfy the stated objectives.
4. Identify clearly the constraints (e.g., technological, political, legal,
social, financial) that exist with the project environment. This step will
help narrow the alternatives generated.
5. Determine and list the benefits and costs associated with each
alternative. Specify each in monetary terms. If this cannot be done for all
factors, then this should be stated clearly in the final report.
6. Calculate the B/C ratios and other indicators (e.g., present value, ROR,
initial investment required, payback period) for each alternative.
7. Prepare the final report comparing the results of the evaluation of each
alternative examined.
5.4.2 Using the Methodology
As with any decision-making process, the first two steps above are to define
the problem and related goals. This may involve identifying a particular
problem to be solved (e.g., pollution) or agreeing on a specific program, such
as landing an astronaut on the moon. Once this is done, it is necessary to
devise a solution that is feasible, not only technically and economically but
also politically.
Implicit in these steps is a twofold selection process: a macro-selection
process whereby we choose from among competing opportunities or
programs (should more federal funds be expended on space research or
pollution cleanup and control?) and a micro-selection process whereby we
strive to find the best of several alternatives (should we build a nuclear- or

coal-fired plant?).
5.4.3 Classes of Benefits and Costs
Once a set of alternatives has been established, the detailed analysis can
begin. The benefits and costs may be broken down into four classes: primary,
secondary, external, and intangible. Primary refers to benefits and costs that
are a direct result of a particular project. If a corporation manufactures
videocassette recorders, then the primary costs are in production, and the
primary benefits are in profits. In building a canal, the construction costs and
the revenues generated from water charges are the primary elements.
“Secondary” benefits and costs are the marginal benefits and costs that accrue
when an imperfect market mechanism is at work. In such instances, the
market prices of a project’s final goods and services do not reflect the “true”
prices. The use of government funds to build and maintain airports is a good
example. There is a hidden cost to society as well as a hidden benefit to the
airlines and their more frequent customers. Increased noise pollution and
traffic congestion around the airport are illustrative of the costs; benefits can
be measured by lower airfares.
External benefits and costs are those that arise when a project produces a
spillover effect on someone other than the intended group. Thus, a
government subsidy to airports produces external benefits by indirectly
boosting the local economy. Massive government spending on space has
yielded extensive benefits to medical science and the microelectronics
industry. Similarly, there are spillover effects of pollution that produce
disutilities in the form of health costs and the loss of recreational facilities.
Intangible benefits and costs are those that are difficult, if not impossible, to
measure on a monetary scale. Examples of intangible benefits include
trademarks and goodwill, whereas examples of intangible costs include costs
associated with increased urban congestion. If intangibles dominate the
decision process, the value of multiple-criteria methods such as multi-
attribute utility theory and the analytic hierarchy process, discussed in
Chapter 6, increases.

After categorizing the benefits and costs in this manner, they should be
allocated to the various stages in a project in which they are expected to
occur. A typical project includes stages such as planning, implementation,
operation, and closeout. This distinction is necessary for proper quantitative
evaluation. For example, the costs associated with noise, traffic disruption,
and hazards of subway construction may occur only in the implementation
stage and must be discounted accordingly.
5.4.4 Shortcomings of the Benefit-
Cost Methodology
Upon completion of the quantitative assessment of the various costs and
benefits, the actual desirability of the project can be determined. Use of the
B/C ratio to rank the best alternative can be deceptive, however, because it
disguises the problem of scale. Two projects may have the same ratio yet
involve benefits and costs that differ by millions of dollars, or one project
may have a lower ratio than another and still possess greater benefits.
Sometimes, therefore, projects will be selected simply on the basis of whether
their benefits exceed their costs; yet again, scale must be considered, for two
projects obviously can have the same net benefit, but one may be far more
costly than the other.
As mentioned, another way to evaluate projects is to compare the expected
ROR on investment with the interest rate on an alternative use of the funds.
This criterion is implicit in most private-sector decisions but generally is
neglected in the public sector, where tangible financial returns are not the
sole criterion for investment allocations. Moreover, there is rarely a
consensus on which discount rate should be used. Economists invariably
dispute the choice, some arguing for the social rate of time preference,
whereas others lean toward the prevailing interest rate. Except when a
particular rate is specified by the decision maker, the NPV calculations
should be repeated using several values to ascertain sensitivity effects.
The difficulty in agreeing on a discount rate is usually secondary to the
problem of determining future costs and benefit streams. Uncertainties in

long-term consequences may be large for extended time horizons of more
than a few years, although frequently, all alternatives will suffer from a
similar fate. Investigating questions of inter-temporal equity and methods for
dealing with uncertain outcomes are central problems of research, and their
logic must be pursued relentlessly. Moreover, all forms of decision making
must resolve these questions, regardless of whether they are dealt with
explicitly.
In practice, it is rare that any one criterion will suffice for making a sound
decision. Several criteria, as well as their many variations, must be examined
in the analysis. The important point, however, is that even if all relevant
factors are addressed, the analysis will still possess a high degree of
subjectivity, leaving room for both conscious and unacknowledged bias. This
leads to the two major shortcomings of B/C analysis.
The first is the need and general failure to evaluate those items that are
unquantifiable in monetary terms. The type of question that continually gets
raised is, “How do you measure the value of harmony between labor and
management?” or “What is the value of a pollution-free environment?” The
development of indicators other than those that reflect dollar values explicitly
present a considerable challenge to analysts. They must depart from the
familiar criteria of economic efficiency as a prime mechanism of evaluation
and venture into the unknown areas of social and environmental concerns.
Interestingly enough, the nonquantifiable elements bear equally on the
governmental, business, and consumer sectors of the economy. In short, these
“unmeasurable” elements may be of utmost significance, as system indicators
must be developed to evaluate their impact on the program. It is here where
judgment and subjectivity come into play.
The second weakness in the practice of B/C analysis arises from the “judge
and jury” characteristic. Invariably, the same organization (either in a private
company or a government agency) that proposes and sponsors a particular
project undertakes the analysis. Whether this is done internally or by a
subcontractor is not important. Rather, the organization and its contractors
will usually display similar attitudes and biases in their approach to a
problem. Independent, unbiased assessments are needed if the process is to
work correctly and produce believable results.

5.5 Cost-Effectiveness Analysis
When comparing two projects that have the same B/C ratio, the one that costs
more will provide greater returns. In some situations, though, there may be a
fixed or upper limit on the budget, so a project that is technically feasible
may not be economically feasible even if it has a high B/C ratio. Economic
barriers to entry are common in many fields, such as automotive or
semiconductor manufacturing where the required initial investment may be as
high as $1 billion.
In the case in which the budget is the limiting factor, a cost-effectiveness (C-
E) study is often performed to maximize the value of an organization’s
investment. In a C-E study, the focus is the performance of the proposed
system (i.e., project) as measured by a composite index that is necessarily
subjective in nature. This is because incommensurable and qualitative factors
such as development risk, maintainability, and ease of use all must be
evaluated collectively.
In general, system effectiveness can be thought of as a measure of the extent
to which a system may be expected to achieve a set of specific mission
requirements. It is often denoted as a function of the system availability,
dependability, and capability.
Availability is defined as a measure of the system condition at the start
of a mission. It is a function of the relationship among hardware,
personnel, and procedures.
Dependability is defined as a measure of the system condition at one or
more points during mission operations.
Capability accounts specifically for the performance spectrum of the
system.
The term effectiveness can be difficult to define precisely. For a product or
service, one definition would be the ability to deliver what is called for in the

technical specification. Among the terms that are related to (or have been
substituted for) effectiveness are value, worth, benefit, utility, gain, and
performance. Unlike cost, which can be measured in dollars, effectiveness
does not possess an intrinsic measure by which it can be uniquely expressed.
Government agencies, in particular, the U.S. Department of Defense, have
been prominent users of C-E analyses. The following eight steps represent a
common blueprint for conducting a C-E study:
1. Define the desired goals.
2. Identify the mission requirements.
3. Develop alternative systems.
4. Establish system evaluation criteria.
5. Determine capabilities of alternative systems.
6. Analyze the merits of each.
7. Perform sensitivity analysis.
8. Document results and make recommendations.
A critical step in the procedure is in deciding how the merits of each
alternative will be judged. After the evaluation criteria or attributes are
established, a mechanism is needed to construct a single measure of
performance. Scoring models, such as those described in Section 5.3, are
commonly used. Here, we assess the relative importance of each system
attribute and assign a weight to each. Next, a numerical value, say between 0
and 100, is assigned to represent the effectiveness of each attribute for each
system. Once again, these values are subjective ratings but may actually be
based on simple mathematical calculations of objective measures, subjective
opinion, or engineering judgments. Where an appropriate physical scale
exists, the maximum and minimum values can be noted and a straight line
between those boundaries can be used to translate outcomes to a scale of 0 to
100. The analyst must ensure that the actual value of the attribute corresponds

to the subjective description; for example, 100≥excellent≥80; 80>good≥60.
In many cases it is useful to compare attribute relative values graphically to
determine whether any obvious errors exist in data entry or logic. Figure 5.3
provides a visual comparison of the ratings of each of five attributes for four
systems. The corresponding data are displayed in Table 5.5.
Figure 5.3
Relative effectiveness of systems.
TABLE 5.5  Data for C-E

Analysis
System 1 System 2 System 3 System 4
Attribute Weight EFF WT EFF WT EFF WT EFF WT
A. Efficiency 0.32 85 27.2 80 25.6 75 24.0 60 19.2
B. Speed 0.24 85 20.4 60 14.4 80 19.2 95 22.8
C. User
Friendly 0.24 85 20.4 50 12.0 70 16.8 90 21.6
D. Reliability 0.12 50  6.0 80  9.6 80  9.6 99 11.9
E.
Expandability 0.08 85  6.8 90  7.2 70  5.6 50  4.0
 Total
effectiveness 80.8 68.8 75.2 79.5
 Costs $450K $250K $300K $350K
At this point in the analysis, two sets of numbers have been developed for
each attribute i: the normalized weights, w i , and the perceived effectiveness
assigned to each system j for each attribute i, s ij . To arrive at a composite
measure of effectiveness, T j , for each system j, we could use Eq. (5.1). The
highest value of T would indicate the system with the best overall
performance.
If this system were within budget and none of its attribute values were below
a predetermined threshold, then it would represent the likely choice.
Nevertheless, effectiveness alone does not tell the entire story, and, whenever
possible, the analysis should be extended to include costs as well. In a similar
manner, cost factors can be combined into a single measure to compare with
effectiveness. Typically, procurement, installation, and maintenance costs are
considered. When the planning horizon extends beyond one year, the effects
of time should be included through appropriate discounting. Table 5.5
contains this information.
The final step of the C-E methodology compares system effectiveness and
costs. A graphical representation may be helpful in this regard. Figure 5.4

plots the two variables for each system (the unlabeled points represent
systems not contained in Table 5.5). The outer envelope denotes the efficient
frontier. Any system that is not on this curve is dominated by one or a
combination of two or more systems, implying that it is inferior from both a
cost and an effectiveness point of view. Systems that fall below the dashed
line (predetermined threshold) are arbitrarily deemed unacceptable. Finally,
note the relationship between systems 1 and 4. Although system 1 has the
highest effectiveness rating, it is only marginally better than system 4. The
fact that it is almost 30% more expensive, however, makes its selection
problematic, as an incremental analysis would indicate.
Figure 5.4

Relationship between system effectiveness and cost.
Figure 5.4 Full Alternative Text

5.6 Issues Related to Risk
In designing, building, and operating large systems, engineers must address
such questions as, “What can go wrong, and how likely is it to happen?”
“What range of consequences might there be, when, and how could they be
averted or mitigated?” “How much risk should be tolerated or accepted
during normal operations, and how can it be measured, reduced, and
managed?”
Formal risk analysis attempts to quantify answers to these questions (Bell
1989, Kaplan and Garrick 1981). In new systems, it is coming to be accepted
as a way of comparing the risks inherent in alternative designs, spotlighting
the high-risk portion of a system, and pointing up techniques for attenuating
those risks. For older systems, risk analysis conducted after systems have
been built and operated have often revealed crucial design faults. One such
fault cost the lives of 167 workers on the British oil production platform
Piper Alpha in the North Sea several years ago. A simple gas leak in the $3
billion rig led to a devastating explosion. The platform had a vertical
structure, and risk analysis was not done on the design. Workers’
accommodations were on top, above the lower compartments, which housed
equipment for separating oil from natural gas. The accommodations were
thought to be immune to mishap, but as a post-accident computer simulation
revealed, the energy from the explosion in the lower level coupled to the
platform’s frame. Stress waves were dissipated effectively into the water
below, but in short order, reflections at the steel–air interface at the upper
levels expanded, weakened, and shattered the structure. In contrast,
Norwegian platforms, which are designed using government-mandated risk
analysis, are long and horizontal like aircraft carriers, with workers’
accommodations at the opposite end of the structure from the processing
facilities and insulated from them by steel doors.
Analysts define risk as a combination of the probability of an undesirable
event and the magnitude of every foreseeable consequence (e.g., damage to
property, loss of money, and delay in implementation). The consequences
considered can range in seriousness from mild setback to catastrophic. Some

related definitions are given in Table 5.6.
TABLE 5.6 Some Definitions
Related to Risk
Term Definition
Failure Inability of a product or system to perform itsrequired function.
Quality
Assurance
Probability that a product or system will perform its
intended function when tested.
Reliability
Probability that a product or system will perform its
intended function for a specified time duration
(assuming under normal conditions).
Risk
A blend of the probability of failure and the
monetary outcome (or equivalent) associated with
failure.
Risk
Assessment
Processes and procedures for identifying and
quantifying risks.
Risk
Management
Techniques used to minimize risk either through
reducing the probability of a failure or reducing the
impact of a failure.
Uncertainty
A measure of the limits of knowledge in a technical
area; for example, uncertainty may be expressed by a
statistical confidence interval (a measure of sampling
accuracy).
The first step in risk analysis is to tabulate the various stages or phases of a
system’s mission and list the risk sensitivities in each phase, including
technical, human, and economic risks. The time at which a failure occurs may
mitigate its consequences. For example, a failure in an air traffic control
system at a major airport would disrupt local air traffic far more at weeknight
rush hour than on a Sunday morning. Similarly, a failure in a chemical
processing plant would be more dangerous if it interfered with an

intermediate reaction that produced a toxic chemical than if it occurred at a
stage when the by-products were more benign.
Next, for each phase of the mission, the system’s operation should be
diagrammed and the logical relationships of the components and subsystems
during that phase determined. The most useful techniques for the job are
failure modes and effects analysis (FMEA), event tree analysis, and fault tree
analysis (Kumamoto and Henley 2001). The three complement one another,
and when taken together, help engineers identify the hazards of a system and
the range of potential consequences. The interactions are particularly
important because one piece of equipment might be caused to fail by
another’s failure to, say, supply fuel or current.
For engineers and managers, the chief purpose of risk analysis—defining the
stages of a mission, examining the relationships between system parts, and
quantifying failure probabilities—is to highlight any weakness in a design
and identify those that contribute most heavily to delays or losses. The
process may even suggest ways of minimizing or mitigating risk.
A case in point is the probabilistic risk analysis on the U.S. space shuttle’s
auxiliary power units, completed for NASA in December 1987 by the
engineering consulting firm Pickard, Lowe & Garrick. The auxiliary power
units, among other tasks, throttle the orbiter’s main engines and operate its
wing ailerons. NASA engineers and managers, using qualitative techniques,
had formerly judged fuel leaks in the three auxiliary fuel units “unlikely” and
the risks acceptable, without fully understanding the magnitude of the risks
that they accepted, even though a worst-case consequence could be the loss
of the vehicle. One of the problems with qualitative assessment is that
subjective interpretation of words such as “likely” and “unlikely” allows
opportunity for errors in judgment about risk. For example, NASA had
applied the word “unlikely” to risks that ranged from 1:250 to 1:20,000.
The probabilistic risk analysis revealed that although the probability of
individual leaks was low, there were so many places where leaks could occur
that five occurred in the first 24 shuttle missions. Moreover, in the ninth
mission on November 28, 1983 the escaping fuel self-ignited while the
orbiter was hurtling back to earth and exploded after it had landed.

The probabilistic analysis pinpointed the fact that an explosion was more
likely to occur during landing than during launch, when the auxiliary power
units are purged with nitrogen to remove combustible atmospheric oxygen. It
also suggested several ways of reducing the risk, such as changing the fuels
or placing fire barriers between the power units.
5.6.1 Accepting and Managing Risk
Once the risks are determined, managers must decide what levels are
acceptable on the basis of economic, political, and technological judgments.
The decision can be controversial because it necessarily involves subjective
judgments about costs and benefits of the project, the well-being of the
organization, and the potential damage or liability.
Naturally, risk is tolerated at a higher level if the payoffs are high or critical
to the organization. In the microcomputer industry, for example, where
product lifetimes may be no greater than 1 or 2 years and new products and
upgrades are being introduced continually, companies must keep pace with
the competition or forfeit market share. Whatever the level of risk finally
judged acceptable, it should be compared with and, if necessary, used to
adjust the risks calculated to be inherent in the project. The probability of
failure may be reduced further by redundant or standby subsystems or by
parallel efforts during development. Also, managers should prepare to
counter the consequences of failure or setbacks by devising contingency
plans or emergency procedures.
5.6.2 Coping with Uncertainty
Two sources of uncertainty still need to be considered: one intrinsic in
probability theory and the other born of all-too-human error. First, the laws of
chance exclude the prediction of when and where a particular failure may
occur. That remains true even when enough statistical information about the
system’s operation exists for a reliable estimate of how likely it is to fail. The
probability of failure, itself, is surrounded by a band of uncertainty that

expands or shrinks depending on how much data are available and how well
the system is understood. This statistical level of confidence is usually
expressed as a standard deviation about the mean or a related measure.
Finally, if the system is so new that few or no data have been recorded for it
and analogous data from similar systems must be used to get a handle on
potential risks, then there is uncertainty over how well the estimate resembles
the actual case.
At the human interface, the challenge is to design a system so that it will not
only operate as it should, but also leave the operator little room for erroneous
judgment. Additional risk can be introduced if a designer cannot anticipate
which information an operator may need to digest and interpret under the
daily pressures of the job, especially when an emergency starts to develop.
From an operational point of view, poor design can introduce greater risk,
sometimes with tragic consequences. After the U.S.S. Vincennes on July 3,
1988, mistook Iran Air Flight 655 for an enemy F-14 and shot down the
airliner over international waters in the Persian Gulf, Rear Admiral Eugene
La Roque blamed the calamity on the bewildering complexity of the Aegis
radar system. He is quoted as saying that “we have scientists and engineers
capable of devising complicated equipment without any thought of how it
will be integrated into a combat situation or that it might be too complex to
operate. These machines produce too much information and don’t sort the
important from the unimportant. There’s a disconnection between technical
effort and combat use.”
All told, human behavior is not nearly as predictable as that of an engineered
system. Today, there are many techniques for quantifying with fair reliability
the probability of slips, lapses, and misperceptions. Still, remaining
uncertainty in the prediction of individual behavior contributes to residual
risk in all systems and projects.
5.6.3 Non-probabilistic Evaluation
Methods when Uncertainty Is

Present
When considering a capital investment, there are four major sources of
uncertainty that are nearly always present in engineering economic studies:
1. Inaccuracy of the cash flow estimates, especially benefits related to new
products or technology.
2. Relationship between type of business and future health of the company.
Certain lines of business are inherently unstable, such as oil drilling,
entertainment, and luxury goods.
3. Type of physical plant and equipment involved. Some structures have
definite economic lives and market values, whereas others are
unpredictable. The cost of specialized plants and equipment is often
difficult to estimate, especially for first-time projects.
4. Length of the project and study period. As the length increases, so does
the variability in the estimates of operations and maintenance costs, as
well as presumed benefits.
As discussed in Chapter 3, breakeven analysis and sensitivity analysis are
two simple ways of addressing uncertainty. Other approaches include
scenario analysis, risk-adjusted MARR, and reduction of useful life.
Breakeven analysis is commonly used when the selection process is
dependent on a single factor, such as capacity, sales, or ROR, and only two
alternatives are being considered. In this case, we identify the one whose
marginal benefit is greater and solve for the value of the factor that makes the
two alternatives equally attractive. Above the breakeven point, the alternative
with the greater marginal benefit is preferable.
Sensitivity analysis is aimed at assessing the relative magnitude of a change
in the measure of interest, such as NPV, caused by one or more changes in
estimated factors, such as interest rate and useful life. The results can often be
visualized graphically, as shown in the following example.

Example 5-5
(Sensitivity Analysis)
Your office is considering the acquisition of a new workstation, but there is
some uncertainty about which model to buy and the expected cash flows.
Before making the investment, your supervisor has asked you to investigate
the NPV of a generic system over a range of ±$40% with respect to (a)
capital investment, (b) annual net cash flow, (c) salvage value, and (d) useful
life. The following data characterize the investment:
Capital investment −$11,500
Annual revenues $5,000
Annual expenses −$2,000
Estimated salvage value $1,000
Useful life 6 years
MARR 10%
Solution
The first step is to compute the NPV for the given data.
Baseline NPV=− $11,500+$3,000( P/A, 10%, 6 )+$1,000( P/F, 10%, 6
)=$2,130
1. When initial investment varies by ±p%,
NPV( p )=−( 1+p/100 )( $11,500 )+$3,000( P/A, 10%, 6 )
+$1000(P/F,10%,6)
2. When revenues vary by ±p%,
NPV( p )=−$11,500+( 1+p/100 )( $3,000 )( P/A, 10%, 6 )
+$1000(P/F,10%,6)

3. When salvage value varies by ±p%,
NPV( p )=−$11,500+$3,000( P/A, 10%, 6 ) +(1+p/100)($1,000)
(P/F,10%,6)
4. When the useful life varies by ±p%,
NPV( p )=−$11,500+$3,000[ P/A, 10%, 6( 1+p/100 ) ] +$1,000[
P/F, 10%, 6( 1+p/100 ) ]
Plotting the functions NPV(p) for −40%≤p≤+40%, gives rise to what is
known as a spider chart, as shown in Figure 5.5. A frame of references is
provided by the baseline result.
Figure 5.5
Spider chart for sensitivity analysis.
Figure 5.5 Full Alternative Text

Scenario analysis, or optimistic-pessimistic estimation, is used to establish a
range of values for the measure of interest. Typically, the optimistic estimate
is defined to have only a 5% chance of being exceeded by the actual
outcome, whereas the pessimistic estimate is defined so that it is exceeded
approximately 95% of the time.
Example 5-6
(Scenario Analysis)
An ultrasound inspection device for which optimistic, most likely, and
pessimistic estimates are given below is being considered for purchase. If the
MARR is 8%, then what course of action would you recommend? Base your
answer on net annual worth (NAW).
Measure Optimistic (O) Most likely (M) Pessimistic (P)
Capital investment −$150,000 −$150,000 −$150,000
Annual revenues  $110,000  $70,000   $50,000
Annual costs −$20,000 −$43,000 −$57,000
Salvage value    $0    $0    $0
Useful life 18 years 10 years 8 years
NAW $73,995 $4,650 −$33,100
Solution
Whether to accept or reject the purchase is somewhat arbitrary, and would
depend strongly on the decision maker’s attitude toward risk. A conservative
approach would be to
accept the investment if NAW( P )>0
reject the investment if NAW( O )<0 or do more analysis Applying this rule tells us that more information is needed. One possible approach at this point is to evaluate all combinations of outcomes and see how many are above some threshold, say $50,000, and below, say $0. Following this idea, we note that annual revenues, annual costs, and the useful life are the independent inputs that vary from one scenario to another. This means that there are 3 3 =27 possible outcomes. The NAW of each is listed in the table below rounded to the nearest $1,000. For example, the first block of 9 data entries represents the results when the annual revenues and useful life are varied over the three scenarios, whereas the annual costs are held fixed at the optimistic estimate. Annual costs O M P Useful life Useful life Useful life Annual revenues O M P O M P O M P O 74 68 64 51 45 41 37 31 27 M 34 28 24 11  5  1 −3 −9 −13 P 14  8  4 −9 −15 −19 −23 −29 −33 The computations indicate that the NAW> $50,000 in 4 of 27 scenarios and
NAW< $0 in 9 out of 27. Coupled with the results for the strictly optimistic, most likely, and pessimistic scenarios, this might not be sufficient for a positive decision. The risk-adjusted MARR method involves the use of higher discount rates for those alternatives that have a relatively high degree of uncertainty and lower discount rates for projects that are at the other end of the spectrum. A higher- than-usual MARR implies that distance cash flows are less important than current or near-term cash flows. This approach is widely used in practice but contains many pitfalls, the most serious being that the uncertainty is not made explicit. As a consequence, the analyst should first try other methods. Example 5-6  (Risk-Adjusted MARRs) As an analyst for an investment firm, you are considering two alternatives that have the same initial cost and economic life but different cash flows, as indicated in the table below. Both are affected by uncertainty to some degree; however, alternative P is thought to be more uncertain than alternative Q. If the firm’s risk-free MARR is 10%, then which is the better investment? End-of-year, k Alternative P Alternative Q 0 −$160,000 −$160,000 1  $120,000  $20,827 2   $60,000   $60,000 3     $0  $120,000 4   $60,000   $60,000 Solution At the risk-free MARR of 10%, both alternatives have the same NPV= $39,659. All else being equal, alternative Q should be chosen because it is less uncertain. To take into account the degree of uncertainty, we now use a prescribed risk-adjusted MARR of 20% for P and 17% for Q. Performing the same computations, we get NPV P ( 20% )=−$160,000+$120,000( P/F, 20%, 1 )+$60,000( P/F, 20%, 2 ) +$60,000( P/F, 20%, 4 )=$10,602 NPV Q ( 17% )=−$160,000+$20,827( P/F, 17%,1 ) +$60,000( P/F, 17%, 2 )+$120,000 (P/F, 17%,3) +$60,000( P/F, 17%, 4 )=$8,575 implying that alternative P is preferable. This is a reversal of the first result. Figure 5.6 plots the NPV of the two alternatives as a function of the MARR. The breakeven point is 10%. For MARRs beyond 10%, P is always the better choice. Figure 5.6 NPV comparisons for risk-adjusted MARRs. Figure 5.6 Full Alternative Text Another technique used to compensate for uncertainty is based on truncating the project life to something less than its estimated useful life. By dropping from consideration those revenues and costs that may occur after the reduced study period, heavy emphasis is placed on rapid recovery of investment capital in the early years. Consequently, this method is closely related to the payback technique discussed in Chapter 3. Implementation can by carried out in one of two ways. The first is to reduce the project life by some percentage and discard all subsequent cash flows. The NPV of the alternatives are then compared for the shortened life. The second is to determine the minimal life of the project that will produce an acceptable ROR. If this life is within the expectations of the decision maker, say, in terms of the maximum payback period, then the project is viewed as acceptable. Example 5-7   (Reduction of Useful life) A proposed new product line requires $2,000,000 in capital over a 2-year period. Estimated revenues and expenses over the product’s anticipated 8- year commercial life are shown in Table 5.7. The company’s maximum payback period is 4 years (after taxes), and its effective tax rate is 40%. The investment will be depreciated by the modified accelerated cost recovery system (MACRS) using a 5-year class life. TABLE 5.7  Data and Results for Reduction of Useful Life Example End of year ($M) Cash flows   −1    0 1 2 3 4 5 6 7 8 Initial investment −0.9 −1.1 0 0  0   0   0   0   0   0   Annual revenues    0    0 1.8 2  2.1  1.9  1.8  1.8  1.7  1.5  Annual expenses    0    0 −0.8   −0.9   −0.9    −0.9   −0.8   −0.8   −0.8   −0.7   ATCF −0.9 −1.1  0.76   0.92 0.88 0.7 0.7 0.65 0.54 0.48 ROR — — — — 10.3% 18.6% 23.6% 26.6% 28.3% 29.4% The company’s management is concerned about the financial attractiveness of this venture should unforeseen circumstances arise (e.g., loss of market or technological breakthroughs by the competition). They are very leery of investing a large amount of capital in this product because competition is fierce and companies that wait to enter the market may be able to purchase improved technology. You have been given the task of assessing the downside profitability of the product when the primary concern is its staying power (life) in the marketplace. If the after-tax MARR is 15%, then what do you recommend? State any necessary assumptions. Solution The first step is to compute the after-tax cash flow (ATCF). To do this, we assume that the salvage value of the investment is zero, that the MACRS deductions are unaffected by the useful life of the product, and that they begin in the first year of commercial operations (year 1). The results are given in Table 5.7. Next we compute the ROR of the investment as a function of the product’s presumed life. For the first 2 years, the undiscounted ATCF is negative so there is no ROR. In year 3, the ROR is 10.3% and climbs to 29.4% if the full commercial life is realized. A plot of the after-tax ROR versus the actual life of the product line is shown in Figure 5.7. To make at least 15% per year after taxes, the product line must last 4 or more years. It can be quickly determined from the data in the table that the simple payback period is 3 years. Consequently, this venture would seem to be worthwhile as long as its actual life is at least 4 years. Figure 5.7 After-tax parametric analysis for product. 5.6.4 Risk-Benefit Analysis Risk-benefit analysis is a generic term for techniques that encompass risk assessment and the inclusive evaluation of risk, costs, and benefits of alternative projects or policies. Like other quantitative methods, the steps in risk-benefit analysis include specifying objectives and goals for the project options, identifying constraints, defining the scope and limits for the study itself, and developing measures of effectiveness of feasible alternatives. Ideally, these steps should be completed in conjunction with a responsible decision maker, but, in many cases, this is not possible. It therefore is incumbent upon the analyst to take exceptional care in stating assumptions and limitations, especially because risk-benefit analysis is frequently controversial. The principal task of this methodology is to express numerically, insofar as possible, the risks and benefits that are likely to result from project outcomes. Calculating these outcomes may require scientific procedures or simulation models to estimate the likelihood of an accident or mishap, and its probable consequences. Finally, a composite assessment that aggregates the disparate measures associated with each alternative is carried out. The conclusions should incorporate the results of a sensitivity analysis in which each significant assumption or parameter is varied in turn to judge its effect on the aggregated risks, costs, and benefits. One approach to risk assessment is based on the three primary steps of systems engineering, as shown in Figure 5.8 (Sage and White 1980). These involve the formulation, analysis, and interpretation of the impacts of alternatives on the needs, and the institutional and value perspectives of the organization. In risk formulation, we determine or identify the types and scope of the anticipated risks. A variety of systemic approaches, such as the nominal group technique, brainstorming, and the Delphi method, are especially useful at this stage (Makridakis et al. 1997). It is important to identify not only the risk elements but also the elements that represent needs, constraints, and alternatives associated with possible risk reduction with and without technological innovation. This can be done only in accordance with a value system. Figure 5.8 Systems engineering approach to risk assessment. Figure 5.8 Full Alternative Text In the analysis step, we forecast the failures, mishaps, and other consequences that might accompany the development and implementation of the project. This will include estimation of the probabilities of outcomes and the associated magnitudes. Many methods, such as cross-impact analysis, interpretive structural modeling, economic modeling, and mathematical programming, are potentially useful at this step. The inputs are those elements determined during problem formulation. In the final step, we attempt to give an organizational or political interpretation to the risk impacts. This includes specification of individual and group utilities for the final evaluation. Decision making follows. The economic methods of B/C analysis are most commonly used at this point. Extension to include the results of the risk assessment, however, is not trivial. A principal problem is that risks and benefits may be measured in different units and therefore may not be strictly additive. Rather than trying to convert everything into a single measure, it may be better simply to present the risks and net benefits in their respective units or categories. To aid in interpreting the results, risk-return graphs, similar to the C-E graph displayed in Figure 5.4, can be drawn to highlight the efficient frontier. Risk profiles may also be useful. Figure 5.9 illustrates a perspective provided by a risk analysis profile. Projects 1 and 2 are most likely to yield lifetime profits of $100,000 and $200,000, respectively. So, for some decision makers, project 2 might be considered superior if the B/C ratio were favorable. Nevertheless, it is worth probing the data a bit more. Project 2 has a finite probability of returning a loss but a higher expected profit than project 1. The probability that project 2 will yield lower profits than project 1 is known as the downside risk and can be found by a breakeven analysis. Given these data, a risk-averse person would be inclined to select project 1, which has a big chance (0.50) of realizing a moderate profit of at least $100K, with little chance of anything much less or much greater; that is, project 1 has a small variance. A gambler would lean toward project 2, which has a small chance at a very large profit. Figure 5.9 Illustration of risk profile. Figure 5.9 Full Alternative Text The types of risk profiles contained in Figure 5.9 make the consequences of outcomes more visible and enable a decision maker to behave in a manner consistent with his or her attitude toward risk, be it conservative or freewheeling. Generally speaking, the amount of data needed to construct a graph such as Figure 5.9 is small and relatively easy to obtain if a historical database exists. It can be solicited from the engineers and marketing personnel who are familiar with an organization’s previous projects. If no collective experience can be found within the organization, then more subjective or arbitrary procedures would be required. A number of software packages are available to help with the construction effort. 5.6.5 Limits of Risk Analysis The ultimate responsibility for project selection and implementation goes beyond any risk assessment and rests squarely on the shoulders of top management. Although formal analysis can reveal unexpected vulnerabilities in large complex projects, it remains an academic exercise unless the managers take the results seriously and ensure that the project is managed conscientiously. Safety must be designed into a system from the beginning, and good operating practice is essential to the success of any continuing program of risk management. Controversy still rages, for example, over whether the vent-gas scrubber—a key element in the safety system of the Union Carbide pesticide plant in Bhopal, India that exploded in 1984, killing more than 3,000 people—was designed adequately to handle a true emergency. But even if it had been, neither it nor a host of other safety features were maintained in working order. For risks to be ascertained at all, project managers must agree on the value of assessing them in engineering design. It has often been said that you can degrade the performance of a system by poor quality control, but you cannot enhance a poor design by good quality control. At the point at which project managers are responsible for crucial decisions, risk assessment is one more tool that can help them weigh alternatives so that their choices are informed and deliberate rather than isolated or worse, repetitions of past mistakes. 5.7 Decision Trees Decision trees, also known as decision flow networks and decision diagrams, may depict and facilitate analysis of problems that involve sequential decisions and variable outcomes over time. They make it possible to look at a large, complicated problem in terms of a series of smaller simple problems while explicitly considering risk and future consequences. A decision tree is a graphical method of expressing, in chronological order, the alternative actions that are available to a decision maker and the outcomes determined by chance. In general, they are composed of the following two elements, as shown in Figure 5.10. Figure 5.10 Structure of decision tree. Figure 5.10 Full Alternative Text 1. Decision nodes. At a decision node, usually designated by a square, the decision maker must select one alternative course of action from a finite set of possibilities. Each possible course of action is drawn as a branch emanating from the right side of the square. When there is a cost associated with an alternative, it is written along the branch. Each alternative branch may result in a payoff, another decision node, or a chance node. 2. Chance nodes. A chance node, designated as a circle, indicates that a random event is expected at this point in the process; that is, one of a finite number of states of nature may occur. The states of nature are shown on the tree as branches to the right of the chance nodes. The corresponding probabilities are similarly written above the branches. The states of nature may be followed by payoffs, decision nodes, or more chance nodes. Constructing a Tree A tree is started on the left of the page with one or more decision nodes. From these, all possible alternatives are drawn branching out to the right. Then, a chance node or second decision node, associated with either subsequent events or decisions, respectively, is added. Each time a chance node is added, the appropriate states of nature with their corresponding probabilities emanate rightward from it. The tree continues to branch from left to right until the final payoffs are reached. The tree shown in Figure 5.10 represents a single decision with two alternatives, each leading to a chance node with three possible states of nature. Finding a Solution To solve a tree, it is customary to divide it into two segments: (1) chance nodes with all their emerging states of nature (Figure 5.11a) and (2) decision nodes with all their alternatives (Figure 5.11b). The solution process starts with those segments that end in the final payoffs, at the right side of the tree, and continues to the left, segment by segment, in the reverse order from which it was drawn. Figure 5.11 Segments of tree. Figure 5.11 Full Alternative Text 1. Chance node segments. The expected monetary value (EMV) of all of the states of nature that emerge from a chance node must be computed (multiply payoffs by probabilities and sum the results). The EMV is then written above the node inside a rectangle (labeled a “position value” in Figure 5.10). These expected values are considered as payoffs for the branch to the immediate left. 2. Decision node segments. At a decision point, the payoffs given (or computed) for each alternative are compared and the best one is selected. All others are discarded. The corresponding branch of a discarded alternative is marked by the symbol ∥ to indicate that the path is suboptimal. This procedure is based on principles of dynamic programming and is commonly referred to as the “rollback” step. It starts at the endpoints of the tree where the expected value at each chance node and the optimal value at each decision node are computed. Suboptimal choices at each decision node are dropped, with the rollback continuing until the first node of the tree is reached. The optimal policy is recovered by identifying the choices made at each decision node that maximize the value of the objective function from that point onward. Example 5-8 (Deterministic Replacement Problem) The most basic form of a decision tree occurs when each alternative results in a single outcome; that is, when certainty is assumed. The replacement problem defined in Figure 5.12 for a 9-year planning horizon illustrates this situation. The numbers above the branches represent the returns per year for the specified period should the replacement be made at the corresponding decision point. The numbers below the branches are the costs associated with that decision. For example, at node 3, keeping the machine results in a return of $3K per year for 3 years, and a total cost of $2K. Figure 5.12 Deterministic replacement problem. Figure 5.12 Full Alternative Text As can be seen, the decision as to whether to replace the old machine with the new machine does not occur just once, but recurs periodically. In other words, if the decision is made to keep the old machine at decision point 1, then later, at decision point 2, a choice again has to be made. Similarly, if the old machine is chosen at decision point 2, then a choice has to be made at decision point 3. For each alternative, the cash inflow and duration of the project is shown above the branch, and the cash investment opportunity cost is shown below the branch. At decision point 2, for example, if a new machine is purchased for the remaining 6 years, then the net benefits from that point on are (6 yr)($6.5K/yr) returns −$17.0K opportunity cost= $22.0K net benefits. Alternatively, if the old machine is kept at decision point 2, then we have ($3.5K/yr)(3 yr) returns −$1.0K opportunity cost +$7K net benefits associated with decision point 3=$16.5K net benefits. For this problem, one is concerned initially with which alternative to choose at decision point 1, but an intelligent choice here should take into account the later alternatives and decisions that stem from it. Hence, the correct procedure in analyzing this type of problem is to start at the most distant decision point, determine the best alternative and quantitative result of that alternative, and then roll back to each successive decision point, repeating the procedure until finally the choice at the initial or present decision point is determined. By this procedure, one can make a present decision that directly takes into account the alternatives and expected decisions of the future. For simplicity in this example, timing of the monetary outcomes first will be neglected, which means that a dollar has the same value regardless of the year in which it occurs. Table 5.8 displays the necessary computations and implied decisions. Note that the monetary outcome of the best alternative at decision point 3 ($7.0K for the “old”) becomes part of the outcome for the old alternative at decision point 2. That is, if the decision at node 2 is to continue to use the current machine rather than replace it, then the monetary value associated with this decision equals the EMV at node 3 ($7K) plus the transition benefit from node 2 to 3 ( $3.5/yr×3 yr−$1K=$9.5K ), or $16.5K. Similarly, the best alternative at decision point 2 ($22.0K for the “new”) becomes part of the outcome for the “old” alternative at decision point 1. TABLE 5.8  Computational Results for Replacement Problem in Figure 5.12 Decision point Alternative Monetary outcome Choice 3 Old ( $3K/yr )( 3 yr )−$2K =$7.0K Old ( $6.5K/yr )( 3 yrs ) New −$18K =$1.5K 2 Old $7K+( $3.5K/yr )( 3 yr )−$1K =$16.5K New ( $6.5K/yr )( 6 yr )−$17K =$22.0K New 1 Old $22.0K+( $4K/yr )( 3 yr)−$0.8K =$33.2K Old New ( $5K/yr )( 9 yr )−$15K =$30.0K By following the computations in Table 5.7, one can see that the answer is to keep the old machine now and plan to replace it with a new machine at the end of 3 years (at decision point 2). In practice, an organization would re- evaluate the decision on a rolling, annual basis and may, in fact, replace the machine prior to three years or may delay machine replacement beyond three years. Example 5-9 (Timing Considerations) For decision tree analyses, which involve working from the most distant decision point to the nearest decision point, the easiest way to take into account the timing of money is to use the present value approach and thus discount all monetary outcomes to the decision points in question. To demonstrate, Table 5.9 gives the computations for the same replacement problem of Figure 5.9 using an interest rate of 12% per year. TABLE 5.9  Computations for Replacement Problem with 12% Interest Rate Decision point Alternative Monetary outcome Choice 3 Old $3K( P/A, 12%, 3 )−$2K=$3K( 2.402 )−$2K =$5.21K Old New $6.5K( P/A, 12%, 3 ) −$18K =$6.5K( 2.402 ) −$18K =−$2.39K 2 Old $3.5K( P/A, 12%, 3 ) −$1K +$5.21K( P/F, 12%, 3 ) =$3.5K( 2.402 )−$1K +$5.21K(0.7118) =$11.11K Old New $6.5K( P/A, 12%, 6 ) −$17K =$6.5K( 4.111 ) −$17K =$9.72K 1 Old $4K( P/A, 12%, 3 ) −$0.8K +$11.11K( P/F, 12%, 3 ) =$4K( 2.402 )−$0.8K +$11.11K(0.7118) =$16.71K Old New $5.0K( P/A, 12%, 9 ) −$15K =$5.0K( 5.328 ) −$15K =$11.64K Note from Table 5.8 that when taking into account the effect of timing by calculating PWs at each decision point, the indicated choice is not only to keep the old at decision point 1, but also to keep the old at decision points 2 and 3. This result is not surprising because the high interest rate tends to favor the alternatives with lower initial investments, and it also tends to place less weight on long-run returns. When the interest rate drops to 10%, the solution is the same as that for Example 5.8. Example 5-10 (Automation Decision Problem with Random Outcomes) In this problem, the decision maker must decide whether to automate a given process. Depending on the technological success of the automation project, the results will turn out to be poor, fair, or excellent. The net payoffs for these outcomes (expressed in NPVs and including the cost of the equipment) are −$90K, $40K, and $300K, respectively. The initially estimated probabilities that each outcome will occur are 0.5, 0.3, and 0.2. Figure 5.13 is a decision tree depicting this simple situation. The calculations for the two alternatives are Automate: −$90K( 0.5 )+$40K( 0.3 )+$300K( 0.2 )=$27K Don’t automate: $0 Figure 5.13 Automation problem before consideration of technology study. Figure 5.13 Full Alternative Text These calculations show that the best choice for the firm is to automate on the basis of an expected NPV of $27K versus $0 if it does nothing. Nevertheless, this may not be a clear-cut decision because of the possibility of a $90K loss. Depending on the decision maker’s attitude toward risk and confidence in the given data, he or she might want to gather more information. Suppose that it is possible for a decision maker to conduct a technology study for a cost of $10K. The study will disclose that the enabling technology is “shaky,” “promising,” or “solid” corresponding to ultimate outcomes of “poor,” “fair,” and “excellent,” respectively. Let us assume that the probabilities of the various outcomes, given the technology study findings, are as shown in Figure 5.14, which is a decision tree for the entire problem. This diagram shows expected future events (outcomes), along with their respective cash flows and probabilities of occurrence. The calculation of these probabilities requires the use of Bayes’ theorem given in Appendix 5A at the end of this chapter and discussed in a later subsection. To use Bayes’ theorem, it is necessary to know all conditional probabilities of the form P( study outcome|state ); e.g., P( shaky|poor ) or P( excellent|promising ). Figure 5.14 Automation problem with technology study taken into account. Figure 5.14 Full Alternative Text The rectangular blocks represent (decision) points in time at which the decision maker must elect to take one and only one of the paths (alternatives) available. These decisions are normally based on a quantifiable measure, such as money, which has been determined to be the predominant “cost” or “reward” for comparing alternatives. As mentioned, the general approach is to find the action or alternative that will maximize the expected NPV equivalent of future cash flows at each decision point, starting with the furthest decision point(s) and then rolling back until the initial decision point is reached. Once again, the chance (circular) nodes represent points at which uncertain events (outcomes) occur. At a chance node, the expected value of all paths that lead (from the right) into the node can be calculated as the sum of the anticipated value of each path multiplied by its respective probability. (The probabilities of all paths that lead into a node must sum to 1.) As the project progresses through time, the chance nodes are automatically reduced to a single outcome on the basis of the “state of nature” that occurs at that time. The solution to the problem in Figure 5.14 is given in Table 5.10. It can be noted that the alternative “technology study” is shown to be best with an expected NPV of $34.62K. (To check the solution in Table 5.10, perform the rollback procedure on Figure 5.14, indicating which branches should be eliminated.) TABLE 5.10  Expected NPV Calculations for the Automation Problem Decision point Alternative Expected monetary outcome Choice 2A Automate −$90K( 0.73 )+$40K( 0.22 ) +$300K( 0.05 ) =−$41.9K Don’t automate =$0 Don’t automate 2B Automate −$90K( 0.43 )+$40K( 0.34 ) +$300K( 0.23 ) =$43.9K Automate Don’t automate =$0 2C Automate −$90K( 0.21 )+$40K( 0.37 ) +$300K( 0.42 ) =$121.9K Automate Don’t automate =$0 1 Automate (see calculationsabove) =$27K Don’t automate =$0 Technology study $0( 0.41 )+$43.9K( 0.35 ) +$121.9K( 0.24 )−$10K =$34.62K Technologystudy 5.7.1 Decision Tree Steps Now that decision trees (diagrams) have been introduced and the mechanics of using them to arrive at an initial decision have been illustrated, the steps involved can be summarized as follows: 1. Identify the points of decision and alternatives available at each point. 2. Identify the points of uncertainty and the type or range of possible outcomes at each point (layout of decision flow network). 3. Estimate the values needed to conduct the analysis, especially the probabilities of different outcomes and the costs/returns for various outcomes and alternative actions. 4. Remove all dominated branches. 5. Analyze the alternatives, starting with the most distant decision point(s) and working back, to choose the best initial decision. In Example 5.9, we used the expected NPV as the decision criterion. However, if outcomes can be expressed in terms of utility units, then it may be appropriate to use the expected utility as the criterion. Alternatively, the decision maker may be willing to express his or her certain monetary equivalent for each chance outcome node and use that as the decision criterion. Because a decision tree can quickly become unmanageably large, it is often best to start out by considering only major alternatives and outcomes in the structure to get an initial understanding or feeling for the issues. Secondary alternatives and outcomes can then be added if they are significant enough to affect the final decision. Incremental embellishments can also be added if time and resources are available. 5.7.2 Basic Principles of Diagramming The proper diagramming of a decision problem is, in itself, very useful to the understanding of the problem, as well as being essential to performing the analysis correctly. The placement of decision points and chance nodes from the initial decision point to any subsequent decision point should give an accurate representation of the information that will and will not be available when the decision maker actually has to make the choice associated with the decision point in question. The tree should show the following: 1. All initial or immediate alternatives among which the decision maker wishes to choose. 2. All uncertain outcomes and future alternatives that the decision maker wishes to consider because they may directly affect the consequences of initial alternatives. 3. All uncertain outcomes that the decision maker wishes to consider because they may provide information that can affect his or her future choices among alternatives and hence, indirectly affect the consequences of initial alternatives. It should also be noted that the alternatives at any decision point and the outcomes at any payoff node must be: 1. Mutually exclusive; that is, no more than one can possibly be chosen. 2. Collectively exhaustive; that is, when a decision point or payoff node is reached, some course of action must be taken. In Figure 5.14, decision nodes 2A, 2B, and 2C are each reached only after one of the mutually exclusive results of the technology study is known; and each decision node reflects all alternatives to be considered at that point. Furthermore, all possible outcomes to be considered are shown, as the probabilities sum to 1.0 for each chance node. 5.7.3 Use of Statistics to Determine the Value of More Information An alternative that frequently exists in an investment decision is to conduct further research before making a commitment. This may involve such action as gathering more information about the underlying technology, updating an existing analysis of market demand, or investigating anew future operating costs for particular alternatives. Once this additional information is collected, the concepts of Bayesian statistics provide a means of modifying estimates of probabilities of future outcomes, as well as a means of estimating the economic value of further investigation study. To illustrate, consider the one-stage decision situation depicted in Figure 5.15, in which each alternative has two possible chance outcomes: “high” or “low” demand. It is estimated that each outcome is equally likely to occur, and that the monetary result expressed as PW is shown above the arrow for each outcome. Again, the amount of investment for each alternative is written below the respective lines. On the basis of these amounts, the calculation of the expected monetary outcomes minus the investment costs (giving expected NPV) is as follows: Old system: E[NPV]=$45M(0.5)+$27.5M(0.5)−$10M=$26.25MNew FMS: E which indicates that the new flexible manufacturing system (FMS) should be selected. Figure 5.15 One-stage FMS replacement problem. Figure 5.15 Full Alternative Text To demonstrate the use of Bayesian statistics, suppose that one is considering the advisability of undertaking a fresh intensive investigation before deciding on the “old system” versus the “new FMS.” Suppose also that this new study would cost $2.0M and will predict whether the demand will be high (h) or low ( ). To use the Bayesian approach, it is necessary to assess the conditional probabilities that the investigation (technology study) will yield certain results. These probabilities reflect explicit measures of management’s confidence in the ability of the investigation to predict the outcome. Sample assessments are P( h|H )=0.70, P( |H )=0.30, P( h|L )=0.20, and P( |L )=0.80, where H and L denote high and low actual demand as opposed to predicted demand. As an explanation, P( h|H ) means the probability that the predicted demand is high (h), given that the actual demand will turn out to be high (H). A formal statement of Bayes’ theorem is given in Appendix 5A along with a tabular format for ease of computation. Tables 5.11 and 5.12 use this format for revision of probabilities based on the assessment data above, and the prior probabilities of 0.5 that the demand will be high and 0.5 that the demand will be low [i.e., P( H )=P( L )=0.5 ]. These probabilities are now used to assess the technology study alternative. Figure 5.16 depicts the complete decision tree. Note that demand probabilities are entered on the branches according to whether the investigation indicates high or low demand. TABLE 5.11  Computation of Posterior Probabilities Given That Investigation-Predicted Demand is High (h) (1) (2) (3) ( 4 )=(2)×(3) ( 5 )=(4)/∑(4) State (actual demand) Prior probability, P(state) Confidence assessment, P( h|state ) Posterior joint probability Probability, P( state|h ) H 0.5 0.70 0.35 0.78    L 0.5 0.20 0.10   0.22 0.45 TABLE 5.12  Computation of Posterior Probabilities Given That Investigation-Predicted Demand is Low ( ) (1) (2) (3) ( 4 )=(2)×(3) ( 5 )=(4)/∑(4) State (actual demand) Prior probability, P(state) Confidence assessment, P( |state ) Posterior joint probability Probability, P( state| ) H 0.5 0.30 0.15 0.27 L 0.5 0.80   0.40   0.73 0.55 Figure 5.16 Replacement problem with alternative of technology study. Figure 5.16 Full Alternative Text The next step is to calculate the expected outcome for the technology study alternative. This is done by the standard decision tree rollback principle, as shown in Table 5.11. Note that the 0.45 and 0.55 probabilities that the investigation-predicated demand will be high and low, respectively, are obtained from the totals in column (4) of the Bayesian revision calculations depicted in Tables 5.11 and 5.12. Thus, from Table 5.13, it can be seen that the “new FMS” alternative with an expected NPV of $29.0M is the best course of action by a slight margin. (As an exercise, perform the calculations on Figure 5.16 and indicate the optimal path.) Although the figures used here do not reflect any advantages to this technology study, the benefit of gathering additional information can potentially be great. TABLE 5.13  Expected NPV Calculations for Replacement Problem in Figure 5.13 Decision point Alternative Expected monetary outcome Choice 2A Old system $45M( 0.78 )+$27.5M(0.22 )−$10M =$31.15M New FMS $80M( 0.78 )+$48M(0.22 )−$35M = $37.96M New FMS 2B Old system $45M( 0.27 )+$27.5M(0.73 )−$10M =$22.23M Old system New FMS $80M( 0.27 )+$48M(0.73 )−$35M =$21.64M 1 Old system (see calculations above) =$26.25M New FMS (see calculations above) =$29.00M NewFMS Technology study $37.96M( 0.45 )+$22.23M( 0.55 )−$2M =$27.31M In practice, firms will conduct market research or spot-market tests before launching a new product to a larger market audience. The research—with a representative sample of customers—will enable the firm to refine its probabilities of successfully launching a new product. The firm may learn, for instance, that a proposed, new product is not well-received by the research panel. In this case, the firm may abandon its broader “go to market” strategy for the new product and save itself from a more catastrophic financial loss. The decisions of (1) whether to conduct a spot-market test and (2) whether to go to market using a broad, national campaign can be modeled with decision trees, assuming that a finite set of possible outcomes, associated with each decision, can be stated and probabilities associated with each of the possible outcomes can be estimated. 5.7.4 Discussion and Assessment One unique feature of decision trees is that they allow management to view the logical order of a sequence of decisions. They afford a clear graphical representation of the various courses of action and their possible consequences. By using decision trees, management can also examine the impact of a series of decisions (over many periods) on the goals of the organization. Such models reduce abstract thinking to a rational, visual pattern of cause and effect. When costs and benefits are associated with each branch and probabilities are estimated for each possible outcome, analysis of the tree can clarify choices and risks. On the down side, the methodology has several weaknesses that should not be overlooked. A basic limitation of its representational properties is that only small and relatively simple decision models can be shown at the level of detail that makes trees so descriptive. Every variable added expands the tree’s size multiplicatively. Although this problem can be overcome to some extent by generalizing the diagram, significant information may be lost in doing so. This loss is particularly acute if the problem structure is highly dependent or asymmetric. Regarding the computational properties of trees, for simple problems in which the endpoints are pre-calculated or assessed directly, the rollback procedure is very efficient. However, for problems that require a roll-forward procedure, the classic tree-based algorithm has a fundamental drawback: it is essentially an enumeration technique. That is, every path through the tree is traversed to solve the problem and generate the full range of outputs. This feature raises the “curse of dimensionality” common to many stochastic models: for every variable added, the computational requirements increase multiplicatively. This implies that the number of chance variables that can be included in the model tends to be small. There is also a strong incentive to simplify the value model, because it is recalculated at the end of each path through the tree. Nevertheless, the enumeration property of tree-based algorithms in theory can be reduced dramatically by taking advantage of certain structural properties of a problem. Two such properties are referred to as “asymmetry” and “coalescence.” For more discussion and some practical aspects of implementation, consult Call and Miller (1990). 5.8 Real Options NPV has been criticized for not properly accounting for uncertainty and flexibility—that is, multistage development funding and abandonment options. Decision trees more accurately capture the multistage nature of development by using probability-based EMVs, but can be time consuming and overly complex when all potential courses of action are included. An alternative to decision trees is real options, a technique that applies financial options theory to nonfinancial assets and encourages managers to consider the value of strategic investments in terms of risks that can be held, hedged, or transferred. Seen through a real options lens, NPV always undervalues potential projects, often by several hundred percent. Real-options analysis offers the flexibility to expand, extend, contract, abandon, or defer a project in response to unforeseen events that drive the value of a project up or down through time. It is good practice to consider these options at the outset of an investment analysis rather than only when trouble arises. Recall that the NPV of a project is estimated by forecasting its annual cash flows during its expected life, discounting them back to the present at a risk- adjusted weighted average cost of capital, then subtracting the initial start-up capital expenditure. There’s nothing in this calculation that captures the value of flexibility to make future decisions that resolve uncertainty. Financial managers often overrule NPV by accepting projects with negative NPVs for “strategic reasons.” Their intuition tells them that they cannot afford to miss the opportunity. In essence, they’re intuiting something that has not been quantified in the project. 5.8.1 Drivers of Value Like options on securities, real options are the right but not the obligation to take an action in the future at a predetermined price (the exercise or striking price) for a predetermined time (the life of the option). When you exercise a real option, you capture the difference between the value of the asset and the exercise price of the option. If a project is more successful than expected, then management can pay an “exercise price” to expand the project by making an additional capital expenditure. Management can also extend the life of a project by paying an exercise price. If the project does worse than expected, then it can be scaled back or abandoned. In addition, the initial investment does not have to be made today—it can be deferred. The value of a real option is influenced by the following six variables: 1. Value of the underlying project. The option to expand a project (a call), for example, increases the scale of operations and therefore the value of the project at the cost of additional investment (the exercise price). Thus, the value of the project (without flexibility) is the value of what, in real- options language, is called the underlying risky asset. If we have flexibility to expand the project—in other words, an option to buy more of the project at a fixed price—then the value of the option to expand goes up when the value of the underlying project goes up. 2. Exercise price/investment cost. The exercise price is the amount of investment required to expand. The value of the option to expand goes up as the cost of expansion is reduced. 3. Volatility of the underlying project’s value. Because the decision to expand is voluntary, you will expand only when the value of expansion exceeds the cost. When the value is less than the cost and there is no variability in the value, the option is worthless, but if the value is volatile, then there’s a chance that the value can rise and exceed the cost, making the option valuable. Therefore, the value of flexibility goes up when uncertainty of future outcomes increases. 4. Time to maturity. The value of flexibility increases as the time to maturity lengthens because there’s a greater chance that the value of expansion will rise the longer you wait. 5. Risk-free interest rate. As the risk-free rate of interest goes up, the present value of the option also goes up because the exercise price is paid in the future, and therefore, as the discount rate increases, the present value of the exercise price decreases. 6. Dividends. The sixth variable is the dividends, or the cash flows, paid out by the project. When dividends are paid, they decrease the value of the project and therefore the value of the option on the project. 5.8.2 Relationship to Portfolio Management The flexible decision structure of options is valid in an R&D context. After an initial investment, management can gather more information about the status of a project and market characteristics and, on the basis of this information, change its course of action. The real option value of this managerial flexibility enhances the R&D project value, whereas a pure NPV analysis understates it. Five basic sources of flexibility have been identified (e.g., Trigeorgis 1997). A defer option refers to the possibility of waiting until more information has become available. An abandonment option offers the possibility to make the investment in stages, deciding at each stage, on the basis of the newest information, whether to proceed further or to stop (this is applied by venture capitalists). An expansion or contraction option represents the possibility to adjust the scale of the investment (e.g., a production facility) depending on whether market conditions turn out favorably or not. Finally, a switching option allows changing the mode of operation of an asset, depending on factor prices (e.g., switching the energy source of a power plant, switching raw material suppliers). One key insight generated by the real options approach to R&D investment is that higher uncertainty in the payoffs of the investment increases the value of managerial flexibility, or the value of the real option. The intuition is clear— with higher payoff uncertainty, flexibility has a higher potential of enhancing the upside while limiting the downside. An important managerial implication of this insight is that the more uncertain the project payoff is, the more efforts should be made to delay commitments and maintain the flexibility to change the course of action. This intuition is appealing. Nevertheless, there is hardly any evidence of real options pricing of R&D projects in practice despite reports that Merck uses the method. Moreover, there is recent evidence that more uncertainty may reduce the option value if an alternative “safe” project is available. This evidence represents a gap between the financial payoff variability, as addressed by the real options pricing literature, and operational uncertainty that pervades R&D. For example, R&D project managers encounter uncertainty about budgets, schedules, product performance, or market requirements, in addition to financial payoffs. The relationship between such operational uncertainty and the value of managerial flexibility (option value of the project) is not clear. For example, should the manager respond to increased uncertainty about product performance in the same way as to uncertainty about project payoffs, by delaying commitments? Questions such as this must be addressed on a case-by-case basis in full view of the scope and consequences of the attending risks. TEAM PROJECT Thermal Transfer Plant On the basis of the evaluation of alternatives, Total Manufacturing Solutions, Inc. (TMS) management has adopted a plan by which the design and assembly of the rotary combustor will be done at TMS. Most of the manufacturing activity will be subcontracted except for the hydraulic power unit, which TMS decided to build “in-house.” There are three functions involved in charging and rotating the combustor. Two of them, the charging rams and the resistance door, naturally lend themselves to hydraulics. The third, turning the combustor, can be done either electromechanically (by an electric motor and a gearbox) or hydraulically. If the hydraulic method is chosen, then there are two alternatives: (1) use a large hydraulic motor as a direct drive or (2) use a small hydraulic motor with a gearbox. Figure 5.17 contains a schematic. Figure 5.17 Hydraulic power unit. TMS engineering has produced the following specifications for the hydraulic power unit: Applicable documents, codes, standards, and requirements National Electric Manufacturers Association (NEMA) American National Standards Institute (ANSI) Pressure Vessels Code, American Society of Mechanical Engineers (ASME) Section VIII Hydraulic rams Two hydraulic cylinders will be provided for the rams. The cylinders will be 8 in. bore ×96 in. stroke. They will operate at 1,500 psi, and will have an adjustable extension rate of 2 to 6 ft/min. They will retract in 15 seconds, will operate 180° out of phase, and will retract in the event of a power failure. Combustor barrel drive A single-direction, variable-speed drive will be provided for the combustor. The output of this drive will deliver up to 1.6 rpm and 7,500 ft-lb of torque. Resistance door cylinder This cylinder will be 6 in. bore ×48 in. stroke and will operate with a constant pressure of 200 psi. Hydraulic power unit The hydraulic power unit will be skid mounted and ready for hookup to interfacing equipment. Mounting and lifting brackets will be manufactured as well. Hydraulic pumps will be redundant so that in the event of the failure of one, another can be started to take over its function. Accumulators will be added to retract the rams and close the resistance door in the event of a power failure. The hydraulic fluid is to be E. F. Houghton’s Cosmolubric or equivalent. Although system operating pressure is to be 1,500 psi, the plumbing will be designed to withstand 3,000 psi. Water-to-oil heat exchangers shall be provided to limit reservoir temperature to 130°C. A method of controlling ram extension speed and combustor rpm within the specifications stated above will be provided. Control concepts may be analog (5 to 20 milliamperes) or digital. Electrical Electric motors will be of sufficient horsepower to drive the hydraulic pumps. Motors shall operate at 1,200 rpm, 220/440 volts, 3 phase, 60 hertz. Solenoids and controls Solenoids are to be 120 volt, 60 hertz and will have manual overrides. Any analog control function is to respond to a 5- to 20- milliampere signal. Combustion drive A single-direction, variable-speed drive will be provided for the combustor. The output of this drive will deliver up to 1.6 rpm and 7,500 ft-lb of torque. Three potential alternatives for the combustor drive are Electric motor and gearbox Hydraulic motor with gearbox (hydraulic power supplied by hydraulic power unit) Hydraulic motor with direct drive (hydraulic power supplied by hydraulic power unit) Your team assignment is to select the most appropriate drive from these candidates. To do so, develop a scoring model or a decision tree and evaluate each alternative accordingly. State your assumptions clearly, regarding technological, economic, and other aspects and explain the methodology used to support your analysis. Initial cost estimates available to your team are: Ram cylinders (two required)  $5,948 each Resistance door cylinder  $1,505 Hydraulic power unit $50,000 Low-speed, high-torque motor $22,780 High-speed motor with gear box  $7,000 Discussion Questions 1. Where would ideas for new projects and products probably originate in a manufacturing company? What would be the most likely source in an R&D organization such as AT&T Laboratories or IBM’s Watson Center? 2. Assume that you work in the design department of an aerospace firm and you are given the responsibility of selecting a workstation that will be used by each group in the department. How would you find out which systems are available? What basic information would you try to collect on these systems? 3. How can you extend a polar graph, similar to the one shown in Figure 5.2, to the case in which the criteria are individually weighted? 4. Identify a project that you are planning to pursue either at home or at work. List all of the components, decision points, and chance events. What is the measure of success for the project? Assuming that there is more than one measure, how can you reconcile them? 5. If you were evaluating a proposal to upgrade the computer-aided design system used by your organization, what type of information would you be looking for in detail? How would your answer change if you were buying only one or two systems as opposed to a few dozen? 6. Which factors in an organization do you think would affect the decision to go ahead with a project, such as automating a production line, other than the B/C ratio? 7. For years before beginning the project to build a tunnel under the English Channel, Great Britain and France debated the pros and cons. Speculate on the critical issues that were raised. 8. The project to construct a subway in Washington, D.C. began in the early 1970s with the expectation that it would be fully operational by 1980. A portion of the system opened in 1977, but as of 2004, approximately 5% remained unfinished. What do you think were the costs, benefits, and risks involved in the original planning? How important was the interest rate used in those calculations? Speculate on who or what was to blame for the lengthy delay in completion. 9. Where does quality fit into the B/C equation? Identify some companies or products that compete primarily on the basis of quality rather than price. 10. A software company is undecided on whether it should expand its capacity by using part-time programmers or by hiring more full-time employees. Future demand is the critical factor, which is not known with certainty but can be estimated only as low, medium, or high. Draw a decision tree for the company’s problem. What data are needed? 11. How could B/C analysis be used to help determine the level of subsidy to be paid to the operator of public transportation services in a congested urban area? 12. Why has the U.S. Department of Defense been the major exponent of C- E analysis? Give your interpretation of what is meant by “diminishing returns,” and indicate how it might affect a decision on procuring a military system versus an office automation system. 13. In which type of projects does risk play a predominant role? What can be done to mitigate the attendant risks? Pick a specific project and discuss. Exercises 1. 5.1 Consider an important decision with which you will be faced in the near future. Construct a scoring model detailing your major criteria and assign weights to each. Indicate which data are known for sure and which are uncertain. What can be done to reduce the uncertainty? 2. 5.2 Use a checklist and a scoring model to select the best car for a married graduate student with one child. State your assumptions clearly. 3. 5.3 Assume that you have just entered the university and wish to select an area of study. 1. Using B/C analysis only, what would your decision be? 2. How would your decision change if you used C-E analysis? Provide the details of your analysis. 4. 5.4 You have just received a job offer in a city 1,000 miles away and must relocate. List all possible ways of moving your household. Use two different analytic techniques for selecting the best approach, and compare the results. 5. 5.5 Three new-product ideas have been suggested. These ideas have been rated as shown in Table 5.14 . TABLE 5.14  Product1 Criteria A B C Weight (%) Development cost P F VG 10 Sales prospects VG E G 15 Producibility P F G 10 Competitive advantage E VG F 15 Technical risk P F VG 20 Patent protection F F VG 10 Compatibility with strategy VG F F   20   100  1 P = poor, F = fair, G = good, VG = very good, E = excellent 1. Using an equal point spread for all five ratings (i.e., P=1, F=2, G=3, VG=4, E=5 ), determine a weighted score for each product idea. What is the ranking of the three products? 2. Rank the criteria, compute the rank-sum weights, and determine the score for each alternative. Do the same using the rank reciprocal weights. 3. What are some of the advantages and disadvantages of this method of product selection? 6. 5.6 Suppose that the products from Exercise 5.5 have been rated further as shown in Table 5.15 . TABLE 5.15  Product A B C Probability of technical success 0.9 0.8 0.7 Probability of commercial success 0.6 0.8 0.9 Annual volume (units) 10,000 8,000 6,000 Profit contribution per unit $2.64 $3.91 $5.96 Lifetime of product (years) 10 6 12   Total development cost $50,000 $70,000 $100,000 1. Compute the expected return on investment over the lifetime of each product. 2. Does this computation change your ranking of the products over that obtained in Exercise 5.5 ? 7. 5.7 The federal government proposes to construct a multipurpose water project. This project will provide water for irrigation and for municipal uses. In addition, there will be flood control benefits and recreation benefits. The estimated project benefits computed for 10-year periods for the next 50 years are given in Table 5.16 . TABLE 5.16  Purpose Firstdecade Second decade Third decade Fourth decade Fifth decade Municipal $ 40,000 $ 50,000 $ 60,000 $ 70,000 $110,000 Irrigation $350,000 $370,000 $370,000 $360,000 $350,000 Flood Control $150,000 $150,000 $150,000 $150,000 $150,000 Recreation  $60,000   $70,000   $80,000   $80,000   $90,000 Totals $600,000 $640,000 $660,000 $660,000 $700,000 The annual benefits may be assumed to be one tenth of the decade benefits. The O&M cost of the project is estimated to be $15,000 per year. Assume a 50-year analysis period with no net project salvage value. 1. If an interest rate of 5% is used and there is a B/C ratio of unity, then what capital expenditure can be justified to build the water project now? 2. If the interest rate is changed to 8%, then how does this change the justified capital expenditure? 8. 5.8 The state is considering the elimination of a railroad grade crossing by building an overpass. The new structure, together with the needed land, would cost $1,800,000. The analysis period is assumed to be 30 years on the theory that either the railroad or the highway above it will be relocated by then. Salvage value of the bridge (actually, the net value of the land on either side of the railroad tracks) 30 years hence is estimated to be $100,000. A 6% interest rate is to be used. At present, approximately 1,000 vehicles per day are delayed as a result of trains at the grade crossing. Trucks represent 40%, and 60% are other vehicles. Time for truck drivers is valued at $18 per hour and for other drivers at $5 per hour. Average time saving per vehicle will be 2 minutes if the overpass is built. No time saving occurs for the railroad. The installation will save the railroad an annual expense of $48,000 now spent for crossing guards. During the preceding 10-year period, the railroad has paid out $600,000 in settling lawsuits and accident cases related to the grade crossing. The proposed project will entirely eliminate both of these expenses. The state estimates that the new overpass will save it approximately $6,000 per year in expenses attributed directly to the accidents. The overpass, if built, will belong to the state. Perform a benefit-cost analysis to answer the question of whether the overpass should be built. If the overpass is built, how much should the railroad be asked to contribute to the state as its share of the $1,800,000 construction cost? 9. 5.9 An existing 2-lane highway between two cities is to be converted to a 4-lane divided freeway. The distance between them is 10 miles. The average daily traffic on the new freeway is forecast to average 20,000 vehicles per day over the next 20 years. Trucks represent 5% of the total traffic. Annual maintenance on the existing highway is $1,500 per lane- mile. The existing accident rate is 4.58 per million vehicle miles (MVM). Three alternative plans of improvement are now under consideration. Plan A: Add 2 lanes adjacent to the existing lanes at a cost of $450,000 per mile. It is estimated that this plan would reduce auto travel time by 2 minutes and truck travel time by 1 minute when compared with the existing highway. The estimated accident rate is 2.50 per MVM, and the annual maintenance is expected to be $1,250 per lane-mile for all 4 lanes. Plan B: Improve along the existing alignment with grade improvements at a cost of $650,000 per mile, and add 2 lanes. It is estimated that this would reduce auto and truck travel time by 3 minutes each compared with current travel times. The accident rate on the improved road is estimated to be 2.40 per MVM, and annual maintenance is expected to be $1,000 per lane-mile for all 4 lanes. Plan C: Construct a new 4-lane freeway on new alignment at a cost of $800,000 per mile. It is estimated that this plan would reduce auto travel time by 5 minutes and truck travel time by 4 minutes compared with current conditions. The new freeway would be 0.3 miles longer than the improved counterparts discussed in plans A and B. The estimated accident rate for plan C is 2.30 per MVM, and annual maintenance is expected to be $1,030 per lane-mile for all 4 lanes. If plan C is adopted, then the existing highway will be abandoned with no salvage value. Useful data: Incremental operating cost  – Autos  6 cents/mile  – Trucks 18 cents/mile Time saving  – Autos  3 cents/minute  – Trucks 15 cents/minute Average accident cost=$1,200   If a 5% interest rate is used, then which of the three proposed plans should be adopted? Base your answer on the individual B/C ratios of each alternative. When calculating these values, consider any annual incremental operating costs due to distance, a user disbenefit rather than a cost. 10. 5.10 A 50-meter tunnel must be constructed as part of a new aqueduct system for a city. Two alternatives are being considered. One is to build a full-capacity tunnel now for $500,000. The other alternative is to build a half-capacity tunnel now for $300,000 and then to build a second parallel half-capacity tunnel 20 years hence for $400,000. The cost of repair of the tunnel lining at the end of every 10 years is estimated to be $20,000 for the full-capacity tunnel and $16,000 for each half-capacity tunnel. Determine whether the full-capacity tunnel or the half-capacity tunnel should be constructed now. Solve the problem by B/C ratio analysis using a 5% interest rate and a 50-year analysis period. There will be no tunnel lining repair at the end of the 50 years. 11. 5.11 Consider the following typical noise levels in decibels (dBA): .2-17 Full Alternative Text 1. Assume that you are responsible for designing a machine shop. How would you determine an acceptable level of noise? What costs and risks should you weigh? 2. What would your answer be for the design of a commercial aircraft? 12. 5.12 Epidemiological data indicate that only a handful of patients have contracted the AIDS (acquired immune deficiency syndrome) virus from health care workers. Many, though, have called for the periodic testing of all health care workers in an effort to protect or at least reduce the risks to the public. Identify the costs and benefits associated with such a program. Develop an implementation plan for nationwide testing. How would you go about measuring the costs of the plan? What are the costs and risks of not testing? 13. 5.13 As chief industrial engineer in a manufacturing facility, you are contemplating the replacement of the spreadsheet procedures that you are now using for production scheduling and inventory control with a material requirements planning system. A number of options are available. You can do it all at once and throw out the old system; you can phase in the new system over time; you can run both systems simultaneously, and so on. Identify the costs, benefits, and risks with each approach. Construct a decision tree for the problem. Assume that the benefits of any option depend on the future state of the economy which may be “good” or “bad” with probabilities 0.7 and 0.3, respectively. 14. 5.14 The daily demand for a particular type of printed circuit board in an assembly shop can assume one of the following values: 100, 120, or 130 with probabilities 0.2, 0.3, and 0.5. The manager of the shop thus is limiting her alternatives to stocking one of the three levels indicated. If she prepares more boards than are needed in the same day, then she must reprocess those remaining at a cost price of 55 cents/board. Assuming that it costs 60 cents to prepare a board for assembly and that each board produces $1.05 in revenue, find the optimal stocking level by using a decision tree model. 15. 5.15 In Exercise 5.14 , suppose that the owner wishes to consider her decision problem over a 2-day period. Her alternatives for the second day are determined as follows. If the demand in day 1 is equal to the amount stocked, then she will continue to order the same quantity on the second day. Otherwise, if the demand exceeds the amount stocked, she will have the options to order higher levels of stock on the second day. Finally, if day 1’s demand is less than the amount stocked, then she will have the options to order any of the lower levels of stock for the second day. Express the problem as a decision tree, and find the optimal solution using the cost data given in Exercise 5.14 . 16. 5.16 Zingtronics Corp. has completed the design of a new graphic- display unit for computer systems and is about to decide on whether it should produce one of the major components internally or subcontract it to another local firm. The advisability of which action to take depends on how the market will respond to the new product. If demand is high, then it is worthwhile to make the extra investment for special facilities and equipment needed to produce the component internally. For low demand it is preferable to subcontract. The analyst assigned to study the problem has produced the following information on costs (in thousands of dollars) and probability estimates of future demand for the next 5- year period: Future demand Action Low Average High Produce $140 $120 $90 Subcontract $100 $110 $160 Probability 0.10 0.60 0.30 1. Prepare a decision tree that describes the structure of this problem. 2. Select the best action on the basis of the initial probability estimates for future demand. 3. Determine the expected cost with perfect information (i.e., knowing future demand exactly). 17. 5.17 Refer to Exercise 5.16 . The management of Zingtronics is planning to hire Dr. Lalith deSilva, an economist and head of a local consulting firm, to prepare an economic forecast for the computer industry. The reliability of her forecasts based on previous assignments is provided by the following table of conditional probabilities. Future demand Economic forecast Low Average High Optimistic 0.1 0.1 0.5 Normal 0.3 0.7 0.4 Pessimistic  0.6   0.2   0.1  1.0 1.0 1.0 1. Select the best action for Zingtronics if Dr. deSilva submits a pessimistic forecast for the computer industry. 2. Prepare a decision tree diagram for the problem with the use of Dr. deSilva’s forecasts. 3. What is the Bayes’ strategy for this problem? 4. Determine the maximum fee that should be paid for the use of Dr. deSilva’s services. 18. 5.18 Allen Konigsberg is an expert in decision support systems and has been hired by a small software engineering firm to help plan their R&D strategy for the next 6 to 12 months. The company wishes to devote up to 3 person-years, or roughly $200,000, to R&D projects. Show how Konigsberg can use a decision tree to structure his analysis. State all of your assumptions. 19. 5.19 The management of Dream Cruises, Ltd., operating in the Caribbean, has established the need for expanding its fleet capacity and is considering what the best plan for the next 8-year planning period will be. One strategy is to buy a larger 40,000-ton cruise ship now, which would be most profitable if demand is high. Another strategy would be to start with a small 15,000-ton ship now and consider buying another medium 25,000-ton ship 3 years later. The planning department has estimated the probabilities for high and low demand for each period to be 0.6 and 0.4 respectively. If the company buys the large ship, then the annual profit after taxes for the next 8 years is estimated to be $800,000 if demand is high and $100,000 if it is low. If the company buys the small ship, then the annual profits each year will be $300,000 if demand is high and $150,000 if it is low. After 3 years with the small vessel, a decision for new capacity will be reviewed. At this time, the firm may decide to expand by adding a 25,000-ton ship or by continuing with the small one. The annual profit after expansion will be $700,000 if demand is high and $120,000 if it is low. 1. Prepare a decision tree that shows the actions available, the states of nature, and the annual profits. 2. Calculate the total expected profit for each branch in the decision tree covering 8 years of operation. 3. Determine the optimum fleet-expansion strategy for Dream Cruises, Ltd. 20. 5.20 Referring to Exercise 5.19 , determine the optimal fleet-expansion strategy if projected annual profits are discounted at the rate of 12%. 21. 5.21 Pipeline Construction Model. This exercise is a variation of the classical “machine setup” problem. The installation of an oil pipeline that runs from an oil field to a refinery requires the welding of 1,000 seams. Two alternatives have been specified for performing the welding: (1) use a team of ordinary and apprentice welders (B-team) only, or (2) use a team of master welders (A-team) who check and rework (as necessary) the welds of the B-team. If the first alternative is chosen, then it is estimated from past experience that 5% of the seams will be defective with probability 0.30, 10% will be defective with probability 0.50, or 20% will be defective with probability 0.20. However, if the B- team is followed by the A-team, then a defective rate of 1% is almost certain. Material and labor costs are estimated at $400,000 when the B-team is used strictly, whereas these costs rise to $530,000 when the A-team is also brought in. Defective seams result in leaks that must be reworked at a cost of $1,200 per seam, which includes the cost of labor and spilled oil but ignores the cost of environmental damage. 1. Determine the optimal decision and its expected cost. How might environmental damage be taken into account? 2. A worker on the pipeline with a Bayesian inclination (from long years of wagering on the ponies) has proposed that management consider x-ray inspections of five randomly selected seams following the work of the B-team. Such an inspection would identify defective seams, which would provide management with more information for the decision on whether to bring in the A- team. It costs $5,000 to inspect the five seams. Financially, is it worthwhile to carry out the inspection? If so, then what decision should be made for each possible result of the inspection? 22. 5.22 A decision is to be made as to whether to perform a complete audit of an accounts receivable file. Substantial errors in the file can result in a loss of revenue to the company. However, conducting a complete audit is expensive. It has been estimated that the average cost of auditing one account is $6. However, if a complete audit is conducted, resulting in the true but unknown proportion p of the accounts in error being reduced, then the loss of revenue may be reduced significantly. Andrew Garland, the audit manager, has the option of first conducting a partial audit before his decision on the complete audit. Using the prior probability distribution and payoffs (costs) given in the table below, develop a single auditing plan based on a partial audit of three accounts. Work with opportunity losses. Proportion of accounts in error, p Prior probability of p, P(p) Conditional cost Do not audit Complete audit 0.05 0.2  $1,000 $10,000 0.50 0.7 $10,000 $10,000 0.95 0.1 $29,000 $10,000 1. Develop the opportunity loss matrix—the matrix derived from the payoff matrix (state of nature versus cost) by subtracting from each entry the smallest entry in its row. 2. Structure the problem in the form of a decision tree. Specify all actions, sample outcomes, and events. Indicate opportunity losses and probabilities at all points on the tree. Show all calculations. 3. Develop the conditional probability matrix, P(X)|p). 4. Develop the joint probability matrix. 5. Is the single auditing plan better than not conducting a partial audit? 1. What is the expected opportunity loss with no partial auditing? 2. What is the expected value of perfect information (EVPI)? Note that EVPI is the difference between the optimal EMV under perfect information and the optimal EMV under the current uncertainty (before collecting more data). 3. What is the expected value of sample information (EVSI), where EVSI=EVPI−EMV? The evaluation of EMV should take into account the results of the partial audit. 4. State how you would determine the optimal number of partial audits in a sampling plan. 23. 5.23 A trucking company has decided to replace its existing truck fleet. Supplier A will provide the needed trucks at a cost of $700,000. Supplier B will charge $500,000, but its vehicles may require more maintenance and repair than those from supplier A. The trucking company is also considering modernizing its maintenance and repair facility either by renovation or by renovation and expansion. Although expansion is generally more expensive than renovation alone, it enables greater efficiency of repair and therefore reduced annual operating costs of the facility. The estimated costs of renovation alone and of renovation and expansion, as well as the ensuing operating costs, depend on the quality of the trucks that are purchased and the extent of the maintenance that they require. The trucking company therefore has decided on the following strategy: purchase the trucks now; observe their maintenance requirements for 1 year; then make the decision as to whether to renovate or to renovate and expand. During the 1-year observation period, the company will get additional information about expected maintenance requirements during years 2 through 5. If the trucks are purchased from supplier A, then first-year maintenance costs are expected to be low ($30,000) with a probability of 0.7 or moderate ($40,000) with a probability of 0.3. If they are purchased from supplier B, then maintenance costs will be low ($30,000) with a probability of 0.3, moderate ($40,000) with a probability of 0.6, or high ($50,000) with a probability of 0.1. The costs of renovation, shown here, depend on the first year’s maintenance experience. One-year maintenance requirements Renovation costs Renovation and expansion costs Low   $150,000 $300,000 Moderate $200,000 $500,000 High   $300,000 $700,000 Expected maintenance costs for years 2 through 5 can best be estimated after observing the maintenance requirements for the first year (Table 5.17 ). Probabilities of various maintenance levels in years 2 through 5 depend on the types of trucks selected and the maintenance experience during year 1 (Table 5.18 ). TABLE 5.17  Renovate Renovate and Supplier First-year maintenance Maintenance years 2–5 expand Maintenance years 2–5 Low Moderate Low Moderate A Low   $100,000 $150,000 $40,000 $60,000 Moderate $100,000 $150,000 $40,000 $60,000 Moderate High   Moderate High   B Low   $150,000 $200,000 $50,000 $90,000 Moderate $150,000 $200,000 $50,000 $90,000 High   $250,000 $300,000 $70,000 $100,000 TABLE 5.18  Supplier First-yearmaintenance Maintenance level, years 2–5 Low Moderate High A Low   0.7 0.3 — Moderate 0.4 0.6 — B Low   — 0.5 0.5 Moderate — 0.4 0.6 High   — 0.3 0.7 Use decision tree analysis to determine the strategy that minimizes expected costs. Bibliography General Models Baker, N. R., “R&D Project Selection Models: An Assessment,” IEEE Transactions on Engineering Management, Vol. EM-21, No. 4, pp. 165– 171, 1974. Davis, J., A. Fusfeld, E. Scriven, and G. Tritle, “Determining a Project’s Probability of Success,” Research Technology Management, Vol. 44, No. 3, pp. 51–57, 2001. Gass, S. I., “Model World: When is a Number a Number?” Interfaces, Vol 31, No. 1, pp. 93–103, 2001. Hobbs, B. F., “A Comparison of Weighting Methods in Power Plant Siting,” Decision Science, Vol. 11, No. 4, pp. 725–737, 1980. Madey, G. R. and B. V. Dean, “Strategic Planning for Investment in R&D Using Decision Analysis and Mathematical Programming,” IEEE Transactions on Engineering Management, Vol. EM-32, No. 2, pp. 84– 90, 1986. Mandakovic, T. and W. E. Souder, “An Interactive Decomposable Heuristic for Project Selection,” Research Management, Vol. 31, No. 10, pp. 1257–1271, 1985. Mintzer, I., Environmental Externality Data for Energy Technologies, Technical Report, Center for Global Change, University of Maryland, College Park, MD, 1990. Shachter, R. D., “Evaluating Influence Diagrams,” Operations Research, Vol. 34, No. 6, pp. 871–882, 1986. Souder, W. E. and T. Mandakovic, “R&D Project Selection Models,” Research Management, Vol. 29, No. 4, pp. 36–42, 1986. Benefit/Cost Analysis Agogino, A. M., O. Nour-Omid, W. Imaino, and S. S. Wang, “Decision- Analytic Methodology for Cost-Benefit Evaluation of Diagnostic Testers,” EE Transactions, Vol. 24, No. 1, pp. 39–54, 1992. Bard, J. F., “The Costs and Benefits of a Satellite-Based System for Natural Resource Management,” Socio-Economic Planning Sciences, Vol. 18, No. 1, pp. 15–24, 1984. Bordman, S. L., “Improving the Accuracy of Benefit-Cost Analysis,” IEEE Spectrum, Vol. 10, No. 9, pp. 72–76, September 1973. Dicker, P. F. and M. P. Dicker, “Involved in System Evaluation? Use a Multiattribute Analysis Approach to Get the Answer,” Industrial Engineering, Vol. 23, No. 5, pp. 43–73, May 1991. Newnan, D. G., J. P. Lavelle, and T. G. Eschenbach, Engineering Economic Analysis, Ninth Edition, Oxford University Press, Cary, NC, 2004. Walshe, G. and P. Daffern, Managing Cost Benefit Analysis, Macmillan Education, London, 1990. Risk Issues Bell, T. E., “Special Report on Designing and Operating a Minimum- Risk System,” IEEE Spectrum, Vol. 26, No. 6, June 1989. Committee on Public Engineering Policy, Perspectives on Benefit-Risk Decision Making, National Academy of Engineering, Washington, DC, 1972. Dougherty, E. M. and J. R. Fragola, Human Reliability Analysis, John Wiley & Sons, New York, 1988. Kaplan, S. and B. J. Garrick, “On the Quantitative Definition of Risk,” Risk Analysis, Vol. 1, No. 1, pp. 1–23, 1981. Kumamoto, H. and E. J. Henley, Probabilistic Risk Assessment and Management for Engineers and Scientists, Second Edition, John Wiley & Sons, New York, 2001. Lowrance, W. W., Of Acceptable Risk: Science and the Determination of Safety, William Kaufmann, Los Altos, CA, 1976. Makridakis, S., S. C. Wheelwright, and R. J. Hyndman, Forecasting: Methods & Applications, Third Edition, John Wiley & Sons, New York, 1997. Sage, A. P. and E. B. White, “Methodologies for Risk and Hazard Assessment: A Survey and Status Report,” IEEE Transactions on System, Man, and Cybernetics, Vol. SMC-10, No. 8, pp. 425–446, 1980. Vose, D., Risk Analysis: A Quantitative Guide, Second Edition, John Wiley & Sons, New York, 2000. Yates, J. F. (Editor), Risk Taking Behavior, John Wiley & Sons, New York, 1991. Decision Trees Call, J. H. and W. A. Miller, “A Comparison of Approaches and Implementations for Automating Decision Analysis,” Reliability Engineering and System Safety, Vol. 30, pp. 115–162, 1990. Canada, J. R., W. G. Sullivan, and J. A. White, Capital Investment Analysis for Engineering and Management, Second Edition, Prentice Hall, Upper Saddle River, NJ, 1996. Clemen, R. T., Making Hard Decisions: An Introduction to Decision Analysis, Second Edition, Duxbury Press, Belmont, CA, 1996. Goodwin, P. and G. Wright, Decision Analysis for Management Judgment, Second Edition, John Wiley & Sons, New York, 2000. Lindley, D.V., Making Decisions, Second Edition, John Wiley & Sons, New York, 1996. Maxwell, D. T., “Decision Analysis: Aiding Insight VI,” OR/MS Today, Vol. 29, No. 5, pp. 44–51, June 2002. Raiffa, H., Decision Analysis: Introductory Lectures on Choices under Uncertainty, Addison-Wesley, Reading, MA, 1968. Real Options Amram, A. and N. Kulatilaka, Real Options: Managing Strategic Investment in an Uncertain World, Harvard Business School Press, Boston, MA, 1999. Boute, R., E. Demeulemeester, and W. S. Herroelen, “A Real Options Approach to Project Management,” International Journal of Projection Research, Vol. 42, No. 9, pp. 1715–1725, 2004. Copeland, T., “The Real-Options Approach to Capital Allocation,” Strategic Finance, Vol. 83, No. 4, pp. 33–37, 2001. Huchzermeier, A. and C. H. Loch, “Project Management Under Risk: Using the Real Options Approach to Evaluate Flexibility in R&D,” Management Science, Vol. 47, No. 1, pp. 85–101, 2001. Trigeorgis, L., Real Options, MIT Press, Cambridge, MA, 1997. Wang, J., and W. L. Hwang, “A Fuzzy Set Approach for R&D Portfolio Selection Using a Real Options Valuation Model,” Omega, Vol. 35, No.3, pp. 247–257, 2007. Appendix 5A Bayes’ Theorem for Discrete Outcomes For a given problem, let there be n mutually exclusive, collectively exhaustive possible outcomes S 1 ,…, S i ,…, S n whose prior probabilities P( S i ) have been established. The laws of probability require ∑i=1nP(Si)=1, 0≤P(Si)≤1, i=1, …,n If the results of additional study, such as sampling or further investigation, are designated as X, where X is discrete and P(X)>0, Bayes’ theorem can be
written as
P(Si|X)=P(X|Si)∑j=1nP(X|Sj)P(Sj) (5A.1)
The posterior probability P(Si|X) is the probability of outcome Si given that
additional study resulted in X. The probability of X and Si occurring,
P(X|Si)P(Si), is the “joint” probability of X and Si or P(X, Si). The sum of all
of the joint probabilities is equal to the probability of X. Therefore, Eq.
(5A.1) can be written
P(Si|X)=P(X|Si)P(Si)P(X) (5A.2)
A format for application is presented in Table 5A.1. The columns are as
follows.
TABLE 5A.1  Format for
Applying Bayes’ Theorem
(1) (2) (3) (4)=(2)×(3) (5)=(4)/∑
State Prior
Probability
of sample Joint probability
Posterior
probability

probability outcome, X P(Si|X)
S1 P(S1) P(X|S1) P(X|S1)P(S1) P(X|S1)P(S1)
S2 P(S2) P(X|S2) P(X|S2)P(S2) P(X|S2)P(S2)
· · · · ·
· · · · ·
· · · · ·
Si P(Si) P(X|Si) P(X|Si)P(Si) P(X|Si)P(Si)
· · · · ·
· · · · ·
Sn P(Sn) P(X|Sn) P(X|Sn)P(Sn) P(X|Sn)P(Sn)
∑i=1n P(Si)=1 ∑i=1nP(X|Si)P(Si)=P(X) ∑i=1nP(Si|X
1. Si: potential states of nature.
2. P(Si): estimated prior probability of Si. (Note: This column sums to
one.)
3. P(X|Si): the conditional probability of getting sample or added study
results X, given that Si is the true state (assumed to be known).
4. P(X|Si)P(Si): joint probability of getting X and Si; the summation of this
column is P(X), which is the probability that the sample or added study
results in outcome X.
5. P(Si|X): posterior probability of Si given that sample outcome resulted
in X; numerically, the ith entry is equal to the ith entry of column (4)
divided by the sum of the values in column (4). (Note: Column (5) sums
to unity.)

Chapter 6 Multiple-Criteria
Methods for Evaluation and Group
Decision Making
6.1 Introduction
It is often the case, particularly in the public sector, that goods and services
are either of a collective nature, such as those for defense and space
exploration, or subsidized so that their prevailing market price is an
unrealistic measure of the actual cost to the community. In these
circumstances, an attempt must be made to find a suitable undistorted
“price.”
When the analysis turns to such intangible considerations as safety, health,
and the quality of life, it is rarely possible to find a single variable whose
direct measurement will provide a valid indicator. Often a surrogate is used.
For example, a city’s environmental character could be evaluated by means
of an index composed of air pollution levels, noise levels, traffic flow rates,
and pedestrian densities. Another index might include crime, fire alarms, and
suicide rates. At the national level, it is common to cite unemployment
percentages, the consumer and producer price indices, the level of the Dow
Jones industrial stocks, and the amount of manufacturer inventories as
indicators of general economic well-being. In fact, each of these measures is
a composite of a multitude of elements, weighted and summed together in
what many would view as an arbitrary manner. A variety of procedures for
doing this were presented in Chapter 5. For evaluating large, complex
projects, more systematic and rational procedures are required. In this
chapter, we focus on methods that have been developed to bring greater rigor
to the evaluation and selection process.

6.2 Framework for Evaluation and
Selection
The success of a project depends on a host of factors, the foremost being its
ability to meet critical performance requirements. Success also depends on
the likelihood that the project will remain within the planned schedule and
budget, the technological opportunities that it offers beyond the immediate
application, and the user’s perception regarding its ability to satisfy long-term
organization goals. For balancing each of these factors, a value model is
needed. Such a model offers the decision maker a framework for conducting
the underlying tradeoffs.
A paradigm for any decision analysis is depicted in Figure 6.1. In the context
of project management, a decision maker must pick the most “preferred”
alternative from a finite set of candidates. Here, the system model may be as
simple as a spreadsheet or as elaborate as a dynamic mathematical
simulation. Consideration should be given to the full range of economic,
technological, and political aspects of the project. Each alternative, together
with the prevailing uncertainties, is fed into the system model, and a
particular outcome is reported.
Figure 6.1

Decision analysis paradigm.
If the uncertainties are minimal and the data are reliable, the outcomes will be
fairly accurate. When uncertainty dominates, it may not be possible to
develop a valid system model. The problems, for which decision analysis is
most effective, lie somewhere between these two extremes. For example, if
an advanced energy system is to be developed, then certain engineering
principles and experience with prototypes should give a good indication of
performance. However, some uncertainties will still exist, such as the cost of
the system in mass production or its reliability in commercial operation.
In the decision analysis paradigm, the outcomes of the system model provide
the input to the value model. The output of the latter is a statement of the
decision maker’s preferences in terms of a rank ordering of the outcomes or
as numerical values that indicate strength of preference as well as rank.
6.2.1 Objectives and Attributes1
1The word attribute is used to describe what is important in a decision
problem and is often interchangeable with objective and criterion. A finer
distinction can be made as follows: an objective represents direction of
improvement or preference for one or more attributes, whereas criterion is a
standard or rule that guides decision making.
For many projects, there are multiple—and, at times—competing objectives
or goals. They are stated in terms of properties, either desirable or
undesirable, that determine a decision maker’s preferences for the outcomes.
For the design of an automobile, for example, several objectives might be to
(1) minimize production costs, (2) minimize fuel consumption, (3) minimize
air pollution, and (4) maximize safety. The purpose of the value model is to
take the outcomes of the system model, determine the degree to which they
satisfy each of the objectives, and then make the necessary tradeoffs to arrive
at a ranking for the alternatives that correctly expresses the preferences of the
decision maker.
The value model is developed in terms of a hierarchy of objectives, as shown

in Figure 6.2 for an automobile design project. To quantify the model, a unit
of measurement must be assigned to the lowest members of the hierarchy.
These members are called attributes and may be scaled in any number of
ways depending on the evaluation technique used. In Figure 6.2, eight
attributes are used to quantify the value model. They may be represented by a
8-component vector: x=( x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 ). A specific
occurrence of an attribute is called a state. An attribute state for the objective
“minimize fuel consumption” might be x 3 =35 miles per gallon.
Figure 6.2
Hierarchy of objectives for advanced vehicle systems.
Figure 6.2 Full Alternative Text
Both theory and practice have shown that the set of attributes should satisfy
the following requirements for the value model to be a valid and useful
representation of the decision maker’s preference structure.
1. Completeness. The set of attributes should characterize all of the factors
to be considered in the decision-making process.
2. Importance. Each attribute should represent a significant criterion in the
decision-making process, in the sense that it has the potential for
affecting the preference ordering of the alternatives under consideration.

3. Measurability. Each attribute should be capable of being objectively or
subjectively quantified. Technically, this requires that it be possible to
establish a utility function (see Chapter 3 for a discussion of utility
functions) for the attribute.
4. Familiarity. Each attribute should be understandable to the decision
maker in the sense that he should be able to identify preferences for
different states.
5. Uniqueness. No two attributes should measure the same criterion, a
situation that would result in double counting.
6. Independence. The value model should be structured so that changes
within certain limits in the state of one attribute should not affect the
preference ordering for states of another attribute or the preference
ordering for gambles over the states of another attribute (more will be
said about this later).
If an attribute does not meet these conditions, then it should either be
redefined by, say, dividing its range into smaller intervals and introducing
“sub-attributes” corresponding to these intervals or be combined with other
attributes.
6.2.2 Aggregating Objectives into a
Value Model
Once attributes have been assigned to all the objectives and attribute states
have been determined for all possible outcomes, it is necessary to aggregate
the states by constructing a single unit of measurement that will accurately
represent the decision maker’s preference ordering for the outcomes. This
was achieved somewhat arbitrarily in Chapter 5 by specifying weights for
each attribute or criterion. A more rigorous and defendable method of doing
this is the “willingness to pay” or “pricing out” technique (Keeney and Raiffa
1976). One attribute is singled out as the reference, preferably an attribute
measured in dollars, and rates of substitution are determined for the others.

Two procedures for operationalizing this concept will now be presented.
Complementary techniques have been developed by Graves et al. (1992),
Lewandowski and Wirezbicki (1989), and Lotfi et al. (1992), just to name a
few.

6.3 Multiattribute Utility Theory
If the set of attributes satisfies the requirements listed above, then it is
possible to formulate a mathematical function called a multiattribute utility
function that will assign numbers, called outcome utilities, to each outcome
state. In general, the utility U( x )=U( x 1 , x 2 ,…, x N ), of any
combination of outcomes ( x 1 , x 2 ,…, x N ) for N attributes can be
expressed as either (1) an additive or (2) a multiplicative function of the
individual attribute utility functions U 1 ( x 1 ), U 2 ( x 2 ),…, U N ( x N )
provided that each pair of attributes is:
1. Preferentially independent of its complement; that is, the preference
order of consequences for any pair of attributes does not depend on the
levels at which the other attributes are held.
2. Utility independence of its complement; that is, the conditional
preference for lotteries (probabilistic tradeoffs) involving only changes
in the levels for any pair of attributes does not depend on the levels at
which the other attributes are held.
To illustrate condition 1, suppose that four attributes for a given project are
profitability, time to market, technical risk, and commercial success.
Preferential independence means that if we judge technological risk, for
example, to be more important than profitability, then this relationship is true
regardless of whether the level of profitability is high, low, or somewhere in
between and also regardless of the value of the other attributes.
The second condition, utility independence, means that if we are deciding on
the preference ordering (ranking) for probabilistic tradeoffs between, for
example, technological risk and time to market, then this can be done
regardless of the value of profitability. An example of preference ordering of
probabilistic tradeoffs between technological risk and time to market is, for
instance, a 25% chance of very low risk and a 70% chance of quick time to
market is preferred to, say, a 15% chance of very low risk and a 90% chance
of quick time to market.

Before proceeding it is necessary to verify that these two conditions are valid,
or more correctly, to test and identify the bounds of their validity. A
procedure for doing this is provided by Keeney (1977). The mathematical
notation used to describe the model is given below:
x i state of the ith attribute
x i 0 least preferred state to be considered for the ith attribute
x i * most preferred state to be considered for the ith attribute
x vector ( x 1 , x 2 ,…, x N ) of attribute states characterizing a specific
outcome
x 0 outcome constructed from the least preferred states of all attributes;
x 0 =( x 1 0 ,…, x N 0 )
x * outcome constructed from the most preferred states of all attributes;
x * =( x 1 * ,…, x N * )
( x i , x ¯ i 0 ) outcome in which all attributes except the ith attribute are
at their least preferred state
U i ( x i ) utility function associated with the ith attribute
U(x) utility function associated with the outcome x
k i scaling constant for the ith attribute; k i =U( x i * , x ¯ i 0 )
k master scaling constant
Now, if the two independence conditions hold, then U(x) assumes the
following multiplicative form:
U( x )= 1 k { ∏ i=1 N [ 1+k k i U i  ( x i ) ]−1} (6.1a)
where the master scaling constant k is determined from the equation 1+k=п
i(1+kki). If Σiki>1, then −10; if Σiki=1, then k=0 and
Eq. (6.1a) reduces to the additive form:

U( x )= ∑ i=1 N k i U i ( x i ) (6.1b)
Because utility is a relative measure, the underlying theory permits the
arbitrary assignment of U i  ( x i 0 )=0 and U i  ( x i * )=1; that is, the worst
outcome for each attribute is given a utility value of 0 and the best outcome is
given a utility value of 1. The shape of the utility function depends on the
decision maker’s subjective judgment on the relative desirability of possible
outcomes. A pointwise approximation of this function can be obtained by
asking a series of lottery-type questions such as the following: For attribute i,
what certain outcome, x i , would be equally desirable as realizing the highest
outcome with a probability p and the lowest outcome with a probability of (
1−p )? This can be expressed in utility terms using the extreme values x i *
and x i 0 as
U i ( x i =? )=p U i  ( x i * )+( 1−p ) U i  ( x i 0 )=p
To construct the curve, p can be varied in fixed increments until either a
continuous function can be approximated or enough discrete points have been
assessed to give an accurate picture. Alternatively, one could specify the
certain outcome x i over a range of values and ask questions such as, “At
what p is the certain outcome x i equally desirable as p U i  ( x i * )+( 1−p ) U
i  ( x i 0 )? ” Graphically, the assessment of p can be represented as the lottery
shown in Figure 6.3.
Figure 6.3
Graphical assessment of indifference probability.

Example 6-1
Suppose that we want to estimate a utility function for the relative fuel
economy of an automobile under development (attribute 3 in Figure 6.2). The
best achievable might be 80 mpg, and the worst might be 20 mpg. These
outcomes would give the utility function values of 1 and 0, respectively. For
p=0.5 (the 50-50 lottery), the question would be, “How many miles per
gallon as a “sure thing” would be equivalent to a gamble if there were a 50%
chance of realizing 80 mpg and a 50% chance of realizing 20 mpg? If the
answer is, say, 60 mpg, the new utility value would be calculated as
U( x=60 ) =0.5U( x=80 )+0.5U( x=20 ) =0.5( 1 )+0.5( 0 )=0.5
Note that the utility of the certain outcome equals the probability of the best
outcome. Figure 6.4 depicts the interview process. A typical utility curve that
resulted from the questioning of a representative of a consumer’s group is
shown in Figure 6.5 (Feinberg et al. 1985).

Figure 6.4
Sample interview question for relative fuel economy.
Figure 6.5
Example of utility curve for representative consumer.
Figure 6.5 Full Alternative Text
Once utility functions for all attributes have been determined, the next step is
to assess the scaling constants, k i . For both the multiplicative Eq. (6.1a) and
additive Eq. (6.1b) models, k i =U( x i * , x ¯ i 0 ), where 0≤ k i ≤1. That is,
k i is the utility value associated with the outcome where attribute i is at its
best value, x i * , and all other attributes are at their worst values, x ¯ i 0 . In

assessing the k i ‘s, the following type of question is usually asked:
For what probability p are you indifferent between:
1. The lottery giving a p chance at x * ≡( x 1 * ,…, x N * ) and a ( 1−p )
chance at x 0 ≡( x 1 0 ,…, x N 0 ), versus
2. The consequence ( x 1 0 ,…, x i−1 0 , x i * , x i−1 0 ,…, x N 0 ).
The interview sheet used for determining the scaling constant associated with
relative fuel economy is shown in Figure 6.6 (the responses to the last two
questions give an indication of the degree to which the independent
conditions hold). The result of the assessment is that, in general, k i =p. Good
practice suggests that before assessing the scaling constants, the attributes
should be ranked in ascending order of importance as they progress from
their worst to their best states. Figure 6.7 displays the question sheet that was
used for this purpose.

Figure 6.6
Sample interview question used to determine scaling constant for
the relative fuel economy attribute.
Figure 6.7
Sample interview question used to determine order of importance
of attributes.

Attribute
Relative
fuel
economy
Initial
cost
Life-cycle
cost/mile
Maintain-
ability Safety
Refuel
time
Unrefueled
range
Best state 80 mpgequivalent $5,000 $0.20/mile 10 10
0.17
hours
(10
min)
250 miles
Worst
state
20 mpg
equivalent $25,000 $1.00/mile 0 0
8.0
hours 50 miles
Order of
importance
The last step in the evaluation and selection process is to rank the
alternatives. This is done by using the multiattribute utility function to
calculate outcome utilities for each alternative under consideration. If two or
more alternatives seem to be close in rank, then their sensitivity to both the
scaling constants and the utility functions should be examined. Appendix 6A
contains a more detailed example of the evaluation process.
A final point to make about multiattribute utility theory (MAUT) concerns
the possibility that the state of an attribute may be uncertain. “Completion
time of a task,” “reliability of a subassembly,” and “useful life of the system”
are some examples of attributes whose states may take on different values
with known (or, more distressingly, with unknown) probability. In these
cases, x i is really a random variable, so it is more appropriate to compute the
expected utility of a particular outcome. For the additive model, this can be
done with the following equation:
E[ U( x ) ]= ∑ i=1 N [ k i ∫ −∞ ∞ U i ( x i ) f i ( x i )d( x i )] (6.2)
where f i ( x i ) is the probability density function associated with attribute i,
and E[ ⋅ ] is the expectation operator (Keeney and von Winterfeldt 1991).
Commercial software is available for helping in the assessment of f i , as well
as the scaling constants k i and the individual utility functions U i . ■

6.3.1 Violations of Multiattribute
Utility Theory
In practice, as pointed out by Schoemaker (1982), among others, MAUT is
rarely used. Human decision makers do not structure decision problems as
holistically and as comprehensively as required and suggested by expected
utility theory. Further, human decision makers do not process information,
particularly probabilities associated with uncertain outcomes, with the rigor
and consistency required by expected utility theory. Human decision makers
tend to use heuristics rules (otherwise referred to as “intuition” or “gut-feel”)
in processing information and making decisions. Ultimately, human decision
makers—even with the aid of advanced computing—satisfice—rather than
optimize.
Schoemaker (1982) surveys a number of controlled experiments that have
proven that human decision makers consistently violate some of the key
axioms and assumptions of MAUT. Coombs (1975) conducted an experiment
in which decision makers were asked to rank three gambles A, B, and C in
order of attractiveness where C was a probability mixture of A and B. For
example, if A offers a 50-50 chance at $3 or $0, and B offers a 50-50 chance
at $5 or $0, then a 40-60 mixture of A and B (i.e., gamble C) offers outcomes
of $5, $3, and $0 with probabilities 0.3, 0.2, and 0.5, respectively. According
to utility theory, gamble C should be ranked in-between A and B in terms of
attractiveness. However, in the Coombs experiment, 46% of participants
ranked the gambles CAB, CBA, ABC, or BAC.
Kahneman and Tversky (1979) described the Allais Paradox. In Situation A,
decision makers must choose between:
(1a) a certain loss of $45 or
(2a) a 0.5 probability of losing $100 and a 0.5 probability of losing $0.
In Situation B, decision makers must choose between:
(1b) a 0.1 probability of losing $45 and a 0.9 probability of losing $0 or

(2b) a 0.05 probability of losing $100 and a 0.95 probability of losing
$0.
Decision makers preferred alternative (2a) to alternative (1a) and alternative
(1b) to alternative (2b). If (2a) is preferred to (1a), then, from utility theory,
U( −45 )<0.5U( −100 )+.5U( 0 ). If (1b) is preferred to (2b), then 0.1U( −45 )+0.9U( 0 )>0.05U( −100 )+0.95U( 0 ) U( −45 )>0.5U( −100
)+0.5U( 0 ).
The Allais Paradox demonstrates that decision makers are not always
consistent with respect to their utility function.
Bar Hillel (1973) conducted an experiment which demonstrated decision
makers’ difficulties with assessing probability (a key tenet of utility theory).
In Bar Hillel’s experiment, participants were asked to consider three
alternatives:
Simple event: drawing a red marble from a bag containing 50% red and
50% white marbles
Conjunctive event: drawing a red marble seven times in succession, with
replacement from a bag containing 90% red marbles and 10% white
marbles
Disjunctive event: drawing a red marble at least once in seven
successive tries, with replacement from a bag containing 10% red
marbles and 90% white marbles
The probabilities of the three events are 0.5, 0.48, and 0.52, respectively.
However, the majority of participants preferred alternative 2 to alternative 1
and alternative 1 to alternative 3. Bar Hillel found that decision makers tend
to over-estimate the probability of conjunctive events and under-estimate the
probability of disjunctive events. This bias may be explained by anchoring.
The stated probability—0.1—of the elementary event provides a natural

starting point from which decision makers make an insufficient adjustment to
arrive at a correct ordering of the events.
Several studies, for example, Hershey and Schoemaker (1980), found that
decision makers are not risk averse, a central premise of utility theory. For
example, fewer than 40% of decision makers were willing to pay $100 to
protect themselves from a 1% chance of losing $10K. Although this
insurance was actuarially fair and risk-neutral, decision makers behaved as if
they were risk-seeking. Hershey and Schoemaker concluded that decision
makers have difficulty in processing information that deals with low
probability, high loss events.
Katona (1965) discussed the role that psychological factors play in economic
behavior. Unlike utility theory which assumes that human decision makers
are fully rational and can optimally assess probabilities of uncertain events
and outcomes, Katona demonstrated that human decision making is often
driven by emotional and psychological factors. He compared private savings
of workers who received a private pension from an employer (“forced
savings”) with private savings of workers who did not receive such a benefit.
Utility theory suggests that workers with forced savings would reduce their
own, private savings (the forced savings, in effect, “substitute” for savings
that a worker would personally contribute in order to reach a savings goal).
However, in Katona’s study, workers with forced savings actually increased
their private savings. Katona attributed this counter-intuitive result to
aspiration-level adjustments and goal-gradient effects. That is, as workers
tended to get closer to their ultimate, overall, savings goals, they tended to
accelerate and increase their personal savings (to complement their forced
savings employment benefit).
Ronen (1973) found that decision makers are sensitive to a problem’s
presentation. For example, interchanging two stages of a multi-stage lottery
can affect preferences. Ronen found that a 70% chance of getting a 30%
chance of receiving $100 was more attractive than a 30% chance of getting a
70% chance of receiving $100. According to utility theory, the two
alternatives are identical.
Related to Ronen’s work, Schoemaker and Kunreuther (1979) discovered a
context effect whereby the wording of decision alternatives can affect

preferences. For example, in an experiment with decision makers, they posed
a gamble formulation:
(1a) a sure loss of $10
(1b) a 1% chance of losing $1,000.
In contrast, they also posed an insurance formulation:
(2a) pay an insurance premium of $10
(2b) remain exposed to a hazard of losing $1,000 with a 1% chance.
Utility theory suggests that these two formulations are identical. However,
56% of decision makers preferred (1a) to (1b) where 81% preferred (2a) to
(2b). From a utility theory perspective, other factors, such as regret, may have
influenced some decision makers to switch from choosing (1b) to favor
purchasing insurance, alternative (2a).
Tversky and Kahnemen (1981) provided a second example of context effects
influencing preferences. Subjects were first asked to choose between two
alternatives for combating a disease which was expected to kill 600 people.
1. (1a) if program A is adopted, exactly 200 people will be saved
2. (1b) if program B is adopted, there is a 33% probability that 600 people
will be saved and a 67% probability that no one will be saved.
76% preferred program A.
A second group of decision makers was given the same choice but in slightly
altered form.
1. (2a) if program A is adopted, exactly 400 people will die
2. (2b) if program B is adopted, there is a 33% probability that nobody will
die and a 67% probability that 600 people will die
13% preferred program A.

The switching between preferring Program A in the first case and preferring
program B in the second case can be explained by the changes in wording
that can affect the reference point that decision makers use to evaluate
outcomes. Utility theory would insist that decision makers remain consistent
between the two cases in stating their preferences.
MAUT is based on decision makers making holistic choices based on
consideration of all relevant information involved in a decision. However,
numerous studies have shown that decisions are made in a decomposed
fashion using relative comparisons (“divide and conquer”). Human beings
find it easier to compare alternatives in a piecemeal, rather than a holistic,
fashion. Oftentimes, human decision makers will use conjunctive or
disjunctive decision making approaches. In a conjunctive decision process,
all attributes must satisfy certain minimum thresholds, whereas in a
disjunctive decision process, at least one critical criterion must be satisfied.
Oftentimes, a lexicographic decision model is used whereby the decision
process follows an elimination by aspect approach. For example, in choosing
a restaurant for dinner, a decision maker can rule out all restaurants that are
more than 10 miles away. Then, all restaurants where the average entrée cost
exceeds $25 can be ruled out, etc. In general, a decision process will vary,
depending on the task complexity (e.g., number of reasonable alternatives,
number of critical considerations, etc.).
According to utility theory, decision making requires a portfolio perspective.
Tversky and Kahnemen (1981), however, demonstrated an “isolation effect”
whereby decisions are made within a narrow, myopic context. For example,
subjects were asked to consider two scenarios:
Scenario A: If you purchase a $20 theater ticket which you lose while
waiting in the lobby—would you buy a new ticket?
Scenario B: If you discover that $20 is missing when you open your
wallet to purchase a theater ticket—would you buy a ticket?
The $20 loss seemed less relevant in Scenario B, although from a portfolio or
total wealth perspective, both scenarios are identical.
Tversky and Kahneman (1981) suggested that reference points are often

utilized by decision makers, in contradiction to utility theory. Tversky and
Kahneman postulated two scenarios.
Scenario A: Suppose you are about to purchase an item for $25; you
then learn that you can purchase the same item for $20 at another,
nearby store.
Scenario B: Same scenario as Scenario A, except now the item is
priced $500 originally and is available for $495 at a nearby store.
Would a decision maker leave the original store and purchase the item at a
nearby store? The 20% savings in Scenario A seems more attractive than the
1% savings in Scenario B. Most people’s reference dimension is percent
savings. However, utility theory suggests that a decision maker should
consider the final asset position in both scenarios. That is, in both scenarios,
the decision maker is exactly $5 ahead by switching stores (i.e., the two
scenarios are identical).
Thaler (1980) identified a sunk-cost fallacy that can influence decision
making. For example, consider a decision maker who bought a case of good
wine for $5 per bottle. A few years later, the decision maker’s wine merchant
offered to buy the wine back for $100 per bottle. The decision maker refused
to sell back the wine, although he never paid more than $35 for a bottle of
wine. The decision maker was influenced by a failure to properly consider
opportunity costs.
Researchers have found that decision makers often employ subjective
probabilities in evaluating uncertainty and making judgments. For example,
wishful thinking influences decision makers to inflate probabilities of
desirable outcomes. Overconfidence leads decision makers to construct
confidence intervals that are too tight. Kahnemen and Tversky (1972)
hypothesized the representative heuristic, characterized by the following
example. A doctor diagnoses a patient as having a certain disease A—rather
than disease B—based on the similarity of the patient’s symptoms to
textbook stereotypes and ignores possible differences in the a priori
probabilities of someone having each of these diseases. Tversky and
Kahneman also hypothesized the availability heuristic. For example, in
judging the chances of dying from a car accident versus lung cancer, people

may base their estimates solely on the frequencies with which they hear of
both events. Finally, Fischoff (1975) discussed hindsight bias which leads to
decision makers distorting probabilities. Specifically, events that happen
appear in retrospect more likely than they did before the outcome was known.
Another blind spot that decision makers have relative to assessing
probabilities is that new information is often underweighted in the revision of
opinions. Decision makers, at times, are conservative and anchor onto old
information with insufficient assimilation of new information.
Finally, Bar Hillel (1980) found that decision makers can be led astray by
perceptions regarding causal connections between pieces of information. For
example, decision makers were told that only 10% of taxi cabs in a city are
blue. Was a taxi cab, involved in a particular traffic accident, green or blue?
According to an eye witness, the taxi cab was blue. Decision makers entirely
focused on the reliability of the eye witness and did not consider the prior
probability of a blue taxi cab being involved in an accident. In contrast, a
second group of decision makers were told that, although there are an equal
number of blue and green cabs, historically only 10% of taxi cabs involved in
traffic accidents were blue. By emphasizing the causal connection of the prior
probabilities to the event markedly improved the decision makers’ posterior
probabilities.
All of these heuristic and sub-optimal rules that decision makers regularly
employ in common and everyday decision processes represent violations of
utility theory and demonstrate a consistent pattern of decision makers
deviating from normative decision making. Human decision makers cannot
and do not structure problems as holistically, and as comprehensively, as
utility theory suggests. Moreover, decision makers cannot process
information—in particular, assess probabilities—according to utility theory.
Human decision makers, ultimately, satisfice—rather than optimize—
decision making (i.e., they make decisions that are “good enough”—and not
necessarily optimal across the full range of alternatives).

6.4 Analytic Hierarchy Process
The analytic hierarchy process (AHP) was developed by Thomas Saaty to
provide a simple, but theoretically sound, multiple-criteria methodology for
evaluating alternatives (Saaty and Vargas 2000). Applications can be found
in such diverse fields as portfolio selection, transportation planning,
manufacturing systems design, and artificial intelligence. The strength of the
AHP lies in its ability to structure a complex, multiperson, multiattribute
problem hierarchically and then to investigate each level of the hierarchy
separately, combining the results as the analysis progresses. Pairwise
comparisons of the factors (which, depending on the context, may be
alternatives, attributes, or criteria) are undertaken using a scale that indicates
the strength with which one factor dominates another with respect to a higher
level factor. This scaling process can then be translated into priority weights
or scores for ranking the alternatives.
The AHP starts with a hierarchy of objectives. The top of the hierarchy
provides the analytic focus in terms of a problem statement. At the next level,
the major considerations are defined in broad terms. This is usually followed
by a listing of the criteria for each of the foregoing considerations. Depending
on how much detail is called for in the model, each criterion may then be
broken down into individual parameters whose values are either estimated or
determined by measurement or experimentation. The bottom level of the
hierarchy contains the alternatives or scenarios underlying the problem.
Figure 6.8 shows a three-level hierarchy developed for evaluating five
different approaches to assembling the U.S. space station while in orbit. The
focus of the problem is “selecting an in-orbit assembly system,” and the four
major criteria are human productivity, economics, design, and operations.
The five alternatives include an astronaut with tools outside the spacecraft, a
dexterous manipulator under human control, a dedicated manipulator under
computer control, a teleoperator maneuvering system with a manipulator kit,
or a computer-controlled dexterous manipulator with vision and force
feedback.

Figure 6.8
Summary three-level hierarchy for selection problem.
In the actual analysis, each of the criteria at level 2 was significantly
expanded to capture the detail necessary to make accurate comparisons (Bard
1986). For example, the criterion, human productivity, was expanded to
include factors such as workload, support requirements, crew acceptability,
and issues surrounding human-machine interfaces. Figure 6.9 depicts the full
portion of the hierarchy used for this criterion.

Figure 6.9
Human productivity objective hierarchy.
Figure 6.9 Full Alternative Text
6.4.1 Determining Local Priorities
Once the hierarchy has been structured, local priorities must be established
for each factor on a given level with respect to each factor on the level
immediately above it. This step is carried out by using pairwise comparisons
between the factors to develop the relative weights or priorities. The weight
of the ith factor is denoted by w i . Because the approach is basically

qualitative, it is arguably less burdensome to implement from both a data
requirement and a validation point of view than by using the multiattribute
utility approach of Keeney and Raiffa. For example, the MAUT’s
independence conditions do not need to be verified and utility preference
functions do not need to be derived. Nevertheless, AHP requires that the
following assumptions, stated in terms of axioms, hold if the methodology is
to be valid (Golden et al. 1989):
Axiom 1. Given any two alternatives (or sub-criteria) i and j from the
set of alternatives , the decision maker is able to provide a pairwise
comparison a ij of these alternatives under criterion c from the set of
criteria X on a reciprocal ratio scale; that is,
a ji = 1 a ij for all i, j∈
Axiom 2. When comparing any two alternatives i,j∈ , the decision
maker never judges one to be infinitely better than another under any
criterion c∈X; that is, a ij ≠∞ for all i, j∈ .
Axiom 3. The decision problem can be formulated as a hierarchy.
Axiom 4. All criteria and alternatives that have an impact on the given
decision problem are represented in the hierarchy. That is, all of the
decision maker’s intuition must be represented (or excluded) in the
structure in terms of criteria or alternatives.
These axioms can be used to describe the two basic tasks in the AHP:
formulating and solving the problem as a hierarchy (3 and 4) and eliciting
judgments in the form of pairwise comparisons (1 and 2). Such judgments
represent an articulation of the tradeoffs among the conflicting criteria and
are often highly subjective in nature. Saaty suggested that a 1 to 9 ratio scale
be used to quantify the decision maker’s strength of feeling between any two
alternatives with respect to a given criterion. The pairwise comparisons give
rise to the elements a ij which are viewed as the ratio of the weights for
factors i and j. In the ideal case, we have a ij = w i / w j . When n alternatives
are being compared, it is easy to see that
a i1 w 1 + a i2 w 2 +…+ a in w n =n w i  i=1,…,n (6.3)

In matrix form, Eq. (6.3) is written as Aw=nw. These equations provide the
basis for deriving the weights w=( w 1 , w 2 ,…, w n ).
An explanation of the 9-point scale is presented in Table 6.1. Depending on
the context, the word factors means alternatives, attributes, or criteria. We
also note that because a ratio scale is being used, the derived weights can be
interpreted as the degree to which one alternative is preferred to another.
TABLE 6.1 Scale used for
Pairwise Comparisons
Value Definition Explanation
1 Equal importance Both factors contribute equally to theobjective or criterion.
3
Weak importance
of one over
another
Experience and judgment slightly favor
one factor over another.
5 Essential or strongimportance
Experience and judgment strongly favor
one factor over another.
7
Very strong or
demonstrated
importance
A factor is favored very strongly over
another; its dominance is demonstrated
in practice.
9
Absolute
importance over
another
The evidence favoring one factor is
unquestionable.
2, 4,
6, 8
Intermediate
values Used when a compromise is needed.
0 No relationship The factor does not contribute to theobjective.
Example 6-2

To illustrate the nature of the calculations, observe the three-level hierarchy
in Figure 6.8. Table 6.2 contains the input and output data for level 2.
When n factors are being compared, n( n−1 )/2 questions are necessary to fill
in the matrix A≡( a ij ). The elements in the lower triangle are simply the
reciprocal of those lying above the diagonal (i.e., a ji =1/ a ij , in accordance
with Axiom 1) and need not be assessed. In this instance, the entries in the
matrix at the center of Table 6.2 are the responses to the 6 ( n=4 ) pairwise
questions that were asked. For example, in comparing “human productivity”
with “economic” considerations (element a 12 of the matrix), it was judged
that the first “weakly” dominates the second. Note that if the elicited value
for this element were 1/3 instead of 3, the opposite would have been true.
Similarly, the value 7 for element a 34 means that design considerations
“very strongly” dominate those associated with operations.■
In general, when comparing two factors, the analyst first discerns which
factor is more important and then ascertains by how much by asking the
decision maker to select a value from the 9-point scale. After the decision
maker supplies all of the data for the matrix, the following equation is solved
to obtain the rankings denoted by w:
Aw= λ max w (6.4)
where w is the n-dimensional eigenvector associated with the largest
eigenvalue λ max of the comparison matrix A. The n components of w are
then scaled so that they sum to 1. The only difference between Eq. (6.3) and
Eq. (6.4) is that n has been replaced by λ max on the right-hand side to allow
for some inconsistency on the part of the decision maker.
In practice, the priority vector w=( w 1 , w 2 ,…, w n ) is obtained by raising
the matrix A to an arbitrarily large power (16 or greater is usually sufficient).
Each element in a given row i converges to the same value, call it v i . The
weights are then computed as follows:
w i = v i ∑ k=1 n v k  i=1,…,n
The value of λ max can be found by solving each row of Eq. (6.4) for λ and
averaging; that is, let λ i be the solution to A i w= λ i w i , where A i is the ith

row of A. Then λmax = 1n Σ i=1 n λi . It should be noted that this procedure
works only for the class of positive reciprocal matrices of which A belongs.
A second but less accurate way of deriving the weights is based on the
geometric mean of the row elements of A. First, we compute
v i = ∏ j=1 n a ij n = a i1 a i2 … a in n i=1,…,n
and then we normalize to get w i = v i v 1 + v 2 +…+ v n for each row i. For
the example in Table 6.2,
TABLE 6.2 Priority Vector
for Major Criteria
Criteria
Criteria 1 2 3 4 Priority Outputparameters
1. Human
productivity 1 3 3 7 0.521 λ max =4.121
2. Economics 0.333 1 1 5 0.205 CI =0.040
3. Design 0.333 1 1 7 0.227 CR =0.045
4. Operations 0.143 0.2 0.143 1 0.047
A=( 1 3 3 7 1/3 1 1 5 1/3 1 1 7 1/7 1/5 1/7 1 )
Row 1:  v 1 = ( 1 )( 3 )( 3 )( 7 ) 4 = 63 4 =2.82
Row 2:  v 2 = ( 1/3 )( 1 )( 1 )( 5 ) 4 = 5/3 4 =1.14
Row 3:  v 3 = ( 1/3 )( 1 )( 1 )( 7 ) 4 = 7/3 4 =1.24
Row 4:  v 4 = ( 1/7 )( 1/5 )( 1/7 )( 1 ) 4 = 1/245 4 =0.25
Normalizing gives the weights

w 1 = 2.82 2.82+1.14+1.24+0.25 = 2.82 5.45 =0.52 w 2 = 1.14 5.45 =0.21 w
3 = 1.24 5.45 =0.23 w 4 = 0.25 5.45 =0.04
To find λ max we solve the following equations for λ i for each row i=1,…,n
A i w= λ i w (where A i is the ith row of the A matrix)
or a i1 w 1 + a i2 w 2 +…+ a in w n = λ i w i .
For the example we have n=4:
Row 1:  λ 1 =2.120/0.52=4.077
Row 2:  λ 2 =0.813/0.21=3.871
Row 3:  λ 3 =0.893/0.23=3.883
Row 4:  λ 4 =0.189/0.04=4.725
Ideally, these values all should be the same but because this is an
approximate method, some variation is inevitable. Setting λ max to the
average of these values is a good compromise:
λ max ≅ 1n (λ1 + λ2 + … + λn) = 1 4  ( 4.077+3.871+3.883+4.725 ) =4.139
The true value of λ max =4.121.
6.4.2 Checking for Consistency
Consistency of response or transitivity of preference is checked by
ascertaining whether
a ij = a ik a kj , for all i, j, k (6.5)
In practice, the decision maker is only estimating the “true” elements of A by
assigning them values from Table 6.1, so the perfectly consistent case
represented by Eq. (6.5) is not likely to occur.

Therefore, as an approximation, the elements of A can be thought to satisfy
the relationship a ij = w i / w j + ϵ ij , where ϵ ij is the error term representing
the decision maker’s inconsistency in judgment when comparing factor i with
factor j. As such, we would no longer expect a ij to equal a ik a kj throughout.
Carrying the analysis one step farther, it can be shown that the largest
eigenvalue, λ max , of the matrix A satisfies λ max ≥n, where equality holds
for perfect consistency only. This leads to the definition of a consistency
index
CI= λ max −n n−1
which can be used to evaluate the quality of the matrix A. To add
perspective, we compare the CI to the index derived from a completely
arbitrary matrix whose entries are randomly chosen. Through simulation,
Saaty has obtained the following results:
n 1 2 3 4 5 6 7 8 9 10
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
where n represents the dimension of the particular matrix and RI denotes the
random index computed from the average of the CI for a large sample of
random matrices. It is now possible to define the consistency ratio (CR) as
CR= CI RI
Experience suggests that the CR should be less than 0.1 if one is to be fully
confident of the results. (There is a certain amount of subjectivity in this
assertion much like that associated with interpreting the coefficient of
determination in regression analysis.) Fortunately, though, as the number of
factors in the model increases, the results become less and less sensitive to
the values in any one matrix.
Returning to Table 6.2, the priorities derived for the major considerations
were 0.521 for human productivity, 0.205 for economics, 0.227 for design,
and 0.047 for operations. These values tend to emphasize the first criterion
over the others, probably because of the implicit mandate that the U.S. space
station must eventually pay for itself. Finally, note that CR=0.045, which is
well within the acceptable range.

6.4.3 Determining Global Priorities
The next step in the analysis is to develop the priorities for the factors on the
third level with respect to those on the second. In our case, we compare the
five alternatives previously mentioned with each of the major criteria. For the
moment, assume that the appropriate data have been elicited and that the
calculations for each of the four comparison matrices have been performed,
with the results displayed in Table 6.3 (note that each column sums to 1). The
first four columns of data represent the local priorities derived from the inputs
supplied by the decision maker. The global priorities are obtained by
weighting each of these values by the local priorities given in Table 6.2 (and
repeated at the top of Table 6.3 for convenience) and summing. The
calculations for alternative 1 are as follows: ( 0.066 )( 0.521 )+( 0.415 )(
0.205 )+ ( 0.122 )( 0.227 )+ ( 0.389 )( 0.047 )= 0.165. To see how the
calculations are performed in general, let
n l =number of factors at level l
w i l =global weight at level l for factor i
w ij l =local weight at level l for factor i with respect to factor j at level l
−1
TABLE 6.3 Local and Global
Priorities for the Problem of
Selecting an In-Orbit Assembly
System
Local priorities
Alternative*
Human
productivity Economies(0.205)
Design
(0.227)
Operations
(0.047)
Global
priorities

(0.521)
1 0.066 0.415 0.122 0.389 0.165
2 0.212 0.309 0.224 0.151 0.232
3 0.309 0.059 0.206 0.178 0.228
4 0.170 0.111 0.197 0.105 0.161
5 0.243 0.106 0.251 0.177 0.214
*1. Astronaut with tools outside the spacecraft;
2. Dexterous manipulator under human control;
3. Dedicated manipulator under computer control;
4. Teleoperator with manipulator kit;
5. Dexterous manipulator with sensory feedback.
The global priorities at level l are obtained from the following equation:
w i l = ∑ j=1 n l−1 w ij i   w j l−1
Continuing with the example, because there are no more levels left to
evaluate, the values shown in the last column of Table 6.3 represent the final
priorities for the problem. Thus, according to the judgments expressed by this
decision maker, alternative 2 turns out to be most preferred.
To complete the analysis, it would be desirable to see how sensitive the
results are to changes in judgment and criteria values; that is, to determine
how changes in the A matrix would affect intra-level, overall priorities, and
consistencies. This feature is built into Expert Choice (Forman et al. 2004),
the most popular commercial code for conducting an AHP analysis, and so
can be done with little effort. HIPRE 3+ (Hamalainen and Mustajoki 2001)
also provided this capability. When uncertainty exists in factor values,
additional attributes can be defined to account for this randomness (Bard
1992).

In summary, the commonly claimed benefits of the AHP are that:
1. It is simple to understand and use.
2. The construction of the objective hierarchy of criteria, attributes, and
alternatives facilitates communication of the problem and solution
recommendations.
3. It provides a unique means of quantifying judgment and measuring
consistency.

6.5 Group Decision Making
When more than one person is responsible for making decisions, the issues
surrounding group dynamics and consensus building become paramount.
Rational procedures must be developed for structuring the problem, soliciting
opinions, and making use of the information collected. In general, there are
two modes of operation: live sessions and some form of correspondence. In
the former, the group takes time to structure its problem, usually weighing all
factors and considering all inputs. Still there is a need to trim the structure
and eliminate redundancies so that the major effort can be brought to bear on
the essential parts of the problem. With regard to judgments, behaviorists
point out that there are four kinds of situations:
1. People are completely antagonistic to the process and do not wish to
participate in a constructive way. In particular, they may believe that the
outcome would dilute their own influence.
2. The participants wish to cooperate to arrive at a rational decision and in
so doing wish to determine every judgment by agreement and
consensus.
3. The group members are willing to have their individual judgments
synthesized after some debate.
4. The group consists of experts each of whom knows his or her mind
exactly and does not wish to interact. They are willing to accept an
outcome but are not willing to compromise on their judgments.
After the session in which the substance is hammered out, the group members
may be willing to revise their structure and judgments by conducting
additional sessions or by correspondence using questionnaires.
The second alternative is to do the entire process by correspondence without
organized meetings. The question here is how to solicit opinions and interact
most effectively. The Delphi method is one particular approach for doing this

that has gained strong adherents.
Several researchers have pointed to the following trends in decision making:
1. Organizational decisions are much more technically and politically
complex and require frequent meetings attended by a wide range of
individuals.
2. Decisions must be reached quickly, usually with greater participation of
low-level or staff personnel than in the past.
3. There is an increasing focus on the development of computer-based
systems that support the formulation and solution of unstructured
decision problems by a group [i.e., a group decision support system
(GDSS)].
In what follows, we highlight some of the important considerations in the
group decision-making process.
6.5.1 Group Composition
The inherent complexity and uncertainty surrounding an organization’s major
activities usually necessitates the participation of many people in the
decision-making process. In some cases, the composition of the group is
fixed (e.g., the board of directors advising the chief executive officer of a
corporation), whereas in others, it is necessary to select a mix of members
(e.g., choosing a panel to investigate the Columbia disaster). The latter
selection process requires specifying the number of experts, nonexperts, staff
personnel, and upper-level managers to participate, as well as choosing the
appropriate people.
This process can be difficult and time consuming for many reasons. First,
participants who are considered “experts” are likely to be troublesome. They
may have strong ideas on the appropriate course of action and may not be
easily swayed in their assessments. Second, decision makers who are
considered “powerful” members of the organization might refuse to

participate. These members are aware that their level of control and influence
might be diminished in a group setting. They fear that the social and
interactive nature of the group process might dilute their power and ability to
direct policy within the organization (Saaty 1989). However, if powerful
people actively participate, then they are likely to dominate the process. In
contrast, results generated by a group that consists solely of “low-level”
managers with little power may not be useful. The danger in all of this is that
powerful managers will implement their preferred solutions without taking
into account the opinions and observations of others.
One way of dealing with the “power differential” problem is to assemble a
group of participants who have equal responsibility and stature within the
organization. Collectively, these people can be treated as a decision-making
“subgroup” that could help formulate and solve a part of the problem with
which they are most knowledgeable. They could also contribute to
discussions that involve higher or lower levels of management. This can be
viewed as a sort of “shared” decision-making responsibility in which high-
level management cooperates with subordinates. In practice, high-level
management often depends on low-level employees to gather the appropriate
information on which to base their decisions.
6.5.2 Running the Decision-Making
Session
After the group has been chosen, the members should begin preparing for the
decision-making session by formalizing their agenda, structuring the
allowable interactions between participants, and clearly defining the purpose
of the session in advance. They can seek answers to several questions (e.g.,
the ones listed below) that are designed to establish the operating ground
rules:
Is the purpose of the session simply to improve the group’s
understanding of the problem, or is the purpose to reach a final solution?
Are the participants committed to generating and implementing a final

solution?
What is the best way to combine the judgments of the participants on
various issues to produce a united course of action?
Often we model decision problems as if the people with whom we are dealing
know their minds and can give answers inspired by a clear or telling
experience. But this is seldom the case. People have a habitual domain. They
are conditioned and biased but also learning and adaptive. Instead of trying to
cajole or coerce them prematurely, they must be given the opportunity to
learn and solidify their ideas. After much experimentation and trial and error,
something useful may emerge. If you hurry, then all you get is a hurried
answer, no matter how scientific you try to be. People must be given an
adequate chance to understand their own minds before they can be expected
to commit themselves. People with different assumptions and different
backgrounds, though, may never be on the same wavelength and will change
their minds later if they are forced to agree. Moreover, interpersonal
comparisons should be undertaken only with the utmost of care. Peer
pressure, concealed and distorted preferences, and the inequalities of power
all conspire to prejudice the group decision-making process.
6.5.3 Implementing the Results
After the final results have been generated, the group should evaluate the
effort and cost of implementing the highest-priority outcome. It must be
determined whether it is likely that the participants and their constituencies
will cooperate in the implementation phase of the effort. To be useful, the
decision-making process must be acceptable to the participants, and the
participants must be willing to abide by the outcome. Finally, it is important
for the group to view whichever GDSS was used, not as a tool for isolated,
one-time applications but rather as a process that has ongoing validity and
usefulness to an organization.
6.5.4 Group Decision Support

Systems
A GDSS aims to improve the process of group decision making by removing
common communications barriers, providing techniques for structuring
decision analysis, and systematically directing the pattern, timing, and
content of the discussion. The more sophisticated the GDSS technology, the
more dramatic the intervention into the group’s natural (unsupported)
environment. Of course, more dramatic intervention does not necessarily lead
to better decisions; but its appropriate design and use can produce the desired
results.
Communications technologies available within a GDSS include electronic
messaging, local- and wide-area networks, teleconferencing, and store-and-
forward facilities. Computer technologies include multiuser operating
systems, fourth-generation languages, databases, data analysis
methodologies, and so on. Decision support technologies include agenda
setting, decision modeling methods (e.g., decision trees, risk assessment,
forecasting techniques, the AHP, MAUT), and rules for directing discussion.
Concerning the information-exchange aspect of group decision making,
DeSanctis and Gallupe (1987) proposed three levels of support. Level 1
GDSSs provide technical features aimed at removing communications
barriers, such as large screens for instantaneous display of ideas, voting
solicitation and compilation, anonymous input of ideas and preferences, and
electronic message exchange between members. Level 1 features are found in
meeting rooms normally referred to as “computer-supported conference
rooms” or “electric board rooms.”
Level 2 GDSSs provide decision modeling and group decision techniques
that are designed to reduce the uncertainty and “noise” that occur in the group
decision process. The result is an enhanced GDSS, as opposed to a level 1
system, which is a communications medium only. A Level 2 GDSS might
provide automated planning tools or other aids found in individual DSSs for
group members to work on and view simultaneously, again using a large,
common screen. Modeling tools to support analyses that ordinarily are
performed in a qualitative manner, such as social judgment formation, risk

assessment, and multiattribute utility methods, can be introduced to the group
via a level 2 GDSS. In addition, group structuring techniques found in the
organizational development literature can be administered efficiently.
Level 3 GDSSs are characterized by machine-induced group communication
patterns and can include expert advice in the selecting and arranging of rules
to be applied during a meeting. As an example, Hiltz and Turoff (1985)
experimented with automating the Delphi method and the nominal group
technique, but to date, very little research has been done with such high-level
systems.
In summary, the objective of GDSSs is to discover and present new
possibilities and approaches to problems. They do this by facilitating the
exchange of information among the group. Message transfer can be hastened
and smoothed by removing barriers (level 1); systematic techniques can be
used in the decision process (level 2); and rules for controlling pattern,
timing, and content of information exchange can be imposed on the group
(level 3). The higher the level of the GDSS, the more sophisticated the
technology and the more dramatic the intervention compared with the natural
decision process. Table 6.4 highlights the major tasks of a decision-related
meeting, the main activities, the corresponding level of GDSS, and the
possible support features.
TABLE 6.4 Example GDSS
Features to Support Six Task
Types
Task
purpose Task type
GDSS
level Possible support features
General: Planning Level1 Large-screen display, graphical aids
Level
Planning tools (e.g., PERT); risk
assessment, subjective probability

2 estimation for alternative plans
Creativity Level1
Anonymous input of ideas, pooling
and display of ideas; search facilities
to identify common ideas, eliminate
duplicates
Level
2
Brainstorming; nominal group
technique
Choose: Objective Level1
Data access and display; synthesis
and display of rationales for choices
Level
2
Aids to finding the correct answer
(e.g., forecasting models,
multiattribute utility models)
Level
3
Rule-based discussion emphasizing
thorough explanation of logic
Preference Level1
Preference weighting and ranking
with various schemes for determining
the most favored alternative; voting
schemes
Level
2
Social judgment models; automated
Delphi method
Level
3
Rule-based discussion emphasizing
equal time to present opinion
Negotiate: Cognitiveconflict
Level
1
Summary and display of members’
opinions
Level
2
Using social judgment analysis, each
member’s judgments are analyzed by
the system and then used as feedback
to the individual member or the
group
Level
3
Automatic mediation; automate
Roberts’ rules
Mixed
motive
Level
1 Voting solicitation and summary
Level

2 Stakeholder analysis
Level
3
Rule base for controlling opinion
expression; automatic mediation;
automate parliamentary procedure
TEAM PROJECT Thermal
Transfer Plant
Total Manufacturing Solutions (TMS) management is considering the
following aspects in selecting a hydraulic power unit for the rotary
combustor:
Size
Weight
Power consumption
Required maintenance
Noise
Cost
Reliability
The power unit provides power to operate three components of the system:
feed rams, resistance door, and combustor. Three design alternatives are
available:
1. Electric motor on a gearbox
2. Low-speed, high-torque hydraulic motor with direct drive
3. High-speed, low torque hydraulic motor on a gearbox

Initial data include the following:
Electromechanical Low speed, hightorque
High speed, low
torque
Delivery 90–120 days 1–6 weeks 90–120 days
Overall
efficiency 96% 94% 88%
Useful life 20 years 25 years 25 years
Noise level 85 dB 78 dB 100 dB
Using the criteria above as guidance, develop an MAUT and an AHP model
for evaluating the three alternatives. It will be necessary to collect data or
make assumptions about the values of all of the attributes. For one of the
models, perform the analysis with the help of a computer program, and give
your recommendation. Be sure to justify and document your results, basing
part of your recommendation on a sensitivity analysis.

Discussion Questions
1. How might you measure the benefits associated with space exploration
or a superconducting supercollider for investigating subatomic particles?
Can you put a dollar value on these benefits? What are the real costs and
opportunity costs of these types of projects?
2. Identify an advanced technology project that you believe should be
undertaken, such as bio-electronic computing or coal gasification. Who
should be responsible for funding the project? The government?
Industry? A consortium? What are the major attributes or criteria
associated with the project?
3. What type of technical background, if any, do you think is needed to
understand MAUT? The AHP?
4. You have just completed an MAUT evaluation of a number of data
communications systems under consideration by your company. How
would you present the results to upper management? Assuming that they
know nothing about the technique, how much background would you
give them? How would your answer differ if the AHP were used
instead?
5. What do you think are the strengths and weaknesses of the AHP and
MAUT?
6. How would you go about constructing an objective hierarchy? Who
should be consulted? Identify a project from your personal experience or
observations, and construct such a hierarchy.
7. When performing an evaluation using any multiple-criteria method,
from whose perspective should the analysis be undertaken? Would the
answer differ if it were a public rather than private project?
8. What experiences have you had with group decision making? What
difficulties do you see arising when trying to perform a multiple-criteria

analysis with many interested parties involved? How might these
difficulties be overcome, or at least mitigated?
9. Are benefit-cost analysis and multiple-criteria analysis mutually
exclusive techniques? In which circumstances is either most
appropriate?
10. You just inherited a large sum of money and would like to develop a
strategy to invest it. Use the AHP to fashion such a strategy. Construct
an objective hierarchy listing all criteria and subcriteria, and principal
alternatives. What data are needed to perform the evaluation? How
would you go about obtaining the data?
11. From a practical point of view, how would you verify the independence
assumptions associated with MAUT?
12. Are the axioms underlying the AHP reasonable and unambiguous? In
which circumstances do you think one or more of them could be
relaxed?
13. Both the AHP and MAUT are value models that facilitate making
tradeoffs between incommensurable criteria. Come up with your own
value model or procedure for doing this.
14. In conducting a group study using a multiple-criteria method, you reach
a point at which two of the participants cannot agree on a particular
response. What course of action would you take to placate the parties
and avoid further delay?
15. For which type of projects or problems might MAUT be more amenable
than the AHP? Similarly, when is the AHP more appropriate than
MAUT?

Exercises
1. 6.1 Assume that you work for a company that designs and fabricates
VLSI chips. You have been given the job of selecting a new computer-
aided design software package for the engineering group.
1. Develop an MAUT model to assist in the selection process.
2. Develop an AHP model to assist in the selection process.
In both cases, begin by enumerating the major criteria and the associated
subcriteria. Explain your assumptions. Who are the possible decision
makers? How do you think the outcome of the analysis would change
with each of these decision makers?
2. 6.2 Develop a flow chart detailing input, output, and processes for a
software package that supports:
1. MAUT applications
2. AHP applications
3. 6.3 Using MAUT and the AHP, perform an analysis to select a graduate
program. Explain your assumptions and indicate which technique you
believe is most appropriate for this application.
4. 6.4 You are the vice president of planning for Zingtronics, a small-scale
manufacturer of IBM-compatible personal computers and peripherals
based in Silicon Valley. Business is growing, and the company would
like to open a second facility. Three options are being considered: (1) a
second plant in Silicon Valley, (2) a new plant in Mexico as a
Maquiladora, and (3) a new plant in Singapore. Most of the workforce
will be low-skilled assembly and machine operators but training in the
use of computers and information systems will be required. It is also
desirable to set up a small design group of engineers for new product
and process development.

Of course, each option has its pros and cons. For example, Silicon
Valley has a high-skill labor pool but is a very expensive place to do
business. Singapore offers the same level of worker skills at lower cost
but is distant from the market and headquarters. Mexico is the least
expensive place to set up a business, as a result of favorable tax laws and
cheap labor, but has a less educated workforce.
Develop two objective hierarchies, one for costs and one for benefits,
that can be used to investigate the location problem. Use the AHP to
rank the three alternatives on both hierarchies, and then compute the
benefit/cost ratios of each. According to your analysis, which alternative
is best?
5. 6.5 Referring to Exercise 6.4 , combine the two hierarchies into one so
that there are no more than eight subobjectives at the bottom level.
Define either a quantitative or a qualitative scale for each of these
subobjectives, and construct a utility function for each. Use MAUT to
evaluate and rank the three alternatives.
6. 6.6 Use the criteria below to construct a two-level objective hierarchy
(major criteria with one set of subcriteria under each) to help evaluate
political candidates. Consider as alternatives the major candidates
running in the last U.S. presidential election, and use the AHP to make
your choice.
Criteria for choosing a national political candidate:
Charisma: Personal leadership qualities, inspiring, enthusiasm, and
support
Glamor: Charm, allure, personal attractiveness; associations with
other attractive people
Experience: Past office holding relevant to the position sought;
preparation for the position
Economic policy: Coherence and clarity of a national economic
policy

Ability to manage international relations: Coherence and clarity of
foreign policy plus ability to deal with foreign leaders
Personal integrity: Quality of moral standards, trustworthiness
Past performance: Quality of role fulfillment—independent of what
the role was—in previous public offices; public record
Honesty: Lawfulness in public life, law-abidingness
7. 6.7 Louise Ciccone, head of industrial engineering for a medium-sized
metalworking shop, wants to move the CNC machines from their
present location to a new area. Three distinct alternatives are under
consideration. After inspecting each alternative and determining which
factors reflect significant differences among the three, Louise has
decided on five independent attributes to evaluate the candidates. In
descending order of importance, they are:
1. Distance traveled from one machine to the next (more distance is
worse)
2. Stability of foundation [strong (excellent) to weak (poor)]
3. Access to loading and unloading [close (excellent) to far (poor)]
4. Cost of moving the machines
5. Storage capacity
(Note: Once the machines have been moved, operational costs are
independent of the area chosen and hence are the same for each area.)
The data associated with these factors for the three alternatives are in
Table 6.5 .
TABLE 6.5 
Alternative

Attribute Area I Area
II
Area
III
Ideal Standard Worst
A 500 ft 300 ft 75 ft 0 ft 300 ft 1,000 ft
B Good Verygood Good Excellent Good Poor
C Excellent Verygood Good Excellent Good Poor
D $7,500 $3,000 $8,500 $0 $5,000 $10,000
E 60,000 ft 2
85,000
ft 2
25,000
ft 2
10,000
ft 2
25,000 ft
2
150,000
ft 2
Using the multiattribute utility methodology, determine which
alternative is best. For at least one attribute, state all of the probabilistic
tradeoff (lottery-type) questions that must be asked together with
answers to obtain at least four utility values between the “best” and
“worst” outcomes so that the preference curve can be plotted. For the
other attributes, you may make shortcut approximations by determining
whether each is concave or convex, upward or downward, and then
sketching an appropriate graph for each. Next, ask questions to
determine the scaling constants k i , and compute the scores for the three
alternatives. [Note: If you follow the recommended procedure for
deriving the scaling constants, probably Σi ki ≠ 1, so you should use the
multiplicative model Eq. 6.1a (). After comparing alternatives by that
model, “normalize” the scaling constants so Σi ki = 1, and then compare
the alternatives using the additive model Eq. 6.1b (). (It is not
theoretically correct to normalize the k i values to enable use of the
additive model.) How much difference does use of the “correct” model
make?]
8. 6.8 Starting with the environmental scoring model in Table 5.3 ,
construct an objectives hierarchy that can be used to evaluate capital
development and expansion projects being considered by an electric
utility company.
9. 6.9 The six major objectives listed below are used by the British
Columbia Hydro and Power Authority to evaluate new projects. Use this

list to construct an objectives hierarchy by providing subobjectives and
their respective attributes where appropriate. Also, estimate the “worst”
and “best” levels for all of the factors at the lowest level of the
hierarchy.
1. Maximize the contribution to economic development
2. Act consistently with the public’s environmental values
3. Minimize detrimental health and safety impacts
4. Promote equitable business arrangements
5. Maximize quality of service
6. Be recognized as public service oriented
10. 6.10
1. Use the three weighting techniques in Section 5.3 to make a
selection of one of the three used automobiles for which some data
are given in Table 6.6 . State your assumptions regarding miles
driven each year, life of the automobile (how long you would keep
it), market (resale) value at end of life, interest cost, price of fuel,
cost of annual maintenance, attribute weights, and other
subjectively based determinations.
TABLE 6.6 
Attribute Alternative
Domestic European Japanese
Price $8,100 $12,600 $10,300
Gas mileage 25 mpg 30 mpg 35 mpg
Type of fuel Gasoline Diesel Gasoline
Aesthetic appeal 5 out of 10 7 out of 10 9 out of 10

Passengers 4 6 4
Performance on road Fair Very good Very good
Ease of servicing Excellent Very good Good
Stereo system Poor Good Excellent
Headroom Excellent Very good Poor
Storage space Very good Excellent Poor
2. Repeat the analysis using MAUT; that is, construct utility functions
and scaling functions for each attribute, and determine the overall
utility of each alternative. Does your answer agree with the one
obtained in part (a)? Explain why they should (or should not) agree.
11. 6.11 An aspiration level for a criterion or attribute is a level at which the
decision maker is satisfied. For example, we all would like our
investment portfolio to provide an annual rate of return of 30% or
higher, but most of us would happily settle for a return of 5% above the
Dow Jones. Develop an interactive multicriteria methodology that is
based on aspiration levels of the criteria. Construct a flow chart for the
logic and computations. Use your methodology to select one of the
alternatives in Exercise 6.10 .

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Additive Value Function,” Decision Science, Vol. 13, pp. 702–713,
1982.
Additional MCDM Techniques
Belton, V. and T. J. Stewart, Multiple Criteria Decision Analysis: An
Integrated Approach, Kluwer Academic, Dordrecht, The Netherlands,
2001.
Graves, S. B., J. L. Ringuest, and J. F. Bard, “Recent Developments in
Screening Methods for Nondominated Solutions in Multiobjective
Optimization,” Computers & Operations Research, Vol. 19, No. 7, pp.
683–694, 1992.
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Support Systems, Vol. 331, Lecture Notes in Economics and
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Model for Multiple Criteria Decision Making,” Computers &
Operations Research, Vol. 19, No. 7, pp. 671–681, 1992.

Appendix 6A: Comparison of
Multiattribute Utility Theory with
the Analytic Hierarchy Process:
Case Study2
2The material presented in this appendix has been excerpted from Bard
(1992).
In this appendix, we present a case study in which the AHP and MAUT are
used to evaluate and select the next generation of rough terrain cargo handlers
for the U.S. Army. Three alternatives are identified and ultimately ranked
using the two methodologies. A major purpose of this study is to demonstrate
the strengths and weaknesses of each methodology and to characterize the
conditions under which one might be more appropriate than the other.
The evaluation team consisted of five program managers and engineers from
the Belvoir Research, Development & Engineering Center. The objective
hierarchy used for both techniques contained 12 attributes. In general, the
AHP was found to be more accessible and conducive to consensus building.
Once the attributes were defined, the decision makers had little difficulty in
furnishing the necessary data and discussing the intermediate results. The
same could not be said for the MAUT analysis. The need to juggle 12
attributes at a time produced a considerable amount of frustration among the
participants. In addition, the lottery questions posed during the data collection
phase had an unsettling effect that was never satisfactorily resolved.
6A.1 Introduction and Background
In an ongoing effort to reduce risk and to boost the productivity of material-
handling crews, the Army is investigating the use of robotics to perform

many of the dangerous and labor-intensive functions normally undertaken by
enlisted personnel. To this end, a number of programs are currently under
way at several government facilities. These include the development of a
universal self-deployable cargo handler (USDCH) at Belvoir Research,
Development & Engineering Center (Belvoir 1987b), the testing of a field
material handling robot (FMR) at the Human Engineering Laboratory, and
the prototyping of an advanced robotic manipulator system (ARMS) at the
Defense Advanced Research Projects Agency (more details are given in
Sievers and Gordon 1986, Sousk et al. 1988).
In each of these efforts, technological risk, time, and cost ultimately intervene
to limit the scope and performance of the final product, but to what extent
and in what manner? To answer these questions, a model that is capable of
explicitly addressing the conflicts that arise among system and organizational
goals is needed. Such a model must also be able to deal with the subjective
nature of the decision-making process. The two approaches examined, the
AHP and MAUT, each offer an analytic framework in which the decision
maker can conduct tradeoffs among incommensurate criteria without having
to rely on a single measure of performance.
6A.2 The Cargo Handling Problem
Although the Army is generally viewed as a fighting force, the bulk of its
activity involves the movement of massive amounts of material and supplies
in the field. This is achieved with a massive secondary labor force whose risk
exposure is comparable to those engaged in direct combat.
From an operational point of view, cargo must be handled in all types of
climates, regions, and environments. At the time of the study, this was
accomplished by three different-sized rough-terrain forklifts with maximum
lifting capacities of 4,000, 6,000, and 10,000 lbs each. These vehicles are
similar in design and performance to those used by industry and, at best, can
reach speeds of 20 mph. For the most part, this means that the fleet is not
self-deployable (i.e., it cannot keep pace with the convoy on most surfaces).
As a consequence, additional transportation resources are required for
relocation between job sites. This restriction severely limits the unit’s

maneuverability and hence its survivability on the battlefield.
A second problem relates to the safety of the crew. Although protective gear
is available for the operator, his or her effectiveness is severely hampered by
its use. Heat exhaustion, vision impairment, and the requirement for frequent
changes are the problems cited most commonly. Logistics units thus lack the
ability to provide continuous support in extreme conditions.
6A.2.1 System Objectives
To overcome these deficiencies as well as to improve crew productivity, a
heavy-duty cargo-handling forklift is needed. This vehicle should be capable
of operating in rough terrain and of traveling over paved roads at speeds in
excess of 40 mph. To permit operations in extreme conditions, internal
cooling (microcooling) should be provided for the protective gear worn by
the operator. As technology progresses, it is desirable that the basic functions
be executable without human intervention, implying some degree of
autonomy.
At a minimum, then, the vehicle should be:
Able to substitute for the existing 4,000, 6,000 and 10,000 (4K, 6K,
10K)-lb forklifts while maintaining current material handling
capabilities
Capable of unaided movement (self-deployability) between job sites at
convoy speeds in excess of 40 mph
Capable of determining whether cargo is contaminated by nuclear,
biological, or chemical agents
Capable of handling cargo in all climates and under all contamination
conditions
Transportable by C-130 and C-141B aircraft
Operable in the near term as a human-machine system expandable to full

autonomy
Capable of robotic cargo engagement
Operable remotely from up to 1 mile away
6A.2.2 Possibility of Commercial
Procurement
A market survey of commercial forklift manufacturers, including those
currently under contract for the 4K-, 6K-, and 10K-lb vehicles, indicates little
opportunity for a suitable off-the-shelf buy. With Army needs constituting
less than 15% of the overall market, lengthy procurement cycles and uneven
demand work to dampen any corporate interest. In the commercial
environment, the use of rough-terrain forklifts is limited to construction and
logging operations; highway travel and teleoperations have no real
applications. Therefore, few, if any, incentives exist for the industry to
undertake the research and development (R&D) effort implied by the design
requirements to build a prototype vehicle.
6A.2.3 Alternative Approaches
To satisfy the system objectives, then, the existing fleet must either be
replaced outright or be substantially overhauled. However, given the low
priority of logistics relative to combat needs, a full-scale R&D program is not
a realistic option. A more likely approach involves an improvement in the
existing system, a modification of a commercial system, or the adaptation of
available technology to meet specific requirements. Each of these approaches
occasions a different level of risk, cost, and performance that must be
evaluated and compared before a final decision can be made. This is the
subject of the remainder of the appendix, but first, the leading alternatives are
defined.
Taking into account mission objectives and the fact that the Army has

functioned with the existing system up until now, the following alternatives
have been identified. This set represents a consensus of the program
managers and engineers at Belvoir and the customer at the Quartermaster
School:
1. Baseline: the existing system comprising the 4K-, 6K-, and 10K-lb
rough-terrain forklifts augmented with the new 6K-lb variable-reach
vehicle
2. Upgraded system: baseline upgraded to be self-deployable
3. USDCH: teleoperable, robotic-assisted USDCH with microcooling for
the protective gear, and the potential for full autonomy
The new 6K-lb variable-reach (telescoping boom) forklift was scheduled to
be introduced into the fleet in early 1990. Its performance characteristics,
along with those of the USDCH, have been discussed in several reports
(Belvoir 1987a, 1987b). Figure 6A.1 depicts a schematic of the robotic-
assisted cargo handler. Note that the field material-handling robot and the
advanced robotic manipulator system have been omitted from the list above.
At this juncture, the primary interest in these systems centers on

Figure 6A.1
Universal self-deployable cargo handler.
Figure 6A.1 Full Alternative Text
their robotic capabilities rather than on their virtues as cargo handlers. In fact,
almost none of the operational deficiencies mentioned previously would be
overcome by either the FMR or the ARMS. Consequently, each was
dismissed from further consideration.
6A.3 Analytic Hierarchy Process
The first step in any multiobjective methodology is to identify the principal
criteria to be used in the evaluation. These should be expressed in fairly
general terms and be well understood by the study participants. For our
problem, the following four criteria were identified: performance, risk, cost,
and program objectives. The next step is to add definition by associating a
subset of attributes (subcriteria) with each of the above. Figure 6A.2 depicts
the resultant objective hierarchy. Risk, for example, has been assigned the
following attributes: system integration, technical performance, cost overrun,
and schedule overrun. The alternatives are arrayed at the bottom level of the
diagram. The connecting lines indicate points of comparison.

Figure 6A.2
Objective hierarchy for next-generation cargo handler.
Figure 6A.2 Full Alternative Text
In constructing the objective hierarchy, consideration must be given to the
level of detail appropriate for the analysis. This is often dictated by the
present stage of the development cycle, the amount of data available on each
alternative, and the relative importance of criteria and attributes. For
example, if human productivity were a major concern, as it is in the space
program, then a fifth criterion might have been included at the second level.

The inclusion or exclusion of a particular attribute depends on the degree to
which its value differs among the alternatives. Although transportability and
survivability are important design considerations, all candidates for the cargo-
handling mission are expected equally to satisfy basic requirements with
respect to these attributes. Consequently, it is not necessary to incorporate
them in the model.
To avoid too much detail, aggregation is recommended. This permits overly
specific factors to be taken into account implicitly by including them in the
attribute definitions. For example, “life-cycle cost” (LCC) could have been
further decomposed into unit purchase price, operations and maintenance
costs, spare parts, personnel and training, and so on, but at the expense of
overtaxing the current database and cost accounting system. As a result, these
factors were left undifferentiated. Similar reasoning applies to the attribute
“reliability/availability/maintainability” (RAM).
6A.3.1 Definition of Attributes
Each of the attributes displayed at level 3 in Figure 6A.2 is described in more
detail below. These descriptions, in the form of instructions, were used by the
analyst to elicit responses from the decision makers during the data collection
phase of the study.
Performance
1. Mission objectives. Compare the alternatives on the basis of how close
they come to satisfying mission objectives and requirements.
Consideration should be given to such factors as lifting capacity,
deployability, productivity improvement, and operation in a nuclear,
biological, chemical (NBC) environment.
2. RAM. Using military standards for RAM, compare the alternatives
relative to the likelihood that each will meet these standards. If possible,
take into account mean-time-between-failures, mean-time-to-repair, and

the most probable failure modes.
3. Safety. Compare the alternatives on the basis of how well they protect
the crew in all climatic conditions and in an NBC environment.
Consider the probable degree of hazard exposure, the vehicle response
under various driving conditions, and the ability of the crew to work
effectively for extended periods.
Risk
4. System integration. Compare the effort required to achieve full system
integration for the alternatives, taking into account the degree of
upgrading and reengineering associated with each.
5. Technical performance. Considering the performance goals of each
system, evaluate the relative likelihood that these goals will be met
within the current constraints of the program. Take into account the
Army’s experience with similar systems and the state of commercially
available technologies.
6. Cost overrun. Based on the maturity of the technology and the funding
histories of similar programs, compare the alternatives as to whether one
is more likely to go over budget than the other.
7. Schedule overrun. Based on the maturity of the technology and the
development histories of similar programs, compare the alternatives as
to whether one is more likely than the other to result in a schedule
overrun.
Cost
8. Research, development, testing, and evaluation (RDT&E). Compare the
alternatives from the standpoint of which is likely to have the least cost
impact during its development cycle. Consideration should be given to
each phase of the program before implementation.

9. LCC. Compare the total cost of buying, operating, maintaining, and
supporting each alternative over its expected lifetime. Exclude RDT&E,
but take into account personnel needs, training, and the degree of
standardization achieved by each system.
Program Objectives
10. Implementation timetable. Compare the alternatives with respect to their
individual schedules for implementation. Consider the effect that the
respective timetables will have on military readiness.
11. Technological opportunities. Compare the alternatives on the basis of
what new technologies might result from their development, as well as
the likelihood that new applications will be found in other areas.
Consideration should be given to the prospect of spinoffs, potential
benefits, and the development of long-term knowledge.
12. Customer acceptability. Compare the alternatives from both the user
representative’s and operator’s points of view. Take into account the
degree to which each alternative satisfies basic objectives, as well as the
potential for growth, risk reduction, and the adaptation of new
technologies. Also consider secondary or potential uses, operator
comfort, and program politics.
6A.3.2 Analytic Hierarchy Process
Computations
To illustrate the nature of the calculations, observe Figure 6A.3 which depicts
a three-level hierarchy—an abbreviated version of Figure 6A.2 used in the
analysis. Table 6A.1 contains the input and output data for level 2.
Recall that when n factors are being compared, n( n−1 )/2 questions are
necessary to fill in the matrix. The elements in the lower triangle (omitted
here) are simply the reciprocal of those lying above the diagonal; that is, a ji

=1/ a ij . The entries in the matrix at the center of Table 6A.1 are the
responses to the 6( n=4 ) pairwise questions that were asked. These responses
were drawn from the 9-point scale shown in Table 6.1. For example, in
comparing “performance” with “risk” (element a 12 of the matrix), it was
judged that the first “strongly” dominated the second. Note that if the elicited
value for this element were 1/5 instead of 5, then the opposite would have
been true.
From Table 6A.1, it can be seen that the priorities derived for the major
criteria were 0.517 for performance, 0.059 for risk, 0.306 for cost, and 0.118
for program objectives. Also note that the consistency ratio (0.097) is a bit
high but still within the acceptable range.
TABLE 6A.1 Priority Vector
for Major Criteria
Criteria
Criteria 1 2 3 4 Priorityweights
Output
parameters
1. Performance 1 5 3 4 0.517 λ max =4.262
2. Risk 1 1/6 1/3 0.059
3. Cost 1 4 0.306 CR=0.097
4. Program
objectives 1 0.118
The next step in the analysis is to develop the priorities for the factors on the
third level with respect to those on the second. In our case, we compare the
three alternatives against the major criteria. For the moment, assume that the
appropriate data have been elicited and that the calculations have been
performed for each of the four comparison matrices, giving the results
displayed in Table 6A.2. The first four columns of data are the local priorities
derived from the inputs supplied by the decision maker; note that each
column sums to 1. The global priorities are found by respectively multiplying
these values by the higher-level local priorities given in Table 6A.1 (and

repeated at the top of Table 6A.2 for convenience) and then summing.
Because there are no more levels left to evaluate, the values contained in the
last column of Table 6A.2 represent the final priorities for the problem. Thus,
according to the judgments expressed by this decision maker, alternative 3
turns out to be most preferred. Finally, it should be noted that other schemes
are available for determining attribute weights.
TABLE 6A.2 Local and
Global Priorities
Local priorities
Alternatives Performance(0.517)
Risk
(0.059)
Cost
(0.306)
Program
obj. (0.118)
Global
priorities
Baseline 0.142 0.704 0.384 0.133 0.248
Upgrade 0.167 0.229 0.317 0.162 0.216
USDCH 0.691 0.067 0.299 0.705 0.536

Figure 6A.3
Abbreviated version of the objective hierarchy.
6A.3.3 Data Collection and Results
for AHP
In the formative stages of the study, two questions quickly arose: (1) Who
should provide the responses? (2) Whose point of view should be
represented? With regard to the first, it was believed that the credibility of the
results depended on having a broad spectrum of opinion and expertise as
input. Subsequently, five people from Belvoir’s Logistics Equipment
Directorate with an average of 15 years’ experience in systems design, R&D
program management, and government procurement practices were
assembled to form the evaluation team. After some discussion it was agreed
that the responses should reflect the position of the material developer––the
U.S. Army Material Command. Other candidates included the Army as a
whole, the customer, and the mechanical equipment division at Belvoir.
At the first meeting, the group was introduced to the AHP methodology and
examined the objective hierarchy developed previously by the analyst.
Eventually, a consensus grew around the attribute definitions, and each
member began to assign values to the individual matrix elements. A bottom
up approach was found to work best. Here the alternatives first are compared
with respect to each attribute; next, a comparison is made among the
attributes with respect to the criteria; and finally, the four criteria at level 2
are compared among themselves. After the data sheets had been filled out for
each criterion, individual responses were read aloud to ascertain the level of
agreement. In light of the ensuing discussion, the participants were asked to
revise their entries to better reflect their renewed understanding of the issues.
This phase of the study took approximately 6 hours and was done in two
sessions over a 5-day period.
As with the Delphi procedure, the challenge was to come as close to a

consensus as possible without coercing any of the team members.
Unfortunately, this proved more difficult than expected as a result of the
speculative nature of much of the attribute data. In practice, many researchers
have found that uniformity within a group rarely can be achieved without
stretching the limits of persuasion (Greenberg and Baron 2003). Biases,
insecurities, and stubbornness often develop their own constituencies.
Although none of these factors was openly present at the meetings,
organizational and program concerns were clearly seen to influence
individual judgments.
In the extreme, when there is no possibility of reconciling conflicting
perceptions, it is best to stratify responses along party lines. In our case,
sufficient agreement emerged to permit the averaging of results without
obscuring honest differences of opinion. Table 6A.3 highlights individual
preferences for the level 2 criteria and for the problem as a whole. The
numbers in parentheses represent the local weights computed for the four
criteria: performance, risk, cost, and program objectives. Global weights and
rankings are given in the last two columns.
Table 6A.4 summarizes the computations for each decision maker and
presents two collective measures of comparison: (1) the arithmetic mean and
(2) the geometric mean. (Issues surrounding the synthesis of judgments is
discussed by Aczel and Alsina 1987.) The latter is obtained by a geometric
averaging of the group’s individual responses at each point of comparison to
form a composite matrix, followed by calculation of the eigenvectors in the
usual manner. As can be seen, both methods give virtually identical results
and rankings. The strongest preference is shown for the USDCH, closely
followed by the baseline. The upgraded system is a distant third.
TABLE 6A.3: Comparison of
Responses Using the AHP
Local results
Performance Risk Cost Program

Respondent Alternative Weight Rank Weight Rank Weight Rank Weight
1 (0.517) (0.059) (0.306) (0.118)
Baseline 0.142 3 0.704 1 0.384 1 0.133
Upgrade 0.167 2 0.229 2 0.317 2 0.162
USDCH 0.691 1 0.067 3 0.229 3 0.705
2 (0.553) (0.218) (0.147) (0.082)
Baseline 0.144 3 0.497 1 0.432 1 0.202
Upgrade 0.213 2 0.398 2 0.383 2 0.269
USDCH 0.643 1 0.105 3 0.185 3 0.529
3 (0.458) (0.240) (0.185) (0.117)
Baseline 0.252 3 0.677 1 0.467 1 0.350
Upgrade 0.273 2 0.249 2 0.375 2 0.371
USDCH 0.474 1 0.074 3 0.158 3 0.280
4 (0.359) (0.315) (0.210) (0.116)
Baseline 0.214 3 0.666 1 0.602 1 0.529
Upgrade 0.263 2 0.266 2 0.313 2 0.313
USDCH 0.524 1 0.068 3 0.085 3 0.158
5 (0.469) (0.252) (0.194) (0.085)
Baseline 0.184 3 0.655 1 0.565 1 0.176
Upgrade 0.227 2 0.274 2 0.285 2 0.178
USDCH 0.589 1 0.071 3 0.150 3 0.646
6A.3.4 Discussion of Analytic
Hierarchy Process and Results
The output in Tables 6A.3 and 6A.4 represents the final judgments of the
participants and was obtained only after holding two additional meetings to
discuss intermediate results. All participants were given the opportunity to
examine the priority weights calculated from their initial responses and to
assess the reasonableness of the rankings. When their results seemed

counterintuitive, they were encouraged to reevaluate their input data,
determine the source of the inconsistency, and make the appropriate changes.
The debate that took place during these sessions proved to be extremely
helpful in clarifying attribute definitions and surfacing misunderstandings. In
a few
TABLE 6A.4: Summary of
Results for the AHP Analysis
Respondent
1 2 3 4
Alternative Weight Rank Weight Rank Weight Rank Weight Rank Weight
Baseline 0.248 2 0.268 3 0.405 1 0.474 1
Upgrade 0.216 3 0.282 2 0.298 2 0.280 2
USDCH 0.536 1 0.450 1 0.297 3 0.246 3
instances, well-reasoned arguments persuaded some people to reverse their
position completely on a particular issue. This was more apt to occur when
the advocate was viewed as an expert and was able to furnish the supporting
data. Ordinarily, one- or two-point revisions were the rule and had no
noticeable effect on the outcome.
Looking at the data in Table 6A.3, a great deal of consistency can be seen
across the group. In all but one instance, performance is given the highest
priority, followed by risk, cost, and program objectives. For the first three
criteria, each alternative has the same ordinal ranking; the only differences
arise in the case of program objectives. Nevertheless, the real conflict is
reflected in the magnitude of the weights. Although some variation is
inevitable, it is frustrating to observe the results for “cost.” In particular, there
is little agreement concerning the extent to which personnel and
transportation resource reductions that accompany the USDCH will be offset
by increased operations and maintenance expenses or how these factors will

affect the LCC. The third and fourth decision makers were more skeptical
than the first two and hence showed a greater preference for the baseline.
The results for “risk” also inform a divergence of opinion. Respondent 1 was
most forthright in acknowledging its presence in the USDCH program by
assigning it an extremely low weight (0.067) relative to the baseline (0.704).
The effect of this assignment was minimal, though, because he judged risk to
be considerably less important than the other three criteria. Compare his
corresponding weight (0.059) with those derived for respondents 2 through 5
(0.218, 0.240, 0.315, and 0.252). From the data in Table 6A.3, it can be seen
that the last four decision makers all viewed risk as the second most
important criterion. This observation was corroborated indirectly in the utility
analysis.
6A.4 Multiattribute Utility Theory
MAUT is a methodology for providing information to the decision maker for
comparing and selecting among complex alternatives when uncertainty is
present. It similarly calls for the construction of an objective hierarchy as
depicted in Figure 6A.2 but addresses only the bottom two levels.
6A.4.1 Data Collection and Results
for Multiattribute Utility Theory
After agreeing on the attributes, the next step in model development is to
determine the scaling constants, k i , and the attribute utility functions, U i .
This is done through a series of questions designed to probe each decision
maker’s risk attitude over the range of permissible outcomes. Before the
interviews can be conducted, though, upper and lower bounds on attribute
values must be specified. Table 6A.5 lists the values elicited from respondent
1 for the 12 attributes. Notice that seven of these are measured on a
qualitative (ordinal) scale, the meanings of which were made precise at the
first group session. Table 6A.6 defines the range of scores for the “mission

objectives” attribute and is typical of the 10-point scales used in the analysis.
TABLE 6A.5 Attribute Data
for Decision Maker 1
Value*
No. Attribute Scale A1 A2 A3 Range Order ofimportance†
Scaling
constant
Performance
1 Mission obj. Ordinal 4 4 8 4–8 1 0.176
2 RAM Ordinal 6 4 3 3–6 11 0.044
3 Safety Ordinal 4 4 10 4–10 2 0.162
Risk
4 Systeminteg. Ordinal 9 7 3 3–7 8 0.059
5 Tech. perf. Ordinal 9 7 3 3–9 9 0.059
6 Cost overrun $M 0 1 5 0–5 12 0.044
7 Sched.overrun Years 0 2 4 0–4 7 0.059
Cost
8 RDT&E $M 0 6 13 0–13 6 0.059
9 LCC $B 3.0 2.8 2.5 2.5–3.0 4 0.088
Program objectives
10 Timetable Years 2 6 8 2-8 10 0.044

11 Tech. opport. Ordinal 1 2 7 1-7 5 0.074
12 Acceptability Ordinal 1 3 9 1–9 3 0.132
* A1=baseline, A2=upgraded system, A3=USDCH.
†Order of importance for the given range of attribute values.
TABLE 6A.6 Scale used for
“Mission Objectives” Attribute
Value Explanation
10
All mission objectives are satisfied or exceeded, and some
additional capabilities are provided. The design is expected
to lead to significant improvements in human productivity
and military readiness.
8
All basic mission objectives are met, and some
improvement in productivity is expected. The design readily
permits the incorporation of new technologies when they
become available.
6
Minor shortcomings in system performance are evident, but
the overall mission objectives will not be compromised.
Some improvement in operator efficiency is expected.
4
Not all performance levels are high enough to meet basic
mission objectives. However, no more than one major
objective (e.g., self-deployability, microcooling) is
compromised, and no threat exists to military readiness.
2
An inability to meet one or more major mission objectives
exists. With the current design, it is not economically
feasible to bring overall performance up to standards.
Significant shortcomings exist with respect to the mission

0 objectives. Implementation or continued use could seriously
jeopardize military readiness.
To determine the scaling constants, the decision maker must specify an
indifference probability, p, related to the best ( x * ) and the worst ( x 0 )
values of the attribute states. The following scenario is posed:
1. Let attribute i be at its best value and the remaining attributes be at their
worst values. Call this situation the “reference.”
2. Assume that a “gamble” is available such that the “best outcome” occurs
with probability p, and the “worst outcome” occurs with probability
1−p. If you can achieve the “reference” for sure, then for what value of p
are you indifferent between the “sure thing” and the “gamble”?
The resultant scaling constants for each of the five decision makers are
displayed in Table 6A.7 along with the corresponding AHP weights. The
former have been normalized to sum to 1 to facilitate the comparison and to
permit the use of the additive model Eq. (6.1b). At a superficial level, the
group showed a remarkable degree of consistency from one set of responses
to the next. (Theoretically speaking, the AHP weights and the MAUT scaling
constants measure different phenomena and hence cannot be given the same
interpretation). In almost all cases, mission objectives, safety, technical
performance, and life-cycle cost emerged as the dominant concerns. A look at
individual values shows some discrepancies, but rankings and orders of
magnitude are similar.
The procedure used to assess the utility functions is nearly identical to that
used for the scaling constants. Not surprisingly, the respondents evidenced a
slight risk aversion for the attribute ranges considered. Further explanation of
the methodology is given by Bard and Feinberg (1989).
TABLE 6A.7  Comparison of
AHP Weights and MAUT

Scaling Constants for the Five
Decision Makers
Respondent
1 2 3 4
No. Attribute AHP MAUT AHP MAUT AHP MAUT AHP MAUT
Performance
1 Missionobjectives 0.324 0.176 0.341 0.287 0.245 0.199 0.215 0.171
2 RAM 0.048 0.044 0.047 0.031 0.092 0.081 0.072 0.105
3 Safety 0.145 0.162 0.164 0.144 0.092 0.103 0.072 0.075
Risk
4 Systemintegration 0.006 0.059 0.080 0.061 0.061 0.016 0.021 0.013
5 Technicalperformance 0.018 0.059 0.080 0.085 0.141 0.093 0.203 0.225
6 Cost overrun 0.018 0.044 0.037 0.074 0.025 0.097 0.058 0.076
7 Scheduleoverrun 0.018 0.059 0.023 0.023 0.013 0.016 0.033 0.047
Cost
8 RDT&E 0.038 0.059 0.018 0.025 0.023 0.038 0.023 0.013
9 Life-cyclecost 0.268 0.088 0.129 0.111 0.162 0.191 0.187 0.170
Program objectives

10 Timetable 0.012 0.044 0.027 0.025 0.066 0.094 0.079 0.032
11 Technicalopportunity 0.030 0.074 0.027 0.057 0.017 0.021 0.010 0.044
12 Acceptability 0.075 0.132 0.027 0.077 0.033 0.051 0.027 0.029
The computational results for the utility analysis are displayed in Table 6A.8
and are seen to parallel closely those for the AHP. Only decision makers 3
and 5 partially reversed themselves but without consequence; the others
maintained the same ordinal rankings. Note again that it would be
inappropriate to compare the final AHP priority weights with the final utility
values obtained for each alternative (see Belton 1986). The former are
measured on a ratio scale and have relative meaning; the latter simply
indicate the order of preference.
An examination of the last four columns of Tables 6A.4 and 6A.8 shows that
the two methods give the same general results. Here the geometric mean, also
known as the Nash bargaining rule, is computed from the five entries in the
table. In making comparisons, only the rankings (and not their relative
values) should be taken into account.
TABLE 6A.8 Summary of
Results for MAUT Analysis
Respondent
1 2 3 4
Alternative Weight Rank Weight Rank Weight Rank Weight Rank Weight
Baseline 0.302 2 0.299 3 0.481 1 0.539 1
Upgrade 0.273 3 0.328 2 0.261 3 0.426 2
USDCH 0.595 1 0.567 1 0.337 2 0.273 3
6A.4.2 Discussion of Multiattribute

Utility Theory and Results
The interview sessions in which the scaling constants and utility functions
were assessed took approximately 30 minutes each and were conducted
individually while the analyst and decision maker were seated at a terminal.
Three difficulties arose immediately. The first related to the probabilistic
nature of the questions. None of the respondents could make sense out of the
relationship between the posed lotteries and the overall evaluation process.
Repeated coaxing was necessary to get them to concentrate on the gambles
and to give a deliberate response.
In this regard, it might have been possible to develop more perspective by
using a probabilistic rather than a deterministic utility model. This would
have required the attribute outcomes to be treated as random variables
(which, in fact, they are) and for probability distributions to be elicited for
each. It was believed, however, that this additional burden would have
strained the patience and understanding of the group without producing
credible results. It was difficult enough to collect the basic attribute data on
each alternative without having to estimate probability distributions.
The second issue centered on the assessment of the scaling constants. Here
the decision makers were asked to balance best and worst outcomes for 12
attributes at a time. This turned out to be nearly impossible to do with any
degree of accuracy and created a considerable amount of tension. The
problem was compounded by the fact that in most instances, the group
believed that a low score on any one of the principal attributes, such as
mission objectives or safety, would kill the program. This produced an
unflagging reluctance to accept the sure thing unless the gamble was
extremely unfavorable. Because most people are unable to deal intelligently
with low probability events, this called into question, at least in our minds,
the validity of the accompanying results.
The third concern relates to the use of ordinal scales to gauge attribute
outcomes. Although time and cost have a common frame of reference,
ordinal scales generally defy intuition. This was the case here. None of the
respondents felt comfortable with this part of the interview, even when they

were willing to accept the overall methodology.
6A.5 Additional Observations
The level of abstraction surrounding the use of MAUT strongly suggests that
the AHP is more acceptable to decision makers who lack familiarity with
either method. For problems characterized by a large number of attributes,
most of whose outcomes can be measured only on a subjective scale, the
AHP once again seems best. When the data are more quantifiable, the major
attributes are few, and the alternatives are well understood, MAUT may be
the better choice.
This is not to say that the AHP does not have its drawbacks. The most serious
relates to the definition and use of the 9-point ratio scale. At some point in
the analysis, each of the decision makers found it difficult to reconcile that by
expressing a “weak” preference for one alternative over another, they were
saying that they preferred it by a factor of 3:1. Although this might have
seemed reasonable in some instances, in others, they believed that a score of
2 was equivalent to showing a “strong” preference. Perhaps this problem
could be alleviated by the use of a logarithmic scale.
From the standpoint of consensus building, the AHP methodology provides
an accessible data format and a logical means of synthesizing judgment. The
consequences of individual responses are easily traced through the
computations and can quickly be revised when the situation warrants. In
contrast, the MAUT methodology hides the implications of the input data
until the final calculations. This makes intermediate discussions difficult
because no single point of focus exists. Sensitivity analysis offers a partial
solution to this problem but in a backward manner that undercuts its
theoretical rigor.
As a final observation, we note that the enthusiasm and degree of urgency
that the participants brought to the study varied directly with their
involvement in the program. Those with vested interests were eager to grasp
the methodologies and were quick to respond to requests for data. The
remainder viewed each new request as a frustrating and unnecessary ordeal

that was best dealt with through passive resistance.
6A.6 Conclusions for the Case Study
The collective results of the analysis indicated that the group had a modest
preference for the USDCH over the baseline. The tradeoff between risk and
performance for the upgraded system did not seem favorable enough to make
it a serious contender for the cargo-handling mission. We therefore
recommended that work continue on the development of the basic USDCH
technologies, including self-deployability and robotic cargo engagement, to
demonstrate the underlying principles. If more supportive data are needed,
then the place to start would be with a full-scale investigation of LCCs, and
some of the more quantifiable performance measures such as reliability. The
effort required to gather these statistics would be considerable, though, and
does not seem justified in light of the overall findings.
In summary, the group believed that the idea of imposing new technologies
on an existing system would probably increase its LCC without achieving the
desired capabilities. The extensive improvements in performance ultimately
sought could best be realized through a structured R&D program that fully
exploited technological advances and innovative thinking in design. Such an
approach would significantly reduce risk while permitting full systems
integration. In fact, this is the approach now being pursued.
References
Bard, J. F., “A Comparison of the Analytic Hierarchy Process with
Multiattribute Utility Theory: A Case Study,” IIE Transactions, Vol. 24,
No. 5, pp. 111–121, 1992.
Belvoir RD&E Center, Test and Evaluation Master Plan for the
Variable Reach Rough Terrain Forklift, U.S. Army Troop Support
Command, Logistics Equipment Directorate, Fort Belvoir, VA, 1987a.
Belvoir RD&E Center, Universal Self-Deployable Cargo Handler,

Contract DAAK-70-87-C-0052, U.S. Army Troop Support Command,
Fort Belvoir, VA, Sept. 25, 1987b.
De Lange, W. J., et al. “Incorporating stakeholder preferences in the
selection of technologies for using invasive alien plants as a bio-energy
feedstock: Applying the analytical hierarchy process.” Journal of
environmental management 99, 76–83, 2012.
Kallas, Z., F. Lambarraa, and J. M. Gil. “A stated preference analysis
comparing the analytical hierarchy process versus choice experiments,”
Food quality and preference, Vol. 22, No. 2, pp. 181–192, 2011.
Saaty, T. L., and L.G. Vargas, Models, methods, concepts &
applications of the analytic hierarchy process, Vol. 175, Springer
Science & Business Media, 2012.
Sievers, R. H. and B. A. Gordon, Applications of Automation
Technology to Field Material Handling, SAIC-86/1987, Science
Applications International Corporation, McLean, VA, December 1986.
Sousk, S. F., H. L. Keller, and M. C. Locke, Science and Technology for
Cargo Handling in the Unstructured Field Environment, U.S. Army
Belvoir RD&E Center, Logistics Equipment Directorate, Fort Belvoir,
VA, 1988.

Chapter 7 Scope and
Organizational Structure of a
Project
7.1 Introduction
Project management deals with one-time efforts to achieve a specific goal
within a given set of resource and budget constraints. It is essential to use a
project organization when the work content is too large to be accomplished
by a single person. The fundamentals of project management involve the
identification of all work required to be performed, the allocation of work to
the participating units at the planning stage, the continuous integration of
output through the execution stage, and the introduction of required changes
throughout the project life cycle. How the efforts of the participants are
coordinated to accomplish their assigned tasks and how the final assembly of
their work is achieved on time and within budget are as much an art as they
are a science. Adequate technical skills and the availability of resources are
necessary but rarely sufficient to guarantee project success. There is a need
for coordinated teamwork and leadership—the essence of sound project
management.
Three types of “structures” are involved in the overall process. Each is
derived from the project scope. They include (1) the work breakdown
structure (WBS), which defines the way the work content is divided into
small, manageable work packages that can be allocated to the participating
units; (2) the organizational structure of each unit participating in the project
(the client, the prime contractor, subcontractors, and perhaps one or more
government agencies); and (3) the organizational breakdown structure (OBS)
of the project itself, which specifies the relationship between the
organizations and people doing the work.

Organizations set up management structures to facilitate the achievement of
their overall mission as defined in both strategic and tactical terms. In so
doing, compromise is needed to balance short-term objectives with long-term
goals. As a practical matter, the project manager has very little say in the final
design of the organization or in any restructuring that might occur from time
to time. Organizations may be involved in many activities and cannot be
expected to reorient themselves with each new project. Nevertheless, both the
project OBS and the WBS should be designed to achieve the project’s
objectives and therefore should be directly under project management
control. The thoughtful design and implementation of these structures are
critical because of their effect on project success.
The design of a project organizational structure is among the early tasks of
the project manager. In performing this task, issues of authority,
responsibility, and communications should be addressed. The project
organizational structure should fit the nature of the project, the nature of the
participating organizations, and the environment in which the project will be
performed. For example, the transport of U.S. forces to remove Saddam
Hussein from Iraq in 2003 required a project organization that was capable of
coordinating logistical activities across three continents (North America,
Europe, and the Arabian Peninsula). The authority to decide which forces to
transport, when and by what means, as well as the channels through which
such decisions were communicated, had to be defined by the project
organizational structure. The participating parties were many, including all
branches of the U.S. armed services and countries such as England, Australia,
and Turkey. To facilitate coordination among these parties, a well-structured
project organization with clear definitions of authority, responsibility, and
communication channels was needed.
The issue of scope underlies the execution of every project. Scope
management includes the processes required to ensure that only the work
necessary to complete the project successfully is identified. It is the project
manager’s responsibility to inform and update the scope at each stage of a
project, starting with the initiation phase, continuing with the introduction of
change requests, and ending with the acceptance of the final deliverables. The
work content of the project, referred to in shorthand as the WBS, can usually
be structured in a variety of ways. For example, if the project is aimed at

developing a new commercial aircraft, then the WBS can be structured
around the main systems, including the body, wings, engines, avionics, and
controls. Alternatively, it can be broken down according to the life-cycle
phases of the project; that is, design, procurement, execution, testing, and so
on. The first critical step after a project is approved is the design of the WBS
by the project manager. The “best” WBS structure is a function of the work
content and the organizational structure used to perform the required tasks.
To reach an optimal design, the project manager needs to know what types of
structures are common, their strengths and weaknesses, and under what
conditions each structure is most effective. These issues are taken up in the
remainder of the chapter.

7.2 Organizational Structures
Projects are performed by organizations using human, capital, and other
resources to achieve a specific goal. Many projects cut across organizational
lines. In order to understand the organizational structure of a project, it first is
necessary to understand the general nature of organizations.
Theorists have devised various ways of partitioning an organization into
subunits to improve efficiency and to decentralize authority, responsibility,
and accountability. The mechanism through which this is accomplished is
called departmentalization. In all cases, the objective is to arrive at an orderly
arrangement of the interdependent components. Departmentalization is
integral to the delegation process. Examples include:
1. Functional. The organizational units are based on distinct common
specialties, such as manufacturing, engineering, and finance.
2. Product. Distinct units are organized around and given responsibility for
a major product or product line.
3. Customer. Organizational units are formed to deal explicitly with a
single customer group, such as the Department of Defense.
4. Territorial. Management and staff are located in units defined along
geographical lines, such as a southern U.S. sales zone.
5. Process. Human and other resources are organized around the flow of
work, such as in an oil refinery.
Thus, organizations may be structured in different ways based on functional
similarity, types of processes used, product characteristics, customers served,
and territorial considerations.
7.2.1 Functional Organization

Perhaps the most widespread organizational structure found in industry is
designed around the technical and business functions performed by the
organization. This structure derives from the assumption that each unit should
specialize in a specific functional area and perform all of the tasks that
require its expertise. Common functional organizational units are
engineering, manufacturing, information systems, finance, and marketing.
The engineering department is responsible, for example, for such activities
as product and process design. The division of labor is based on the function
performed, not on the specific process or product. Figure 7.1 depicts a typical
functional structure.
Figure 7.1
Portion of a typical functional organization.
When the similarity of processes is used as a basis for the organizational

structure, departments such as metal cutting, painting, and assembly are
common in manufacturing, and departments such as new policy development,
claims processing, and information systems are common in the service sector.
When similar processes are performed by the same organizational elements,
capital investment is minimized and expertise is built through repetition
within the particular group.
In a functional organization structure, no strong central authority is
responsible for integration of the various, detailed aspects of each particular
project. Major decisions relating to resource allocation and budgets are
seldom based on what is best for a particular project but rather on how they
affect the strongest functional unit. In addition, considerable time is spent in
evaluating alternative courses of action, because each project decision
requires coordination and approval of all functional groups, in addition to
upper management. Finally, there is no single point of contact for the
customer.
Despite these limitations, the functional organization structure offers the
clearest and most stable arrangement for large organizations. Advantages and
disadvantages are as follows:
Advantages
Efficient use of collective experience and facilities
Institutional framework for planning and control
All activities receive benefits from the most advanced technology
Allocates resources in anticipation of future business
Effective use of production elements
Career continuity and growth for personnel
Well-suited for mass production of items

Disadvantages
No central project authority
Little or no project planning and reporting
Weak interface with customer
Poor horizontal communications across functions
Difficult to integrate multidisciplinary tasks
Tendency of decisions to favor strongest functional group
7.2.2 Project Organization
In this type of structure, each project is assigned to a single organizational
unit and the various functions, such as engineering and finance, are
performed by personnel within the unit. This results in a significant
duplication of resources. Because similar activities and processes are
performed by different organizational elements on any particular project,
there could be a widespread disparity in methods and results. Another
disadvantage can be attributed to the limited life span of projects. Since work
assignments and reporting hierarchies are subject to continuous change,
workers’ career paths and professional growth may be negatively impacted.
Figure 7.2 depicts a project-oriented organizational structure. As can be seen,
functional units are duplicated across projects. These units are coordinated
indirectly by the corresponding central functional unit, but the degree of
coordination may vary sharply. The higher the level of coordination, the
closer the organizational structure is to a pure functionally oriented structure.
Low levels of coordination represent organizational structures closer to the
project-oriented structure. For example, consider an organization that has to
select a new CAD/CAM (computer-aided design/computer-aided
manufacturing) system. In a functional organization, the engineering

department might have the responsibility of selecting the most appropriate
system. In a project-oriented organization, each engineering group will select
the system that fits its needs best. If, however, it is desirable to achieve
commonality and have all engineering groups use the same system, then the
central engineering department will have to solicit input from the various
groups and on the basis of this input, make a decision that balances the
concerns of each. Characteristics of an organization, geared to optimize
project performance—as opposed to developing functional skillset
capabilities—are highlighted below.
Figure 7.2

Project-oriented organizational structure.
Advantages
Strong control by a single project authority
Rapid reaction time
Encourages performance, schedule, and cost tradeoffs
Personnel loyal to a single project
Interfaces well with outside units
Good interface with customer
Disadvantages
Inefficient use of resources
Does not develop technology with an eye on the future
Does not prepare for future business
Less opportunity for technical interchange among projects
Minimal career continuity for project personnel
Difficulty in balancing workloads, as projects phase in and out
In addition to the functional organization and project organization, the
following structures are also common.
7.2.3 Product Organization

In a mass-production environment where large volumes are the norm, such as
in consumer electronics or chemical processing, the organizational structure
may be based on the similarity among products. An organization specializing
in domestic appliances, for example, may have a refrigerator division,
washing machine division, and small appliances division. This structure
facilitates the use of common resources, marketing channels, and
subassemblies for similar products. By exploiting commonality, it is possible
for mixed model lines and group technology cells, handling a family of
similar products, to achieve performance that rivals the efficiency of
dedicated facilities designed for a unique product.
7.2.4 Customer Organization
Some organizations have a few large customers. This is frequently the case in
the defense industry, where contractors deal primarily with one branch of the
service. By structuring the contractor’s organization around its principal
client, it is much easier to establish good working relationships. In many such
organizations, as exemplified by consulting firms and architecture and
engineering firms, there is a tendency to hire veteran employees from the
customer’s organization to smooth communications and exploit personal
friendships.
7.2.5 Territorial Organization
Organizational structures can be based on territorial considerations, too.
Service organizations that have to be located close to the customer tend to be
structured along geographical lines. With the push toward reduced
inventories and just-in-time delivery, large manufacturers are encouraging
their suppliers to set up plants, or warehouses, in the neighborhood of the
main facility. The same rationale applies to advertising agencies that need to
be in close contact with specific market segments, although this
need continues to shrink with the widespread use of both the Internet and
video conferencing.

7.2.6 The Matrix Organization
A hybrid structure known as the matrix organization provides a sound basis
for balancing the use of human resources and skills, as workers are shifted
from one project to another. The matrix organization can be viewed as a
project organization superimposed on a functional organization, with well-
defined interfaces between project teams and functional elements. In the
matrix organization, duplication of functional units is eliminated by assigning
specific resources of each functional unit to each project. Figure 7.3 depicts
an organization that is performing several projects concurrently. Each project
has a manager who must secure the required skills and resources from the
functional groups. Technical support, for example, is obtained from the
engineering department, and the marketing department provides sales
estimates. The project manager’s request for support is handled by the
appropriate functional manager, who assigns resources on the basis of their
availability, the project’s need, and the project’s priority as compared with
other projects. Project managers and functional managers must act as partners
to coordinate operations and the use of resources. It is the project manager,
though, who is ultimately responsible for the success or failure of the project.
Important advantages of the matrix organization are:

Figure 7.3
Typical matrix structure.
Figure 7.3 Full Alternative Text
1. Better utilization of resources. Because the functional manager assigns
resources to all projects, he or she can allocate resources in the most
efficient manner. The limited life span of projects does not reduce
utilization of resources, because they can be reassigned to other projects
and tasks as the need arises.

2. State-of-the-art technology. The knowledge gained from various
projects is accumulated at the functional level. The most sophisticated
projects are sources of new technology and skills that can be transferred
to other projects and activities performed by the organization. Therefore,
the functional departments become knowledge centers.
3. Adaptation to changing environment. The matrix organization can adapt
to changing conditions, including the arrival of new competition in the
market, the termination of existing projects, and the realignment of
suppliers and subcontractors. The functional skeleton is not affected by
such changes, and resources can be reallocated and rescheduled as
needed. No loss of knowledge is experienced when projects terminate,
because the experts are kept within the functional units.
The matrix organization benefits from having focused effort in both the
functional and the project dimensions. However, this advantage may be offset
by several potential difficulties.
1. Authority. Although personnel resources are under the control of the
functional manager in the long run, they are accountable, day-to-day, to
the project manager. In a matrix organization, this can lead to a conflict
of interest and to a “dual boss” phenomenon.
2. Technical knowledge. The project manager is not an expert in all
technical aspects of a project. He or she has to rely on functional experts
and functional managers for their inputs. But, once again, the project
manager is responsible for the overall outcome.
3. Communications. Workers have to report to their functional manager
and to the project manager for whom they perform specific tasks.
Double reporting and simultaneous horizontal/vertical communication
channels are difficult to develop, manage, and maintain.
4. Goals. The project manager tends to see the short-term objectives of the
project most clearly, whereas the functional manager typically focuses
on the longer-term goals, such as accumulation of knowledge and the
acquisition and efficient use of resources. These different perspectives
frequently conflict and create friction within an organization.

The design and operation of a matrix organization are complicated, time-
consuming tasks. A well-conceived and well-managed structure is necessary
if the impact of the problems listed above is to be minimized.
In general, each project and functional unit has a set of objectives that must
be balanced against a set of mutually agreed-on performance measures. This
balance depends on the weight given to each objective and is an important
determinant in selecting the organizational structure. For example, if the
successful completion of projects on time and within budget is considered
most important, the matrix organization will be more project oriented. In the
case in which functional goals are emphasized, then the matrix organization
can be designed to be functionally oriented.
The orientation of a matrix organization can be measured to some degree by
the percentage of workers who are fully committed to single projects. If this
number is 100%, then the organization has a perfect, project-oriented
structure. If none are fully committed, then the organization has a functional
structure. A range of matrix organizations can be defined between these two
extremes as depicted in Figure 7.4. In this figure, functional organizations are
located on the left-hand side, and project-oriented organizations are on the
right. Those in between are hybrids of varying degree. An organizational
structure that is based on one part-time person managing each project while
everyone else is a member of a functional unit represents a very weak matrix
structure with a strong functional orientation. Conversely, if the common
arrangement is project teams with only a few shared experts among them,
then the matrix organization has a strong project orientation, sometimes
called a “strong matrix” structure.

Figure 7.4
Level of employee commitment as a function of organizational
structure.
Figure 7.4 Full Alternative Text
In summary, the principal advantages and disadvantages of the matrix
organization are:
Advantages
Effective accumulation of know-how
Effective use of resources
Good interface with outside contacts
Ability to use multidisciplinary teams
Career continuity and professional growth

Perpetuates technology
Disadvantages
Dual accountability of personnel
Conflicts between project and functional managers
Profit-and-loss accountability difficult
7.2.7 Criteria for Selecting an
Organizational Structure
The decision to adopt a specific organizational structure is based on several
criteria, as discussed below.
1. Technology. A functional organization and a process-oriented
organization have one focal point for each type of technology. The
knowledge gained in all operations, projects, and products is
accumulated at that focal point and is available to the entire
organization. Furthermore, experts in different areas can be used
efficiently, because they, too, are a resource available to the whole
organization.
2. Finance and accounting. These functions are easier to perform in a
functional organization, where the budgeting process is controlled by
one organizational element that is capable of understanding the “whole
picture.” Such an entity is in the best position to develop a budget that
integrates the organizational goals within individual project objectives.
3. Communications. The functional organization has clear lines of
communication that follow the organizational structure. Instructions
flow from the top down, whereas progress reports are directed over the
same channels from the bottom up. The functional organization provides

a clear definition of responsibility and authority and thus minimizes
ambiguity in communications.
Product-, process-, or project-oriented structures have vertical as well as
horizontal lines of communication. In many cases, communication
between units that are responsible for the same function on different
projects, processes, or product lines might not be well defined. The
organizational structure itself is subject to frequent changes as new
projects or products are introduced, existing projects are terminated, or
obsolete lines are discontinued. These changes affect the flow of
information and cause communications problems.
4. Responsibility to a project/product. The product- or project-oriented
organization removes any ambiguity over who has responsibility for
each product manufactured or project performed. The project manager
has complete control over all resources allocated to the project, along
with the authority to use those resources as he or she sees fit. The one-
to-one relationship between an organizational element and a project or
product eliminates the need for coordination of effort and
communication across organizational units and thus makes management
easier and more efficient.
5. Coordination. As mentioned, the project/product-oriented structure
reduces the need for coordination of activities related to the project or
product; however, more coordination is required between organizational
units that perform the same function on different products.
6. Customer relations: The project/product-oriented organization provides
the customer with a single point of contact. Any need for service,
documentation, or support can be handled by the same organizational
unit. Accordingly, this structure supports better communications and
frequently better service for the customer, compared with the functional
structure. Its performance closely approximates that of a pure customer
oriented organizational structure.
This partial list demonstrates that there is no single structure that is optimal
for all organizations, in all situations. Therefore, each organization must
analyze its own operations and select the structure that best fits its needs, be it

functional, process oriented, customer oriented, project/product oriented, or a
combination thereof.

7.3 Organizational Breakdown
Structure of Projects
The OBS should be designed as early as possible in the project’s life cycle.
An unambiguous definition of communication channels, responsibilities, and
the authority of each participating unit is a key element that affects project
success. The most appropriate structure depends on the nature of the project,
on the environment in which the work is performed, and on the structure of
the participating organizations. For example, if a computer company believes
that the development of a lighter laptop is crucial to maintaining its market
share, then it is likely that either a project structure or a strong matrix
structure would be used for this purpose. In these structures, team members
report directly to the project manager and, as a result, are able to maintain a
strong identification with the project, thus increasing the probability that the
project will be completed successfully.
In most projects, it is not enough to adopt the organizational structure of the
prime contractor. At a minimum, both the client and the contractor
organizations must be considered. The client organization usually initiates the
project by defining its specific needs, whereas the contractor is responsible
for developing the plan to satisfy those needs. The two may be elements of
the same organization (e.g., an engineering department that develops a new
product “for” the marketing department), or they may be unrelated (e.g., a
contractor for the National Aeronautics and Space Administration). In either
case, the relationship between these organizations is defined by the project
organizational structure. This definition should specify the responsibility of
each party, the client’s responsibility to supply information or components
for the project, such as government-furnished equipment, and the contractor’s
responsibility to perform certain tasks, to provide progress reports, to consult
periodically with the client, and so on.
7.3.1 Factors in Selecting a

Structure
The primary factors that should be taken into consideration when selecting an
organizational structure for managing projects are as follows.
1. Number of projects and their relative importance. Most organizations
are involved in projects. Common examples are the installation of a new
enterprise resource planning system, the integration of a new acquisition
into the company structure, or the cultivation of a new market. If an
organization is dealing with projects only infrequently, then a functional
structure supported by ad hoc project coordinators may be best. As the
number of projects increases and their relative importance (measured by
the budget of all projects as a percentage of the organizational budget, or
any other method) increases, the organizational structure should adapt
by moving to a matrix structure with a stronger project orientation.
2. Level of uncertainty in projects. Projects may be subject to different
levels of uncertainty that affect cost, schedule, and performance. To
handle uncertainty, a feedback control system is used to detect
deviations from original plans and to detect trends that might lead to
future deviations. It is easier to achieve tight control and to react faster
to the effects of uncertainty when each project manager controls all of
the resources used in the project and gets all the information regarding
actual performance directly from those who are actively involved.
Therefore, a project-oriented structure is preferred when high levels of
uncertainty are presented.
3. Type of technology used. When a project is based on a number of
different technologies and the effort required in each area does not
justify a continuous effort throughout the project life cycle, the matrix
organization is preferred. When projects are based on several
technologies and the work content in each area is sufficient to employ at
least one full-time person, then a strong matrix or a project-oriented
structure is preferred.
Research and development projects in which new technologies or

processes are developed are subject to high levels of uncertainty. The
uncertainty is expressed through parameters such as task completion
times, the likelihood of a contemplated breakthrough, or simply the
chances that the project’s components can be integrated successfully.
Therefore, to struggle better with this high uncertainty, stronger
commitment for the project is needed, calling for the use of a project-
oriented structure.
4. Project complexity. High complexity that requires very good
coordination among the project team is best handled in a project-
oriented structure. Here communication is most rapid and unobstructed.
Low-complexity projects can be handled effectively in a functional
organization or a matrix arrangement with a functional orientation.
5. Duration of projects. Short projects do not justify a dedicated project
organization and are best handled within a functional structure or a
matrix organization. For certain shorter projects, a functional manager—
for example, the manager of a function that has a key role on the project
—may assume project manager responsibilities. Long projects that span
many months or years justify a project-oriented structure.
6. Resources used by projects. When common resources are shared by two
or more projects, the matrix arrangement with a functional orientation
tends to be best. This is the case when expensive resources are used or
when each project does not need a fully devoted unit of a resource. If the
number of common resources among projects is small, then the project-
oriented structure is preferred.
7. Overhead cost. By sharing facilities and services among projects, the
overhead cost of each project is reduced. A matrix organization should
be preferred when an effort to reduce overhead cost is required.
8. Data requirements. If many projects have to share the same databases
and it is desirable to make available as quickly as possible the
information generated by a set of projects to other elements in the
organization not directly involved in these projects, then a weak matrix
structure is preferred.

In addition to the above factors, the organizational structures of the client and
the contractor must be taken into account. If both have a functional
orientation, then direct communication between similar functions in the two
organizations might be best. If both are project/product oriented, then an
arrangement that supports direct communication links between project
managers in their respective organizations would be most efficient.
The situation is complicated when the contractor and the client do not have
similar organizational structures or when there are several participating units.
If the organizational structure of the contractor is functionally oriented, then
the client project manager may have to deal simultaneously with many
departments as well as a host of subcontractors, government agencies, and
private consultants.
7.3.2 The Project Manager
The success of a project is highly correlated with the qualities and skills of
the project manager. In particular, a project manager must be capable of
dealing with a wide range of issues that include refining and promoting
project objectives, translating those objectives into plans, and obtaining the
required resources to execute each phase of the project. On a day-to-day
basis, a project manager copes with issues related to budgeting, scheduling,
and procurement. He or she must also be able to respond to the needs and
expectations of key stakeholders, including customers, subcontractors, and
government agencies. It is often the case that the project manager has most of
the responsibilities of a general manager but almost none of the authority.
In Section 1.4.2, we highlighted some of the important attributes that a
project manager should have if he or she is to grapple successfully with the
above issues. These attributes are now discussed in detail.
Leadership
The most essential attribute of a project manager is leadership. The project

manager has to lead the project team through each phase of its life cycle,
dealing swiftly and conclusively with any number of problems as they arise
along the way. This is made all the more difficult given that the project
manager usually lacks full control and authority over the participants. An
ability to guide the project team smoothly from one stage to the next depends
on the project manager’s stature, temperament, skills of persuasion, and the
degree of commitment, self-confidence, and technical knowledge. A manager
who possesses these characteristics, in some measure, is more likely to be
successful even when his or her formal authority is limited.
Interpersonal skills
The project manager (as any manager) has to achieve a given set of goals
through other people. The manager must deal with senior management,
members of the project team, functional managers, and perhaps an array of
clients. In addition, a project manager frequently must interact with
representatives from other organizations, including subcontractors,
laboratories, and government agencies. To achieve the goals of the project,
the ability to develop and maintain good personal relationships with all
parties is crucial.
Communication skills
Communication skills The interaction between groups involved in a project
and the project manager takes place through a combination of verbal and
written communications. The project manager must be kept abreast of
progress and be able to transmit directions in a succinct and unambiguous
manner. By building reliable communication channels and by using the best
channel for each application, the project manager can achieve a fast, accurate
response from the team with some degree of confidence that directions will
be carried out correctly. The more up to date and comprehensive the
information, the smoother the implementation route will be.

Decision-making skills
The project manager has to establish procedures for documenting and dealing
with problems as they arise. Once the source and the nature of a problem are
identified, the manager must evaluate alternative solutions, select the best
corrective action, and ensure that it is implemented. These are the
fundamental steps in project control.
In some instances, the project manager gets involved early enough to
participate in discussions regarding the organizational structure of the project
and the choice of technology to be used. An understanding of the basic
technical issues gives the project manager the credibility needed to influence
resource allocation, budget, and schedule decisions before they are finalized.
A project manager’s input on these matters in the initial stages increases the
probability that the project will get started in the right direction.
Negotiation and conflict resolution
Many of the problems that the project manager faces do not have a “best
solution,” for example, when a conflict of interest exists between the project
manager and the client over a contract issue contingent on various
interpretations. There are many sources of conflict, including:
Scheduling
Disagreements that develop around the
timing, sequencing, and duration of projects
and feasibility of schedule for project-
related tasks or activities.
Managerial and
administrative
procedures
Disagreements that develop over how the
project will be managed: the definition of
reporting relationships and responsibilities,
interface relationships, project scope, work
design, plans of execution, negotiated work
agreements with other groups, and
procedures for administrative support.

Communication
Disagreements resulting from poor
information flow among staff or between
senior management and technical staff,
including such topics as misunderstanding
of project-related goals, the strategic
mission of the organization, and the flow of
communication from technical staff to
senior management.
Goal or priority
Disagreements arising from lack of goals or
poorly defined project goals, including
disagreements regarding the project mission
and related tasks, differing views of project
participants over the importance of
activities and tasks, or the shifting of
priorities by superiors/customers.
Resource allocation
Disagreements resulting from the
competition for resources (e.g., personnel,
materials, facilities, equipment) among
project members or across teams or from
lack of resources or downsizing of
organizations.
Reward
structure/performance
appraisal
Disagreements that originate from
differences in understanding the reward
structure or from the insufficient match
between the project team approach and the
performance appraisal system.
Personality and
interpersonal
relations
Disagreements that focus on interpersonal
differences rather than on “technical”
issues; includes conflicts that are ego-
centered, personality differences, or
conflicts caused by prejudice or
stereotyping.
Costs
Disagreements that arise from the lack of
cost control authority within the project
office or with a functional group.
Disagreements related to the allocation of

funds.
Technical opinion
Disagreements that arise, particularly in
technology-oriented projects, over technical
issues, performance specifications,
technical tradeoffs, and the means to
achieve performance.
Politics
Disagreements that center on issues of
territorial power (not-invented-here
attitudes) or hidden agendas.
Poor input or
direction from leaders
Disagreements that arise from a need for
clarification from upper management on
project-related goals and the strategic
mission of the organization.
Ambiguous
roles/structure
Disagreements, especially in the matrix
structure, in which two or more people or
sections have related or overlapping
assignments or roles.
Tradeoff analysis skills
Because most projects have multidimensional goals (e.g., performance,
schedule, budget), the project manager often has to perform tradeoff analyses
to reach a compromise solution. Questions such as, “Should the project be
delayed if extra time is required to achieve the performance levels specified?”
or, “Should more resources be acquired at the risk of a cost overrun to reduce
a schedule delay?” are common and must be resolved by trading off one
objective for another.
In addition to these skills and attributes, a successful project manager will
embody good organizational skills, the ability to manage time effectively, a
degree of open mindedness, and loyalty to his or her charge. The correct
selection of the project manager and the project organizational structure are
two important decisions that are made early in the life cycle of a project and
have a lasting impact.

A major difficulty that a project manager faces in a matrix structure (which is
the most common one) is related to the nature of the relationship with the
functional managers. To understand the sources of the difficulties, let us
compare the roles of the two by referring to the four following domains:
responsibility, authority, time horizon, and communication.
Responsibility
The project manager is responsible for ensuring that the project is completed
successfully, as measured by time, cost, system or product performance, and
stakeholder satisfaction. The functional manager is responsible for running a
department so that all the department’s customers are served efficiently and
effectively. To be successful, the functional manager must continuously
upgrade the technical ability of the department and take care of staff needs.
Inherent in these responsibilities is the following conflict: Assume that a
project manager needs a certain job done by one of the functional
departments in the organization. The project manager would like a specific
individual to do the work. However, the functional manager plans to assign
another person to do the job because the preferred employee is needed
elsewhere. In these situations, the functional manager is inclined to do what’s
best for the department, and not necessarily what’s best for a particular
project.
Authority
Authority is measured by the amount of resources that a manager can allocate
without the need to get higher-level approval. Whenever external contractors
are used, the project manager is the one who approves payment in accordance
with the terms of the contract. This is not the case when the work is
performed by a functional department within the organization, particularly in
a matrix environment, because payment is little more than an accounting
entry. This means that if the functional department is late with a deliverable,
then the project manager cannot withhold payment, implying that he has little

leverage over his functional counterpart. In situations such as this, in which
unresolved internal conflicts hurt the chances of the project being completed
on time, the project manager should seek resolution with higher-level
management. In contrast, the functional manager has the authority over all of
the resources that belong to his department, including material, equipment,
and employees.
Time horizon
Because projects have a limited time horizon, the project manager is
necessarily short-term oriented and is interested in immediate impacts. A
functional manager has an ongoing department to run whose mission remains
in effect beyond the project’s lifetime. A functional manager receives work
orders that have to be executed for different customers and may not have the
vision to view the full scope or importance of different, individual projects. A
project can be viewed as a small business within a larger enterprise whose
ultimate goal is to go out of business when all tasks are completed. At the
same time, functional departments should be viewed as permanent entities
striving to maximize the benefits that they provide to the organization.
Communication
In allocating work, a project manager has to interact with many individuals,
often from different companies. With some individuals, such as a contractor
or consultant, he or she has a formal relationship established through a
signed, legally binding contract. With others, such as functional managers
within the organization undertaking the project, he or she does not have a
formal contract, although there is generally an explicit agreement on the work
to be performed. In most cases, specific tasks are carried out not by the
person who negotiated the scope of work, but by his or her subordinates.
Depending on the established line of communications, the project manager
may not be able to communicate directly with those charged with the work;
however, in many cases, there is a continuing need for communication and
coordination between two individuals who belong to two different

organizational units. Using formal communication channels, the project
manager should approach those individuals through their managers.
Unfortunately, this process may complicate the communications and increase
the response time to unacceptable levels. To circumvent this difficulty, the
execution of projects in a matrix environment often requires that the project
manager communicate informally with those who are working on his or her
project.
Projects are essentially horizontal, whereas the functional organization, as
exemplified by the traditional organization chart, is vertical. The basic
dichotomy between the two can be better understood by comparing the types
of questions that project and functional managers ask. Table 7.1 highlights
the differences.
TABLE 7.1 Concerns of
Project and Functional
Managers
Project manager Functional manager
What is to be done?
When will the task be
done?
What is the importance of
the task?
How much money is
available to do the task?
How well has the total
project been done?
How will the task be done?
Where will the task be done?
Who will do the task?
How well has the functional input been
integrated into the project?

7.3.3 Project Office
The project office is a functional department that specializes in the
development and implementation of project management methodologies and
processes. This department offers its services to all other units in the
organization in the same manner as any other functional department. It may
be directly under the general manager or may be a subunit in, say, the
research and development (R&D) department or the information systems
department. These two departments are the ones typically involved in most
projects, especially in technology-oriented companies.
The following is a list of tasks that fall within the scope of the project office:
Support in data entry, presentation, and analysis
Development and introduction of project management body of
knowledge (PMBOK)-related methods, tools, and techniques
Training project and functional managers
Supplying professional project managers to the organization
Multi-project management support
Maintaining the company’s project management know-how
Coordination between organizational strategy and project portfolio
Contract management
Developing infrastructure required for effective project management
Increased reliance of the use of a project office within large organizations
over the last decade can be traced to the need to overcome the following
problems:
High failure rate of project completion with respect to budget and

schedule
Constant complaints of overwork by project teams
Departments within the same organization manage projects differently,
making it complicated to integrate interdepartmental projects
Insufficient correlation between organizational strategy and the project
portfolio
Lack of a standardized way to perform projects
A major concern of many organizations is the process by which data and
information are collected and stored. If this process is handled diligently, then
its output can be used as a vehicle for improving future project planning and
execution. An enterprise-wide, information warehouse, operated and
maintained by the IT organization, is typically established to standardize data
processing and information procedures across all departments. The
development of a project office is not a straightforward job and should be
treated as a project in and of itself. The following may serve as guidelines for
such a project:
The project office should be developed in stages, beginning with the
most painful problems faced by the organization. Long-term objectives
can be deferred until a structure is in place, a manager and staff are
chosen, and operational procedures are established.
In the early stages, the project office may offer support on issues such as
report design, tracking progress, budgeting, methods for analyzing
performance, and standardizing processes by developing templates.
There is a need to meet with different stakeholders, such as project
managers and functional managers, and identify their immediate needs.
A list of current projects along with their status should be developed to
help determine the most pressing organizational needs.
A respected officer in the organization who believes in the need for a

project office should be recruited to champion its development.
A project office is typically called on to support one or more of the following
activities:
1. Developing a performance measure and control system. Monitoring the
use of resources such as money, labor hours, and material is a basic need
of any project.
2. Developing project managers. It is common for a technically competent
person to be nominated to be a project manager without having any
training or experience in management. A technical perspective is likely
to be much different than the perspective needed to plan, schedule,
monitor, and control the various aspects of a project. One of the primary
functions of a project office is to offer training programs for
inexperienced project managers.
3. Formulating project management processes. Training effectiveness
depends highly on the organizational commitment to implement
standard methods for managing projects. Therefore, the organization
should first make a decision on which project management processes it
wishes to adopt. If the project is to be managed with the help of
software, for example, then it will be necessary to plan for the
acquisition, installation, training, and maintenance of the selected
product.
4. Developing technological infrastructure. As with any process, project
management processes require a technological infrastructure for their
implementation. For example, an intranet (internal organizational
Internet) is an infrastructure that facilitates the integration of information
and effort across all projects within an organization.
5. Developing processes used to manage contractors. Managing work
performed by contractors is different from managing work performed by
internal units. Because many organizations outsource a significant
portion of a project, there is a need to develop a standard process for
contract management that will be used by all projects.

6. Continuous improvement. To compete effectively in open markets, there
is an ongoing need to improve product performance and quality. This
translates into a continuing need for an organization to learn and
improve the way it initiates, manages, and administers projects. The
development of systematic procedures for incorporating the experience
and knowledge gained at the project level and accumulated over time
falls within the domain of the project office.
The specific unit within an organization that carries out the above functions
may be called by one of several names rather than the “project office.” The
name chosen may better characterize its responsibilities. Table 7.2 presents a
list of names and their common meaning.
TABLE 7.2 Similar
Organizational Units that
Perform Project Management
Related Tasks
Level Organizational unit Major activity
1 Project Support Office Administrative support forprojects
2 Project Tool Support Office Support for tools andtechniques
3 Project Office Overall project managementsupport
4 Project Management Office Overall project managementsupport
5 Program Office Program and projectmanagement support
6 Master Program Office Same as above but with moreauthority

7 Enterprise Project
Management Office
Project and portfolio
management
— Virtual Project ManagementOffice
Project management via the
Internet
The first column in the table specifies the sophistication level of the
departmental activities: level 1 means that the project management
department performs very basic tasks, whereas level 7 is associated with the
most complicated tasks. Project management departments that belong to
levels 1 to 4 focus mostly on managing single projects, whereas departments
that belong to levels 5 to 7 deal not only with single projects but also with the
coordination and integration of project activities with organizational strategy.
No level is specified for the virtual project management office because it may
be anywhere from 1 to 7, depending on the organization. This type of office
is becoming more and more common as “virtual companies” set up shop in a
single office and do all of their business with subcontractors over the Internet
and with telecommuting employees. Projects are typically managed with
templates that are used by all participants.
It is difficult to quantify the benefit that a project office offers in monetary
terms. Therefore, without the sponsorship and ongoing support of upper
management, the chances of establishing and maintaining an effective project
office are slim. In reality, though, an increasing number of large corporations
are establishing project management offices or setting up functional
departments devoted to project management. Furthermore, companies are
increasingly viewing project management as a desirable skillset in
recruitment of new employees for functional areas such as marketing and
engineering.

7.4 Project Scope
This section highlights issues and concepts associated with the project scope.
We begin with the following definitions.
Project scope. The work that must be done to deliver a product that is able to
perform a specified set of functions and incorporates a predetermined set of
features. If all of the required work is not delineated, then some of the
deliverables may be excluded. If more than the required work is delineated,
then unplanned and unbudgeted items will be delivered. This will have a
negative impact on the cost and schedule of the project and may lead to
excessive delays.
Project scope management. The processes required to ensure that the project
includes all of the work required, and only the work required, for successful
completion. The scope plays a role at each stage of a project, starting with
initiation, continuing with change orders, and terminating with the approval
of the deliverables. The following is an outline of the scope-related concepts
that arise throughout the project life cycle.
Scope in the initiation stage. When a need for a project is identified, possible
technical alternatives are explored, their feasibility is evaluated, and a “go/no
go” decision is made. At this point, the work required to design, build, and
implement a system that responds to the defined need has to be estimated.
The end result of the initiation stage is a project charter that provides a
summary description of the project content, the project sponsor, and the
management approach that should be used. A project charter for an internal
project is similar to but not necessarily as detailed as a contract signed with
an outside vendor.
Scope planning. This process includes a short description of the project
scope, called a scope statement, which is used as the basis for future project
decisions and for establishing an understanding between the project team and
the customer. The primary components of the scope statement are:

1. Justification for the project
2. Project objectives
3. Sponsor of project
4. Major stakeholders
5. Project manager
6. Major project deliverables
7. Success criteria
The seventh component is used to determine whether each major phase, as
well as the project as a whole, has been completed successfully. If a request
for proposal (RFP) has already been issued, then it may serve as the basis for
the scope statement document, because it includes most of the required
information. An example of a scope statement is given in Figure 7.5.
Composing the scope statement starts during the final phase of project
initiation and ends before the start of any significant planning efforts.
Scope definition. Although key stakeholders—with input from the project
manager—typically define the scope of a project, the project manager has full
responsibility for implementation. The major output of this process is the
WBS, which is developed right after scope planning. Details are given in the
next section.
Scope verification. This process consists of comparing the planned scope
with the actual outputs. Deliverables are accepted, rejected, or modified as
required, based on comparisons with the original project scope definition.
Verification and user acceptance of project deliverables are performed
throughout the life cycle of a project.
Scope change. Because no project of any consequence is completed as
originally planned, there is a need for a mechanism that will govern the way
scope changes are introduced and implemented throughout the project life
cycle. A large component of a project manager’s day-to-day responsibility

involves change management. If every module of a project ran according to
plan (for example, the project was on schedule, under budget, and all
resources were functioning at 100% efficiency), then the role of a project
manager would be greatly simplified.
7.4.1 Work Breakdown Structure
The scope definition process involves subdividing the major project
deliverables into smaller, manageable components called work packages,
which can be assigned to
Figure 7.5
Scope statement for a project.
Project justification:
The lack of qualified managers within the region is one of the
principal reasons that economic growth has stagnated over the past
decade. After evaluating a variety of alternatives, community
leaders decided that the best way to respond to this problem was to
create a college.
Project objectives:
1. To open a top management school within a year, equipped
with advanced computer systems and high-tech teaching
facilities.
2. The school will run two major programs: (a) an MBA program
and (b) focused seminars that will serve managers who wish to
improve their leadership and communications skills.

3. The school will use an existing building that will be renovated
to fit its needs.
Sponsor of the project:
The local mayor is the chief supporter and fundraiser.
Major stakeholders:
1. The mayor.
2. Big State University, an internationally renowned institution
situated in the region that will help structure the program. Dr.
Knowly has been nominated to be the coordinator on behalf of
the university.
3. Regional Management Association, which will be involved in
identifying the region’s management needs and in helping to
promote the program. Ms. Simpson has been nominated to be
the coordinator on behalf of the association.
4. Regional industry—organizations that wish to upgrade the
managerial skills of their current and future employees. There
is an emerging high-tech concentration in the area on which to
draw students.
The project manager:
The mayor has nominated Seymour Smyles as the project manager.
Dr. Smyles has 10 years of project experience in the
telecommunications industry and has recently earned an MBA.

Major project deliverables:
1. Recognized MBA program
2. Published catalog with courses and instructors
3. Web presence
4. Registered students for the first year
5. High-tech classroom facilities
6. Administrative staff
7. Faculty offices and teaching resources
Success criteria:
1. On-time completion within budget
2. Number of students registered for the first year of the program
3. Number of advanced seminars offered the first year
4. Operating costs for first year
organizational units that are then responsible for their execution. As stated in
the beginning of the chapter, the division of the work content into lower level
components is called the WBS. According to the PMBOK, the WBS is a
deliverable-oriented grouping of project elements that organizes and defines
the total scope of the project. Each descending level represents an
increasingly detailed definition of project components.
The notion of a WBS was initiated by the U.S. Department of Defense
(1975), which also has published guidelines relating to the design of military
systems. “A work breakdown structure is a product-oriented family tree

composed of hardware, services and data which result from project
engineering efforts during the development and production of a defense
material item, and, which completely defines the project/program. A WBS
displays and defines the product(s) to be developed or produced and relates
the elements of work to be accomplished to each other and to the end
product.”
The concept of a “WBS dictionary” is widely used as well and consists of a
set of documents that includes the WBS and a detailed description of each
work package. The conscientious and meticulous development, maintenance,
and use of the WBS contribute significantly to the probability that a project
will be completed successfully.
The WBS provides a common language for describing the work content of a
project. This language centers on the work package definitions and a
hierarchical coding scheme for representing each WBS element. It enables all
stakeholders, such as customers, suppliers, and contractors, to communicate
effectively throughout a project.
The resources required for a project can be determined by summing the
resources required to execute each work package and the level-of-effort
(LOE) resources used to maintain the project infrastructure. Typical LOE
resources are project management, quality assurance personnel, and
information systems.
The first level of the WBS hierarchy represents the entire project. Subsequent
levels reflect the decomposition of the project according to a number of
possible criteria, such as product components, organization functions, or life-
cycle stages. Different WBSs are obtained by applying the criteria at different
levels of the hierarchy.
The division of the work content into work packages should reflect the way
in which the project will be executed. If, for example, a university initiates a
project to create an executive MBA program, then the development of a
specific course for the program can be defined as a task and the
organizational unit responsible for that course (a professor) can be associated
with the task to form a work package. There are, however, different ways to
decompose the work content of this project. One way is to divide the entire

project directly into work packages. If there are 30 courses required in the
program and each course is developed by one professor, then there will be 30
work packages in the WBS. This is illustrated in Figure 7.6. The following
coding scheme can also be used:
Figure 7.6
Two-level WBS for curriculum development project.
1. Development of an MBA program curriculum
1. 1.1 Introduction to Finance
2. 1.2 Introduction to Operations
.
.
.
3. 1.30 Corporate Accounting
Alternatively, the project manager may decide to disaggregate the project
work content by functional area and have each such area divide the work
content further into specific courses assigned to professors. This situation is
illustrated in Figure 7.7. Using an expanded coding scheme, the WBS in this
case might take the following form:

1. Development of an MBA program curriculum
1. 1.1 Development of courses in Finance
1. 1.1.1 Introduction to Finance
2. 1.1.2 Financial Management
.
.
.
2. 1.2 Development of courses in operations
1. 1.2.1 Introduction to Operations
2. 1.2.2 Practice of Operations Management
.
.
3. 1.6 Development of courses in accounting
1. 1.6.1 Fundamentals of Accounting
.
.
2. 1.6.4 Corporate Accounting
A third option that the project manager might consider is to divide the work
content according to the year in the program in which the course is taught and
then divide it again by functional areas. This WBS is illustrated in Figure 7.8
and might take the following form:

1. Development of an MBA curriculum
1. 1.1 First-year courses
1. 1.1.1 Development of courses in Finance
1. 1.1.1.1 Introduction to Finance
.
.
.
Figure 7.7
Three-level WBS for curriculum development project.
Figure 7.7 Full Alternative Text

Figure 7.8
Four-level WBS for curriculum development project.
Figure 7.8 Full Alternative Text
2. 1.2 Second-year courses
1. 1.2.1 Development of courses in finance
1. 1.2.1.1 Financial Management
.
.
.
2. 1.7.6 Development of courses in accounting
1. 1.7.6.1 Management Information Systems in Accounting

.
.
.
2. 1.7.6.4 Corporate Accounting
For all three WBSs, the same 30 tasks are performed at the lowest level by
the same professors. However, each WBS represents a different approach to
organizing the project. The first structure is “flat.” There are only two levels,
and from the organizational point of view, all of the professors report directly
to the project manager, who has to deal with the integration of all 30 work
packages. In the second WBS, consisting of three levels, there is one
intermediate level—the functional committee—in which each functional
committee is responsible for integration of the work packages that are
directly under them. In the third example of the WBS chart, there are four
levels. That is, two intermediate levels that deal with integration.
As a second example, let us consider the construction of a new assembly line
for an existing product. To capitalize on experience and minimize risk, the
design may be identical to that of the existing facilities; alternatively, a new
design that exploits more advanced technology may be sought. In the latter
case, the WBS might include automated material handling equipment, an
updated process design, and the development of production planning and
control systems. One possible WBS follows:
1. New assembly line
1. 1.1. Process design
1. 1.1.1 Develop a list of assembly operations
2. 1.1.2 Estimate assembly time for each operation
3. 1.1.3 Assignment of operations to workstations
4. 1.1.4 Design of equipment required at each station

2. 1.2 Capacity planning
1. 1.2.1 Forecast of future demand
2. 1.2.2 Estimates of required assembly rates
3. 1.2.3 Design of equipment required at each station
4. 1.2.4 Estimate of labor requirements
3. 1.3 Material handling
1. 1.3.1 Design of line layout
2. 1.3.2 Selection of material handling equipment
3. 1.3.3 Integration design for the material handling system
4. 1.4 Facilities planning
1. 1.4.1 Determination of space requirements
2. 1.4.2 Analysis of energy requirements
3. 1.4.3 Temperature and humidity analysis
4. 1.4.4 Facility and integration design for the whole line
5. 1.5 Purchasing
1. 1.5.1 Equipment
2. 1.5.2 Material handling system
3. 1.5.3 Assembly machines
6. 1.6 Development of training programs
1. 1.6.1 For assembly-line operators

2. 1.6.2 For quality control personnel
3. 1.6.3 For foremen and managers
7. 1.7 Actual training
1. 1.7.1 Assembly-line operators
2. 1.7.2 Quality control
3. 1.7.3 Foremen, managers
8. 1.8 Installation and integration
1. 1.8.1 Shipment of equipment and machines
2. 1.8.2 Installations
3. 1.8.3 Testing of components
4. 1.8.4 Integration and testing of line
5. 1.8.5 Operations
9. 1.9 Management of project
1. 1.9.1 Design and planning
2. 1.9.2 Implementation monitoring and control
The decision on how to disaggregate the work content of a project is related
to the decision on how to structure the project organization. In making these
decisions, the project manager not only establishes how the work content will
be decomposed and then later integrated, but also lays the foundation for
project planning and control systems.
The WBS of a project can be defined in several ways. The choice depends on
a number of factors, such as the complexity of the project, duration of the
project, the work content of the project, risk levels, the organizational

structure, resource availability, and management style. There is no one
“correct” way. Nevertheless, the WBS selected should be complete in the
sense that it captures all of the work to be performed during the project. It
should be detailed in the sense that, at its lowest level, executable work
packages with specific objectives, resources, budgets, and durations are
specified; and it should be accurate in the sense that it represents the way
management envisions first decomposing the work content and then
integrating the completed tasks into a unified whole.
The following general guidelines may be used when considering a WBS:
The WBS represents work content and not an execution sequence.
The second level of the WBS may be components, functions, and
geographical locations.
Managerial philosophy often influences the structure.
The WBS and its derived work packages should be compatible with
organizational working procedures.
The WBS should be generic in nature so that it may be used in the future
for similar projects.
The WBS is not a product structure tree or bill of materials, both of
which refer to a hierarchy of components that are physically assembled
into a product.
7.4.2 Work Package Design
Each work package (WP) requires a certain amount of planning, reporting,
and control. As described by Raz and Globerson (1998), organizations use
general guidelines to size WPs. These guidelines are typically expressed in
terms of effort (e.g., person-days, dollar value) or in terms of elapsed time
(e.g., days, weeks). One possible principle is that a WP should last not more
than four weeks.

Ideally, the project manager should ensure that each WP is assigned to a
single person or organizational unit and that this unit has the capabilities
required to execute it. Smaller WPs mean more frequent deliveries to the
customer and earlier payments, reducing finance charges to the contractor
and increasing them for the customer.
The definition of a WP—the lowest level of the WBS—should include the
following elements:
Objectives. A statement of what is to be achieved by performing this WP.
The objectives may include tangible accomplishments, such as the successful
production of a part or a successful integration of a system. Nontangible
objectives are also possible, such as learning a new computer language.
Deliverables. Every WP has deliverables, which may consist of hardware
components, software modules, reports, economic analyses, or a
recommendation made after evaluating different alternatives.
Responsibility. The organizational unit that is responsible for proper
completion of each WP has to be defined. This unit may be a component of
the organization or be an outside contractor.
Required inputs. These include data, documents, and other material needed
for the execution of the WP. They are provided by various sources, such as
the stakeholders, company records, contractors, and marketing studies. The
information derived from these inputs is used by the project manager to
establish the order in which all of the WPs will be executed.
Resources. The unit that is responsible for executing the WP should estimate
resources that are required for the task (e.g., labor hours, material, and
equipment).
Duration. After estimating the resource required for each WP, the responsible
party should estimate the duration required for its completion. Resource
availability and possible delays must be taken into account.
Budget. A time-phased budget should be prepared for each WP. The budget
is a function of the resources allocated to the WP and the duration that each

will be used.
Performance measures. Whether a WP has been completed successfully is
determined by a predefined set of performance measures and standards.
These elements are used during project execution to compare actual versus
planned performance and to establish project control.
Because a WP is the smallest manageable unit of a project, the success of the
project depends to a large extent on the ability of the project manager to deal
properly with each WP. A powerful tool for this purpose is the WP
description form, which contains a description of all relevant WP attributes. It
is also used as the basis for a contract, either formal or informal, between the
project manager and the supplier of the WP. Figure 7.9 depicts a sample form
for the MBA project. The form is generic and may be used for different WPs.
The nature of the required resources, for example, will obviously change
from one WP to another.

Figure 7.9
Work package definition form.
Figure 7.9 Full Alternative Text

Points to remember when defining a WP:
A WP is the lowest level in the WBS.
A WP always has a deliverable associated with it.
A WP should have one responsible party, called the WP owner.
A WP may be considered by the WP owner as a project in itself.
A WP may include several milestones.
A WP should fit organizational procedures and culture.
Many projects, for a particular company or organization, are likely to be
similar in nature. In such cases, developing a generic approach to defining
WPs and constructing WBSs can prove extremely advantageous. Although
no two projects are identical, many will have enough similarities to allow the
same WBS template to be used as a starting point with the necessary
modifications made as the requirements unfold. Using this approach will
enable a company to improve its performance and perhaps gain a competitive
edge.

7.5 Combining the Organizational
and Work Breakdown Structures
The two structures—the OBS and the WBS—form the basis for project
planning, execution, and control. Building blocks, called work packages, are
formed at the intersection of the lowest levels of these structures. A specific
organizational unit is assigned a specific WP that includes tasks that reside at
the lowest level of the WBS. The WP is further divided by the organizational
unit into specific activities, each defined by its work content, expected
output, required resources, time table, and budget. The hierarchical nature of
these structures provides for a roll-up mechanism wherein the information
gathered and processed at any level can be aggregated and rolled up to its
higher level.
In operational terms, the WP is the smallest unit used by the project manager
for planning and control, although internal milestones may be defined to
allow for better visibility of progress. Further disaggregation of a WP is
undertaken by the person who is charged with getting the work done (e.g., a
group leader) and converts the WP into a set of basic tasks and activities. For
example, “Introduction to Operations” is a WP in the project outlined in
Figure 7.6. Let’s assume that the corresponding execution responsibilities
have been assigned to an operations management instructor. To complete the
assignment properly, the instructor must divide the WP into tasks and
activities. These might include collecting syllabi from institutions that offer a
similar course, establishing a list of possible topics, deciding what material to
cover on each topic, developing a detailed bibliography, evaluating case
studies, generating exercises and discussion questions, and so on.
The person who is responsible for a WP is responsible for detailed resource
planning, budgeting, and scheduling of its constituent tasks. The development
of the OBS–WBS relationship is a major step in the responsibility assignment
task faced by the project manager. By planning, controlling, and managing
the execution of a project at the WP level, lines of responsibility are clarified
and the effect of each decision made on each element of the project can be

traced to any level of the OBS or the WBS.
7.5.1 Linear Responsibility Chart
An important tool for the design and implementation of the project’s work
content is the linear responsibility chart (LRC). The LRC, also known as the
matrix responsibility chart or responsibility interface matrix, summarizes the
relationships between project stakeholders and their responsibilities in each
project element. An element can be a specific activity, an authorization to
perform an activity, a decision, or a report. The columns of the LRC represent
project stakeholders; the rows represent project elements performed by the
organization. Each cell corresponds to an activity and the organizational unit
to which it is assigned. The level of participation of the organizational unit is
also specified.
By reading down a column of the LRC, one gets a picture of the nature of
involvement of each stakeholder; reading across a row gives an indication of
which organizational unit is responsible for that element, as well as the nature
of involvement of other stakeholders with that element. An example of an
LRC is shown in Table 7.3. The notation used in the table is defined as
follows:
TABLE 7.3 Example of an
LRC
Activity Engineering Manufacturing Contracts Projectmanager Marketing
Respond to
RFP I I O, A P B
Negotiating
contract I, N I, N I, R P –
Preliminary
design P A R O, B –

Detailed
design P A R O –
Execution R P – O, B –
Testing I I – O, B –
Delivery N N P A N
A Approval. Approves the WP or the element.
P Prime responsibility. Indicates who is responsible for accomplishing theWP.
R Review. Reviews output of the work package. For example, the legaldepartment reviews a proposal of a bid submitted by the team leader.
N
Notification. Notified of the output of the WP. As a result of this
notification, the person makes a judgment as to whether any action should
be taken.
O
Output. Receives the output of the work package and integrates it into the
work being accomplished. In other words, the user of that package. For
example, the contract administrator receives a copy of the engineering
change orders so that the effects of changes on the terms and conditions of
the contract can be determined.
I
Input. Provides input to the WP. For example, a “bid/no bid” decision on a
contract cannot be made by a company, unless inputs are received from
the manufacturing manager, financial manager, contract administrator, and
the marketing manager.
B
Initiation. Initiates the WP. For example, new product development is the
responsibility of the R&D manager, but the process generally is initiated
with a request from the marketing manager.
If A, R, and B are not separately identified, then P is assumed to include
them. The LRC in Table 7.3 corresponds to a single project. Similar charts
can be constructed for each project in the portfolio, as well as for each WP in
a project.
The LRC clarifies authority, responsibility, and communication channels
among project stakeholders. Taken as a whole, it is a blueprint of the activity
and information flows that occur at the interfaces of an organization. Once

the LRC for a project is developed, it can be sorted for each organizational
unit by the nature of its involvement. When a manager reviews the sorted
WPs associated with his unit, he can identify those activities for which he has
direct responsibility and others in which he plays a supportive role.
The LRC conveys information on job descriptions and organizational
procedures. It provides a means for all stakeholders in a project to view their
responsibilities and agree upon their assignments. It shows the extent or type
of authority exercised by each participant in performing an activity in which
two or more parties have overlapping involvement, and it clarifies
supervisory relationships that may otherwise be ambiguous when people
share work.
To generate the LRC, the OBS should be complete, detailed, and accurate:
complete in the sense that it should depict all of the stakeholders and
organizational units that will participate in the project; detailed in the sense
that each organizational unit is represented down to the level where the work
is actually being performed; and accurate in the sense that it reflects the true
lines of authority, responsibility, and communication. The LRC integrates the
two structures by assigning bottom-level WBS elements to bottom-level OBS
elements. This can be done only when the WBS and the OBS are accurate
and comprehensive.
Although both the LRC and WPs are formed from elements at the lowest
levels of the WBS and the OBS, they take different forms and serve different
purposes. The LRC defines the nature of the organizational interaction
associated with each major WP. For example, it identifies the responsible
stakeholders who have to be consulted with regard to each WP and indicates
who should be notified when the WP is completed. Each row in the LRC
represents the decision-making process for the specific WP, and each column
represents the job description of a specific organizational unit/stakeholder
with regard to the project.
The integration of the WBS, the OBS, and the LRC forms the cornerstone of
project management and provides the framework for developing and
integrating tools needed for scheduling, budgeting, management, and control.
It also aids in defining the relationship among the project manager, client
representatives, functional managers, and other stakeholders.

7.6 Management of Human
Resources
Of the many types of resources used in projects (people, equipment,
machinery, data, capital), human resources are the most difficult to manage.
Unlike other resources, human beings seek motivation, satisfaction, and
security and need an appropriate climate and culture to achieve high
performance. The problem becomes even more complicated in a project
environment because the successful completion of the project is primarily
dependent on team effort. Working groups, or teams, are the common
organizational units within which individual efforts are coordinated to
achieve a common goal. A team is well integrated when information flows
smoothly, trust exists among its members, each person knows his or her role
in the project, morale is high, and the desire is for a high level of
achievement.
7.6.1 Developing and Managing the
Team
In a project environment where workers from many disciplines join to
perform multifunctional tasks, the importance of teamwork is paramount. The
issues center on how to build a team, how to manage it, and which kind of
leadership is most appropriate for a project team. The objective of team
building is to transform a collection of individuals with different objectives
and experiences into a well-integrated group in which the objectives of each
person promote the goals of the group. The limited life of projects and the
frequent need to cross the functional organizational lines make team building
a complicated task.
Members of a new project team may come from a variety of organizational
units or may be new employees. To build an efficient team, organizational

uncertainty and ambiguity must be reduced to a minimum. This is done by
clearly defining, as early as possible, the project, its goals, its organizational
structure (organizational chart), and the procedures and policies that will be
followed during execution.
Each person who joins the project must be given a job description that
defines reporting relationships, responsibilities, and duties. Task
responsibilities must also be defined. The LRC is a useful tool for defining
individual tasks and responsibilities. Once the roles of all team members have
been established, they should be introduced to each other properly and their
functions explained. Continuous efforts on the part of the project manager are
required to keep the team organized and highly motivated. An ongoing effort
is also required to detect any problems and to ensure that appropriate
correction measures are taken.
The roles of team members tend to change over time as the project evolves.
Because confusion and uncertainty cause conflict and inefficiency, the project
manager should frequently update team members regarding their roles.
Furthermore, the manager should detect any morale problems as early as
possible in an effort to identify and eliminate the cause of such problems. For
example, the appearance of cliques or isolated members should serve as a
signal that the team is not being managed properly.
The project manager should also help in reducing anxieties and uncertainty
related to “life after the project.” When a project reaches its final stages, the
project manager, together with relevant functional managers, should discuss
the future role in the organization of each team member and prepare a plan
that ensures a smooth transition to that new role. By providing a stable
environment and a clear project goal, team members can focus on the job at
hand.
A recommended practice for management is to conduct regular team
meetings throughout the life cycle of the project but more frequently in the
early phases, when uncertainty is highest. In a team meeting, plans, problems,
operating procedures, and policies should be discussed and explained. By
anticipating potential sources of “issues” and preparing an agreed-on plan,
the probability of success is increased and the probability of conflicts is
reduced or eliminated altogether.

Despite the pragmatic guidelines specified above, if the team is not properly
developed, there is a high probability that it will not perform its functions
effectively. If, for the moment, we associate an iceberg with the processes of
a project, then we might see something similar to relationships depicted in
Figure 7.10.
Figure 7.10
Iceberg model of project processes.
Figure 7.10 Full Alternative Text
The tip of the iceberg, the part first to be seen and supported by the
submerged structure, represents the project deliverables. The middle of the
iceberg, still above water (and supporting the tip) contains all of the
supporting project management tools and processes. Finally, below the
surface lie all of the human processes. These are hidden from the eye in the
sense that we can see their results but not their essence; that is, we can see the
product of a committed team or an unmotivated team, but we cannot see the
commitment or the lack of motivation itself. Like the iceberg base, any
movement below the surface will affect the entire structure. The stability of
the iceberg as a whole is only as strong as the stability of its base; and yet
although the human processes are of critical importance, they are often left
relatively unattended, at least until they rumble and threaten to undermine the

project.
One of the paradoxes of project management is that a project manager may
be chosen for technical/professional expertise, rather than for leadership
skills, but is then given the task of leading a group of people to achieve
collaboratively what may be a set of unfamiliar and conflicting goals. The
following paragraphs outline typical team development stages. By
recognizing these stages, the project manager will be in a better position to
bring out the full potential of the team.
When individuals get together to form a team, they are concerned with four
issues:
1. Identity: Who will they be in the team? What role will they play? Will
their role be meaningful?
2. Power: How much power and influence will they have in the team? Will
their voice be heard? Will they be able to change the course of events
and influence team decisions?
3. Interface (conflict or overlap) between their needs as individuals and the
needs of the team: Will they benefit from working in this team
(materially, professionally)? What will they have to give up to stay in
line with the team?
4. Acceptance: Will they be accepted and liked? Will they fit in? Will they
belong?
At any given point, individuals may be concerned with one or more of these
issues although it is unlikely that they will formulate and express them
precisely. A project manager will be better able to respond to a dissatisfied
team member by understanding that, often, behind complaints related to, say,
scheduling/workload/role definitions, lie concerns of
identity/acceptance/power and so on.
A team, as a collective, tends to go through the following four stages:
forming, storming, norming, and performing. These stages give rise to what
is known as a performance model. As the team moves from one stage to the

next, its competence in performing its task grows. More precisely, we have
the following:
Forming
task performance at a lower level
lack of clarity regarding roles and expectations
lack of norms governing team interactions
relatively low commitment to both team and task
low trust
high dependence on project manager
high curiosity, expectations
boundaries begin to form (who is/is not a part of the team)
Storming
roles and responsibilities understood (accepted or challenged)
open confrontations and power struggles
open expression of disagreement
high competition
“subgroups” formed
little or no team spirit
lots of testing of authority

feeling of being “stuck”
low motivation
Norming
roles and responsibilities accepted
purpose clear
agreement on working procedures
trust built
confidence rises
openness to give and receive feedback
conflict resolution strategies formed
task orientation
feeling of belonging
very strong norms may suffocate individual expression and creativity
Performing
cooperation and coordination
strong sense of team identity
high commitment to task
mutual support

high confidence in team ability
high task performance
networks created with other teams/parts of the organization
leadership role moves informally between members
high motivation (with occasional dips)
How can this model benefit the project manager? First, many project
managers find a familiarity with this model helpful in that it can predict and
explain some of the phenomena that they may be observing in their team.
Most salient is the storming stage, which project managers often view with
distress and come to the conclusion that “something is wrong with the team”
or “we’ll never be able to work together,” rather than viewing it as an
integral––even necessary––part of team development.
Second, there are operational implications associated with the model; that is,
the project manager can, to a certain extent, manage the process of team
development. With this in mind, his or her role becomes one of leading the
team through the first three stages as smoothly as possible so that they all
arrive at the performing stage at the earliest possible time.
In the ambiguity of the forming stage, the project manager may facilitate the
team process by being directive and ensuring clarity; that is, by setting a clear
mission and set of objectives for the team, by establishing clear roles and
reporting procedures, by defining human resource processes, and, in general,
by being the authority for the team’s uncertainty and questions.
In the storming stage, the project manager’s role calls for a more supportive
and flexible attitude: supporting members, facilitating and reconciling
differences, setting boundaries through persuasion, spending time building
trust between team members, and constantly reminding the team of their
superordinate goals and mission—which tend to get lost in the day-to-day
struggles.
In the norming stage, the project manager must constantly be aware of the

team norms that are being created regarding planning and schedules,
feedback loops, meetings, communication (quantity and quality), expressing
disagreement, and changing priorities as some examples. At this stage, the
team forms its own particular style of working, or, in other words, its own
culture, which can sometimes be effective and sometimes serve as a real
obstacle to effectiveness. (An example of ineffective norm might concern
meetings: “We have far too many meetings, people come unprepared for the
most part, and the first 15 minutes are spent on socializing––no wonder
people are no longer coming as frequently.”) It is important for the project
manager to remember that it is much easier to set a desired norm than it is to
change an undesirable one.
Finally, in the performing stage, the project manager is called on to become
more of a coach: delegating responsibilities as team members become more
proficient at taking them on, giving feedback on performance and advice on
problems, generating team spirit and motivation, and generally directing and
supporting the team’s work.
A revision of the model added a fifth stage, “adjourning,” which is especially
relevant in project management because the team is, a priori, a temporary
one. Although this is not really a stage like the others, it is sometimes
characterized by lowered motivation, by people moving on to the next project
(in their minds, if not in reality), and by a scattering of focus and attention.
The project manager needs to be aware when he or she sees these things
happening and to take steps in two directions. The first is to encourage people
to “run the last leg,” mainly through motivational techniques and
encouragement. The second is to make sure that the project ends on a positive
note—both in the sense of joint celebration and in a process of “lessons
learned.” This is particularly important in organizations that are based on
project structures because the end of each project leaves all involved with
either a positive experience and an enthusiasm to go on to the next project or,
the contrary, a negative experience that generates a lack of energy and will to
commit to the next project.
7.6.2 Encouraging Creativity and

Innovation
The one-time nature of projects requires solutions to problems that have not
been dealt with in the past. The ability to apply past solutions to present
problems may be limited. The human ability to innovate and create new ideas
needs to be stimulated by the project manager.
In order for creativity and innovation to flourish, a project manager—with
support from senior management—must create an appropriate climate and
culture. The various ways and means by which management has tried to
establish the proper conditions have been well documented in literature and
include quality circles, suggestion boxes, and rewards for new ideas that are
implemented. Sherman (1984) interviewed key executives in eight leading
U.S. companies to study the techniques used to encourage innovation.
Following are some of his findings:
Organizational level
The search for new ideas is part of the organizational strategy.
Continuous effort is encouraged and supported at all levels.
Innovation is seen as a means for long-term survival.
Small teams of people from different functions are used frequently.
New organizational models such as quality circles, product development
teams, and decentralized management are tested frequently.
Individual level
Creative and innovative team members are rewarded.
Fear that the status quo will lead to disaster is a common motivator for
individual innovation.

The importance of product quality, market leadership, and innovation is
stressed repeatedly and thus is well known to employees.
To put it more succinctly, innovation and creativity should be encouraged
and properly managed. To enhance innovation, a systematic process that
starts by analyzing the sources of new opportunities in the market is required;
namely, users’ needs and expectations. Techniques such as quality function
deployment and the house of quality have proved to be very effective in this
regard (Cohen 1995, Hauser and Clausing 1988).
Once a need is identified, a focused effort is required to fulfill that need. Such
an effort is based on knowledge, ingenuity, free communication, and well-
coordinated hard work. The entire process should aim at a solution that will
be the standard and trend setter for that industry. Techniques that support
individual creativity and innovation are usually designed to organize the
process of thinking and include:
1. A list of questions regarding the problem, or the status quo.
2. Influence diagrams that relate elements of a problem to each other.
3. Models that represent a real problem in a simplified way, such as
physical models, mathematical programs, and simulation models.
A project manager can enhance innovation by selecting team members who
are experts in their technical fields with a good record as problem solvers and
innovators in past projects. The potential of individuals to innovate is further
enhanced by teamwork and the application of proper techniques, such as
brainstorming and the Delphi method.
Brainstorming is used as a tool for developing ideas by groups of individuals
headed by a session chairman. The session starts by the chairman presenting
a clear definition of the problem at hand. Group members are invited to
present ideas, subscribing to the following rules:
Criticism of an idea is barred absolutely.
Modification of an idea or its combination with another idea is

encouraged.
Quantity of ideas is sought.
Unusual, remote, or wild ideas are encouraged.
A major function of the chairman is to stimulate the session with new ideas
or direction. A typical session lasts up to an hour and is brought to an end at
the onset of fatigue.
The Delphi technique is used to structure intuitive thinking. It was developed
by the Rand Corporation as a tool for the systematic collection of informed
opinions from a group of experts. Unlike brainstorming, the members of the
group need not be in the same physical location. Each member gets a
description of the problem and submits a response. These responses are
collected and fed back anonymously to the group members. Each person then
considers whether he or she wants to modify earlier views or contribute more
information. Iterations continue until there is convergence to some form of
consensus.
In addition to these two approaches, a number of other techniques are
available to support creativity and innovation by groups. For a
comprehensive review, see Warfield et al. (1975). As a final example, we
mention the nominal group technique, which works as follows:
1. A problem or topic is given and each team member is asked to prepare a
list of ideas that might lead to a solution.
2. Participants present their ideas to the group, one at a time, taking turns.
The team leader records the ideas until all lists are exhausted.
3. The ideas are presented for clarification. Team members can comment
on or clarify each of the ideas.
4. Participants are asked to rank the ideas.
5. The group discusses the ranked ideas and ways to expand or implement
them.

7.6.3 Leadership, Authority, and
Responsibility
Because of the cross-functional nature of most project teams, organizations
tend to be matrix oriented. This means that at any given moment, each team
member may have two bosses—the project manager and his functional
manager. Often, a person is also a part of two or more project teams and may
be faced with conflicting priorities and demands. Similarly, the project
manager may be constrained by the limited options available for managing
the team (e.g., lack of control over compensation and other types of rewards).
In the absence of full authority, managing teams becomes both more complex
and more challenging. Often the only way a project manager can achieve
outstanding results is to motivate the team through a sense of pride,
belonging, and commitment. Whereas in other areas such as scheduling and
budgeting, a project manager is able to manage, in the “people management”
area, a project manager is expected to “lead” rather than to “manage.” Indeed,
one definition of “leadership” is precisely the ability to motivate people to
achieve a goal through the use of informal motivational techniques, rather
than those associated with formal authority.
One way of differentiating between management and leadership would be to
consider the sources or bases of power that a project manager has. In general,
we tend to speak of five main power bases:
1. Formal/position: the power a manager has over subordinates as given by
the organization—to hire and fire, to compensate, to promote, and so on.
2. Reward/coercive: the power to use the “carrot and stick” method.
Although there is a large overlap with the first power base, the two are
not identical. People have the ability to punish and reward others even
when they are not formally responsible for them, for example, by
withholding valuable information or resources.
3. Professional expertise: the power to influence people or events through
in-depth knowledge, skills, and experience in a certain discipline.

4. Interpersonal skills: the power to create and maintain relationships,
which includes the ability to listen, to empathize, and to resolve
conflicts.
5. Ability to create identification/commitment: the power to create a sense
of meaningfulness for people through a connecting of their own wishes,
desires, and ambitions to the task in question.
In a matrix environment, a project manager rarely has the first source of
power (formal). The second (reward/coercive) is one that a project manager
can exercise to a certain extent, but with limits. Coercion, whether implicit or
explicit, creates a type of “transactional” relationship whereby a subordinate
will perform according to a perception of the value of the reward that will be
received for successful results—or conversely, according to a fear of possible
punishment for not performing well (e.g., not being assigned to desired
project in the future). The obvious problem is that team members will be
cooperative as long as the promise of significant reward or punishment holds
out; when neither is there, motivation disappears.
Professional expertise is and has always been a prime power base used by
project managers. This is frequently the reason they are chosen for the role in
the first place, and it is in using their expertise that they usually feel the most
comfortable, seeing themselves and being seen by others as adding value.
Although this is both a necessary and an effective power base, it is most often
not sufficient by itself. It enables the project manager to manage and control
task processes but not necessarily people.
Interpersonal skills are also a critical power base at the disposal of the project
manager. One common misperception concerning this power base is that it is
inborn, that is, either you have it or you don’t. Although some people may
have a head start in interpersonal skills, anyone can acquire a good
understanding of them through focus and attention, training, practice, and the
intelligent use of several commercial methodologies.
It is, however, the ability to create identification/commitment that
differentiates between a good project manager and an outstanding one. This
is where “intangible” motivational abilities come into play, first to bring out
team members’ inner need to excel and to be a part of a team that is doing

something meaningful and, second, to generate the commitment that can lead
people to perform above and beyond their normal levels. These abilities
include:
Giving meaning to the tasks by linking them to the project and to the
larger organizational picture. This involves generating an ongoing
dialogue concerning the “what,” the “how,” and especially the “why” of
the project.
Setting an example: being a role model is one of the most difficult but
effective ways in which the project manager can motivate his team. A
project manager must set standards of behavior, integrity, commitment,
and sensitivity to others, and abide by those standards and guiding
principles. There is probably nothing as demotivating as a manager who
does not “walk his or her talk.”
Creating trust: this relates to the fact, consistently upheld by research,
that mutual trust is the primary condition under which people will
commit themselves—their knowledge, skills, and spirit—to a team
project. When trust does not exist, an inordinate amount of energy is
channeled from task–related issues to political or power issues or toward
self-justification and protection from criticism.
Creating intellectual and emotional stimuli: both of these relate to the
question that each team member asks him- or herself at the beginning of
the project: “What’s in it for me?” The answer lies not on the material
level but rather in terms of challenge, professional growth, experience,
and development in more generic project management areas as well as
in a member’s specific professional field. If a project manager can create
an environment in which team members can both contribute to and learn
from others and can take on meaningful responsibilities, and in which
each individual’s unique voice will be heard and heeded, then he or she
will have gone a long way towards ensuring the project’s success, for his
or her team will give it the best they have.
Leading a team to the successful completion of a project is no simple task.
Whereas prediction and control have always been the staples of effective
management, they are not easy to implement in today’s turbulent and

constantly changing environment. The “grand paradox” of management,
according to management theorist Peter Vaill (1990), is that being a manager
in our complex reality is taking responsibility for what is less and less stable
and controllable. In the same vein, project managers are expected to work
within a paradoxical framework: they need to predict and control the many
variables that affect their project, at the same time as planning for the
inevitable changes and surprises that cannot be predicted and controlled.
This becomes very clear in the team leadership role of a project manager. He
or she needs to understand that effective teamwork does not “just happen”
automatically. It requires attention to and engagement in human processes
that are often “messy,” emotional, and sometimes irrational. It requires
knowledge of group processes and individual preferences and tendencies,
together with the understanding that there is no model that can completely
capture the complexity of thought processes, behavior, and interaction. It
requires an understanding that people are motivated to do their best only
when their heart and spirit are involved in the project, rather than only their
professional and technical expertise.
Finally, perhaps the biggest paradox of all lies in the fact that although
project managers need to be adept in the theory and practice of “people
management,” “it is the ability to meet each situation armed not with a
battery of techniques but with openness that permits a genuine response. The
better managers transcend technique. Having acquired many techniques in
their development as professionals, they succeed precisely by leaving
technique behind.” (Farson 1996).
The responsibility of a project manager is typically to execute the project in
such a way that the pre-specified deliverables will be ready within the time
and budget planned. This responsibility must come with the proper level of
legal authority, implying that leadership and authority are related. A manager
cannot be a leader unless he or she has authority. Authority is the power to
command or direct other people. There are two sources of authority: legal
authority and voluntarily accepted authority. Legal authority is based on the
organizational structure and a person’s organizational position. It is delegated
from the owners of the organization to the various managerial levels and is
usually contained in a document. Voluntarily accepted authority is based on

personal knowledge, interpersonal skills, or a person’s experience that
enables him or her to exercise influence over and above their legal authority.
The project manager should have well-defined legal authority in the
organization and over the project. However, a good project manager will seek
voluntarily accepted authority from the team members and organizations
involved in the project on the basis of his or her personal skills.
The importance of legal authority is most pronounced in a matrix
organization in which the need to work with functional managers and to
utilize resources that “belong” to functional units can trigger conflicts.
Reduction of these conflicts depends on the formal authority definition, as
well as on the ability of both the project manager and the functional manager
to be flexible.
7.6.4 Ethical and Legal Aspects of
Project Management
The legal authority of a project manager and his or her role as a leader require
proper understanding of the legal and ethical aspects of project management.
The Project Management Institute (PMI)1 developed a code of ethics.
1 PMI Member Ethical Standards, Project Management Institute Inc., 2000.
Copyright and all rights reserved. Material from this publication has been
reproduced with the permission of PMI.
A project manager’s legal responsibilities are set by the organization
sponsoring the project and depend, in part, on any contracts involving the
projects and the laws of the country where the project is performed. The
following legal aspects are common to most projects:
Contractual issues regarding clients, suppliers, and subcontractors
Government laws and regulations
Labor relations legislation

As a rule of thumb, whenever the project manager is not sure of the legal
aspects of a decision or a situation, he or she should consult the legal staff of
the organization.
Legalities are very important when an organization contracts to carry out a
project or parts of a project for a customer or when an organization uses
subcontractors. A large variety of contract types exist, commonly classified
into fixed-cost and cost-reimbursable contracts, and each requires a different
legal orientation. Among the first class, two major subclasses can be
identified: (1) firm fixed price (FFP) contracts and (2) fixed price incentive
fee (FPIF) contracts. Under FFP contracts, the contractor assumes full
responsibility for cost, schedule, and technical aspects of the project. This
type of contract is suitable when the levels of uncertainty are low, technical
specifications are well defined, and schedule and cost estimates are subject to
minimal errors. The FPIF contract is designed to encourage performance
above a preset target level. Thus, if a project is completed ahead of schedule
or under cost, then an incentive is paid to the contractor. In some FPIF
contracts, a penalty is also specified in case of cost overruns or late
deliveries. By specifying a target that can be achieved with high probability,
the risk that the contractor takes is minimized, while the incentive motivates
the contractor to try to do better than the specified target.
Cost-reimbursable contracts are also classified into two major types: (1) cost
plus fixed fee and (2) cost plus incentive fee (CPIF) contracts. The former are
designed for projects in which most of the risk associated with cost overrun is
borne by the customer. This type of contract is appropriate when it is
impossible to estimate costs accurately, as, for example, in R&D projects. On
top of the actual cost of performing the work, an agreed-on fee is paid to the
contractor. CPIF contracts are designed to guarantee a minimum profit to the
contractor while motivating the contractor to achieve superior cost, schedule,
and technical performance. This is done by paying an incentive for
performance higher than expected and tying the level of incentive to the
performance level.
Within the four types of contracts, there are many variations. The proper
contract for a specific project depends on the levels of risk involved, the
ability of each party to assume part of the risk, and the relative negotiating

power of the participants. Although the legal staff is usually responsible for
contractual arrangements, the project manager has to execute the contract, so
his or her ability to establish good working relationships with the client,
suppliers, and subcontractors within the framework of the contract is
extremely important.
In addition to contracts, the project manager should be familiar with
government laws and regulations in areas such as labor relations, safety,
environmental issues, patents, and trade regulations. Whenever a question
arises, the project manager should consult the legal staff.
Each country has its own labor relations legislation, and managers of
international projects must not assume that these regulations are the same or
even similar from one country to the next. Typically, these regulations have
to do with minimum wages, benefits, work conditions, equal employment
opportunity, employment of individuals with disabilities, and occupational
safety and health.
To summarize, management of human resources is probably the most
difficult aspect of project management. It requires the ability to create a
project team, to manage it, to encourage creativity and innovation without
being threatened, and to deal with human resources in and out of the
organization. The project manager can learn some of these skills, but a
majority of them come only with experience, common sense, and inherent
leadership qualities.
TEAM PROJECT Thermal
Transfer Plant
At the last Total Manufacturing Solutions, Inc. (TMS) board meeting,
approval was given to develop a new area of business: recycling and waste
management. Because your supporting analysis was the determining factor,
your team has been asked to develop for TMS an organizational structure that
will integrate this new area with its current business. You are also required to
develop a detailed OBS and WBS for a project aimed at designing and

assembling a prototype rotary combustor for which only the power unit will
be manufactured in-house; other parts will be purchased or subcontracted. In
developing the OBS and WBS for the project, clearly identify the
corresponding hierarchies and show who has responsibility at each level.
In your report explain your objectives and the criteria used in reaching a
decision. Show why the selected structure is superior to the alternatives
considered, and explain how this structure relates to the TMS organization as
a whole. Your report will be submitted to TMS management for review. Be
prepared to present the major points to your management and to defend your
recommendations.

Discussion Questions
1. Describe the organizational structure of your school or company. What
difficulties have you encountered working within this structure?
2. Explain how a matrix organization can perform a project for a functional
organization. What are the difficulties, contact points, and
communication channels?
3. In the matrix management structure, the functional expert on a project
has two bosses. What considerations in a well-run organization reduce
the potential for conflict?
4. Write a job description for a project manager in a matrix organization.
Assume that only the project manager is employed full time by the
project.
5. How does the WBS affect the selection of the OBS of a project?
6. Under what conditions can a functional manager act as a project
manager?
7. Develop a list of advantages and disadvantages of the following
structures:
1. Product organization
2. Customer organization
3. Territorial organization
8. Which kind of OBS is used in the company or organization to which you
now or used to belong? What are the limitations that you have
perceived?
9. What are the activities and steps involved in developing an LRC?

10. Describe the “team building” inherent in the development of an LRC.
How is team building accomplished on large projects? How does this
relate to development of the LRC?
11. Discuss the applicability of the nominal group technique, the Delphi
method, and brainstorming to the process of scheduling and budgeting a
project.
12. Compare the advantages and disadvantages of the four types of contracts
discussed in this chapter.
13. Of the types of leadership discussed, which is most appropriate for a
high-risk project?

Exercises
1. 7.1 Develop an organizational structure for a project performed in your
school (e.g., the development of a new degree program). Explain your
assumptions and objectives.
2. 7.2 You are in charge of designing and building a new solar heater.
Develop the OBS and the WBS. Explain the relationship between the
two.
3. 7.3 Develop an OBS for an emergency health care unit in a hospital.
How should this unit be related to the other departments in the hospital?
4. 7.4 Develop a WBS for a construction project.
5. 7.5 Consider the development of a new electric car by an auto
manufacturer and a manufacturer of high-capacity batteries.
1. Develop an appropriate four-level WBS.
2. Develop the OBS.
3. Define several WPs to relate the WBS elements to the OBS.
6. 7.6 Suggest three approaches (OBS–WBS combinations) for the
development of a new undergraduate program in electrical engineering.
7. 7.7 Develop an LRC for a project done for a client who has a functional
organization by a contractor who has a customer-oriented organization.
1. Describe the project and its WBS.
2. Describe the OBS of the client and the contractor.
8. 7.8 You are the president of a startup company that specializes in
computer peripherals such as optical backup units, tape drives, signature

verifications systems, and data transfer devices. Construct two OBSs,
and discuss the advantages and disadvantages of each.
9. 7.9 List two activities that you have recently performed with two or
more other people. Explain the role of each participant using an OBS, a
WBS, and an LRC.
10. 7.10 Give an example of an organization with an ineffective or
cumbersome structure. Explain the problems with the current structure
and how these problems could be solved.
11. 7.11 You have been awarded the contract to set up a new restaurant in
an existing building at a local university (i.e., there is no need for
external construction). The WBS for the project, as developed by the
planning team, is presented in Figure 7.11 . Using this WBS, carry out
the following exercises:
Figure 7.11

WBS for new restaurant.
Figure 7.11 Full Alternative Text
1. Develop a coding system for the project.
2. Identify other types of projects that could use this coding system.
For which types of projects would it be inappropriate? Explain.
3. If you wish to use a more general coding system that deals with
construction, what would be the differences between the latter and
the more specific coding system developed in part (a)?
12. 7.12 You have been offered a contract to undertake the restaurant
project in Exercise 7.11 at several campuses that belong to the same
university.
1. Suggest an OBS for these projects.
2. Generate three WPs and assign them to the appropriate
organizations.
3. Identify some areas that will require coordination among the
organizations included in the OBS to ensure that the three WPs will
be completed properly.
4. Construct an LRC for coordinating the work among the various
functions that are to be carried out.
13. 7.13 For the restaurant project in Exercise 7.11 :
1. Develop another WBS, making sure that it includes the same WPs
that are shown in the original WBS in Figure 7.11 .
2. Generate additional WPs for the project and add them to the new
WBS.
14. 7.14 You have been assigned the task of developing a network
representation of the project in Exercise 7.11 (network construction is

taken up in much greater detail in Chapter 9 ).
1. Design the network for the WBS in Figure 7.11 . In so doing, each
WP in the WBS should correspond to a node in the network, and
each arc should indicate a precedence relation. Include in your
diagram a dummy start node and a dummy end node.
2. Extend your network by including several activities for each WP.
15. 7.15 Prepare a Delphi session for selecting the best project manager for
a given project.
16. 7.16 Develop a set of guidelines for project managers in international
projects that deal with legal and ethical issues.
17. 7.17 Generate an example of a project management-related ethical issue,
and discuss possible ways to resolve it.
18. 7.18 Generate a WP template and test it on a selected WP.

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Chapter 8 Management of Product,
Process, and Support Design
8.1 Design of Products, Services,
and Systems
Design is the conversion of an idea or a need into information from which a
new service, product, or system can be developed. It is the “transformation
from vague concepts to defined objects, from abstract thoughts to the solution
of detailed problems” (Hales 1993). Design is an important part of the life
cycle of any product or system. It is also part of any project, either as a phase
in the project life cycle or as a process used to introduce changes in existing
designs as a result of new information and changes in the environment.
Design has an impact on the deliverables of the project as well as on its cost,
schedule, and risk. Furthermore, the satisfaction of project stakeholders
depends to a large extent on management of the design process and its
results.
The project manager should not assume that good engineers are guaranteed to
produce good designs. It is the project manager’s responsibility to implement
an appropriate design process and to manage the design effort throughout the
life cycle of the project to maximize the project’s technological competitive
edge.
A good design starts with the selection of the right technology, where “right”
connotes the following two primary benefits. First, it provides a market
advantage through differentiation of value added, and second, it provides a
cost advantage through improved overall system economies. To use
technology effectively, an organization must address four elementary
questions: (1) What is the basis of competition in our industry? (2) To
compete, which technologies must we master? (3) How competitive are we in

these areas? (4) What is our technology strategy? In embryonic and growth
industries, technology frequently drives the strategy, whereas in more mature
fields, technology must be an enabling resource for manufacturing,
marketing, and customer service. The United States excels at technology-
driven innovation that creates whole new enterprises. By contrast, Japan
excels at incremental advances in existing products and processes.
In the following sections, general purpose tools and techniques for managing
the design process are presented. Specific applications, such as CASE
(Computer-Aided Software Engineering) tools for software design, though
interesting in their own right, fall outside the scope of the text and will not be
discussed.
8.1.1 Principles of Good Design
The success of products, services, and systems is heavily dependent on the
quality of the design process. Most product or service characteristics and
corresponding performance measures are determined in the design phase,
including:
1. Operational or functional capability. This is a measure of the system’s
ability to perform tasks and satisfy the market’s or customer’s needs.
For example, the range of an electric passenger vehicle, its payload, and
its speed are possible measures of operational or functional capabilities.
In software selection, the ability to perform all required functions within
acceptable time standards is an operational performance measure.
2. Timeliness. This measure relates to the time at which the system is
available to perform its mission (i.e., the successful completion of
acceptance tests and the start of regular operations).
3. Quality. Quality measures the system’s design with respect to market or
customer needs and with respect to its design specifications. Therefore,
the quality of an alternative design refers to the system’s components,
the integration of those components, and the compatibility of the
proposed system with the environment in which it will interact. Quality

is defined in specific terms for systems such as planes, boats, buildings,
and computers, where a host of national and international standards
exist. The Institute of Electrical and Electronics Engineers is in the
forefront of setting standards for electrical equipment and devices. If
adequate standards are not available, then desired quality levels should
be specified for both the operational (functional) and the technical
(design and workmanship) aspects of the system. The Software
Engineering Institute, based at Carnegie-Mellon University, has taken
the lead in setting standards for software quality and reliability.
4. Reliability. This measure relates to the probability that a product,
system, or service will operate properly for a specified period of time
under specified conditions without failure. In the simplest form, two
factors—the mean time between failures (MTBF) and the mean time to
repair (MTTR) the system—can be combined to calculate the proportion
of time that the system is available.
Reliability= MTBF MTBF + MTTR ×100%
There is a correlation between reliability and quality, as a high quality of
design, workmanship, and integration usually leads to a high level of
reliability. However, reliability also depends on the type of technology
used and the operating environment.
5. Compatibility. This measure corresponds to the system’s ability to
operate in harmony with existing or planned systems. For example, a
new management information system has a higher degree of
compatibility if it can use existing databases. Electronic systems are said
to be compatible when they can operate without interference from the
electromagnetic radiation put out by other systems in the same vicinity.
A new software package is compatible when it has the ability to import
and export data from other information systems and databases.
Organizations seek to minimize disruption and costs associated with
implementing changes.
6. Adaptability. This measure evaluates a system’s ability to operate in
conditions other than those initially specified. For example, a
communication system that is designed for ground use would be

considered a highly adaptable system if it could be used in high-altitude
supersonic aircraft without losing any of its functionality. Systems with
high adaptabilities are preferred when future operating conditions are
difficult to forecast. A highly adaptable software package is one that can
run on different computer types under a variety of operating systems in
addition to the computer and operating system specified.
7. Life span. This measure has a direct impact on both cost and
effectiveness. Because of learning and efforts at continued process
improvement, systems with a longer life span tend to improve over time.
This eliminates the need for frequent capital investments and hence
reduces total LCC.
8. Simplicity. The process of learning a new system while it is being
introduced into an organization depends on its simplicity. A system that
is easy to maintain and operate is usually accepted faster and creates
fewer difficulties for the user. Furthermore, complicated systems may
not be maintained and exercised adequately, especially during startup or
periods of change when there is high turnover in the organization. A
software package that is simple to operate and maintain is one that is
developed according to software engineering standards regarding
modularity, documentation, and so on.
9. Safety. The methods by which a system will be operated and maintained
should be considered in the advanced development phase. Safety
precautions should be introduced and evaluated to minimize the risk of
accidents. As with quality, designing a safe system from the start can
provide significant benefits over the long run.
10. Commonality. A high level of commonality with other systems either
used by or produced by the organization should be a driving force in the
design. Commonality has many facets, such as common parts and
subsystems, input sources, communication channels, databases, and
equipment for troubleshooting and maintenance. Many airlines insist
that all aircraft that they buy within a particular class, regardless of
manufacturer, have the same engines. Some airlines have taken this one
step further and buy only one type of aircraft. In a similar vein, the U.S.
Department of Defense developed the computer language Ada in the late

1970s and for many years required that all programs commissioned by
any of its branches be written in Ada.
11. Maintainability. Providing adequate maintenance for a system is
essential. The loss in operational time due to preventative maintenance
must be weighed against the probability of system failure and the need
for unscheduled maintenance, which in turn, reduces the system’s
overall effectiveness. Higher levels of maintainability lead to better
labor utilization and lower personnel training costs. Part of
maintainability is testability—the ability to detect a system failure and
pinpoint its source in a timely manner. Higher levels of maintainability
and testability contribute to the effectiveness of a system. In software
design, a well-documented source code and clearly defined interfaces
between modules of a software package help in detection and correction
of bugs.
12. Friendliness. This performance measure quantifies the effort and time
required to learn how to operate and maintain a system. A friendly
system requires less time and skill to learn and hence reduces both direct
and indirect labor costs. In software, the use of menus, on-line help, and
pointing devices such as a mouse can increase the friendliness of the
software package.
8.1.2 Management of Technology
and Design in Projects
Although some projects do not have a design phase in their life cycle (these
are known as built-to-print projects), almost every project must have a
mechanism for addressing design changes. Configuration management
systems that deal with design changes will be discussed later in the chapter.
Design changes are common in all projects because new information that was
not available during the design phase may call for a reassessment of the
original assumptions and decisions.
Design activities begin with “the voice of the customer,” an analysis of the

client’s or organization’s needs, which are translated into technical factors
and operation and maintenance plans. A common tool for this process is
quality function deployment or the house of quality (see Section 8.4). Once
approved by the client or upper management, these requirements are
transformed into functional and technical specifications. The last link in the
chain is detailed product, process, and support design. Product design centers
on the structure and shape of the product. Performance, cost, and quality
goals all must be defined. Process design deals with the preparation of a
series of plans for manufacture, integration, testing, and quality control. In
the case of an item to be manufactured, this means selecting the processes
and equipment to be used during production, setting up the part routings,
defining the information flows, and ensuring that adequate testing procedures
are put into place.
Support design is responsible for selecting the hardware and software that
will be used to track and monitor performance once the system becomes
operational. This means developing databases, defining report formats, and
specifying communication protocols for the exchange of data. A second
support function concerns the preparation of manuals for operators and
maintenance personnel. Related issues center on the design of maintenance
facilities and equipment and development of policies for inventory
management. Both process design and support design include the design of
training for those who manufacture, test, operate, and maintain the system.
Design efforts are also relevant to many non-engineering projects. Such
efforts are required to transform needs into the blueprint of the final product.
For example, consider the design of a new insurance policy or a change in the
structure of an organization. In the first case, new needs may be detected by
the marketing department; for example, a need to provide insurance for pilots
of ultra-light airplanes. The designer of the new policy should consider the
various risks involved in flying ultra-lights and the cost and probability of
occurrence associated with each risk. In addition to the risk to the pilot as a
result of accidents, damage to the ultra-light or to a third party must be
considered. The designer of the new policy has to decide which options
should be available to the customer and how the different options should be
combined.

Changes in the business environment and new technologies may generate a
need to restructure an organization. For example, if a new product is very
successful in a traditional organization and the business associated with this
product becomes critical to the financial well-being of the organization, then
a special division may be needed to manufacture, market, and support this
product. The designer of the new organizational structure should consider
questions related to the size of the new division and its mission and
relationship with the existing parts of the organization.
In some projects, the design effort represents the most important component
of the work. Examples are an architect who is designing a new building and a
team of communication experts who are designing a satellite relay network.
Usually, design is the basis for production or implementation, depending on
the context. In many situations, the design effort may consume only a small
portion of the assigned budget and resources. Nevertheless, decisions made in
the conceptual design and advanced development phases are likely to have a
significant effect on the total budget, schedule, resource requirements,
performances, and overall success of the project.
Management of the design effort, from identifying a specific need to
implementation of the end product, is the core of the technological aspect of
project management. That design takes place in the early stages of most
projects does not imply that technological management efforts cease once the
blueprints are drawn. Changes in design are notoriously common throughout
the life cycle of a project and have to be managed carefully.

8.2 Project Manager’s Role
The project manager is responsible for assigning the total work content
specified in the statement of work (SOW) to the participating units. In
Chapter 7, we explained how work packages are constructed from the work
breakdown structure (WBS) and assigned to the lowest level units in the
organizational breakdown structure (OBS). Design efforts are part of the
SOW and are similarly allocated to members of the performing organization
or outsourced. In either case, it is the responsibility of the project manager to
oversee both the design process and the change process throughout the
project life cycle. In doing so, five major factors must be considered: quality,
cost, time, risk, and performance, the last being measured by the functional
attributes of the system. The tools for assessing each of these factors in the
initial stages of a project were discussed in Chapter 3, Engineering Economic
Analysis; Chapter 5, Project Screening and Selection; and Chapter 6,
Multiple-Criteria Methods for Evaluation. In Chapter 4, we discussed life-
cycle costing and showed how (design) decisions made early in the project
affect the total LCC. To underscore the importance of a good design, a
National Science Foundation study showed that more than 70% of the LCC
of a product is defined at the conceptual and preliminary design stages.
Information and decision support systems play a dynamic role in these stages
by focusing management’s efforts on technology and providing feedback to
the design team in the form of assessment data.
Techniques discussed previously can be used throughout the life cycle of a
project to manage its design processes and thus its technological aspects.
Frequently, the design is subject to change as a result of newly identified
needs, changing business conditions, and the evolution of the underlying
technology. Therefore, management of the design (or technological
management) is a continuous process. Manufacturer warranties and an
insistent desire for product improvement in some markets may keep a project
alive well after delivery of the product(s).

8.3 Importance of Time and the Use
of Teams
In the global market, successful companies will be those that learn to make
and deliver goods and services faster than their competitors. “Turbo
marketers,” a term coined by Kotler and Stonich (1991), have a distinct
advantage in markets where customers highly value time compression and
are willing to pay a premium or to increase purchases. Moreover, in certain
high-tech areas, such as semiconductor manufacturing and
telecommunications, where performance is increasing and price is decreasing,
survival depends on the rapid introduction of new technologies.
Once a company has examined the demand for its product, it can begin to
reduce cycle time. Although the implementation effort and cost required to
reduce cycle time will be substantial, the payoff can be great. To create a
sustainable advantage, companies must couple the so-called “soft” aspects of
management with programs aimed at achieving measurable time-based
results.
A trend in technology management is to perform all major components of
design concurrently. This approach, aptly known as concurrent engineering,
is based on the concept that the parallel execution of the major design
components will shorten project life cycles and thus reduce the time to
market for new products. In an era of time-based competition when the shelf
life of some high-tech items may be as short as six months, this can make the
difference between mere survival and material profits.
Studies by the consulting firm, McKinsey & Co., have shown repeatedly that
being a few months late to market is even worse than having a 30%
development cost overrun. Figure 8.1 points up the difference in revenue
when a product is on time or late. The model underlying the graph assumes
that there are three phases in the product’s commercial life: a growth phase
(when sales increase at a fixed rate regardless of entry time), a stagnation
phase (when sales level off), and a decline phase (when sales decrease to

zero). Figure 8.1 shows that a delay causes a significant decline in revenue.
Suppose that a market has a six-month growth period followed by a year of
stagnation and a decline to zero sales in the succeeding eight months. Then,
being late to market by three months reduces revenues by 36%. Thus, a delay
of one-eighth of the product lifetime reduces income by more than one-third.
Such a loss can be especially severe because the largest profits are usually
realized during the growth phase.
Figure 8.1
Lost revenue as a result of delay in reaching market.
Figure 8.1 Full Alternative Text
The application of concurrent engineering principles to technology
management requires thoughtful planning and oversight. There is a clear need
to inform the product engineers, process engineers, and support specialists of
the current status of the design and to keep them updated on all change
requests. This is accomplished by the configuration management systems
discussed later in the chapter.
In the following sections, we explore the issues surrounding concurrent
engineering, configuration management, and describe the risk and quality
aspects of technological management.

8.3.1 Concurrent Engineering and
Time-Based Competition
The ability to design and produce high quality products that satisfy a real
need at a competitive price was, for many years, almost a sure guarantee for
commercial success. With the explosion of electronic and information
technology, a new factor—time—has become a critical element in the
equation. The ability to reduce the time required to develop new products and
bring them to market is considered by many the next industrial battleground.
For example, the Boeing 777 transport design took a year and a half less than
its predecessor the 767, permitting the company to introduce it in time to
stave off much of the competition from the European Airbus. Similarly, John
Deere’s success in trimming development time for new products by 60% has
enabled it to maintain its position as world leader in farm equipment in the
face of a growing challenge from the Japanese. This was done using the
concurrent engineering (CE) approach to support time-based competition.
CE’s major advantage is in creating designs that are more easily
manufactured (Fleischer and Liker 1997).
CE uses project scheduling and resource management techniques in the
design process. These techniques, discussed in Chapters 9 and 10, have
always been common to the production phase but are now recognized as vital
to all life-cycle phases of a project from start to finish. In a CE environment,
teams of experts from different disciplines work together to ensure that the
design progresses smoothly and that all of the participants share the same,
most recent information.
The CE approach replaces the conventional sequential engineering approach
in which new product development starts by one organizational unit (e.g.,
marketing), which lays out product specifications based on customer needs.
These specifications are used by engineers to come up with a product design,
which in turn serves as the basis for manufacturing engineering to develop
the production processes and flows. Only when this last step is approved does
support design begin.

Sequential engineering takes longer because all of the design activities are
strictly ordered. Furthermore, the design process may be cyclic. For example,
if product specifications prepared by marketing cannot be met by available
technology, then marketing may have to modify its specifications. Similarly,
manufacturing engineering may not be able to translate product design into
process design, as a result of technological difficulties or the absence of
adequate support (e.g., it may not be economically practical to develop test
equipment for a product that has not been designed with testing in mind). In
each of these examples, primary activities have to be repeated, increasing
time and cost associated with the design process.
CE depends on designing, developing, testing, and building prototype parts
and subsystems concurrently, not serially, while designing and developing
the equipment to fabricate the new product or system. This does not
necessarily mean that all tasks are performed in parallel but rather that the
team members from the various departments make their contribution in
parallel. A prime objective of CE is to shorten the time from conception to
market (or deployment, in the case of government or military systems), so as
to be more competitive or responsive to evolving needs.
The basis of CE is teamwork, parallel operations, information sharing, and
constant communication among team members. In recent years, the terms
integrated product team (IPT) and integrated product development have been
used to describe a team that is responsible for the whole design and support
process. The IPT concept is discussed in more detail in the next subsection.
To be most effective, the team should be multidisciplinary, composed of one
or more representatives from each functional area of the organization. The
watchword is cooperation. After a century of labor–management
confrontation and sequestering employees in job categories, hierarchies, and
functional departments, many manufacturers are now seeking teamwork,
dialogue, and barrier bashing. By performing product, process, and support
design in parallel, there is a much greater likelihood that misunderstandings
and problems of incompatibility will be averted over the project’s life cycle.
By reducing the length of the design process, overhead and management
costs are reduced proportionally, while the elimination of design cycles
reduces direct costs as well. These cost-related issues are discussed in detail
in Chapter 11. From a marketing point of view, a shorter design process

results in the ability to introduce new models more frequently and to target
specific models to specific groups of customers. This strategy leads to a
higher market share.
Implementation of CE is based on shared databases, good management of
design information (this is the subject of configuration management), and
computerized design tools such as CAD/CAM (computer-aided
design/computer-aided manufacturing) and CASE (computer-aided software
engineering). CE is risky and, without proper technological and risk
management, results can be calamitous. The two most prominent risks are:
1. Organizational risks. The attempt to cross the lines of functional
organizations and to introduce changes into the design process is often
met with resistance. One way to overcome this resistance is to form IPTs
that are made up of people from the various functional areas. In
addition, an educational effort aimed at teaching the advantages and the
logic of CE can create a positive atmosphere for this new approach.
2. Technological risks. The simultaneous effort of product, process, and
support design should be well-coordinated. Configuration management
systems are the key to ensure that the information that is used by all of
the designers is current and correct. The risks associated with a failure to
manage this design information in the CE environment is much higher
than in sequential engineering, where it is possible to freeze product
design once process design starts and to freeze process design once
support design starts.
Companies that are considering the introduction of CE techniques should
consider projects that have the following characteristics:
1. The project can be classified as developmental (novel applications of
known technology) or applied (routine applications of known
technology).
2. The team has experience with the technology.
3. The team has received training in quality management and has had the
opportunity to apply the concepts in its work.

4. The scale of the project falls somewhere in the range of 5 to 35 full-time
staff members for a period of 3 to 30 months.
5. The goal is a product or family of products with clearly defined features
and functions.
6. Success is not dependent on invention or significant innovation.
8.3.2 Time Management
One of the goals of CE is to reduce the time that it takes to develop and
market new products. Before we can say that a reduction has been achieved,
we must have some idea of what the current standards are and what controls
them. This is not as clear-cut as it sounds, because few projects proceed
smoothly without interference from outside forces. Also, most companies
modify their goals as work progresses, making it that much more difficult to
measure project length.
Every industry and its constituent firms are in continuous flux, but they all
are limited in their flexibility to achieve change. A number of inhibiting
factors combine to create a rhythm or tempo in a company that is very
difficult to break. Table 8.1 lists some of these factors for manufacturing
companies, although each may not be universally applicable at all times.
Thoughtful engineering managers develop a feeling for the important factors
in their business and how these affect their operations. If possible, they
quantify them. This provides a baseline against which improvement can be
measured. It is clear that many time-sensitive decisions have an impact on the
successful operation of a business and that focusing on only one or two
factors to the exclusion of the others is rarely optimal. CE is a business
activity, not just an engineering activity. Market success is a function of a
firm’s ability to improve all of its key tempo factors by integrating current
engineering decisions with business decisions. Important issues are:
TABLE 8.1 Factors that

Affect the Tempo of
Manufacturing Firms
Technology life Market forces
Product lifetime costs Product life
Product development cycle Process development cycle
Market development cycle Economic cycle
Workforce hiring/training Capital/loan acquisition
Long-lead items Access to limited resources
Manufacturing capacity
planning
Competitive product
introductions
Integrated Product Team. Many people have written about time management
for individuals. CE requires time management for organizations. The
principles are the same, but their implementations are somewhat different.
Two notorious time wasters are senior people doing junior work and
everyone repeating the same tasks. These are both addressed by the IPT
approach—forming a multifunctional project team from the appropriate
departments and carefully assigning responsibilities to the members. Not
everyone is needed full-time on every team, but the organizing plan should
indicate where to get resources when needed on a part-time basis. All team
members, whether active or not, should be kept informed of progress so that
they do not have to waste time catching up when called into play. Examples
of people who fall into this category are patent attorneys, illustrators, and
technical specialists needed for tricky problems.
The participation of staff from all major functions—marketing, development,
manufacturing, finance, and so on—from the first day of the project makes a
direct contribution to the reduction of duplicate effort. The marketing person
can immediately comment on the desirability of some feature before the
development person has spent time on it. Similarly, the development staff can
get immediate feedback from manufacturing on the feasibility of a particular
design.

Tools. The team organization will lose effectiveness if its members are not
provided with appropriate tools. Today, this usually means access to
applications software and system support for CAD, CAE, CIM, CASE, and
other computer-aided disciplines. Team members must also be trained in the
effective use of the tools.
Team empowerment. The IPT organization will also lose effectiveness if
there are unnecessary delays in decision making. An empowerment approach
enables a team to make the majority of the decisions. The initial program plan
should include some major review milestones, called design reviews, when
upper management and peer evaluation can influence the course of the
project. These meetings should not be determined by the calendar but rather
by progress. The same principle is true of meetings among team members.
Setting them up every Tuesday at 8:00 a.m. usually leads people to spend all
day Monday preparing for Tuesday and all day Wednesday responding to
Tuesday. Have frequent team meetings, but schedule them at short notice to
deal with issues as they arise. To use the project scheduling terminology,
team meetings are activities, not events. Many companies have difficulty
implementing the empowerment requirement because it encroaches on
established lines of authority. This is one area where CE can actually increase
risks.
If there is an important role for upper management to play during the course
of day-to-day activities, then it is in assigning access to limited resources. If
two or more teams need access to a special piece of equipment, say, for
production trials, then there has to be a responsive mechanism in place to set
priorities. Again, the initial project plan must cover this situation.
Use of design authorities. Another approach to facilitate decision making is
to appoint design authorities in various areas. For example, there could be a
technology design authority, a product design authority, a process design
authority, and an equipment design authority. The authorities must be
legitimate experts in their fields. They do not necessarily do the design work
and may not, in fact, be full-time members of the team. Their role is to help
the project manager make the final decision when two or more conflicting
approaches have been recommended and to provide peer evaluation and
review when needed. The design authority should not be called in until the

competing approaches have been documented in equivalent detail. He or she
is a last resort to help resolve sticky issues. By having them available and
identified in the plan with their role clearly spelled out, it is possible to
facilitate decision making even in complex situations. Nevertheless, the
ultimate decision maker is the project manager. The design authorities are
consultants who are called on only to evaluate competing solutions and offer
their expertise.
Quality. A major time waster is repeating work because of poor quality.
Developing procedures that focus on delivering satisfaction to customers,
both internal and external, goes a long way in reducing the need to correct or
redo work. Obviously, careful selection of team members also goes a long
way in ensuring high-quality results. Here is where the best interests of a CE
team can conflict with the best interests of individuals. Unless the company
implementing the procedures takes special steps to prevent it, working on a
CE team can limit growth opportunities for individuals and even eliminate
career paths. The project manager wants to be assured of high-quality work in
all areas and will tend to select people who have already demonstrated their
ability to deliver. The problem can be especially acute for junior staff
members who have demonstrated their skills in one area but are not given a
chance to expand into other areas because they are continually asked to work
on projects that require their known skills.
Bureaucracy. The final time waster of note is lengthy administrative and
bureaucratic procedures. Eberhardt Rechtin, a former vice president of
engineering at Hewlett-Packard, once said that an approval takes 2n days,
where n is the number of levels of approval needed. The obvious solution to
this problem is to empower the project team in advance with all of the
necessary approval authority. Again, this means that the initial project plan
must be prepared very carefully. Another approach to shortening the time
required for administration is to provide the team leader with the authority to
eliminate competitive bidding procedures on certain development items
involving known vendors. Other bureaucratic red tape should also be
eliminated, although this makes sense even in the absence of CE. Many
companies assign a full-time administrator/facilitator to CE teams to assist
the project manager.

External participation. The best users of CE also extend the concept of the
project team to involve key vendors and customers. The customers can help
minimize the time required to define and specify the product, facilitate
product acceptance procedures, and reduce project risk by either ordering
early or at least indicating through a letter of intent what their purchases may
be. Vendors can be extraordinarily helpful members of the team by providing
technical support for the application of their products and materials and by
providing preferential access to scarce resources. In return, they get some
indication of likely sales. If a company uses formal vendor certification
procedures, they should extend them to “certifying” selected key vendors as
participants in CE development programs.
Toyota example
To cut the length of the design cycle and to improve the quality of the design,
Toyota implemented a design process in which IPT plays a major role. Each
IPT is headed by a shusa, or big boss whose name becomes synonymous with
the project. Members are assigned to the project for its life but retain ties with
the functional area (continuity) from which they were drawn. Team member
performance is evaluated by the shusa and is used to determine subsequent
assignments. Team members sign pledges to do exactly what everyone has
agreed on as a group and try to resolve critical design tradeoffs early. The
number of team members is highest at the outset of a project. As
development proceeds, the number dwindles as certain specialties (e.g.,
market assessment) are no longer needed.
8.3.3 Guideposts for Success
Tom Peters (1991), a well-known management consultant, postulated the
following guideposts to help organizations implement the team concept:
1. Set goals, deadlines, or key subsystem tests. Successful project teams
are characterized by a clear goal, although the exact path is left unclear
to induce creativity. Also, three to six strict due dates for subsystem

technical and market tests/experiments are set and adhered to
religiously.
2. Insist on 100% assignment to the team. Key function members must be
assigned full time for the project’s duration.
3. Place key functions on-board from the outset. Members from sales,
distribution, marketing, finance, purchasing, operations/manufacturing,
and design/engineering should be part of the project team from day 1.
Legal, personnel, and others should provide full-time members for part
of the project.
4. Give members authority to commit to their function. With few
exceptions, each member should be able to commit resources from his
or her function to project goals and deadlines without second-guessing
from higher-ups. Top management must establish and enforce this rule
from the start.
5. Keep team-member destiny in the hands of the project leader. For
consulting firms such as Booz, Allen & Hamilton and McKinsey & Co.,
life is a series of projects. The team leader might be from San Francisco
or Sydney, Australia; either way, his or her evaluation of team members’
performance will make or break a career. In general, then, the project
boss rather than the functional boss should evaluate team members.
Otherwise, the project concept falls flat.
6. Make careers a string of projects. A career in a “project-minded
company” is viewed as a string of multifunction tasks.
7. Live together. Project teams should be sequestered from headquarters as
much as possible. Team camaraderie and commitment depend to a
surprising extent on “hanging out” together, isolated from one’s normal
set of functional colleagues.
8. Remember the social element. Spirit is important: “We’re in it together.”
“Mission impossible.” High spirits are not accidental. The challenge of
the task per se is central. Beyond that, the successful team leader
facilitates what psychologists call “bonding.” This can take the form of

“signing up” ceremonies upon joining the team, frequent (at least
monthly) milestone celebrations, and humorous awards for successes
and setbacks alike.
9. Allow outsiders in. The product development team notion is incomplete
unless outsiders participate. Principal vendors, distributors, and “lead”
(future test-site) customers should be full-time members. Outsiders not
only contribute directly but also add authenticity and enhance the sense
of distinctiveness and task commitment.
10. Construct self-contained systems. At the risk of duplicating equipment
and support, the engaged team should have its own workstations, local
area network, database, and so on. This is necessary to foster an “its-up-
to-us-and-we’ve-got-the-wherewithal” environment. However, the
additional risk created by too much isolation must be balanced with the
need for self-sufficiency. Problems may arise when it comes time to
integrate the project with the rest of the firm.
11. Permit the teams to pick their own leader. A champion blessed by
management gets things under way, but successful project teams usually
select and alter their own leaders as circumstances warrant. It is
expected that leadership will shift over the course of the project, as one
role and then another dominates a particular stage (engineering first,
then manufacturing, and distribution later).
12. Honor project leadership skills. No less than a wholesale reorientation of
the firm is called for away from “vertical” (functional specialists
dominate) and toward “horizontal” (cross-functional teams are the
norm). In this environment, horizontal project leadership becomes the
most cherished skill in the firm, rewarded by dollars and promotions.
Good team skills, for junior members, are also valued and rewarded.
8.3.4 Industrial Experience
Consider a few of the real-world success stories of CE implementation that
have been documented and reported at professional conferences.

For Cadillac, a winner of the Malcolm Baldrige National Quality Award, CE
involved a new culture and a new way of designing and building its
extraordinary, complex product—luxury cars. Engineers, designers, and
assemblers are now members of vehicle, vehicle-systems, and product (parts)
teams that work in close coordination rather than belonging to separate,
isolated functional areas as before. Assembly line workers, dealers, repair
shop managers, and customers provide insight to engineers involved in all
stages of design. To inspire cultural change, Cadillac created a position of
champion of simultaneous engineering (a role that combines keeping the
process on track, preaching to the believers, and motivating the recalcitrant)
and sent 1,400 employees to seminars on quality management. They also
established an “Assembly Line Effectiveness Center,” where production
workers rub shoulders with engineers, critiquing prototypes for
manufacturability.
John Deere’s Industrial Equipment Division in Moline, Illinois, has had two
CE efforts. The first, begun in 1984, failed because management retained the
traditional manufacturing departments. Designers and process engineers who
were assigned to task groups remained loyal to the interests of their
disciplines rather than to the overall enterprise. In 1988, the division
reorganized. Staff members now report to product teams and answer to team
leaders, not functional department heads. Early in the design stage, teams
create a product definition document that describes the product precisely, sets
deadlines, and lays out the manufacturing plan. Products no longer change as
departments work on them. The result has been gradual improvements in
manufacturing processes. There are now fewer experimental designs, and it is
possible to produce prototypes in the production environment. The advantage
of this is that in addition to checking for flaws in the prototypes themselves,
engineers can simultaneously perfect the manufacturing process.
A third example of a successful CE implementation is Federal-Mogul, a
precision parts manufacturer in Southfield, Michigan. The first Federal-
Mogul unit to adopt CE was its troubled oil-seal business. Other units quickly
followed. Success in the oil-seal business, in which products are simple but
must meet exacting standards, requires rapid turnaround on bids and
prototypes and strong customer service. By providing estimates to customers
in minutes instead of weeks and producing sample seals in 20 working days

instead of 20 weeks, market share soared. Federal-Mogul accomplished this
by adopting a cross-functional product team approach to manufacturing,
encouraging consensus building and empowerment, and introducing new
information technologies. Key applications include networks that allow all
plants to share CAD drawings and machine tools, a scheduling system that
automatically notifies appropriate team members when a new order comes in,
an engineering data management system, and an on-line database of past
orders.
8.3.5 Unresolved Issues
From a technical point of view, recent advances in hardware and software,
database systems, electronic communications, and the various components of
computer-integrated manufacturing facilitated the implementation of CE. At
the first International Workshop on CE Design, sponsored by the National
Science Foundation (Hsu et al. 1991), four themes emerged from the
discussions: models, tools, training, and culture. Participants identified
measurement issues and tradeoffs that will inform future models of new
product development. They concluded that tools must focus on expanded
CAD/CAM/CAPP capabilities with strong interfaces. Training is needed for
multiple job stations, in the impact of design on downstream tasks, and in
teamwork and individual responsibility. Corporate culture—and how to
change it—must be better understood. Important aspects of culture to be
clarified include incentives and performance, myths that inhibit an
organization’s progress, and the management of change.
One of the primary roles of CE is identifying the interdependencies and
constraints that exist over the life cycle of a product and ensuring that the
design team is aware of them. Nevertheless, care must be taken in the early
stages to avoid overwhelming the design team with constraints and stifling
their creativity for the sake of simplicity. A truly creative design that satisfies
customer requirements in a superior manner may justify the expense of
relaxing some of the development and process guidelines.
Although a basic tenet of CE is that input to the design process should come
from all life cycle stages, there is much ambiguity about how to achieve this.

At exactly what point in the CE process should discussion of assembly,
sequences, tolerances, and support requirements be introduced? Also,
tradeoffs abound. For example, consolidation of parts is desirable, yet too
much consolidation implies costly and inefficient procurement and
inventorying. A balance must be struck between meeting the customer’s
specifications, designing for manufacturability, and LCC. This means that
cost information should be available to the design teams throughout a project.

8.4 Supporting Tools
8.4.1 Quality Function Deployment
A quality product is one that meets or exceeds stakeholders’ needs and
expectations. Thus, the design quality is the degree to which product, process,
and support design meets or exceeds stakeholders’ needs and expectations,
and the quality of conformance is the degree to which the product, service, or
system delivered meets the design specifications.
Clearly, a quality design is the translation of needs and expectations into the
blueprints of the product, process, and support system. An important
technique that accompanies quality design and CE is quality function
deployment (QFD), introduced by Yoji Akao. QFD is based on using
interdisciplinary teams. The members of the teams study the market
(customers) to determine the required characteristics of the product or
system. These characteristics are classified into customer attributes and are
listed in order of their relative importance to the customer.
The ranked attributes, also called the “What’s,” are input to a second step in
which team members translate the attributes into technical specifications, or
“How’s.” Thus, an attribute such as “a tape recorder that is easy to carry
around” can be translated into physical dimensions and weight that can be
used to guide product development. This example, of course, led to Sony’s
Walkman. The joint effort by the team members promotes CE while ensuring
better communication and easier integration of the basic functions.
A matrix called the quality chart is used in the QFD process. The rows of the
quality chart list in hierarchical order the attributes (the “What’s”); the design
characteristics (the “How’s”) are similarly listed across the columns. Each
cell in the resulting matrix corresponds to a lower level attribute intersection
with a lower level design characteristic. Entries indicate the correlation
between the corresponding attribute and design characteristic. From the
matrix, team members can infer the relative importance of the attributes along

with their correlated design characteristics and the degree of correlation. On
the basis of this information, a weight, w i , is calculated for each design
characteristic, i. This weight is the sum of all attribute weights, a j , multiplied
by the corresponding correlation, c ij , between the specific design
characteristic and the particular attribute. The formula for calculating w i is
w i = Σ j   a j   c ij .
QFD is a powerful tool that helps the CE team focus on the design
characteristics that influence the attributes viewed as most important by
customers. To illustrate the ideas behind QFD, consider a project aimed at
designing a new cross-country bicycle. By using market research, the project
team can identify the most important attributes of this product for its potential
customers. Suppose that the four top-ranking attributes were found to be
durability, convenience, speed, and cost. Next, the team considers the three
major components of the new bicycle: the frame, the gears, and the wheels.
Table 8.2 illustrates the relationship between the attributes required by the
customers and the design characteristics.
TABLE 8.2 Quality Chart for
New Bicycle Design
Design characteristics
1. Frame 2. Gears
1.1 1.2 2.1 2.2
Attributes Weight( a j ) Material Design Material Design Material
1. Durability 1.1Corrosion 2 H L H M
1.2 Impact 1 H H H H
1.3 Pressure 3 H H H M
1.4 Wear 2 M L H H
2. 2.1 Carrying 3 H M L L

Convenience
2.2 Riding 3 M M M H
2.3
Maintenance 2 L M H H
3. Speed 3.1 Flatsurface 1 M H M H
3.2 Up hill 3 M H M H
3.3 Down
hill 2 M H M H
4. Cost 4.1 Purchase 2 H H M H
4.2
Maintenance 2 M M H H
4.3 Salvage
value 1 H H M H
H=high correlation; M=medium correlation; L=low correlation
Now, assuming that the correlations used are H=0.9, M=0.5, L=0.3, the
weight of, say, the frame material ( w 1 ) is
w 1 = ∑ j=1 13 a j c 1 j = 2×0.9+1×0.9+3×0.9+2×0.5+3×0.9+3×0.5
+2×0.3+1×0.5+3×0.5+2×0.5+2×0.9 +2×0.5+1×0.9=17.9
In Table 8.2, only two levels of attributes and design characteristics are
presented. Lower levels, such as the dimensions and shape of the frame and
the size of each gear in the transmission, can be added if more detail is
deemed necessary. Additional information frequently found in the quality
chart is the relative importance of each attribute, target value of design
characteristics, information about similar products available in the market,
and the correlation between design characteristics.
QFD uses the house of quality, shown conceptually in Figure 8.2, to integrate
the informational needs of marketing, engineering, R&D, manufacturing, and
management. For new-product development, the team begins by obtaining
the “voice of the customer” in the form of 200 to 300 detailed customer

needs, such as (on-screen programming) “a menu appears on the TV screen
with easy-to-read instructions.” These customer needs are grouped
hierarchically into a relatively few primary needs (to establish the strategic
position), 20 to 30 secondary needs (to design the basic product and its
marketing), and 150 to 250 tertiary needs (to provide specific design
direction to engineers). Customer perceptions of competitive products
provide goals and opportunities for new products. The importance of
customer needs establishes design priorities.
Figure 8.2
House of quality.
Figure 8.2 Full Alternative Text
The relationship matrix translates customer needs, the language of marketing,
into engineering language. Engineering design attributes, such as an
automatic shutoff time delay, provide the means to satisfy customer needs.
Performance measures of the design attributes (seconds of delay, etc.)
establish competitor capabilities. Finally, the “roof matrix” (upper triangle in

Figure 8.2) quantifies the physical interrelations among the design attributes
—instructions must be succinct and correlate with the design.
The house of quality encourages cooperation and communication among
functions by requiring input from marketing (the customer’s voice) and from
engineering (engineering measures and the roof matrix), and agreement on
interrelationships. The entire team should participate with all members,
understanding and accepting these inputs and relationships. Further
discussion can be found in Hauser and Clausing (1988).
8.4.2 Configuration Selection
Configuration is a term that refers to the complete description of the physical
and functional characteristics of a product or a system. Configuration is the
output of the design process. In large, technologically sophisticated projects,
selection of the best design is a complex decision due to technological
uncertainties, the absence of a single agreed-on objective, the size of the
system, and the system’s complexity. In such projects, it might not be
appropriate to make a decision solely on the basis of cost of development and
manufacturing. System operations and maintenance costs may be significant,
even after discounting over the system’s useful life, to warrant consideration
when the original design decision is made.
Cost-effectiveness and benefit-cost (B/C) analyses are intended to assist in
the selection of the most appropriate design alternative for system
development or system modification-type projects. These techniques are
supported by a variety of models used to estimate the functional efficiency,
the risk, and the LCC of each technological alternative.
The selection process may be driven by the available budget or by the
functional requirements. In the first case, the available budget for the project
is viewed as a binding constraint and an effort is made to design a system
with the best possible capabilities without exceeding the budget. This is
known as the design-to-cost approach. In the second case, the design effort is
aimed at minimizing the ratio between the cost of the system and its
effectiveness. This is known as the cost-effectiveness approach. In either case,

there is a need to define and estimate the value of some performance
measures for cost and effectiveness. Both approaches are used in the process
of configuration selection. This process takes place before and during the
detailed design phase, when the exact configuration of the system and each of
its components are selected.
The techniques discussed earlier in this book for project selection are used for
configuration selection as well. Checklists and scoring models, B/C analysis,
cost-effectiveness analysis, and multiple-criteria methods all have a role. In
the configuration selection process, each alternative design (configuration) is
analyzed with respect to its LCCs and is evaluated with respect to its
expected performance. Performance measures are project dependent (they
would be different for the development of a new car and for the construction
of a new building), but some are common to many systems. Those discussed
in Section 8.1.1 are general indicators that should be taken into account when
evaluating a system from a technological point of view. By combining them
with specific project objectives related to budget and schedule, they provide a
framework for selecting the design configuration and foreshadow the
capabilities of the final system.
For a particular project, each measure should be subdivided until the desired
level of detail is reached. For example, compatibility might be broken down
by hardware, software, operations and maintenance personnel, training
requirements, and logistics support. Software then might be decomposed into
databases, controls, interface protocols, and applications. Quantifying each
element in the resultant hierarchy for each alternative is the first step in the
analysis. The selection process can be supported by scoring models,
multiattribute utility theory-based models, or the analytic hierarchy process,
as discussed in Chapters 5 and 6.
The cost of each alternative must also be evaluated. The LCC of a system is
defined as its total cost from the start of the conceptual design phase until it
completes active service. Related methodologies and techniques are
discussed in Chapter 4.
Along with a B/C analysis, a risk analysis of each alternative design should
be conducted. Risk analysis includes the following steps:

Identification of risk drivers
Estimation of probabilities of undesired outcomes
Evaluation of the impact of each undesired outcome (on cost, schedule,
quality, and operational and technological capabilities)
Elimination and reduction of risks
Preparation of contingency plans
The procedures used for selecting the best design alternative can also be
adopted for managing configuration changes. This is discussed later, but first
we offer some guidelines for system definition. The selection process is
complete when the specifications of the proposed system are robust enough
to at least answer the following questions:
Technological specifications
Operational/functional: What tasks should the system perform and what
performance levels are expected?
Timeliness: When should the system be operational?
Quality: Which standards are applicable? Which customer needs are to
be supported by the system, and to what extent?
Reliability: What are the expected MTBF and MTTR in the environment
in which the system has to operate?
Compatibility: With which other systems must the system contemplated
operate in harmony? What interfaces are required?
Adaptability: Under what environmental conditions is the system
designed to operate, be maintained, and stored? Under what conditions
is the system required to operate, be maintained, and stored?

Life span: For how long is the system expected to be in service?
Simplicity: What level of training is required to operate and to maintain
the system?
Safety: What safety standards are applicable to the system?
Commonality: What level of commonality is required with each existing
or planned system?
Maintainability: What logistics support is required: spare parts, training,
technical manuals, test equipment, and so on?
Friendliness: What features should be included in the system to enhance
its friendliness?
Life-cycle cost
What are the estimated costs of design, manufacture, operation,
maintenance, and phase-out for the system?
What is the expected timing of each cost component?
Risk assessment
What are the major risk drivers?
What are the probabilities of undesired outcomes?
What is the expected impact of each undesired outcome?
What are the plans to handle undesired outcomes?
The selected design alternative defines the technological aspects of the
project. Based on the specifications, estimates of cost and schedule are made,
and the proposed project is either approved or rejected. Project approval is a

management decision that may affect the entire organization. When several
projects are being considered, the final choice is based on strategic and
tactical considerations, including:
General considerations
Organizational goals
Current or pending projects
Existing and future products and markets
Introduction of new technologies
Image of the organization
Organizational growth
R&D
Availability of required technology
Future use of new technologies developed or acquired for the project
Development risks
Opportunity to acquire new technologies and new knowledge
Availability of resources required
Future use of new resources acquired for the project
Logistics and production

Project’s need for logistics support
Future use of investment in logistics support
Project’s production resource requirements
Availability of production resources needed
Effect on utilization of existing resources
Need for new facilities
Future use of facilities required for the project
Marketing
Potential markets
Estimate of future sales or business
Availability of marketing resources
Effect on existing products markets
Finance
Project net present value
Project rate of return
Project payback period
Project budgetary risks
Project cash flow

This partial list, together with any specific considerations unique to the
organization, underlies the selection process. Decision making can be based
on any of the other methods discussed for evaluating and selecting
alternatives.
8.4.3 Configuration Management
Configuration management (CM) concentrates on the management of
technology by identifying and controlling the physical and functional
characteristics of a product or a system as well as its supporting
documentation. The medium of implementation is a set of tools designed to
provide accurate information on what is to be built, what is currently being
built, and what has been built in the past. The mission of CM is to support CE
and to assist management in evaluating and controlling proposed
technological changes. Through quality assurance activities, CM ensures the
integrity of the design and engineering documentation, and supports
production, operation, and maintenance of the system.
In configuration management, a baseline is established in each phase of the
system’s life cycle with well-defined procedures for handling proposed
deviations. The initial baseline, known as the functional (or program
requirements) baseline, is prepared in the first phase of the life cycle—the
conceptual design phase. This baseline contains technical data regarding
functional characteristics, demonstration tests, interface and integration
characteristics, and design constraints imposed by operational,
environmental, and other considerations. Approval is subject to a preliminary
design review (PDR). The PDR and other design reviews serve as gates for
subjecting projects to peer evaluation and stakeholder “go/no go” decisions.
Gating is critical when it comes to project termination. Because it is unlikely
that the project team will decide to terminate a project, design reviews or
gates should serve as “kill points” for the stakeholders to assess performance
and evaluate the probability of successful completion.
The advanced development phase, also known as the definition phase,
produces the second baseline, the allocated (or design requirements)
baseline. This document contains performance specifications guiding the

development of subsystems and components including characteristics derived
from the system’s design. Laboratory or computer simulation may be used to
demonstrate achievement of functional characteristics, interface
requirements, and design constraints. This baseline is subject to a critical
design review.
The product (or product configuration) baseline is last and includes
information on the system as built, including results of acceptance tests for a
prototype, supporting literature, operation and maintenance manuals, and part
lists. Acceptance is subject to a physical configuration audit. In addition to
these three baselines, other baselines and additional design reviews are
frequently needed when complicated systems are involved. Examples are a
baseline that defines the initial design and a baseline that defines the detailed
design of the system. The transition from one baseline to the next is
controlled by design reviews.
The CM system ensures smooth transition and provides updated information
on the configuration of the system and all pending change requests at all
times. To function properly, it should perform the tasks discussed in the
following subsections.
Configuration identification
This function is at the heart of the CM system. It starts with the selection of
configuration items, both software and hardware that have one or more of the
following characteristics:
End-use function
New or modified design
Technical risk or technical complexity
Many interfaces with other items
High rate of future design changes expected

Logistic criticality
The selection of configuration items is a critical task of systems engineering.
Too few configuration items will not provide adequate management control,
and too many may overload the system, sparking a waste of time and money.
Configuration change control
This function involves the development of procedures that govern the
following three steps.
1. Preparation of a change request. This step requires that a formal change
request be prepared and submitted. The initiation of a change can be
internal (the project team) or external (any other stakeholder, e.g., the
customer, a subcontractor, or a supplier). The change request specifies
the reason for the modification and forewarns management of increases
in cost, schedule, and risk, as well as changes in quality, contractual
arrangements, and system performance. Each change request is assigned
an identification number and is evaluated after input is received from all
organizational units affected. The principal aim is to collect the relevant
data on each proposed change and to assess its expected impact.
A typical change request form will include the following information:
Change request number
Originator
Date issued
Contract or project number
Configuration items affected by the change
Type of changes: temporary permanent
Description of change

Justification for change
From serial number through serial number
Priority
Effect on:
Cost
Schedule
Resource requirements
Operational aspects
Timeliness
Quality
Reliability
Compatibility
Life span
Simplicity
Safety
Commonality
Maintainability
Friendliness

Remarks:
Engineering
Marketing
Manufacturing
Logistics support
Configuration management
Other organizational units
CCB decision:
Accept Reject More information needed
Acceptance date
Rejection date
2. Evaluation of a change request. A team of experts representing the
different organizational functions and the project stakeholders are
responsible for the evaluation of change requests. This team, known as a
change control board (CCB), or configuration management board,
evaluates each proposed change on the basis of its effect on the form, fit,
and function of the system, logistics (manuals, training, support
equipment, spare parts, etc.), and project cost, risk, and schedule. This
review leads to a decision to approve or reject the change request or to
reconsider it after more data are collected.
Changes are classified as either permanent or temporary. A temporary
change might be needed for test programs or debugging software.
Approval can usually be obtained in a short time compared with a
request for a permanent change.

Changes are also classified by type of change. Major changes are
handled by the CCB, whereas minor changes can be approved by the
project manager or a subcommittee that consists of some of the CCB
members. This classification can be based on the effect of the proposed
change on the form, fit, and function of the system or product as well as
on the effect on the cost schedule and risk of the project.
All information regarding each proposed change is accumulated and
analyzed by CM, which also functions as a central repository for
historical records. The decision to accept a proposed change is based on
cost-effectiveness and risk analysis in which the need for the change and
its expected benefits are weighed against implementation and project
LCCs, its impact on project quality and schedule, and the expected risks
associated with implementation.
3. Management of the implementation of approved changes. Approved
changes are integrated into the design. This is accomplished by
preparing and distributing a change approval form or an engineering
change order to all parties involved, including engineering,
manufacturing, quality control, and quality assurance.
The CCB is responsible for the pivotal task of conducting a comprehensive
impact analysis of each change proposed. A well-functioning change control
system ensures tight control of the technological aspects of a project. In
addition, it provides accurate configuration records for the smooth,
coordinated implementation of changes and effective logistics support during
the life cycle of the system.
Configuration status accounting
This task provides for the updated recording of:
Current configuration identification, including all baselines and
configuration items
Historical baselines and the registration of approved changes

Register and status of all pending change requests
Status of implementation of approved changes
Configuration status accounting provides the link between different baselines
of the system. It is the tool that supports the CCB in its analysis of new
change requests. The effect of these changes on the current baseline must be
evaluated and their relationship to all pending change requests must be
determined before a decision can be made.
Review and audits
This CM task provides all stakeholders (e.g., the contractor, the customer)
with the assurance that test plans demonstrate the required performance and
that test results prove conformance to requirements. Functional configuration
audit includes a review of development test plans and test results, as well as a
list of required tests not performed, deviations from the plan, and waivers. In
this task, the relationship between quality assurance and CM is established.
CM provides the baselines and a record of incorporated and outstanding
changes. Quality assurance first checks the configuration documentation to
gauge requirements; then it verifies that the system conforms to the approved
configuration.
CM is a tool that supports the project team in all phases of product
development and implementation. It specifies the procedures and information
required for the project to be carried out in the most cost-effective manner.
8.4.4 Risk Management
Risk is a major factor in the management of projects because of their one-
time nature and the uniqueness of the deliverables. The highest levels of
uncertainty and risk appear early in the project life cycle. Whenever the
design process or the design itself deviates from existing procedures and
established techniques, technological risks are introduced. These risks can be
related to the product design, to the process design, or to the design of the

support system and can vary widely in magnitude. For example, in product
design, modification of an existing subassembly is an example of a low-level
risk. A moderate-level risk would involve, for example, the design of a new
product based on currently used technologies and parts (integration risks); a
third, even higher level of risk is related to the use of new materials, such as
ceramics, in a product that was previously fabricated out of conventional
metal alloys.
The development of the first transistor was a high-risk project involving a
completely new technology. Sony’s work on the first radio transistor was also
a high-risk project because this technology was being implemented in a new
product—the portable radio. However, development of subsequent models of
the transistor radio represented much lower risks, as both the technology and
the basic product were known.
The probability of success (or the risk of failure) should be estimated and
monitored throughout the life cycle of a project including project selection,
evaluation of alternative designs, change management, and implementation.
The scope of activities associated with risk management includes:
Risk management planning
Risk identification
Risk analysis
Risk response planning
Risk monitoring and control
Risk can be measured as a function of the probability of an undesirable event
and the severity of the consequences of that event. In general, high risk
corresponds to a strongly adverse event that has a high probability of
occurrence, whereas low risk corresponds to a low probability of occurrence
and low severity. Moderate levels of risk correspond to combinations of
probabilities and consequences that fall between these extremes.
A project may face multiple sources of risk: a schedule risk related to the

event of delays, a cost risk associated with the event of a budget overrun, one
or more performance risks accompanying the failure to achieve
technical/operational goals, and a program risk related to the success of the
project as perceived by the stakeholders. The multiple sources of risks, for
example, technological, political, environmental, and marketing/business, and
the different aspects of a project that are subject to failure or delay make risk
management a demanding, time-consuming activity.
To demonstrate the problems faced by management during the various
project phases, consider an organization that has decided to initiate a project
aimed at automating its production planning and control system. Among the
large number of available options, the organization focuses on two
alternatives: (1) purchasing the most suitable system off the shelf and
modifying it according to its individual needs or (2) developing a system that
will support all of the specific production planning policies and procedures
currently in use. In this example, the first alternative represents a project of
relatively low development risk; however, the benefits may be minimal. This
is because most off-the-shelf software packages have limited flexibility and
can only rarely be made 100% compatible with the existing work
environment. The second alternative offers a higher chance of achieving the
technological and functional goals but involves a significant software
development effort. As such, development and integration risks and,
consequently, the risk of schedule delays and budget overruns are higher.
To perform a tradeoff analysis between the two alternatives, the techniques
presented in earlier chapters can be implemented. The decision process
should be inclusive and, ideally, a consensus can be formed. Achieving a
high level of satisfaction depends on the process used in selecting and
implementing the alternative. When management, potential users, and future
operators of the new system select the alternative and define its specific
configuration, the probability that the project will be successful is
greatly increased.
In addition to the economic, scheduling, and cost aspects that have to be
analyzed, risk analysis is part of the selection process. Risk analysis starts
with identification of all possible events that might have a negative impact on
the project. In the example above, typical negative events for the first

alternative are an inability to modify the software to accommodate a given
need and an inability to integrate the package with existing management
information systems and databases. Negative events for the second
alternative include unexpected difficulties in integrating the modules of the
new software package and excessive CPU time requirements that slow down
information processing and retrieval.
In the next stage of the analysis, the severity of each event is estimated and
the level of risk (based on the severity of the event and the probability of
occurrence) is calculated. The events are then ranked, with those exhibiting
the highest risks placed at the top of the list. Next, the source of high-risk
events is investigated, and, if needed, actions are taken to eliminate, reduce,
or mitigate the risk. In some cases, it may be appropriate to contract with an
outside consultant to undertake the assessment. By initiating a risk
management activity at the outset of the project, unnecessary risks can be
avoided, whereas those that are deemed necessary can be minimized or
transformed. A formal description of the processes involved in risk
management follows.
Risk management planning
Major sources of risk require special attention. The risk management plan
starts by identifying each of these sources and their magnitude, their relation
to the various design stages, and their possible effects on cost, schedule,
quality, and performance. The next step is to develop a plan to manage,
monitor, and control these risks. One component of the plan includes the
identification of modifications or alternatives that would either reduce or
eliminate some of the risks altogether. Continuing with the example above,
the thoughtful selection of a computer language or an operating system may
reduce some of the integration risks. If management decides to develop a new
software package, then contingency plans that cut expenses and development
time at the cost of lower performance should be prepared. These plans would
be used in case one or more undesired events take place. By preparing
contingency plans in advance, time is saved when the anticipated problem
surfaces.

Risk identification
Risks are caused by several factors and can affect different aspects of the
project. A list of such factors can help the project manager focus on potential
risks. In organizations that perform similar projects, it is possible to develop a
checklist of risk sources based on past occurrences of such risks.
1. Technology. The rapid pace with which technology (e.g., information
systems and integrated circuits) is expanding may make a new product
obsolete the day the first unit rolls off the production line. To avoid this
risk, design engineers prefer to use the latest technologies available,
which frequently are immature and unproved. This increases the risk of
technological failure. Simple lack of experience heightens the chances
that the project will be saddled with unforeseen problems. The tradeoff
between well-proven technologies with lower performance levels and
new, unproven technologies requires detailed risk analysis. When
NASA decided to build a new space shuttle in 1987 to replace the
Challenger, which exploded on launch, it opted for a design that was
nearly identical to the original. Rather than exploit recent advances in
microelectronics, expert systems, and robotics, 20-year-old technology
was used to avoid additional risks.
2. Complexity and integration. The adoption of well-known technologies
for a project reduces the risk of component failure but may do little to
mitigate the risk of integration failure. Modern, complex systems are
based on the integration of parts and subsystems, the compatibility of
software modules, and integration of hardware and software. The
interfaces between components of a system are a source of integration
problems and risks. For example, problems related to RFI (radio-
frequency interference) or EMI (electromagnetic interference) should be
considered in the design of electrical devices. Parts of the same system
may affect each other in an undesired and unexpected manner. Complex
interfaces within a system, between systems, and between systems and
humans are sources of risk that need management’s attention throughout
the project.

3. Changes. Virtually all projects are subject to design changes throughout
their life cycle. A reassessment of needs, revitalized competition, and
emerging technologies are some of the factors that may call the original
design into question. Design changes are risky, as each change may have
a different effect on the system or its components. As a result, the risks
of integration may go up sharply. A configuration management system
is required that can evaluate each proposed change and its possible
consequences. This system should provide information on approved
changes to the design engineers in an effort to reduce the risk of
integration failure. The same system should provide updated design
information to manufacturing and quality control so that the product is
manufactured and tested according to the most recent configuration.
4. Supportability. Good design and workmanship are not enough to
guarantee a successful project. The ultimate test of success is customer
or end-user satisfaction. To achieve this goal, the design should be based
on the customer’s needs, and the product should conform to the design.
At the conclusion of the project, the product or system delivered should
be operational (i.e., all of the support required for maintenance and
operations should be available). In the case of the rough-terrain cargo
handler, for example, this includes trained personnel, transportation,
storage and maintenance facilities, spare parts, and manuals. To prepare
for worst-case contingencies, the design effort should cover the risks of
a system delivered without adequate logistics support.
To summarize, technological risks are usually generated by one or more of
the following factors:
Unproven technology
System complexity
Integration requirements
Physical or chemical properties
Modeling assumptions

Interfacing with other systems
Interfacing with operators and service personnel
Operating environment
In addition to technological risks, other sources of risk should be identified,
including political risks, environmental risks, and marketing/business risks.
Risk analysis
Risk identification, when done properly in complex projects, may produce a
long list of risk events or sources of risk. The most important potential risks
should be analyzed in-depth. Each risk event should be classified according
to the impact that it has on the project, for example, separating schedule risks
from budget risks from performance-related risks. Another classification is to
distinguish between “known unknowns” versus “unknown unknowns.”
Known unknowns are risk events that occurred in past projects so
information on their probability and severity is available. Unknown
unknowns are risk events associated with a new technology, a new market, or
a new environment for which no past information is available.
The next step is to assess the magnitude of each type of risk and identify
those that seem to be the most serious. The analysis of risk is based on
experience gathered in past projects, expert opinion, and physical or
mathematical models. If the project manager does not have the technical
expertise to perform the job, then he or she should call on those in the
organization who are more qualified.
Response planning
The next step in the analysis is to decide how to handle the risks identified.
Possible alternatives are:
Information gathering. Because risk is generated by uncertainty, an

effort to collect information and to reduce the level of uncertainty can
reduce or eliminate the risk. Such an effort in the form of literature
search, feasibility studies, purchasing of knowledge or patents, reverse
engineering, hiring of new employees that have the needed know-how,
and designing and executing experiments all are common in the high-
tech industry.
Risk elimination. Eliminating the probability of occurrence (bringing it
to zero) or eliminating the impact, for example, by selecting a different
technology.
Risk reduction. Reducing the probability of occurrence, say, by
redundancy (e.g., having two independent R&D groups each develop a
new component) or by reducing the impact of the risk event should it
happen (or doing both).
Risk sharing. Sharing the risk with another stakeholder in the project,
such as a subcontractor, a partner, or a client.
Risk transfer. Transferring the risk to a third party; for example, by
purchasing insurance that pays any penalties associated with schedule
delays.
Risk buffering. Adding a buffer of extra time to the schedule or a buffer
of management reserve to the budget to protect the project from risk by
absorbing it.
Contingency planning. Preparing plans that will be used as soon as a risk
event occurs, thus reducing the response time and the impact of the risk
on the project.
The problem of how to handle risk is a function of the degree to which it can
undermine performance, the cost to the organization, and the tolerance of the
stakeholders. If the stakeholders are not sensitive to schedule delays, for
example, then all that needs to be done is to identify and monitor the related
risks; if the stakeholders are highly concerned about delays, however, then
staying on schedule should be a top priority.

Risk monitoring and control
Throughout the life cycle of a project, new information is collected, leading
to a better understanding of the hurdles faced by the project team. As new
risks are identified, the probability and impact of existing risks may change,
and the stakeholders may realign their tolerances and expectations. It is
important to continuously monitor existing risks and to identify new risks as
soon as possible to keep the risk management plan updated. Risk
management is a critical component of project management and deserves a
prominent place in the budget.

8.5 Quality Management
The industrial world witnessed a quality revolution brought on by the
Japanese. The introduction of just-in-time philosophy, supported by Kanban
for production and inventory control, continuous process improvement on the
shop floor, and the general goal of zero part and product defects enabled
Japanese firms to capture the bulk of the consumer electronics market, a large
share of the semiconductor market, and a significant proportion of the U.S.
automobile market. This success, which has come in less than two decades,
can be attributed to a knack for squeezing a few more percentage points of
performance out of a system or process after logic and economics indicate
that diminishing returns have long set in, but such an explanation is too glib.
At the heart of Japanese manufacturing is an emphasis on education and
training, a cross-functional workforce, teamwork, and a commitment to
excellence; these are some of the basic components of quality management.
8.5.1 Philosophy and Methods
Quality management is a system that combines quality planning, quality
assurance, and quality control techniques. It is a logical evolution of
management by objectives, strategic planning, quality circles, and many other
systems. The three major components are:
Quality planning: identifying the needs and expectations of stakeholders
and which quality standards are relevant to the project and determining
how to satisfy them.
Quality assurance: all of the planned and systematic activities
implemented to provide confidence that the project will satisfy the needs
and expectations of stakeholders and the quality standards.
Quality control: monitoring of specific project results to determine
whether they comply with requirements and identifying ways to
eliminate causes of unsatisfactory results.

Quality management typically involves one or more of the following
approaches developed by such leaders in the field as W. Edwards Deming,
Joseph Juran, Philip Crosby, and Masaaki Imai. Their message is basically
the same:
Commit to quality improvement throughout the organization.
Attack the process, not the employees.
Strip down the process to find and eliminate problems that diminish
quality.
Identify your customers and satisfy their requirements.
Instill teamwork, and create an atmosphere for innovation and
permanent quality improvement.
The leitmotif is worker enablement and empowerment; that is, train the
workers and give them responsibility. In the project context, the leitmotif is
enablement and empowerment of the IPT.
In the remainder of this section, we highlight the main points made by each
of these pioneers and mention how they can be applied to project
management. Lean Principles, which encompass ideas from these various
quality management approaches, are also summarized below. Lean thinking
represents the most recent evolution in the practice of quality management,
and its principles may be integrated into project management.
Deming approach
Deming, originally a physicist with a Ph.D. from Yale, after many years in
industry came to believe: “Improve quality and you automatically improve
productivity. You capture the market with lower prices and better quality.
You stay in business and you produce jobs. It’s so simple.” In his work, he
stressed statistical process control, statistical quality control, and a 14-point
plan for managers that emphasized the human element. The philosophy is to
treat people as intelligent human beings who want to do a good job. Although

statistical control methods are difficult to implement in a project environment
as a result of the one-of-a-kind nature of projects, the 14 points are readily
adoptable.
Deming was the American who took his message to Japan in 1950 after being
shown the door by most major U.S. corporations. It was a time when U.S.
firms dominated international markets; there was virtually no competition
from abroad, so as long as the product worked, the concern for quality was
minimal. Deming was instrumental in changing this attitude and in turning
Japanese industry into an economic world power. His 14 principles for
achieving competitiveness through quality are as follows (Deming 1986):
1. Create constancy of purpose toward improvement of products and
services; emphasize long-term needs rather than short-term objectives.
This principle should guide management throughout the life cycle of a
project. In early stages of project selection, long-term goals should be
emphasized. The acquisition of new knowledge and the ability to master
new technologies are leading considerations. Furthermore, the specific
configuration selected for a project should support these long-term
objectives. The required constancy can be achieved only by a learning
process that promotes improvement from one project to the next.
Whatever was learned during project execution is as important as the
final results and deliverance. Top management has to facilitate the
diffusion of knowledge throughout the organization and the transfer of
technology between different projects. This calls for an investment in
resources and the development of procedures to support these activities.
2. Adopt a new philosophy. The philosophy that productivity and cost are
the most important performance measures should be modified based on
the recognition that improved quality can reduce cost and improve
productivity while increasing customer satisfaction. Thus, quality is the
most important performance measure. Defects are unacceptable, and
problem-solving tools should be used at all levels of the organization to
eliminate their sources.
3. Cease dependence on mass inspection. Quality should be built into the
product design, the process design, and the support design. The
responsibility for quality should not be with quality control and quality

assurance but rather with all members of the organization regardless of
function and level. Advanced presentation analysis should be used to
support group problem solving so that improved process capability is
maintained.
4. Reduce the number of vendors; don’t select vendors on the basis of cost.
The decision on selecting suppliers and subcontractors should be based
on quality considerations; that is, employ a limited number of high-
quality subcontractors with whom long-term relationships, predicated on
loyalty and trust, can be established. Quality should be the predominant
factor in choosing a supplier or a subcontractor, rather than price tag
alone.
5. Search for problems and improve constantly. Successful project
management is based on good design and good planning, and a dogged
determination to remain on course. Thus, an ongoing effort to identify
and solve problems is the key to smooth implementation. The earlier a
problem is detected, the easier it is to correct. Control systems should be
established with this adage in mind. One way of doing this is with trend
analysis in areas such as cost, schedule, and quality, as discussed in
Chapter 12.
6. Institute on-the-job training to make better use of human resources.
Instituting training in technological and managerial fields supports
continuous improvement in the process. Training employees in new
technologies developed in one project enhances the likelihood of success
for future projects. It is an investment that will pay for itself many times
over. Promulgating the philosophy of built-in quality enables the whole
organization to move in the direction of quality improvement. Training
in managerial techniques used for planning, scheduling, budgeting, and
control is important for the project manager and the project team. Input
from the various team members on all problems that are relevant to their
expertise should be continually sought, and the reasons behind all
decisions should be made explicit.
7. Improve supervision. The role of supervision is leadership aimed at
helping people do a better job. Because recognition and reinforcement
are critical to good job performance, supervisors should be trained to

support this continued improvement process by providing leadership,
example, and training to their teams. Managers should become coaches
rather than feudal lords.
8. Drive out fear. Encourage open communication. Open communication
lines and the ability to report problems without fearing the consequences
are essential to the ongoing improvement process. Employees are the
first to know about problems in their specific areas of responsibility.
Problems can be resolved early in the project life cycle by encouraging
open discussion. Furthermore, employees usually know their part of the
project better than anybody else. By encouraging them to initiate change
in the product, process, or support design when they see fit, a continuous
improvement is likely to take place. Management should institute
an “open door” policy and a “How can I help you in doing your job?”
approach to promote communication and to effectively use its most
important resource—people.
9. Break down barriers and promote communication among the different
organizations that are participating in the project. By eliminating
communication barriers between functional areas, departments, and
subcontractors, CE can be implemented. All of the participants in the
project should be viewed as a team with a common goal. The OBS
should clearly define the formal communication channels within the
team. However, informal communication between the members of the
project should also be encouraged. Each organization that participates in
a project should learn to view the other organizations as its customers
(or suppliers), striving to understand their needs in performing their
tasks. By adopting the customer-provider point of view, integration
between the various elements of the WBS assigned to different
organizations in the OBS greatly increases the likelihood that it will be
smooth and error-free.
10. Eliminate slogans, posters, and targets for the workforce that demand a
new level of productivity without appropriate methods and solutions.
The focus should be on the process as well as the outcome. An effort to
improve the process of design and implementation will result in higher
levels of achievement. Management should help employees develop

better ways of performing their tasks; that is, provide leadership in
problem identification and problem-solving methodologies.
11. Use work standards (quotas) carefully. Work standards that are used in a
project environment can be dangerous, especially when they depend on
environmental factors external to the project. Standards and quotas are
important in the planning process (they can be used for time and cost
estimates as explained in Chapters 9 and 11) but should be used
carefully as a foundation for performance evaluations. When standard
time or cost goals are not achieved, the source of the problem should be
determined. It is rarely a good idea to apply sanctions solely on the basis
of cost or schedule overruns, as the cause may be outside the worker’s
control.
12. Remove barriers that eliminate the worker’s pride of workmanship. The
responsibility of supervisors must be changed from sheer numbers to
quality. Employees should be permitted to evaluate their own work and
to take pride in it. This means the abolishment of annual or merit rating
and of management by objectives. By assigning the responsibility of
better quality to the employees who perform the work, a link is
established between their satisfaction and the improvement process. This
link is necessary to promote improvement in quality.
13. Institute an education and training program to teach workers new skills
as new technologies are developed or assimilated by the organization.
Technologies that are developed or acquired for one project can serve
the whole organization in future projects if proper training regimens are
established. In addition to on-the-job training, a training program should
be instituted to transfer knowledge between different parts of the
organization.
14. Everyone in the organization should team up in the quality improvement
process. This process should not be an isolated effort of the quality
control or quality assurance departments. Everyone in the organization
should be involved in the transformation to quality. Too often, advice
and opinions of low-level staff are either not sought or ignored. Too
many times, managers act as if they know the answer to every problem.
Top management must set the example in implementing a quality

management program by insisting that the basic principles be adopted
by each unit in the organization.
Juran approach
Juran (1998) believes that management must establish top-level plans for
annual improvement and encourage projects as a means to achieve this end.
Juran asserts that poor planning by management results in poor quality. His
approach for improving quality, known as the Juran trilogy, is to (1) plan, (2)
control, and (3) improve. More specifically:
Quality planning. In preparing to meet organizational goals, the end
result should be a process that is capable of meeting those goals under
operating conditions. Quality planning might include identifying internal
and external customers, determining customer needs; developing a
product or service that responds to those needs, establishing goals that
meet the needs of customers and suppliers at a minimum cost, and
proving that the process is capable of meeting quality goals under
operating conditions. A necessary step is for managers to engage cross-
functional teams and openly supply data to team members so that they
may work together with unity of purpose.
Quality control. At the heart of this process is the collection and analysis
of data for the purpose of determining how best to meet project goals
under normal operating conditions. Project management is responsible
for choosing control subjects, units of measurement, standards of
performance, and degrees of conformance. To measure the difference
between the actual performance before and after the process or system
has been modified, the data should be statistically significant and the
processes or system should be in statistical control. Task forces that
work on various problems need to establish baseline data so that they
can determine whether the implemented recommendations are
responsible for the observed improvements.
Quality improvement. This process is concerned with breaking through
to a new level of performance. The end result is that the particular

process or system is obviously at a higher level of quality in delivering
either a product or a service.
Juran’s approach, like those of his colleagues, stresses the involvement of
employees in all phases of a project. The philosophy and procedures require
that managers listen to employees and help them rank the processes and
systems that require improvement. This can be done with the help of any of
the techniques described in Chapters 5 and 6.
Crosby approach
Crosby’s philosophy enforces the belief that quality is a universal goal and
that management must provide the leadership to compel an enterprise in
which quality is never compromised. Crosby defines quality as conformance
to requirements and asserts that the mechanism for attaining quality is
prevention. He encourages a performance standard of zero defects. He
believes that managers should be facilitators.
Like Deming, Crosby (1984) has 14 steps for quality improvement. They are:
1. Management commitment
2. Quality improvement teams
3. Measurement
4. Cost of quality
5. Quality awareness
6. Corrective action
7. Zero-defect planning
8. Employee education
9. Zero-defect day

10. Goal setting
11. Error-cause removal
12. Recognition
13. Quality councils
14. Doing it over again
Imai approach
Imai (1986) supports the continuous improvement process, whereby people
are encouraged to focus on the environment in which they work rather than
on the results. He believes that by continually improving processes and
systems, the end result will be a better product or service. This has become
known as the “P,” or process approach, rather than the “R,” or results
approach of Frederick Taylor, a pioneer in work measurement and the father
of industrial management. The process approach is also known as the Kaizen
approach.
In the “R” approach, management examines the anticipated result(s), usually
specified by a management-by-objectives plan, and then rates the
performance of the individual(s). A person’s performance is influenced by
reward and punishment; that is, the use of “carrot and stick” motivation. In
the “P” approach, management supports individual and team efforts to
improve the processes and systems that lead to the end result.
The effects of the continuous improvement, or Kaizen approach, can be
elusive because they are long term and often undramatic. Change is gradual
and consistent. The approach involves everyone, with the group effort
focused on processes and systems rather than on one person’s performance
evaluation. Although the monetary investment is low, a great deal of
management support is required to maintain the momentum of the group. The
Kaizen approach is people oriented.

Lean Approach:
Lean project management, often referred to as “Lean,” is a Quality
Management tool that focuses on elimination of waste. A Lean project creates
value while minimizing waste and is based on the following six Lean
Principles.
1. Principle 1: Value: A Lean project captures value, as defined by internal
and/or external customer stakeholders. A project’s value proposition
(i.e., key milestones and deliverables) may change over the lifetime of a
project, so a Lean project will update its requirements in an effective and
efficient manner. A Lean project must delicately balance between
frequent changes in requirements which are costly versus insufficient
changes in requirements which could lead to a project becoming
obsolete before it is delivered.
2. Principle 2: Value Stream: A Lean project will create a map or flow
diagram of all linked tasks and control/decision points that are needed to
execute a project and create customer value. The mapping process
serves to identify and eliminate non-value tasks, minimize all required—
but non-value—tasks, and enable the value-added activities to efficiently
proceed. A flow diagram illustrates how material and/or information is
created, transformed, and moved from task-to-task, adding value at each
step. Lean project management utilizes shared databases, rapid and
pervasive communication among team members, and frequent
integrative activities in order to achieve real-time issue resolution and
decision making.
3. Principle 3: Flow: A Lean project carefully plans and streamlines value-
added tasks and processes, minimizing unplanned rework and idle
resource time. This principle can be difficult to execute in practice. For
example, Toyota required several decades of practicing Lean in order to
perfect its execution. Lean encourages a “fail early—fail often”
philosophy in early design phases of a project, as it seeks to promote
discovery and innovation. However, once an approach for a project is
chosen, Lean seeks to efficiently drive it forward.

4. Principle 4: Pull: A Lean project enables customer stakeholders to “pull”
or drive value. That is, the inclusion of any task in a project plan should
be associated with a specific need or request from a customer.
Furthermore, the completion time of any task should be synchronized
with customer needs. Customer requests and change orders should be
managed and controlled to ensure a Lean outcome.
5. Principle 5: Perfection: A Lean project pursues perfection in all
processes related to the project. A project’s final deliverables are
constrained by budget, schedule, and project scope. A project
management team, in consultation with subject matter experts, is
responsible for deciding when an output is “good enough.” In contrast,
processes should be continuously improved and perfected. Lean
prioritizes process improvements by making process imperfections
visible and seeking to eliminate activities that represent the biggest
impediments to smooth process flow. By identifying and communicating
problems in project flow early in a project life cycle, an organization
increases its ability to fix imperfections in a minimum-cost fashion.
Otherwise, if problems are unnoticed and allowed to fester, they will
grow to crisis proportions and require expensive corrective actions.
6. Principle 6: Respect for People: A Lean project prioritizes people
management and respect for people involved in the project. A Lean
project emphasizes open communication and encourages workers to
proactively identify gaps and corrective actions. A Lean project
empowers workers to solve problems on the spot with minimal/no
bureaucratic involvement. This Principle is often seen as the most
significant of the six Lean principles.
Lean project management emphasizes identification and elimination of waste.
In general, all work activities may be classified into one of the following
three categories:
Value-added activity
Required non-value-added activity
Non-value activity

A value-added activity satisfies one of the following three conditions:
Transforms (i.e., enhances) material or information or reduces
uncertainty
A customer must be willing to pay for the activity
The activity is done right the first time (excludes “legitimate” trial and
error experimentation)
A required non-value added activity does not meet the previous
definition of a value-added activity. However, it cannot be eliminated
since it is required by law or contract.
A non-value added activity consumes resources and creates no value, for
example, unneeded reports and communications, idle or delay time,
defects that require rework.
The Lean philosophy strives to make work programs simple enough to
understand, execute, and manage. Lean principles have been
successfully applied in a broad range of industries across the
manufacturing and service sectors.
The first Lean Principle, Value, may be maximized and enabled by deploying
the following tactics:
Establish the value and benefit of the project to stakeholders—for
example, an organization formally documents specific benefits that each
major stakeholder receives from a particular project, enabling each
stakeholder to better define its needs prior to embarking on detailed
design and development.
Focus all project tasks on project benefits and deliverables—for
example, each task in a project plan must be specifically linked to a
clearly defined project benefit.
Frequently engage stakeholders throughout a project’s life cycle—for
example, a project management office will establish regular, periodic

meetings with various stakeholder groups (e.g., weekly project steering
committee meetings, bi-weekly governance meetings that review
policies and practice for compliance purposes, and monthly progress
updates for senior management).
With customer stakeholder input, develop high-quality project
requirements before issuing requests for proposals (RFPs)—for
example, an organization may initially procure certain parts or
equipment that are needed for a project; subsequent RFPs require that
the bidding contractors propose designs that are compatible with the
earlier-purchased parts and equipment.
Clarify, derive and prioritize project requirements, early and often in a
project life cycle—a “tight” project scope definition and meticulous
management of changes are critical to keeping changes in requirement to
an absolute minimum and managing a project’s budget.
Actively minimize the bureaucratic, regulatory, and compliance burden
on the project team—for example, an organization may train the project
management office and key project participants in inter-personal skills
as workflow management for working teams, e-mail management,
meeting management, and people development.
The second Lean Principle, Value Stream, may be optimized and enabled by
deploying the following tactics:
Map the project work streams and eliminate non-value added activities
—for example, each work stream may be broken into key tasks,
identifying ownership, due dates, and major hand-offs from task to task.
Comprehensively architect and manage a project so that its performance
as a system is optimized—a project may choose to subcontract with a
systems integrator that is responsible for providing capabilities—rather
than subcontracting for individual components or units with different
subcontractors.
Pursue multiple solution approaches in parallel—in order to minimize
risk and consider a broad set of alternatives, a project may choose to

evaluate multiple, detailed designs simultaneously.
Early detection of issues—if a potential barrier is identified in the early
stages of a project, the cost of developing a contingency plan is reduced.
Use probabilistic estimates in project planning—a computer simulation
model may forecast the operational efficiency of deploying different
asset mixes under various scenarios; a simulation model takes a variety
of factors into account including historical data, upon which probability
estimates are based, and subject matter experts’ opinions.
Proactively coordinate with suppliers to avoid conflict and mitigate
project risk—a project manager can require certain standardization
across deliverables provided by different contractors, thereby mitigating
risk in a project’s system integration phase; as system integration
typically occurs in the later stages of a project, rework at that point tends
to be costly and time-consuming.
Develop leading indicators and key metrics to manage a project—a
project management office may develop a set of critical success factors
related to a project’s budget, schedule, and quality of deliverables that
are continuously tracked and communicated via a dashboard in order to
visibly track project performance.
Develop an integrated and progressively detailed master project
schedule—a project may concurrently track each of its major work
streams, updating each work stream with additional, detailed
information as it progresses.
Manage technology readiness—in order to mitigate risk, a project
manager may freeze the process design so that new technologies may be
implemented with minimal disruption.
Develop a communication plan—in addition to developing internal
communication protocols, a project manager may also develop a media
relations plan, a crisis communication plan, and a comprehensive
community/public relation plan.

The third Lean Principle, Flow, may be created and enabled with the
following tactics:
Use of systems engineering methodologies to coordinate and integrate
project activities—a project management office can assume complete
responsibility for overseeing development of a full system capability
(i.e., no coordination required among subcontractors); internal
management of all day-to-day project functions requires that the project
management office be staffed by appropriate personnel that are subject
matter experts in the underlying technology as well as having project
management skill sets.
Ensure clear project responsibility, accountability, and authority
throughout all phases of a project—a project manager can document and
communicate a visible staffing matrix that tracks responsibilities of all
resources assigned to a project.
Deploy a project manager to lead and integrate a project from start to
finish—project performance has shown to be more successful if the
project manager is involved in development of the original project
proposals.
An effective project management office—the project management office
should be staffed by personnel who are experienced with working on
inter-disciplinary project teams and have been trained in project
management.
Develop a collaborative and inclusive decision making process to
resolve root causes of issues—an organization may adapt a structure to
foster collaboration and accelerate decision making; an effective
organization structure facilitates sharing of relevant information in a
timely fashion.
Maintain a project governance function to oversee integration of project
elements and functions—a project oversight (or governance) committee
may be established to oversee project planning and project management
as well as the system integration process.

Efficient and effective communication and coordination within the
project team—a project manager may convene “skip-level” meetings
which enable members of the working team (e.g., engineers) to
communicate directly with senior leadership, enabling streamlined and
transparent communication across the organization.
Standardize key elements across a project to increase efficiency and
facilitate collaboration—a project manager may document standard
workflows and procedures for all project activities.
Use Lean thinking to facilitate smooth, integrated project flow—certain
tasks on a project may be highly interdependent; in these cases, it is
helpful to have frequent integrated project team meetings where team
members from the different, but integrated, work streams may engage
one another.
Provide visibility to project progress—as project activities progress and
milestones are achieved, the project management office can publicize
project accomplishment to all members of the broader organization, for
example, using large wall posters, status updates and key project metrics
can be visibly tracked.
The fourth Lean Principle, Pull, may be facilitated and enabled with the
following tactics:
Include tasks and deliverables into a project plan, based on customer and
stakeholder needs only—interviews with customers and stakeholders
can reveal specific needs; the project design can tightly link activities to
these needs, rejecting all other potential system features as not needed
and non-value added (i.e., waste).
Establish effective contracting strategies that support the project in
achieving the required and planned benefits—a project may incentivize
contractors to propose innovative ideas to reduce costs by sharing any
savings with the contractor for ideas that are successfully implemented.
The fifth Lean Principle, Perfection, may be pursued and enabled with the
following tactics:

Implement effective project management methods and standards—a
project management office may compile a manual of Best Practices to
serve as a reference for all projects in an organization.
Deploy Lean practices for the long term—an organization can establish
training courses in Lean practices for corporate employees and key,
external sub-contractors.
Strive for excellence in project management and systems engineering—
an organization may utilize pilot or smaller projects to test project
management techniques; if a technique proves to be successful in a pilot,
then it may roll out to other projects on a broader scale.
Continuous improvement through applying lessons learned from
existing projects to future projects—a project management office may
be responsible for collecting and disseminating lessons learned from
each project in an organization; some organizations have found that
keeping a project management team intact from project-to-project will
increase the likelihood of improved project management.
Effective use of change management to continually align a project with
unexpected changes—a project management team must establish a
formal change control process including: each request is submitted in
writing to a centralized management team, each request is formally
evaluated, and a decision on each request is communicated.
Proactively manage uncertainty and associated risk factors—a
comprehensive risk management plan requires subject matter experts
and the project management team to identify all possible risks associated
with each project activity; each risk should be evaluated for its potential
impact and mitigation strategies should be prepared.
Strive for seamless communication, coordination, and collaboration
across all project sub-teams—a project management office can establish
and maintain guidelines on communication protocols; for example,
project team members may be limited to one response per e-mail—if
additional communication is required, a personal meeting would need to
occur.

Promote continuous improvement by encouraging creativity from all
stakeholders and project team members—an organization can create a
culture that rewards employees for making suggestions to improve the
business; establishment of such a culture requires management to
engage employees and directly respond to and take action on incoming
suggestions.
The sixth Lean Principle, Respect for People, may be operationalized and
enabled with the following tactics:
Create a culture based on respect for people—project reviews can
include reports on people development and recognition; each employee
should have a development plan to gain required skill sets for current
and future projects.
Motivate people by transparently communicating project objectives—if
all team members buy into the strategic purpose of a project, the
likelihood of successfully completing the project increases.
Support an autonomous working style—if team members are given
certain flexibilities in satisfying requirements, the final project output
may be more creative and innovative compared with deliverables of a
project that is micro-managed with rigid guidelines.
Support professional development and growth of employees—an
organization can establish training and mentoring programs that foster
career growth by providing employees with new skills and professional
guidance, respectively.
Promote a culture of “learning”—an organization can provide “on the
job” training and mentoring to junior employees by having them work
alongside experienced personnel on a project, thereby providing for the
transfer of knowledge.
Encourage personal networks and engagement—a project manager can
organize informal, off-site team meetings and celebrations that promote
interaction among team members and increase team bonding.

8.5.2 Importance of Quality in
Design
The likelihood of achieving superior quality in both production and process is
increased when product design, process design, and support design are
integrated. A major goal of quality management is defect prevention. To
achieve this end, design should be started only after requirements are clearly
understood. Product, process, and support design should be integrated so that
manufacturing technology is compatible with product complexity, and all
training requirements are identified and performed before the production
phase. The configuration management system provides the project manager
with updated configuration and engineering information needed as references
for quality control. When applying quality management, the detection of a
defect not only is a trigger for rework but also initiates a study aimed at
eliminating future defects; that is, a study of the process and product design,
as well as the processes and methods used in manufacturing that might be the
source of the problem. Quality management tries to eliminate the source of
defects so that defect detection and rework do not become the normal mode
of operation.
When a project consists of building several identical units in series like
apartments in a construction project, product trend analysis is used to avoid
repeating mistakes. This is done by monitoring the performance of
consecutive units and studying related trends. When the trend is toward
higher performance as a result of learning, no special action is required. If,
however, deterioration (or simply no improvement) in performance is
observed, then the source of such a trend should be identified and corrective
action taken.
The integration of quality management with CE and CM greatly facilitates
the design of quality into new products and their manufacturing processes.
This minimizes dependence on inspection and the need for costly rework.
8.5.3 Quality Planning

Quality planning is based on the philosophy that quality should be designed
into the product and process and that defects should be avoided at almost all
cost. Defect detection by itself is expensive and prone to error. Once a defect
is created, it might be a nightmare to find and remove it. In electronic
assembly, for example, an often-cited rule of thumb is that the cost of finding
a defective component goes up by a factor of 10 for each level of assembly:
device, board, system, and field installation. Furthermore, even if the defect is
found, correction is not only expensive and time-consuming but also is likely
to reduce the quality of the product. A reworked part will often not measure
up to the same standards of one manufactured properly the first time.
8.5.4 Quality Assurance
There are many definitions of quality, such as “meeting or exceeding
customer requirements” or “fitness for use,” but these can be vague and
difficult to quantify in the conceptual design phase of a system. However,
even if this problem is remedied, there is another problem that catches most
people unaware—the lack of planning. To rephrase a point made above,
quality cannot be added to a system upon completion; it must be built in.
The vehicle for doing this is the quality assurance (QA) plan. This is a
before-the-fact document that states the rules that will be followed during
project execution. Of course, whenever there is a plan, there must be a way to
verify that it is being carried out correctly. This is the function of the QA
review, which is an after- the-fact checkpoint. The QA plan and the QA
review provide a means for close monitoring of a project, in terms of both
meeting requirements and conformance to standards.
The underlying rationale and expected benefits are outlined below:
1. A plan is needed to enforce discipline on a project. For example, there
may be an implicit requirement to conduct a walk-through on test plans.
If the project falls behind schedule, the temptation may be great to skip
over this step and lose the benefit of peer criticism on the testing
procedures.

2. A plan is a statement of procedures. It describes how quality will be
examined and measured. If prototypes are subjected to quality
inspection, for example, then the people involved should know
beforehand what is going to be examined and measured.
3. A plan states the amount of time and money required. Thus, quality is
less susceptible to cuts when it is planned for explicitly. It becomes a
stated requirement of the system being developed for which resources
must be allocated.
4. A QA plan tends to generate uniform quality. Differing levels of
experience, ability, style of work, and even attitude can cause variations
in quality levels within the same company or department. If quality
plans are mandatory and are produced according to standard guidelines,
variations in quality should diminish.
5. Finally, quality plans encourage attention to standards. The problem is
not that standards do not exist but simply that they frequently fall into
disuse. If at the start of a project, team members were informed of the
relevant standards, then the chances of the standards being applied
correctly would increase.
Plans must be tailored. No single quality plan will suit all project
environments and circumstances. The IEEE Standard for Software Quality
Assurance Plans is a comprehensive document that can provide guidance for
software development. For two-party contractual arrangements, the
International Standards Organization (ISO) has propounded a series of
standards known as ISO 9000. Coverage includes the selection and use of
equipment, the development, installation, and servicing of facilities, final
inspection and test, and general management responsibilities. ISO 9000
certification is critical for companies that wish to compete in the European
marketplace. Additional references for military system standards are given in
the reference list at the end of the chapter.
A customized QA plan can be developed easily by asking some fundamental
“what–who–how” questions: What has to be accomplished? Who has the
responsibility? How are the tasks to be done? Consolidating answers to these
questions into a concise, practical format yields a simple yet effective plan

for ensuring a smooth, problem-free transition from one phase of a project to
the next. A common format is the QA matrix, which arrays standards for each
task against the three headings “what,” “who,” and “how.”It is similar to the
linear responsibility chart discussed in Chapter 7.
Generally, the QA matrix is developed by the QA team if one exists or,
alternatively, by the project manager. Regardless of who prepares it,
agreement must be obtained from all parties mentioned before work begins.
Consensus carries with it a number of automatic benefits:
It verifies responsibility and identifies the type of involvement for each
of the participants.
It presents a complete picture of responsibility, such that one party can
see the involvement of other responsible parties.
It is a forewarning of required standards knowledge.
It is an explicit sign of acceptance of shared responsibility for
deliverable quality.
A QA matrix can be developed at several different levels: deliverables,
project phase, or the complete system life cycle. In each case, the elements
remain the same.
8.5.5 Quality Control
Quality control is based on the collection and analysis of data for the purpose
of determining whether project results comply with the selected quality
standards. Quality control should be performed throughout the project life
cycle to detect problems as early as possible and to eliminate them.
Every step in the project, including design and execution, should be subject
to quality control. During the design phase, peer evaluation in the form of
design reviews and laboratory and field tests are used to detect faulty design.
In the implementation phase, acceptance testing of parts, modules, and
complete systems serves as a major building block of quality control. In

software development, for instance, quality control starts with unit testing,
whereby each program or module in a program is tested. Integration testing is
the next step whenever two or more modules are integrated. The final step is
to verify that the entire system performs as it should. Although the cost of
testing is high, the alternative—releasing a defective product to the market—
is more expensive.
8.5.6 Cost of Quality
The modeling and analysis of tradeoffs are at the very foundation of decision
making. For example, in manufacturing planning, tradeoff involves quality
and cost.
The traditional view holds that approaches to zero-defect operations are too
expensive for manufacturers of most products to attempt to reach. When the
manufacturer must work to such high quality standards, production costs
require that the price of the finished product is outrageously high. As a
consequence, a balance is struck between cost and quality, as shown in
Figure 8.3. This frequently leads to the distribution of goods and services that
fail to meet customer expectations.
Figure 8.3

Relationship between quality and cost.
Figure 8.3 Full Alternative Text
What drives the tradeoff according to this traditional view? Vaughn (1990)
explained that efforts to reduce rework, repairs, warranty costs, and liability
losses generate increasing costs associated with the time, materials,
engineering, and overhead required. From an economic perspective, the point
at which the marginal cost of improving quality by one unit equals the
marginal loss as a result of poor quality is the point at which the optimum is
achieved. Juran, the quality guru mentioned previously who has long been at
the forefront of the quality improvement movement, encouraged this kind of
thinking. In his optimization model, he showed how failure costs decline until
they are overtaken by the increasing costs of appraisal and prevention. At this
point, total quality costs begin to rise.
Why, then, have the Japanese been so successful in increasing quality while
simultaneously bringing down production costs? As Cole (1992) asked,
“Have they abolished the laws of economics?” Hardly, but they have made us
realize that the point at which total quality costs start to rise again as failure
costs are driven down has shifted sharply to the right in Figure 8.3.
Moreover, continuous improvement makes perfect economic sense as long as
the search follows a minimum cost path. In terms of organizational dynamics,
it has long been observed that quality achievements tend to regress over time.
Thus, to press for continuous improvement helps ensure that no ground is
lost.
According to Cole, the Japanese achievement was based on six guiding
principles. First, Japanese managers realized that the traditional calculations
dramatically underestimated the costs of poor quality. Typically, such
calculations ignored the customers who were lost or who had never bought
the product. A declining reputation among customers and the effects of
negative word-of-mouth publicity were never considered, partly because they
were difficult to quantify. There is every reason to believe that these effects
are substantial. Japanese managers recognize these costs. They stress the
fragility of their reputations with their customers and the importance of
winning the customer’s trust. They find it entirely appropriate to spend
generously for this purpose. In short, once one recognizes the high costs

associated with poor quality, one sees that it is economically rational to invest
more in quality improvement.
Second, the traditional approach vastly underestimated the payback that a
corporate-wide quality improvement culture yields in terms of worker
motivation and a broad array of performance indicators. A 1991 study
undertaken by the U.S. General Accounting Office (GAO) of the 1988 and
1989 Baldrige Award finalists revealed that companies that adopted total
quality management (TQM) practices achieved better employee relations,
higher productivity, greater customer satisfaction, increased market share,
and improved profitability. The GAO calculated that, on average, these
measures increased 4.5% per year from the mid-1980s on. Other measures,
related to employee turnover, product reliability, number of employee
suggestions, on-time delivery, order processing time, number of defects,
production lead times, customer complaints, and inventory turnover,
improved at even greater rates. Although these findings are still preliminary
(no control groups were included, and some data were missing), they are
suggestive of the broad impact that a quality initiative can have.
Third, the Japanese pursuit of quality is accompanied by intense pressure to
minimize the attending costs. In the United States, some of the quality zealots
have made the mistake of separating the two and regarding support for any
quality initiative as a kind of litmus test of enlightened management. This
misses the point altogether and substitutes an unthinking repetition of the
quality mantra for real understanding. The widespread mobilization of
production workers, equipped with elementary but powerful statistical
problem-solving methods to improve quality, is a concrete illustration of this
low-cost approach. Such employees are a lot less expensive than design
engineers, and there are a lot more of them to work on an endless supply of
problems. The incremental costs of their involvement in quality improvement
are extremely modest, as found in a study by Schneiderman (1986).
Fourth, preventing problems at the source has become the preferred approach
to improving quality. The Japanese gradually recognized that the costs of
poor quality could be reduced more effectively by moving their efforts
“upstream.” In practice, this means concentrating on the process of new
product development. This approach dramatically reduces appraisal costs and

has the beneficial side effect of eliminating white-collar rework (e.g.,
downstream engineering changes).
Fifth, Japanese managers came to see quality improvement not as a matter of
adding product attributes (which inevitably add costs) but as a matter of
improving the quality of all business processes. By doing things right the first
time, massive amounts of rework could be avoided and costs could actually
be reduced. Those who are involved in the business processes were trained
and given responsibility for improving them. Proceeding along these lines,
Japanese firms actually eliminated a good deal of the traditional quality-cost
tradeoff.
Finally, the traditional tradeoff model assumed that what the customer
wanted in quality and was willing to pay for did not change over time. In fact,
what Japanese manufacturers discovered was that by achieving the highest
quality standards, they could charge a premium for their products, and
thereby educate consumers to demand higher and higher quality. As the
company that changed customers’ tastes, they were then in a unique market
position to satisfy those new tastes. This in turn could be translated into
higher prices or a greater share of the market.
Cole’s analysis has a resonance that is being felt by all players in the global
marketplace. New attitudes toward quality improvement and the results that
have been achieved have made the traditional quality-cost tradeoff model
obsolete. The Japanese have not abolished the economics of quality, but they
have changed the way we approach, conceive of, and measure the relevant
variables.
TEAM PROJECT Thermal
Transfer Plant
The approved rotary combustor project is now in the detailed design phase.
Recently, the chief operating officer (COO) at Total Manufacturing Solutions
(TMS) was exposed to the following three concepts: time-based competition,
TQM, and configuration management. Because a task force under his

supervision is now examining the potential benefits and risks of the rotary
combustor project, you have been asked to explain how these three concepts
will be implemented in the project’s production phase to maximize its
probability of success.
Specifically, the COO would like to know how the configuration will be
managed, what level of CE will be implemented, and which aspects of and by
what means TQM will be designed into the project. Your analysis is part of
the detailed design phase, and the COO is expecting a thoroughly
documented report that at least answers the following questions:
1. Which forms will be used for change requests?
2. Who should sit on the CCB?
3. How can continuous improvement be encouraged? Be specific.
Submit a detailed report that can be implemented within TMS’s current
organizational structure. Discuss the costs and benefits of introducing each of
these ideas into the project on the basis of your assumptions and analysis.

Discussion Questions
1. What are the design aspects of writing a term paper?
2. Describe a design process with which you are familiar that is performed
sequentially. Explain how CE could be implemented (if possible) in this
case. If, in your opinion, CE is not possible, explain why.
3. Give an example in which CE cannot be applied.
4. What is the difference between the communication needs in sequential
engineering and CE?
5. What is the relationship between CM and quality?
6. In what ways does CE affect quality?
7. What are the similarities and differences between Deming’s 14 points
and Crosby’s 14 points?
8. The Kaizen approach of Imai stresses gradual, long-term improvement.
In what situation or under what conditions might this approach not work
very well? Under what conditions is this approach best?
9. Contrast Juran’s approach with the Kaizen approach. Identify situations
in which either would be more or less appropriate.
10. Is it possible to implement the idea of training workers and giving them
responsibility for improving the processes with which they are involved
in a project environment?
11. Henry Ford invented mass production. In doing so, he perfected the
assembly line concept in which each worker does only one job or a
handful of jobs and is given little other responsibility. This worked well
for 70 years; however, it became apparent in the 1990s that an
increasing number of U.S. companies could not produce a high-quality

product by sticking to the assembly line model. What has changed?
12. U.S. manufacturers spend approximately 80% of their R&D budgets on
new technology, whereas their Japanese counterparts spend
approximately 80% on process improvement. What do you think have
been the positive and negative impacts of these allocations on product
quality? What, in your opinion, is the best division of the R&D budget?
Your answer should be industry specific.
13. Discuss the problem of assigning weights and estimating correlations in
QFD. Suggest a way to solve this problem.
14. Discuss the risks involved in the project “buying a used car.” Develop a
risk management plan for this project.
15. One of the requirements for graduation in engineering is the successful
completion of a design project. Discuss the criteria and the logic that a
student should use in selecting a project.
16. What are the risks associated with the project in Question 15?

Exercises
1. 8.1 Prepare a risk management plan for the project of finding a job after
graduation.
2. 8.2 Select a project with which you are familiar and explain the most
important factors that affect the configuration selection decisions of this
project.
3. 8.3 Prepare a configuration identification system for the project you
have selected in Exercise 8.2 .
4. 8.4 Prepare a form for a configuration change request for the project
selected in Exercise 8.2 .
5. 8.5 Write a job description for the configuration manager of a project.
6. 8.6 Develop a flow diagram for the data handling and data processing
required for CM, including
1. definition of files
2. sources of data
3. data processing requirement
4. required output
7. 8.7 Assume that you are an instructor in either an engineering or a
business college. Interpret the meaning of and indicate how you would
apply each of Deming’s 14 points to a typical class that you teach.
8. 8.8 Do the same as in Exercise 8.7 for Crosby’s 14 points.
9. 8.9 Quality principles are only now being adopted by universities.
Develop a plan for the administration in your college for implementing

Juran’s approach.
10. 8.10 Do the same as in Exercise 8.9 for the chairman of an academic
department.
11. 8.11 Develop a reward system for motivating IPT members to do their
jobs more conscientiously and to take on more responsibility.
12. 8.12 How would the reward system developed in Exercise 8.11 be
different for (a) matrix organization and (b) project organization?
13. 8.13 Use QFD to analyze the project “developing a new course in
project management.”
14. 8.14 List the major risks of a military operation such as the United
States’ effort to oust Saddam Hussein from power in Iraq in 2003.
Outline a risk management plan for such projects.
15. 8.15 Explain the relationship among time-based competition, cost-based
competition, and CE.
16. 8.16 Explain why configuration management is needed when concurrent
engineering is used.
17. 8.17 List the pros and cons of CE.

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Chapter 9 Project Scheduling
9.1 Introduction
Project scheduling deals with the establishment of timetables and dates
during which various resources, such as equipment and personnel, will be
used to perform the activities required to complete a project. Schedules are
the cornerstone of the planning and control system, and, because of their
importance, are often written into the contract by the customer. For some
projects, achievement of a scheduling milestone is a paramount objective and
is not negotiable. For example, preparation of the halftime show at the Super
Bowl must be completed on time, even if on-time completion results in
budget overruns.
The scheduling activity integrates information on several aspects of the
project, including the estimated duration of activities, the technological
precedence relations among activities, constraints imposed by the availability
of resources, the budget, and if applicable, due-date requirements. This
information is processed into an acceptable schedule with the help of a
decision support system that may include network models, a resource
database, cost-estimating relationships, and options for accelerating
performance. The aim is to answer the following questions:
1. If each activity goes according to plan, then when will the project be
completed?
2. Which tasks are most critical to ensure the timely completion of the
project?
3. Which tasks can be delayed, if necessary, without delaying project
completion, and by how much?
4. More specifically, at what times should each activity begin and end?

5. At any given time during the project, how much money should have
been spent?
6. Is it worthwhile to incur extra costs to accelerate some of the activities?
If so, then which ones?
The first four questions relate to time, which is the chief concern of this
chapter; the last two deal with the possibility of trading off time for money
and are taken up in Chapter 11.
The schedule itself can be presented in several ways, such as a timetable or a
Gantt chart, which is essentially a bar chart that shows the relationship of
activities over time. Different schedules can be prepared for the various
participants in the project. A functional manager may be interested in a
schedule of tasks performed by members of his or her group. The project
manager may need a detailed schedule for each work breakdown structure
(WBS) element and a master schedule for the entire project. The vice
president of finance may need a combined schedule for all projects that are
under way in the organization to plan cash flows and capital requirements.
Each person involved in the project may need a schedule with all of the
activities in which he or she is involved.
Schedules provide an essential communications and coordination link
between the individuals and organizations that are participating in the project.
They facilitate the coordination of effort among people coming from different
organizations and working on different elements of the WBS in different
locations at different times. By developing a schedule, the project manager is
planning the project. By authorizing work according to the scheduled start of
each task, he or she triggers execution of the project; and by comparing the
actual execution dates of tasks with the scheduled dates, he or she monitors
the project. When actual performance deviates from the plan to such an
extent that corrective action must be taken, the project manager is exercising
control.
Although schedules come in many forms and levels of detail, they all should
relate to the master program schedule, which gives a time-phased picture of
the principal activities and highlights the major milestones associated with
the project. For large programs, a modular approach that reduces the

prospects of getting bogged down in the excess detail that necessarily
accompanies work assignments is recommended. To implement this
approach, the schedule should be partitioned according to its functions and/or
phases and then disaggregated to reflect the various work packages (WPs).
For example, consider the WBS shown in Figure 9.1 for the development of a
microcomputer. One possible modular array of project schedules is depicted
in Figure 9.2. The details of each module would have to be worked out by the
individual project leaders and then integrated by the project manager to gain
the full perspective.
Schedules are working tools for program planning, evaluation, and control.
They are developed over many iterations with project team members and
with continuing feedback from the client. The reality of changing
circumstances requires that they remain dynamic throughout the project life
cycle. Every project has unique management requirements. When preparing
the schedule, it is important that the dates and time allotments for the WPs be
in precise agreement with those set forth in the master schedule. These times
are control points for the project manager. It is his or her responsibility to
insist on and maintain consistency, but the actual scheduling of tasks and
WPs is usually done by those who are responsible for their accomplishment
—after the project manager has approved the due dates. This procedure
assures that the final schedule reflects the interdependencies among all of the
tasks and participating units and that it is consistent with available resources
and upper management expectations.
It is worth noting that the most comprehensive schedule is not necessarily
best in all situations. In fact, too much detail can impede communications and
divert attention

Figure 9.1
WBS for a microcomputer.
Figure 9.1 Full Alternative Text

Figure 9.2
Modular array of project schedules.
from critical activities. Nevertheless, the quality of a schedule has a major
impact on the success of the project and frequently affects other projects that
compete for the same resources.
9.1.1 Key Milestones
A place to begin the development of any schedule is to define the major
milestones for the work to be accomplished. For ease of viewing, it is often
convenient to array this information on a time line depicting events and their
due dates. Once agreed on, the resultant milestone chart becomes the skeleton
for the master schedule and its disaggregated components.
A key milestone is defined as an important event in the project life cycle.
Ideally, the completion of a milestone should be easily verifiable, but in
practice, this may not be the case. Key milestones should be defined for all
major phases of the project before startup. Care must be taken to arrive at an
appropriate level of detail. If the milestones are spread too far apart,
continuity problems in tracking and control can arise. Conversely, too many
milestones can result in unnecessary busywork, micromanagement,
confusion, and increased overhead costs. As a guideline for long-term
projects, four key milestones per year seem to be sufficient for tracking
without overburdening the system.
The project office, in close cooperation with the customer and the
participating organizations, typically has the responsibility for defining key
milestones. Selecting the right type and number is critical. Every key
milestone should represent a checkpoint for a collection of activities at the
completion of a major project phase. Some examples with well-defined
boundaries include:
Project kickoff

Requirements analysis complete
Preliminary design review
Critical design review
Prototype fabricated
Integration and testing completed
Quality assurance review
Start volume production
Marketing program defined
First shipment
User-acceptance test complete
9.1.2 Network Techniques
Project scheduling can be approached with a network diagram that
graphically portrays the relationships between tasks and milestones in the
project. Several techniques evolved in the late 1950s for organizing and
representing this basic information. Best known today are the program
evaluation and review technique (PERT) and the critical path method (CPM).
PERT was developed by Booz, Allen & Hamilton in conjunction with the
U.S. Navy in 1958 as a tool for coordinating the activities of more than
11,000 contractors involved with the Polaris missile program. CPM was the
result of a joint effort by DuPont and the UNIVAC division of Remington
Rand to develop a procedure for scheduling maintenance shutdowns in
chemical processing plants. Interestingly, both methods were developed by
applied organizations, that is, industrial and government research
laboratories, rather than by academia. Industry and government’s leading role
in developing PERT and CPM reflects the applied nature of the project
management discipline.

The major difference between the PERT and CPM is that CPM assumes
activity times are deterministic, whereas PERT views activity times as being
stochastic. For example, PERT assumes that a task’s duration time is a
random variable that can be characterized by an optimistic, a pessimistic, and
a most likely estimate. Over the years, a host of variants has arisen, to address
specific aspects of the planning, tracking, and control problems, such as
budget fluctuations, complex intertask dependencies, and the multitude of
uncertainties found in the research and development (R&D) environment.
Nevertheless, PERT/CPM represents the standard for project management
techniques in practice.
PERT/CPM is based on a diagram that represents an entire project as a
network of arrows and nodes. The two most popular approaches are either to
place the activities on the arrows (AOA) and have the nodes signify
milestones (i.e., start and end of particular activities) or to place activities on
the nodes (AON) and let the arrows represent precedence relations among
activities. A precedence relation states, for example, that activity X must be
completed before activity Y can begin, or that X and Y must end at the same
time. It allows tasks that must precede or follow other tasks to be clearly
identified, in time as well as in function.
Precedence constraints arise due to fundamental or technological aspects
involved between multiple tasks of a project. For example, in home
construction, a foundation for a new home must be set before indoor work,
such as plumbing, can begin. In airline scheduling, a leg is characterized by
origin and destination airports and departure and arrival times. A leg can only
be flown if both the necessary crew and aircraft are in the origin location at
the planned departure time.
A project manager is responsible for verifying precedence constraints. In
practice, a particular resource or function may deliberately or unintentionally
state precedence constraints. For example, an IT group may claim that a
certain report or analysis cannot be run until a larger server is purchased. The
project manager must interrogate the key parties and determine whether such
a claim is true. In many cases, an effective project manager is one who is able
to “break” precedence constraints through innovative ideas and workarounds.
Removing or lessening certain precedence constraints can reduce the overall

duration of a project, lowering costs and potentially increasing revenues. A
project manager must be organizationally savvy, understand the personalities
of project team members, and have some subject-matter expertise that will
allow a proper vetting of claimed precedence constraints.
To apply PERT/CPM, a thorough understanding of a project’s requirements
and structure is needed. The effort spent in identifying activity relationships
and constraints yields valuable insights. In particular, four questions must be
answered to begin the modeling process:
1. What are the project activities?
2. What are the sequencing requirements or constraints for these activities?
3. Which activities can be conducted simultaneously?
4. What are the estimated time requirements for each activity?
PERT/CPM networks are an integral component of project management and
have been shown to provide the following benefits (Clark and Fujimoto 1989,
Meredith and Mantel 1999):
They furnish a consistent framework for planning, scheduling,
monitoring, and controlling projects.
They illustrate the interdependencies of all tasks, WPs, and work units.
They aid in setting up the proper communications channels between
participating organizations and points of authority.
They can be used to estimate the expected project completion dates as
well as the probability that the project will be completed by a specific
date.
They identify so-called critical activities that, if delayed, will delay the
completion of the entire project.
They also identify activities that have slack and so can be delayed for
specific periods of time without penalty or from which resources may

temporarily be borrowed without negative consequences.
They determine the dates on which tasks may be started or must be
started if the project is to stay on schedule.
They illustrate which tasks must be coordinated to avoid resource or
timing conflicts.
They also indicate which tasks may be run or must be run in parallel to
achieve the predetermined completion date.
PERT and CPM are easy to understand and use. Although computerized
versions are available for both small and large projects, manual calculation is
quite suitable for many everyday situations. Unfortunately, though, some
managers have placed too much reliance on these techniques at the expense
of good management practice. For example, when activities are scheduled for
a designated time slot, there is a tendency to meet the schedule at all costs.
This may divert resources from other activities and cause much more serious
problems downstream, the effects of which may not be felt until a near-
catastrophe has set in. If tests are shortened or eliminated as a result of time
pressure, design flaws may be discovered much later in the project. As a
consequence, a project that seemed to be under control is suddenly several
months behind schedule and substantially over budget. When this happens, it
is convenient to blame PERT/CPM even though the real cause is poor
management.
In the remainder of this chapter, we discuss and illustrate the techniques used
to estimate activity durations, to construct PERT/CPM networks, and to
develop project schedules. PERT/CPM is focused on the timing of activities.
Issues related to resource and budget constraints, as they affect a project’s
schedule, are taken up in Chapters 10 and 11.

9.2 Estimating the Duration of
Project Activities
A project is composed of a set of tasks. Each task is performed by one
organizational unit and is part of a single WP. Most tasks can be broken
down into activities. Each activity is characterized by its technological
specifications, drawings, list of required materials, quality control
requirements, and so on. The technological processes selected for each
activity affect the resources required, the materials needed, and the timetable.
For example, to move a heavy piece of equipment from one point to another,
resources such as a crane and a tractor-trailer might be called for, as well as
qualified operators. If the piece of equipment is mounted on a special fixture
before moving, then the required resources and the performance time might
be affected. Thus, the schedule of the project, as well as its cost and resource
requirements, is a function of technological and operational decisions.
Some activities cannot be performed unless certain activities are completed
beforehand. For example, if a piece of equipment to be moved is very large,
then it might be necessary to disassemble it or at least remove a few of its
parts before loading it onto a truck. Thus, the “moving” task has to be broken
down into activities, with precedence relations among them.
The process of dividing a task into activities and dividing activities into
subactivities should be performed carefully to strike a proper balance
between size and duration. The following guidelines are recommended:
1. The length of each activity should be approximately in the range of
0.5% to 2% of the length of the project. Thus, if the project takes
approximately 1 year, then each activity should be between a day and a
week.
2. Critical activities that fall below this range should be included. For
example, a critical design review that is scheduled to last two days on a
3-year project should be included in the activity list because of its

pivotal importance.
3. If the number of activities is very large (e.g., above 250), then the
project should be divided into subprojects, perhaps by functional area as
suggested in Section 9.1, and individual schedules should be developed
for each. Schedules with too many activities quickly become unwieldy
and are difficult to monitor and control.
We start our discussion with techniques commonly used to estimate the
length of activities. We then describe the effects that precedence relations
among activities have on the overall schedule.
Two approaches are used for estimating the length of an activity: the
deterministic approach and the stochastic approach. The deterministic
approach ignores uncertainty and, thus, results in a point estimate. The
stochastic approach addresses the probabilistic elements in a project by
estimating both the expected duration of each activity and its corresponding
variance. Although tasks are subject to random forces and other uncertainties,
a majority of project managers prefer the deterministic approach because of
its simplicity and ease of understanding. A corollary benefit is that it yields
satisfactory results in most instances. If an activity is one that has been
performed many times in the past, the project manager, together with a
subject-matter expert, can estimate activity duration as a point estimate by the
mean of historical, actual duration times. This approach assumes that the
coefficient of variation (i.e., the ratio of the standard deviation to the mean) is
relatively small.
9.2.1 Stochastic Approach
In some cases, a project activity’s duration time may vary significantly
between projects. In these cases, a deterministic modeling approach is not
suitable. Rather, a project manager may construct a frequency distribution of
related activity durations, based on actual duration times associated with
previous projects. An example of such a distribution is illustrated in Figure
9.3. From the plot, we observe that previously the activity under
consideration was performed 40 times and required anywhere from 10 to 70

hours. We also see that in 3 of the 40 observations the actual duration was 45
hours and that the most frequent duration was 35 hours. That is, in 8 out of
the 40 repetitions, the actual duration was 35 hours.
Figure 9.3
Frequency distribution of an activity duration.
Figure 9.3 Full Alternative Text
The information in Figure 9.3 can be summarized by two measures: the first
is associated with the center of the distribution (commonly used measures are
the mean, the mode, and the median), and the second is related to the spread
of the distribution (commonly used measures are the variance, the standard
deviation, and the interquartile range). The mean of the distribution in Figure
9.3 is 35.25, its mode is 35, and its median is also 35. The standard deviation
is 13.3 and the variance is 176.89.

When working with empirical data, it is often desirable to fit the data with a
continuous distribution that can be represented mathematically in closed
form. This approach facilitates the analysis. Figure 9.4 shows the
superposition of a normal distribution with the parameters μ=35.25 and
σ=13.3 on the original data.
Figure 9.4
Normal distribution fitted to the data.
Whereas the normal distribution is symmetrical and easy to work with, the
distribution of activity durations is likely to be skewed. Furthermore, the
normal distribution has a long left-hand tail, whereas actual performance time
cannot be negative. A better model of the distribution of activity lengths has
proved to be the beta distribution, which is illustrated in Figure 9.5.

Figure 9.5
Beta distribution fitted to the data.
A visual comparison between Figures 9.4 and 9.5 reveals that the beta
distribution provides a closer fit to the frequency data depicted in Figure 9.3.
The left-hand tail of the beta distribution does not cross the zero duration
point, neither is it necessarily symmetric. Nevertheless, in practice, a
statistical test (e.g., the chi-square goodness-of-fit test or the Kolmogorov–
Smirnov test; Banks et al. 2001) must be used to determine whether a
theoretical distribution is a valid representation of the actual data.
In project scheduling, probabilistic considerations are incorporated by
assuming that the time estimate for each activity can be derived from three

different values:
a=optimistic time, which will be required if execution goes extremely
well
m=most likely time, which will be required if execution is normal
b=pessimistic time, which will be required if execution goes badly
Statistically speaking, a and b are estimates of the lower and upper bounds of
the frequency distribution, respectively. If the activity is repeated a large
number of times, then only in approximately 0.5% of the cases would the
duration fall below the optimistic estimate, a, or above the pessimistic
estimate, b. The most likely time, m, is an estimate of the mode (the highest
point) of the distribution. It need not coincide with the midpoint ( a+b )/2 but
may occur on either side.
To convert m, a, and b into estimates of the expected value d ^ and variance v
^ of the elapsed time required by the activity, two assumptions are made. The
first is that the standard deviation, s ^ (square root of the variance) equals one-
sixth the range of possible outcomes; that is,
s ^ = b−a 6 (9.1)
The rationale for this assumption is that the tails of many probability
distributions (e.g., the normal distribution) are considered to lie about 3
standard deviations from the mean, implying a spread of approximately 6
standard deviations between tails. In industry, statistical quality control charts
are constructed so that the spread between the upper and lower control limits
is approximately 6 standard deviations ( 6σ ). If the underlying distribution is
normal, then the probability is 0.9973 that d ^ falls within b−a. In any case,
according to Chebyshev’s inequality, there is at least an 89% chance that the
duration will fall within this range (see, e.g., Banks et al. 2001).
The second assumption concerns the form of the distribution and is needed to
estimate the expected value, d ^ . In this regard, the definitions of the three
time estimates above provide an intuitive justification that the duration of an
activity may follow a beta distribution with its unimodal point occurring at m

and its end points at a and b. Figure 9.6 shows the three cases of the beta
distribution: (a) symmetric, (b) skewed to the right, and (c) skewed to the left.
The expected value of the activity duration is given by
d ^ = 1 3  2m+ 1 2  ( a+b )= a+4m+b 6 (9.2)
Figure 9.6
Three cases of the beta distribution: (a) symmetric, (b) skewed to
the right, and (c) skewed to the left.
Figure 9.6 Full Alternative Text
Notice that d ^ is a weighted average of the mode, m, and the midpoint ( a+b
)/2, where the former is given twice as much weight as the latter. Although the
assumption of the beta distribution is an arbitrary one and its validity has
been challenged from the start (Grubbs 1962), it serves the purpose of
locating d ^ with respect to m, a, and b in what seems to be a reasonable way
(Hillier and Lieberman 2001).
The following calculations are based on the data in Figure 9.3 from which we
observe that a=10, b=70, and m=35:
d ^ = 10+( 4 )( 35 )+70 6 =36.6and s ^ = 70−10 6 =10
Thus, assuming that the beta distribution is appropriate, the expected time to
perform the activity is 36.6 hours with an estimated standard deviation of 10
hours.
In practice, many project managers are challenged to estimate the parameters
a, b, and m. Typically, a project manager may survey one or more subject-

matter experts and obtain estimates of the three parameters. However, some
subject-matters experts—perhaps influenced by a particular senior manager
involved with the project—may provide estimates that tilt and politically
influence certain decisions. Also, in some cases, subject-matter experts, like
other decision makers, are not adept at estimating low-probability events such
as severe weather events. Therefore, the parameters a and b may not be
appropriately characterized.
9.2.2 Deterministic Approach
When past data for an activity similar to the one under consideration are
available and the variability in performance time is negligible, the duration of
the activity may be estimated by its mean; that is, the average time required
for the activity in the past. A problem arises when no past data exist. This
problem is common in organizations that do not have an adequate
information system to collect and store past data and in R&D projects in
which an activity is performed for the first time. To deal with this situation,
three techniques are available: the modular technique, the benchmark job
technique, and the parametric technique. Each is discussed below.
9.2.3 Modular Technique
This technique is based on decomposing each activity into subactivities (or
modules), estimating the performance time of each module, and then totaling
the results to get an approximate performance time for the activity. As an
example, consider a project to install a new flexible manufacturing system
(FMS). A training program for employees has to be developed as part of the
project. The associated task can be broken down into the following activities:
1. Definition of goals for the training program
2. Study of the potential participants in the program and their qualifications
3. Detailed analysis of the FMS and its operation

4. Definition of required topics to be covered
5. Preparation of a syllabus for each topic
6. Preparation of handouts, transparencies, and so on
7. Evaluation of the proposed program (a pilot study)
8. Improvements and modifications
If possible, the time required to perform each activity is estimated directly. If
not, then the activity is broken into modules, and the time to perform each
module is estimated based on past experience. Although the new training task
may not be wholly identical to previous tasks undertaken by the company, the
modules themselves should be common to many training programs, so
historical data may be available.
9.2.4 Benchmark Job Technique
This technique is best suited for projects that contain many repetitions of
some standard activities. The extent to which it is used depends on the
performing organization’s diligence in maintaining a database of the most
common activities along with estimates of their duration and resource
requirements.
To see how this technique is used, consider an organization that specializes in
construction projects. To estimate the time required to install an electrical
system in a new building, the time required to install each component of the
system would be multiplied by the number of components of that type in the
new building. If, for example, the installation of an electrical outlet takes on
average 10 minutes and there are 80 outlets in the new building, then a total
of 80×10=800 minutes is required for this type of component. After
performing similar calculations for each component type or job, the total time
to install the electrical system would be determined by summing the resultant
times.
The benchmark job technique is most appropriate when a project is composed

of a set of basic elements whose execution time is additive. If the nature of
the work does not support the additivity assumption, then another method––
the parametric technique––should be used.
9.2.5 Parametric Technique
This technique is based on cause–effect analysis. The first step is to identify
the independent variables. For example, in digging a tunnel an independent
variable might be the length of the tunnel. If it takes on average 20 hours to
dig 1 ft, then the time to dig a tunnel of length L can be estimated by T( L
)=20×L, where time is considered the dependent variable and the length of the
tunnel is considered the independent variable.
When the relationship between the dependent variable and the independent
variable is known exactly, as it is in many physical systems, one can plot a
response curve in two dimensions. Figure 9.7 depicts two examples of length
versus time: line (a) represents a linear relationship between the independent
and dependent variables, and line (b) is a nonlinear one. In general, if the
dependent variable, Y, is believed to be a linear function of the independent
variable, X, then regression analysis can be used to estimate the parameters of
the line Y= b 0 + b 1 X. Otherwise, either a transformation is performed on
one or both of the variables to establish a linear relationship and then
regression analysis applied, or a nonlinear curve-fitting technique is used.

Figure 9.7
Two examples of activity duration as a function of length.
In the simple case, we have n pairs of sample observations on X and Y which
can be represented on a scatter diagram as in Figure 9.8. Because the line Y=
b 0 + b 1 X is unknown, we hypothesize that
Y i = b 0 + b 1 X i + u i ,i=1, … , n E[ u i ]=0,i=1, … , n E[ u i u j ]= 0,
for i≠j; i, j=1, … n σ u 2 , for i=j; i, j=1, … n

Figure 9.8
Typical scatter diagram.
Figure 9.8 Full Alternative Text
where E[ ⋅ ] is the expected value operator and b 0 , b 1 , and σ u 2 are
unknown parameters that must be estimated from the sample observations X
1 , … , X n and Y 1 , … , Y n . It is usually assumed that u i ∼N( 0, σ u 2 );
i.e., u i is normally distributed with mean 0 and variance σ u 2 .
To begin, denote the regression line by
Y ^ = b ^ 0 + b ^ 1 X
where b ^ 0 and b ^ 1 are estimates of the unknown parameters b 0 and b 1 ,
and Y ^ is the value of the dependent variable for any given value of X. To fit
such a line we must develop formulas for b ^ 0 and b ^ 1 in terms of the
sample observations. This is done by the principle of least squares (Draper
and Smith 1998) as discussed in Appendix 9A.

With some activities more than one independent variable is required to
estimate the performance time. For example, consider the activity of
populating a printed circuit board. The use of three independent variables
might be appropriate, the first being the number of components to be
inserted, the second being the number of setups or tool changes required, and
the third being the type of equipment used (here a qualitative rather than a
quantitative measure is called for).
In general, if we start with m independent variables, then the regression line
is
Y= b 0 + b 1 X 1 + b 2 X 2 +⋯+ b m X m +u
The coefficients b 0 , b 1 , … , b m are also estimated by using the principle
of least squares. Goodness of fit is measured by the R 2 value which ranges
from 0 (no correlation) to 1 (perfect correlation). The formula used in its
calculation is given in Appetndix 9A. However, some analysts prefer to use a
normalized version of R 2 known as adjusted R 2 given by
R a 2 =1−( 1− R 2 ) n−1 n−m−1
where n is the total number of observations and m+1 is the number of
coefficients to be estimated. By working with the adjusted R 2 it is possible
to compare regression models used to estimate the same dependent variable
using different numbers of independent variables.
Guidelines for developing a regression equation include the following steps:
Identify the independent variables that affect activity duration.
Collect data on past performance time of the activity for different values
of the independent variables.
Check the correlation between the variables. If necessary, use
appropriate transformations and only then generate the regression
equation.
In the case that several potential independent variables are considered, a

technique called stepwise regression analysis can be used. This technique is
designed to select the independent variables to be included in the model. At
each step, at most one independent variable is added to the model. In the first
step, a simple regression equation is developed with the independent variable
that is the best predictor of the dependent variable (i.e., the one that yields the
highest value of R 2 ). Next, a second variable is introduced. This process
continues until no improvement in the regression equation is observed. The
final form of the model includes only those independent variables that
entered the regression equation during the stepwise iterations.
The quality of a regression model is assessed by analysis of residuals. These
residuals ( e i = Y i − Y ^ i ) are assumed to be normally distributed with a
mean of zero. If this is not the case or a trend in the value of the residuals as a
function of any independent variable exists, then the dependent variable or
some of the independent variables may require a transformation.
Example 9-1
An organization decides to use a regression equation to estimate the time
required to develop a new software package. The candidate list of
independent variables includes
X 1 =number of subroutines in the program
X 2 =average number of lines of code in each subroutine
X 3 =number of modules or subprograms
Table 9.1 summarizes the data collected on 10 software packages. The time
required in person-months, denoted by Y, is the dependent variable (the
duration is given by the number of person-months divided by the number of
programmers assigned to the project). Running a stepwise regression on the
data yields the following equation:
Y=−0.76+0.13 X 1 +0.045 X 2
with R 2 =0.972 and R a 2 =0.964. Figure 9.9 plots the data points and the

fitted line.
The value of R a 2 is lower than R 2 because
R a 2 =1−( 1− R 2 ) n−1 n−m−1 =1−( 1−0.972 ) 9 7 =0.964
TABLE 9.1 Data for
Regression Analysis
Package number Time required, Y X 1 X 2 X 3
 1  7.9  50 100 4
 2  6.8  30  60 2
 3 16.9  90 120 7
 4 26.1 110 280 9
 5 14.4  65 140 8
 6 17.5  70 170 7
 7  7.8  40  60 2
 8 19.3  80 195 7
 9 21.3 100 180 6
10 14.3  75 120 3

Figure 9.9
Data points and regression surface for the example.
By introducing the third candidate X 3 into the regression model the value of
R a 2 is reduced to 0.963; consequently, it is best to use only the independent
variables X 1 and X 2 as predictors, although the difference is negligible.
If a new software package similar to the previous 10 is to be developed and it
contains X 1 =45 subroutines with an average of X 2 =170 lines of code in
each, then the estimated development time is

Y=−0.76+( 0.13 )( 45 )+( 0.045 )( 170 )=12.7 person-months
In general, the following points should be taken into account when using and
evaluating the results of a regression analysis:
For the activity under investigation, only data collected on similar
activities performed by the same work methods should be used in the
calculations.
When the value of R 2 or R a 2 is low (below 0.5), the independent
variables may not be appropriate.
If the distribution of the residuals is not close to normal or there is a
trend in the residuals as a function of any independent variable, then the
regression model may not be appropriate.

9.3 Effect of Learning
The ability to learn is translated into improved performance as experience is
gained, at both the organizational and individual levels. Improved
performance can be measured by reductions in activity times or lower direct
costs per repetition. Experience is usually measured by the number of
repetitions of a given activity.
Most organizations have the potential to improve performance. This potential
will be realized, however, only if sufficient motivation exists on the part of
management and the workforce. Improvement at the individual level stems
from the ability of a person to move faster and more accurately as experience
is gained. Details of the work to be performed are memorized and the time
spent on reading instructions, looking at drawings, and experimenting with
different procedures decreases. At the organizational level, the potential for
improvement is found largely in the areas of communications and logistics
and may be achieved with the use of more efficient equipment and work
methods.
The relationship between performance time and experience (number of
repetitions) can conveniently be represented by a learning curve. The
underlying model relates the direct labor required to perform an activity to
the experience gained in its execution. The basic learning curve equation
(Wright 1936) is
T( n )=T( 1 ) n β (9.3)
where
T( n )=expected number of direct labor hours required to perform the
activity in the nth repetition
n=repetition number
T( 1 )=expected number of direct labor hours required to perform the
activity the first time

β=learning coefficient
A common practice is to describe this learning curve by the percentage
decline of labor hours required for repetition 2n compared with the required
labor hours for repetition n. A 90% learning curve means that the time
required for repetition 2n is 90% of that required for n; thus
T( 2n ) T( n ) = T( 1 ) ( 2n ) β T( 1 ) ( n ) β = 2 β =0.9
so
β log 10 2= log 10 0.9
or
β= log 10 0.9 log 10 2 =−0.15
If we assume a 100×L percent learning curve (where L is a fraction between 0
and 1), then
β= log 10 L log 10 2 (9.4)
Other learning curve models are discussed in Yelle (1979) and Smunt (1986).
The effect of learning is most important during startup when the cumulative
number of repetitions is small. This is because the same relative improvement
takes place whenever the number of repetitions is doubled; that is,
T( 2n ) T( n ) = 2 β
Thus the relative improvement between the first and second repetitions is the
same as the improvement between the 10th and the 20th repetitions.
This observation suggests that, in projects where a small number of identical
units are to be produced, the careful assignment of workers to activities is
crucial. By assigning the same workers to perform an activity on all units,
direct labor costs and time can be saved as a result of learning. The
scheduling of projects under learning is discussed in detail by Shtub (1991)
and LeBlanc et al. (1992).

Consider the following example: An activity is to be repeated four times in a
project. Its duration is estimated to be 100 hours if performed by a single
worker. The learning percentage defined is estimated as 80%. Solving Eq.
(9.4) for β, we get
β= log 10 0.8 log 10 2 =−0.322
Based on the initial estimate, the time to perform this activity is as follows:
Repetition number Performance time
1 100
2 100× 2 −0.322 =80
3 100× 3 −0.322 =70
4 100× 4 −0.322 =64
314
Tables 9B.1 and 9B.2 in Appendix 9B can replace the calculations above. In
Table 9B.1, the values of n β are given for different values of n and
100×L percent. Using Table 9B.1, the performance time for the activity when
n=3 (and assuming an 80% learning curve) is 100×0.7021=70. Using Table
9B.2, the total time for the four repetitions is 100×3.142=314.
Thus, in this example, the total time to perform the activity is 314 hours if the
same worker is assigned to the activity and learning takes place. If, however,
the four repetitions are assigned to four different workers, then the total time
required would be 100×4=400 hours.
The learning curve can also be used to update time and cost estimates.
Suppose that the actual time for the first repetition was 105 hours, whereas
the actual time for the second repetition was 90 hours. In this case T( 1 )=105
and T( 2 )=90, so from Eqs. (9.3) and (9.4)
2 β = T( 2 ) T( 1 ) = 90 105 =0.857orβ= log 10 0.857 log 2 2 =−0.22
By using the learning curve model for time and cost estimation and by
scheduling workers so that learning is maximized, the project manager can
take advantage of the learning effect.

In practice, a project manager may not be able to continuously assign the
same resource to a particular activity, if the resource is a human being.
Typically, an organization strives to develop its people by giving them new
positions and opportunities every few years. It is not uncommon for a person
to be pulled out of a function at the point at which the person has, in fact,
mastered the function. Therefore, in practice, an organization’s goal of people
development can often negate the benefits of learning, reducing the potential
for a project to take advantage of learning efficiencies.

9.4 Precedence Relations Among
Activities
The schedule of activities is constrained by the availability of resources
required to perform each activity and by technological constraints known as
precedence relations. Four general types of precedence relations exist among
activities. The most common, termed “finish to start,” requires that an activity
can start only after its predecessor has been completed.
A lag or time delay can be added to any of these connections. In some
situations the relationship between activities is subject to uncertainty. For
example, after testing a printed circuit board that is to be part of a prototype
communications system, the succeeding activity might be to install the board
on its rack, to repair any defects found, or to scrap the board if it fails the
functionality test.
The four types of precedence relations are illustrated in Figure 9.10. A formal
definition of each follows:
Figure 9.10

Lead-lag relationships in precedence diagramming.
Figure 9.10 Full Alternative Text
F S AB (finish to start): This relation specifies that activity B cannot
start until at least FS time units after the completion of activity A. Note
that the PERT/CPM approaches use F S AB =0 for network analysis.
S S AB (start to start): In this case, activity B cannot start until activity
A has been in progress for at least SS time units.
F F AB (finish to finish): Here, activity B cannot finish until at least FF
time units after the completion of activity A.
S F AB (start to finish): There must be at least SF time units between the
start of activity A and the completion of activity B.
The leads or lags may also be expressed in percentages, rather than time
units. For example, we may specify that 20% of the work content of activity
A must be completed before activity B can start. If percentage of work
completed is used for determining lead–lag constraints, then a reliable
procedure must be used for estimating the percentage completion. If the
project work is broken up properly in the WBS, then it will be much easier to
estimate percentage completion by evaluating the work completed at the
elementary task levels. The lead–lag relationships may also be specified in
terms of at most relationships instead of at least relationships. For example,
we may have at most an FF lag requirement between the finish time of one
activity and the finish time of another activity.
In the following sections, we concentrate on the analysis of “finish to start”
connections, which are most prevalent in practice. Other types of connections
are examined in Section 9.8 and the effect of uncertainty on precedence
relations is discussed in Section 9.11. Uncertainty gives rise to probabilistic
networks.
The large number of precedence relations among activities makes it difficult
to rely on verbal descriptions alone to convey the effect of technological
constraints on scheduling. Graphical representations are frequently used. In

subsequent sections, a number of such representations are illustrated with the
help of an example project. Table 9.2 contains the relevant activity data.
TABLE 9.2 Data for Example
Project
Activity Immediate predecessors Duration (weeks)
A – 5
B – 3
C A 8
D A, B 7
E – 7
F C, E, D 4
G F 5
In this project, only “finish to start” precedence relations are considered.
From Table 9.2, we see that activities A, B, and E do not have any
predecessors and thus can start at any time. Activity C, however, can start
only after A finishes, whereas D can start after the completion of A and B.
Further examination reveals that F can start only after C, E, and D are
finished and that G must follow F. Because activity A precedes C, and C
precedes F, A must also precede F by transitivity. Nevertheless, when using a
network representation, it is necessary to list only immediate or direct
precedence relations; implied relations are taken care of automatically.

9.5 Gantt Chart
The most widely used management tool for project scheduling and control is
a version of the bar chart developed by Henry L. Gantt. The Gantt chart, as it
is called, enumerates the activities to be performed on the vertical axis and
their corresponding duration on the horizontal axis. It is possible to schedule
activities by either early-start or late-start logic. In the early-start approach,
each activity is initiated as early as possible without violating the precedence
relations. In the late-start approach, each activity is delayed as much as
possible as long as the earliest finish time of the project is not compromised.
A range of schedules is generated on the Gantt chart when a combination of
early and late starts is applied. The early-start schedule is performed first and
yields the earliest finish time of the project. That time is then used as the
required finish time for the late-start schedule. Figure 9.11 depicts the early-
start Gantt chart schedule for the example above. The bars denote the
activities; their location with respect to the time axis indicates the time over
which the corresponding activity is performed. For example, activity D can
start only after activities A and B finish, which happens at the end of week 5.
A direct output of this schedule is the earliest finish time for the project (22
weeks for the example).

Figure 9.11
Gantt chart for an early-start schedule.
Figure 9.11 Full Alternative Text
On the basis of the earliest finish time, the late-start schedule can be
generated. This is done by shifting each activity to the right as much as
possible while still starting the project at time 0 and completing it in 22
weeks. The resultant schedule is depicted in Figure 9.12. The difference
between the start (or the finish) times of an activity on the two schedules is
called the slack (or float) of the activity. Activities that do not have any slack
are denoted by a shaded bar and are termed critical. The sequence of critical
activities connecting the start and end points of the project is known as the
critical path, which logically turns out to be the longest path in the network.
A delay in any activity along the critical path delays the entire project. The

sum of durations for critical activities represents the shortest possible time to
complete the project. The time required to complete all of the critical tasks
pertaining to a particular project is known as the makespan.
Gantt charts are simple to generate and interpret. In construction of a Gantt
chart, there should be a one-to-one correspondence between the listed tasks
and the WBS and its numbering scheme. As shown in Figure 9.13, which
depicts the Gantt chart for the microcomputer development project, a separate
column can be added for this purpose. In fact, the schedule should not contain
any tasks that do not appear in the WBS. Often, however, the Gantt chart
includes milestones such as project kickoff and design review, which are
listed along with the tasks.
In addition to showing the critical path, Gantt charts can be modified to
indicate project and activity status. In Figure 9.13, a bold border is used to
identify a critical

Figure 9.12
Gantt chart for a late-start schedule.
Figure 9.12 Full Alternative Text
Figure 9.13
Gantt chart for the microcomputer development example.
Figure 9.13 Full Alternative Text
activity, and a shaded area indicates the approximate completion status at the
August review. Accordingly, we see that tasks 2, 5, and 8 are critical, falling
on the longest path. Task 2 is 100% complete, task 4 is 65% complete, task 7
is 55% complete; tasks 5, 6, and 8 have not yet been started.
Gantt charts can be modified further to show budget status by adding a
column that lists planned and actual expenditures for each task. This is taken

up in Chapter 11. Many variations of the original bar graph have been
developed to provide more detailed information for the project manager. One
commonly used variation that replaces the bars with lines and adds triangles
to indicate project status and revision points is shown in Figure 9.14. To
explain the features, let us examine task 2, equipment design. According to
the code given in the lower left-hand corner of the figure, this task was
rescheduled three times, finally starting in February, and finishing at the end
of June. Note the two rescheduled start milestones and the two rescheduled
finish milestones.
Figure 9.14
Extended Gantt chart with task details.
Figure 9.14 Full Alternative Text
The problem with adding features to the bar graph is that they take away

from the clarity and simplicity of the basic form. Nevertheless, the additional
information conveyed to the user may offset the additional effort required in
generating and interpreting the data. A common modification of the analysis
is the case when a milestone has a contractual due date. Consider, for
example, activity 8 (WBS No. 5.0) in Figure 9.14. If management decides
that the required due date for this activity is the end of February (instead of
the end of January) then a slack of 1 month will be added to each activity in
the project. If, however, the due date of activity 8 is the end of December,
then the schedule in Figure 9.14 is no longer feasible because the sequence of
activities 2, 5, and 8 (i.e., the critical sequence) cannot be completed by the
end of December. In Section 9.13, scheduling conflicts and their management
are discussed in detail.
The major limitation of bar graph schedules is their inability to show task
dependencies and time–resource tradeoffs. Network techniques are often used
in parallel with Gantt charts to compensate for these shortcomings.

9.6 Activity-on-Arrow Network
Approach for CPM Analysis
Although the AOA model is most closely associated with PERT, it can be
applied to CPM as well (it is sometimes called activity-on-arc). In
constructing a network, an arrow is used to represent an activity, with its head
indicating the direction of progress of the project. The precedence relations
among activities are introduced by defining events. An event represents a
point in time that signifies the completion of one or more activities and the
beginning of new ones. The beginning and ending points of an activity, thus,
are described by two events known as the head and the tail. Activities that
originate from a certain event cannot start until the activities that terminate at
the same event have been completed.
Figure 9.15a shows an example of a typical representation of an activity (i, j)
with its tail event i and its head event j. Figure 9.15b depicts a second
example, in which activities (1, 3) and (2, 3) must be completed before
activity (3, 4) can start. For computational purposes, it is customary to
number the events in ascending order so that, compared with the head event,
a smaller number is always assigned to the tail event of an activity.
Figure 9.15

Network components.
The rules for constructing a diagram are summarized below.
Rule 1
Each activity is represented by one and only one arrow in the network.
No single activity can be represented twice in the network. This is to be
differentiated from the case in which one activity is broken down into
segments wherein each segment may then be represented by separate arrows.
For example, in designing a new computer architecture, the controller might
first be developed followed by the arithmetic unit, the I/O processor, and so
on.
Rule 2
No two activities can be identified by the same head and tail events.
A situation such as this may arise when two or more activities can be
performed in parallel. As an example, consider Figure 9.16a, which shows
activities A and B running in parallel. The procedure used to circumvent this
difficulty is to introduce a dummy activity between either A or B. The four
equivalent ways of doing this are shown in Figure 9.16b, where D 1 is the
dummy activity. As a result of using D 1 , activities A and B can now be
identified by a unique set of events. It should be noted that dummy activities
do not consume time or resources. Typically, they are represented by dashed
lines in the network.

Figure 9.16
Use of a dummy arc between two nodes.
Figure 9.16 Full Alternative Text
Dummy activities are also necessary in establishing logical relationships that
cannot otherwise be represented correctly. Suppose that in a certain project,
tasks A and B must precede C, whereas task E is preceded only by B. Figure
9.17a shows an incorrect depiction of this part of the network. The difficulty
is that although the relationship among A, B, and C is correct, the diagram
implies that E must be preceded by both A and B. The correct representation
using dummy D 1 is depicted in Figure 9.17b.

Figure 9.17
(a) Incorrect and (b) correct representation.
Figure 9.17 Full Alternative Text
Rule 3
To ensure the correct representation in the AOA diagram, the following
questions must be answered as each activity is added to the network:
1. Which activities must be completed immediately before this activity can
start?
2. Which activities must immediately follow this activity?
3. Which activities must occur concurrently with this activity?
This rule is self-explanatory. It provides guidance for checking and
rechecking the precedence relations as the network is constructed.
The following examples further illustrate the use of dummy activities.
Example 9-2

Draw the AOA diagram so that the following precedence relations are
satisfied:
1. E is preceded by B and C.
2. F is preceded by A and B.
Solution
Consider Figure 9.18. Part (a) shows an incorrect precedence relation for
activity E. According to the requirements, B and C precede E, and A and B
precede F. The dummy D 1 therefore is inserted to allow B to precede E.
Doing so, however, implies that A also must precede E, which is incorrect.
Part (b) in the figure shows the correct relationships.
Figure 9.18
Subnetwork with two dummy arcs: (a) incorrect, (b) correct.

Figure 9.18 Full Alternative Text
Example 9-3
Draw the precedence diagram for the following conditions:
1. G is preceded by A.
2. E is preceded by A and B.
3. F is preceded by B and C.
Solution
An incorrect and correct representation is given in Figure 9.19. The diagram
in part (a) of the figure is wrong because it implies that A precedes F.
Figure 9.19

Subnetwork with complicated precedence relations: (a) incorrect,
(b) correct.
It is good practice to have a single start event, common to all activities, that
has no predecessors and a single end event, for all activities, that has no
successors. The actual mechanics of drawing an AOA network are illustrated
using the data in Table 9.2.
The process begins by identifying all activities that have no predecessors and
joining them to a unique start node. This is shown in Figure 9.20. Each
activity terminates at a node. Only the first node in the network is assigned a
number (1); all other nodes are labeled only when network construction is
completed, as explained presently. Because activity C has only one
predecessor (A), it can be added immediately to the diagram (see Figure
9.20).
Figure 9.20
Partial plot of the example AOA network.
Activity D has both A and B as predecessors; thus, there is a need for an
event that represents the completion of A and B. We begin by adding two
dummy activities D 1 and D 2 . The common end event of D 1 and D 2 is now
the start event of D, as depicted in Figure 9.21. As we progress, it may
happen that one or more dummy activities are added that really are not
necessary. To correct this situation, a check will be made once the network
graph is completed, and redundant dummy arcs will be eliminated.

Figure 9.21
Using dummy activities to represent precedence relations.
Before starting activity F, activities C, E, and D must be completed.
Therefore, an event that represents the terminal point of these activities
should be introduced. Notice that C, E, and D are not predecessors of any
other activity but F. This implies that we can have the three arrows
representing these activities terminate at the same node (event)—the tail of F.
Activity G, which has only F as a predecessor, can start from the head of F
(see Figure 9.22).
Figure 9.22
Network with activities F and G included.
Figure 9.22 Full Alternative Text
Once all of the activities and their precedence relations have been included in
the network diagram, it is possible to eliminate redundant dummy activities.
A dummy activity is redundant when it is the only activity that starts or ends

at a given event. Thus, D 2 is redundant and is eliminated by connecting the
head of activity B to the event that marked the end of D 2 . The next step is to
number the events in ascending order, making sure that the tail always has a
lower number than the head. The resulting network is illustrated in Figure
9.23. The duration of each activity is written next to the corresponding arrow.
The dummy D 1 is shown like any other activity, but with a duration of zero.
Figure 9.23
Complete AOA project network.
Example 9-4
Construct an AOA diagram that comprises activities A, B, C, . . . , L such that
the following relationships are satisfied:
1. A, B, and C, the first activities of the project, can start simultaneously.
2. A and B precede D.
3. B precedes E, F, and H.
4. F and C precede G.
5. E and H precede I and J.

6. C, D, F, and J precede K.
7. K precedes L.
8. I, G, and L are the terminal activities of the project.
Solution
The resulting diagram is shown in Figure 9.24. The dummy activities D 1 and
D 2 are needed to establish correct precedence relations. D 3 is introduced to
ensure that the parallel activities E and H have unique finish events. Note that
the events in the project are numbered in such a way that if there is a path
connecting nodes i and j, then i0 is an arbitrarily small constant. Conceptually, in the augmented
formulation, the computations are done in two stages. First, t n is found.
Then, given this value, a search is conducted over the set of alternative
optima to find the minimum values of t i , i=2,… , n−1. In reality, the
computations all are done in one stage, not two.
We may also wish the LP solution to set the start of noncritical activities to
their latest possible start times. In this case, we must apply the following trick

to the objective function. Let M denote some large constant, for example
M=10×tn. We can then formulate the following objective function:
Min M×tn−(t1+t2+…tn−1).
The resulting LP will result in prioritizing the minimization of the makespan
and, secondarily, result in setting the start times of all activities to their
respective latest start times. When noncritical activities are set to start at their
latest possible start times, the resulting total slack values are known as safety
slacks.

9.10 Aggregating Activities in the
Network
The detailed network model of a project is very useful in scheduling and
monitoring progress at the operational (short-term) level. Management
concerns at the tactical or strategic level, however, create a need for a focused
presentation that eliminates unnecessary clutter. For projects that span a
number of years and include hundreds of activities, it is likely that only a
portion of those activities will be active or require close control at any point
in time. To facilitate the management function, there is a need to condense
information and aggregate tasks. The two common tools used for this purpose
are hammock activities and milestones.
9.10.1 Hammock Activities
When a group of activities has a common start and a common end point, it is
possible to replace the entire group with a single activity, called a hammock
activity. For example, in the network depicted in Figure 9.37, it is possible to
use a hammock activity between events 4 and 6. Activities F and G are
collapsed into FG whose duration is the sum of L 45 and L 56 .
Figure 9.37

Example of a hammock activity.
In general, the duration of a hammock activity is equal to the duration of the
longest sequence of activities that it replaces. If another hammock activity is
used to represent A, B, C, D, and E, then its length would be
max L 12 + L 24 L 13 + L 34 L 12 + L 23 + L 34 L 14 =max 5+8 3+7 5+0+7
7 =13
Hammock activities reduce the size of a network while preserving, in general,
information on precedence relations and activity durations. By using
hammock activities, an upper-level network that presents a synoptic view of
the project can be created. Such networks are useful for medium (tactical)
and long-range (strategic) planning. The common practice is to develop a
hierarchy of networks in which the various levels correspond to the levels of
either the WBS or the OBS. Higher level networks contain many hammock
activities and provide upper management with a general picture of flows,
milestones, and overall status. Lower level networks consist of single
activities and provide detailed schedule information for team leaders. Proper
use of hammock activities can help in providing the right level of detail to
each participant in the project.
9.10.2 Milestones
A higher level of aggregation is also possible by introducing milestones to
mark the completion of significant activities. As explained in Section 9.1.1,
milestones are commonly used to mark the delivery of goods and services, to
denote points in time when payments are due, and to flag important events
such as the successful completion of a critical design review. In the simplest
case, a milestone can mark the completion of a single activity, as event 2 in
our example marks the completion of activity A. It can also mark the
completion of several activities as exemplified by event 4, which denotes the
completion of C, D, and E.
By using several levels of aggregation––that is, networks with various layers
of hammock activities and milestones––it is possible to design the most

appropriate decision support tool for each level of management. Such an
exercise should take into account the WBS and the OBS. At the lowest levels
of these structures, a detailed network is essential; at higher levels,
aggregation by hammock activities and milestones is the norm.

9.11 Dealing with Uncertainty
CPM either assumes that the duration of an activity is known and
deterministic or that a point estimate, such as the mean or mode, can be used
in its place. It makes no allowance for activity variance. When fluctuations in
performance time are low, this assumption is logically justified and has
empirically been shown to produce accurate results. When high levels of
uncertainty exist, however, CPM may not provide a very good estimate of the
project completion time. In these situations, there is a need to account
explicitly for the effects of uncertainty. Monte Carlo simulation and PERT
are the two most common approaches that have been developed for this
purpose.
9.11.1 Simulation Approach
Simulation is applied by randomly generating performance times for each
activity from some perceived, underlying distribution. In most cases, it is
assumed that activity times follow a beta distribution, as discussed in Section
9.2.1. In each simulation run, a sample of the performance time of each
activity is taken, and a CPM analysis is conducted to determine the critical
path and the project finish time for that realization. By repeating the process a
large number of times, it is possible to construct a frequency distribution or
histogram of the project completion time. This distribution then may be used
to calculate the probability that the project finishes by a given date, as well as
the expected error of each estimate.
A single simulation run would consist of the following steps:
1. Generate a random value for the duration of each activity from the
appropriate distribution.
2. Determine the critical path and its duration using CPM.
3. Record the results.

The number of times that this procedure must be repeated depends on the
error tolerances deemed acceptable. Standard statistical tests can be used to
verify the accuracy of the estimates. Typically, a few hundred replications of
a simulation are sufficient to generate stable results.
To understand the calculations, let us focus on the AOA network in Figure
9.23 for the example project and assume that each activity follows a beta
distribution with parameter values given in Table 9.8. After performing 10
simulation runs, the results listed in Table 9.9 for activity durations, critical
path, and project completion time were obtained. Additional data collected,
but not presented, include the earliest and latest start and completion times of
each event and activity slacks.
TABLE 9.8 Statistics for
Example Activities
Activity Optimistictime a
Most
likely
time, m
Pessimistic
time, b
Expected
value, d
^
Standard
deviation, s
^
A 2 5  8 5 1
B 1 3  5 3 0.66
C 7 8  9 8 0.33
D 4 7 10 7 1
E 6 7  8 7 0.33
F 2 4  6 4 0.66
G 4 5  6 5 0.33
TABLE 9.9 Summary of
Simulation Runs for Example
Project

Activity Duration
Run
number A B C D E F G
Critical
path
Completion
time
 1 6.3 2.2 8.8 6.6 7.6 5.7 4.6 A-C-F-G 25.4
 2 2.1 1.8 7.4 8.0 6.6 2.7 4.6 A-D-F-G 17.4
 3 7.8 4.9 8.8 7.0 6.7 5.0 4.9 A-C-F-G 26.5
 4 5.3 2.3 8.9 9.5 6.2 4.8 5.4 A-D-F-G 25.0
 5 4.5 2.6 7.6 7.2 7.2 5.3 5.6 A-C-F-G 23.0
 6 7.1  .4 7.2 5.8 6.1 2.8 5.2 A-C-F-G 22.3
 7 5.2 4.7 8.9 6.6 7.3 4.6 5.5 A-C-F-G 24.2
 8 6.2 4.4 8.9 4.0 6.7 3.0 4.0 A-C-F-G 22.1
 9 2.7 1.1 7.4 5.9 7.9 2.9 5.9 A-C-F-G 18.9
10 4.0 3.6 8.3 4.3 7.1 3.1 4.3 A-C-F-G 19.7
Looking at the first run in Table 9.9, we see that the realized duration of
activity A is 6.3, the duration of activity B is 2.2, etc. In the second run, the
duration of A is 2.1, and so on. Note that the critical path differs from one
replication to the next depending on the randomly generated durations of the
activities. In the 10 runs reported, the sequence A-D-F-G is the longest
(critical) in two replications, whereas the sequence A-C-F-G is critical in the
other eight. Activities A, F, and G are critical in 100% of the replications,
whereas activity C is critical in 80% and activity D is critical in 20%.
A principal output of the simulation runs is a frequency distribution of the
project makespan (the length of the critical path). Figure 9.38 plots the results
of some 50 replications for the example. As can be seen, the project length
varied from 17 to 29 weeks, with a mean of 22.5 weeks and a standard
deviation of 2.9 weeks.

Figure 9.38
Distribution of project length for simulation runs.
Figure 9.38 Full Alternative Text
Now let X be a random variable associated with project completion time. The
probability of finishing the project within, say, τ weeks can be estimated from
the following ratio:
P( X≤τ )= number of times project finished in ≤ τ weeks
total number of replications
For the example, if τ=20 weeks, then the number of runs in which the length
of the critical path was ≤ 20 weeks is seen to be 13, so P( X≤20 )= 13 50
=26%.
In addition, it is possible to estimate the criticality of each activity. The

criticality index (CI) of an activity is defined as the proportion of runs in
which the activity was on the critical path (i.e., it had a zero slack). Dodin
and Elmaghraby (1985) provided some theoretical background on this
problem as well as extensive test results for large PERT networks.
An advantage of a simulation is that it produces arbitrarily accurate results as
the number of runs increases. Unlike PERT, described below, some
computational effort is required to build the simulation. Simulation methods
are not typically available with project management software, thus limiting
the popularity of the approach in practice.
9.11.2 PERT and Extensions
PERT and extensions of PERT are the common analytical approaches used to
assess uncertainty in projects. PERT and its derivatives are based on the
central limit theorem which states that the distribution of the sum of
independent random variables is approximately normal when the number of
terms in the sum is sufficiently large.
The first approach yields a rough estimate and assumes that the duration of
each project activity is an independent random variable. Given probabilistic
durations of activities along specific paths, it follows that elapsed times for
achieving events along those paths are also probabilistic. Now, suppose that
there are n activities in the project, k of which are critical. Denote the
durations of the critical activities by the random variables d i with mean d ¯ i
and variance s i 2 , i=1, … , k. Then the total project length is the random
variable
X= d 1 + d 2 +⋯+ d k
It follows that the mean project length, E[X], and the variance of the project
length, V[X], are given by
E[ X ]= d ¯ 1 + d ¯ 2 +⋯+ d ¯ k V[X]=s12s22+⋯+sk2
These formulas are based on elementary probability theory, which tells us

that the expected value of the sum of any set of random variables is the sum
of their expected values, and the variance of the sum of independent random
variables is the sum of the variances.
Now, invoking the central limit theorem, we can use normal distribution
theory to find the probability of completing the project in less than or equal to
some given time τ as follows:
P( X≤τ )=P X−E[ X ] V [ X ] 1/2 ≤ τ−E[ X ] V [ X ] 1/2 =P Z≤ τ−E[ X ] V [
X ] 1/2 (9.12)
where Z is the standard normal deviate with mean 0 and variance 1. The
desired probability in Eq. (9.12) can be looked up in Table 9C.1 in Appendix
9C.
Continuing with the example project, if (based on the simulation) the mean
time of the critical path is 22.5 weeks and the variance is ( 2.9 ) 2 , then the
probability of completing the project within 25 weeks is found by first
calculating
z= 25−22.5 2.9 =0.86
and then looking up 0.86 in Table 9C.1. Doing so, we find that P( Z≤0.86
)=0.805, so the probability of finishing the project in 25 weeks or less is
80.5%. This solution is depicted in Figure 9.39.

Figure 9.39
Example of probabilistic analysis with PERT.
Figure 9.39 Full Alternative Text
If, however, the mean project length, E[X], and the variance of the project
length, V[X], are calculated using the assumption that the critical activities are
only those that have a zero slack in the deterministic CPM analysis (A-C-F-
G), we get
E[ X ]=5+8+4+5=22 V[X]1/2 = 12+0.332+0.662+0.332=1.285
On the basis of this assumption the probability of completing the project
within 25 weeks is
P Z≤ 25−22 1.285 =P( Z≤2.33 )=0.99
This probability is higher than 0.805, which was computed using data from
the simulation in which both sequences A-C-F-G and A-D-F-G were critical.
The procedure above, in which only a single critical path is considered based
on expected duration times of the activities, is, in essence, PERT.

Summarizing for an AON network:
1. For each activity i, assess its probability distribution or assume a beta
distribution and obtain estimates of a i , b i , and m i . These values
should be supplied by the project manager or experts who work in the
field.
2. If a beta distribution is assumed for activity i, then use the estimates a, b,
and m to compute the variance s ^ i 2 and mean d ^ i from Eqs. (9.1) and
(9.2) in Section 9.2.1. These values then are used in place of the true but
unknown values of si2 and d i ¯ , respectively, in the above formulas for
V[X] and E[X].
3. Use CPM to determine the critical path given d ^ i , i=1, … , n.
4. Once the critical activities are identified, sum their means and variances
to find the mean and variance of the project length.
5. Use Eq. (9.12) with the statistics computed in step 4 to evaluate the
probability that the project finishes within some desired time.
Using PERT, it is possible to estimate completion time for a desired
completion probability. For example, for a 95% probability the corresponding
z value is z .95 =1.64. Solving for the time τ for which the probability to
complete the project is 95%, we get
z 0.95 = τ−22.5 2.9 =1.64orτ=( 1.64 )( 2.9 )+22.5=27.256 weeks
A shortcoming of the standard PERT calculations is that it ignores all
activities that are not on the critical path. A more accurate analytical
approach is to identify each sequence of activities that lead from the start
node of the project to the finish event, and then to calculate separately the
probability that the activities that compose each sequence will be completed
by a given date. This step can be done as above by assuming that the central
limit theorem holds for each sequence and then applying normal distribution
theory to calculate the individual path probabilities. It is necessary, though, to
make an additional assumption that the sequences themselves are statistically
independent. This implies that the time to traverse each path in the network is

independent of what happens on the other paths. Although it is easy to see
that this is rarely true because some activities are sure to be on more than one
path, empirical evidence suggests that good results can be obtained if there is
not too much overlap.
Once these calculations are performed, assuming that the various sequences
are independent of each other, the probability of completing the project by a
given date is set equal to the product of the individual probabilities that each
sequence is finished by that date. That is, given n sequences with completion
times X 1 , X 2 , … , X n , the probability that X≤τ is found from
P( X≤τ )=P( X 1 ≤τ )P( X 2 ≤τ )⋯P( X n ≤τ ) (9.13)
where now the random variable X=max{ X 1 , X 2 , … , X n }.
Example 9-5
Consider the simple project in Figure 9.40. If no uncertainty exists in activity
durations, then the critical path is A-B and exactly 17 weeks are required to
finish the project. Now if we assume that the durations of all four activities
are normally distributed (the corresponding means and standard deviations
are listed under the arrows in Figure 9.40), then the durations of the two
sequences are also normally distributed [i.e., N( μ, σ ) ], with the following
parameters:
length( A-B )= X 1 ∼N( 17, 3.61 ) length( C-D )= X 2 ∼N( 16, 3.35 )

Figure 9.40
Stochastic network.
The accompanying probability density functions are plotted in Figure 9.41. It
should be clear that the project can end in 17 weeks only if both A-B and C-
D are completed within that time. The probability that A-B finishes within 17
weeks is
P( X 1 ≤17 )=P Z≤ 17−17 3.61 =P( Z≤0 )=0.5
Figure 9.41
Performance time distribution for the two sequences.
and similarly for C-D,
P( X 2 ≤17 )=P Z≤ 17−16 3.35 =P( Z≤0.299 )=0.62
Using Eq. (9.13), we now can find the probability that both sequences finish
within 17 weeks:

P( X≤17 )=P( X 1 ≤17 )P( X 2 ≤17 )=( 0.5 )( 0.62 )=0.31
Thus, the probability that the project will finish by week 17 is approximately
31%. A similar analysis for 20 weeks yields P( X≤20 )=0.7 or 70%.
Consider now the case when one or more activities are members of two or
more sequences, for example, the project in Figure 9.42. In this example, the
two sequences are not truly independent since they share a common activity.
Activity E is a member of the two sequences that connects the start of the
project (event 1) to its termination node (event 5). The expected lengths and
standard deviations of these sequences are
Figure 9.42
Stochastic network with dependent sequences.
Sequence Expected length Standard deviation
A-B-E 8+9+3=20 2 2 + 3 2 + 4 2 =5.39
C-D-E 10+6+3=19 3 2 + 1.5 2 + 4 2 =5.22
The probability that the sequence A-B-E will be completed in 17 days is
calculated as follows:
z= 17−20 5.39 =−0.5565implying that P=0.29
which is obtained from Table 9C.1 by noting that
P( Z≤−z )=1−P( Z≤z )

Similarly, the probability that the sequence C-D-E will be completed in 17
days is calculated by determining z=( 17−19 )/5.22=−0.383 and then using
Table 9C.1 to find P=0.35.
Thus, the simple PERT estimate (based on the critical sequence A-B-E)
indicates that the probability of completing the project in 17 days is 29%. If
both sequences A-B-E and C-D-E are taken into account, then the probability
of completing the project in 17 days is estimated as
P( X ABE ≤17 )P( X CDE ≤17 )=( 0.29 )( 0.35 )=0.1 or 10%
assuming that the two sequences are independent. However, because activity
E is common to both sequences, the true probability of completing the project
in 17 days is somewhere between 10 and 29%.
To illustrate the effect of uncertainty, consider the example project. Four
sequences connect the start node to the finish node. The mean length and the
standard deviation of each sequence are summarized in Table 9.10.
TABLE 9.10 Mean Length
and Standard Deviation for
Sequences in Example Project
Sequence Mean length Standard deviation
A-C-F-G 22 1.285
A-D-F-G 21 1.595
B-D-F-G 19 1.407
E-F-G 16 0.808
The probability of completing each sequence in 22 weeks is computed next
and summarized in Table 9.11.

TABLE 9.11 Probability of
Completing Each Sequence in
22 Weeks
Sequence z value Probability
A-C-F-G 22−12 1.285 =0 0.5 
A-D-F-G 22−21 1.595 =0.626 0.73
B-D-F-G 22−19 1.407 =2.13 0.98
 E-F-G 22−16 0.808 =7.42 1.0 
Based on the simple PERT analysis, the probability to complete the project in
22 weeks is 0.5. If both sequences A-C-F-G and A-D-F-G are considered and
assumed to be independent, the probability is reduced to ( 0.5 )( 0.73 )=0.365.
Because three activities (A, F, G) are common to both sequences, the actual
probability to complete in 22 weeks is closer to 0.5 than to 0.365. Based on
the data in Figure 9.38, we see that in 24 of 50 simulation runs, the project
duration was 22 weeks or less. This implies that the probability of completing
the project in 22 weeks is 24/50=0.48, or 48%.
Continuing with this example, if the Chebyshev’s inequality is used for the
critical path ( μ=22, σ=1.285 ), then the probability of completing the project
in, say, 22+( 2 )( 1.285 )=24.57 weeks is approximately
1− 1 2 2 = 3 4 =0.75.
By way of comparison, using the normal distribution assumption, the
corresponding probability is
P Z≤ 24.57−22 1.285 =P( Z≤2 )=0.97.
Of the two, the Chebyshev estimate is likely to be more reliable given that
there are only a few activities on the critical path.

Because uncertainty is bound to be present in most activities, it is possible
that after determining the critical path with CPM, a noncritical activity may
become critical as certain tasks are completed. From a practical point of view,
this suggests the basic advantage of early-start schedules. Starting each
activity as soon as possible reduces the chances of a noncritical activity
becoming critical and delaying the project.

9.12 Critique of Pert and CPM
Assumptions
PERT and CPM are models of projects and hence are open to a wide range of
technical criticism including (1) the difficulty in accurately estimating
durations, variances, and costs; (2) the validity of using the beta distribution
in representing durations; (3) the validity of applying the central limit
theorem; and (4) the heavy focus on the critical path for project control. Table
9.12 highlights some of the more significant shortcomings. In addition, PERT
and CPM analysis is based on the precedence graph, which contains only two
types of information: activity times and precedence constraints. The results
may be highly sensitive to data estimates and defining relationships.
TABLE 9.12 Principal
Assumptions and Criticisms of
PERT/CPM
Source: Adapted from Chase et al. (2003).
1. Assumption: Project activities can be identified as entities;
that is, there is a clear beginning and ending point for each
activity.
Criticism: Projects, especially complex ones, change in
content over time, and therefore a network constructed in the
planning phase may be highly inaccurate later. Also, the very
fact that activities are specified and a network is formalized
tends to limit the flexibility that is required to handle
changing situations as the project progresses.

2. Assumption: Project activity–sequence relationships can be
specified and arranged in a directed network.
Criticism: Sequence relationships cannot always be specified
beforehand. In some projects, in fact, the ordering of certain
activities is conditional on previous activities. (PERT and
CPM, in their basic form, have no provision for treating this
problem, although some other techniques have been proposed
that present the project manager with several contingency
paths, given different outcomes from each activity.)
3. Assumption: Project control should focus on the critical path.
Criticism: It is not necessarily true that the longest path
obtained from summing activity expected duration values will
ultimately determine project completion time. What often
happens as the project progresses is that some activity that is
not on the critical path becomes delayed to such a degree that
it extends the entire project. For this reason, it has been
suggested that a critical activity concept replace the critical
path concept as the focus of managerial control. Under this
approach, attention would center on those activities that have
a high potential variation and lie on a near-critical path. A
near-critical path is one that does not share any activities with
the critical path and project plan, and could become critical if
one or a few activities along it become delayed.
4. Assumption: The activity times in PERT follow the beta
distribution, with the variance of the project assumed to be
equal to the sum of the variances along the critical path.
Criticism: As mentioned in the discussion in Section 9.2.1,
the beta distribution was selected for a variety of good
reasons. Nevertheless, each component of the statistical
treatment in PERT has been brought into question. First, the
formulas are in reality a modification of the beta distribution
mean and variance, which, when compared with the basic
formulas, could be expected to lead to absolute errors on the

order of 10% for the mean and 5% for the individual
variances. Second, given that the activity–time distributions
have the properties of unimodality, continuity, and finite
positive endpoints, other distributions with the same
properties would yield different means and variances. Third,
obtaining three “valid” time estimates to put into the PERT
formulas presents operational problems: it is often difficult to
arrive at one activity–time estimate, let alone three, and the
somewhat subjective definitions of a and b do not help the
matter. (How optimistic and pessimistic should one be?)
In addition to the criticisms listed in Table 9.12, Schonberger (1981) showed
that a PERT estimate that is based on the assumption that the variance of a
sequence of activities is equal to the sum of the activity variances (i.e., that
activities and sequences are independent) can lead to a consistent error in
estimating the completion time of a project.
A related problem, investigated by Britney (1976), concerns the cost of over-
and underestimating activity duration times. He found that underestimates
precipitate the reallocation of resources and, in many cases, engender costly
project delays. Overestimates, conversely, result in inactivity and tend to
misdirect management’s attention to relatively unfruitful areas, causing
planning losses. (Britney recommends a modification of PERT called
BPERT, which uses concepts from Bayesian decision theory to consider
these two categories of cost explicitly in deriving a project network plan.)
Another problem that sometimes arises, especially when PERT is used by
subcontractors who work with the government, is the attempt to “beat” the
network in order to get on or off the critical path. Many government contracts
provide cost incentives for finishing a project early or are negotiated on a
“cost-plus-fixed-fee” basis. The contractor who is on the critical path
generally has more leverage in obtaining additional funds from these
contracts because he or she has a major influence in determining the duration
of the project. In contrast, some contractors deem it desirable to be less
“visible” and therefore adjust their time estimates and activity descriptions in
such a way as to ensure that they will not be on the critical path. This

criticism, of course, reflects more on the use of the method than on the
method itself, but PERT and CPM, by virtue of their focus on the critical
path, enable such ploys to be used.
Finally, the cost of applying critical path methods to a project is sometimes
used as a basis for criticism. However, the cost of applying PERT or CPM
rarely exceeds 2% of total project cost. Thus, this added cost is generally
outweighed by the savings from improved scheduling and reduced project
time.
As with any analytic technique, it is important when using CPM and PERT to
understand fully the underlying assumptions and limitations that they impose.
Management must be sure that the people who are charged with monitoring
and controlling activity performance have a working knowledge of the
statistical features of PERT as well as the general nature of critical path
scheduling. Correct application of these techniques can provide a significant
benefit in each phase of the project’s life cycle as long as the above-
mentioned pitfalls are avoided.

9.13 Critical Chain Process
Goldratt (1997) developed the critical chain buffer management (CCBM)
process, which is an application of his theory of constraints to managing and
scheduling projects. The CCBM method addresses several of the criticisms of
PERT. It was noted above that PERT often underestimates the true makespan
of a project. A project manager who solely relies on PERT to determine the
completion time of a project is liable to be overly optimistic and will,
ultimately, disappoint senior management and project sponsors. As a
defensive mechanism against being blamed for a project’s lateness, project
team members respond by inflating or “sandbagging” estimates of activity
duration times.
With CCBM, several alterations are made to traditional PERT in an attempt
to circumvent these shortcomings. First, all individual activity slack, or
“buffer,” becomes a single, project buffer. Each team member, responsible
for his or her component of the activity network, creates a duration estimate
free from any padding—one, say, that is based on a 50% probability of
success. All activities on the critical chain (path) are linked with minimal
time padding. Even if the delivery date of individual activities is missed, as
they are likely to do 50% of the time, the overall effect on the project’s
duration is minimized because of the downstream aggregated buffer.
CCBM distinguishes between its use of buffer and the traditional PERT use
of project slack. With the PERT approach, project slack is a function of the
overall completed activity network. In other words, slack is an outcome of
the task dependencies. In contrast, CCBM’s buffer is used as an a priori
planning input that is based on the application of an aggregated project buffer
that is added onto the schedule.
Setting the size of the project buffer follows a heuristic rule of thumb.
According to Newbold, “In practice, we want buffer sizes that are good
enough . . . The data just aren’t good enough to support precision or complex
calculations.” He suggests that the project buffer be set to

( ∑ tasks j on the critical path ( b j − d ^ j ) 2 ) ) 0.5 .
The value of the project buffer is added to the PERT estimate of makespan in
order to provide a more realistic and more conservative estimate of overall
project completion time.
Intuitively, the buffer can be conceptualized as follows. In practice, project
managers will not allocate all of a project’s budget. Some funds are purposely
held out in order to protect the project from cost overruns and to have funds
available for contingencies that inevitably arise in the real word. Similarly,
the buffers in the CCBM method represent a holding back of time that is
available for the project. A project manager recognizes that schedule
overruns—much like cost overruns—are inevitable in practice. By setting up
time buffers as proscribed by CCBM, a project manager has spare time left
over to handle activities that are delayed and still meet a project’s due date.
Proponents of CCBM argue that it is more than a new scheduling technique,
representing instead a different paradigm by which project management
should be viewed. The CCBM paradigm argues for truth in activity duration
estimation, a “just in time” approach to scheduling noncritical activities, and
greater discipline in project scheduling and control as a result of more open
communication among internal project stakeholders.
The newness of CCBM is a point refuted by some who see the technique as
either ill-suited to many types of projects or simply a reconceptualization of
well-understood scheduling methodologies (e.g., PERT). Nevertheless, a
growing body of case studies and proponents is emerging to champion the
CCBM process as it continues to diffuse throughout project organizations.
Nevertheless, critical chain project management is not without its critics.
Several arguments against the process include the following charges and
perceived weaknesses in the methodology:
1. Lack of project milestones makes coordinated scheduling, particularly
with external suppliers, highly problematic. Critics contend that the lack
of in-process project milestones adversely affects the ability to
coordinate schedule dates with suppliers who provide the external
delivery of critical components.

2. Although it may be true that CCBM brings increased discipline to
project scheduling, efficient methods for applying this technique to a
firm’s portfolio of projects are unclear; that is, CCBM seems to offer
benefits on a project-by-project basis, but its usefulness at the program
level has not been proved. Furthermore, because CCBM argues for
dedicated resources in a multiproject environment where resources are
shared, it is impossible to avoid multitasking, which severely limits its
power.
3. Evidence of its success is still almost exclusively anecdotal and based on
single-case studies. Debating the merits and pitfalls of CCBM has
remained largely an intellectual exercise among academics and writers
of project management theory. No large-scale empirical research exists
to either confirm or refute its efficacy.
4. Critics also charge that Goldratt’s evaluation of duration estimation is
overly negative and critical, suggesting that his contention of huge levels
of activity duration estimation “padding” is exaggerated.
Of course, it must be remembered that models, whether associated with
CPM, PERT, or CCBM, are simplifications of reality designed to support
analysis and decision making by focusing on the most important aspects of
the problem. They should be judged not so much by their fidelity with the
actual system but by the insight that they provide, by the certainty with which
they show the correct consequences of the working assumptions, and by the
ease with which the problem structure can be communicated.

9.14 Scheduling Conflicts
The discussion so far assumed that the only constraints on the schedule are
precedence relations among activities. On the basis of these constraints, the
early and late time of each event and the early and late start and finish of each
activity are calculated.
In most projects, there are additional constraints that must be addressed, such
as those associated with resource availability and the budget. In some cases,
ready time and due-date constraints also exist. These constraints specify a
time window in which an activity must be performed. In addition, there may
be a target completion date for the project or a due date for a milestone. If
these due dates are earlier than the corresponding dates derived from the
CPM analysis, then the accompanying schedule will not be feasible.
There are several ways to handle these types of infeasibilities, such as
Reducing some activity durations by allocating more resources to them.
This approach is discussed in Chapter 10.
Eliminating some activities or reducing their lengths by using a more
effective technology. For example, conventional painting, which
requires the application of several layers of paint and a long drying time,
may be replaced by anodizing—a faster but more expensive process. In
practice, the scope of a project may also be reduced. For example,
certain features of an IT system may be scaled back during the course of
a project, as a project team realizes that delivering the original, specified
requirements is infeasible. As resources were required to be allocated to
unplanned contingencies, fewer resources were left over to meet the
project’s original scope.
Replacing some precedence relations of the “finish to start” type by
other precedence relations, such as “start to start,” without affecting
quality, cost, or performance. When this is possible, a significant
amount of time may be saved.

It is common to start the scheduling analysis with each activity being
performed in the most economical way and assuming “finish to start”
precedence relations. If infeasibility is detected, then one or more of the
foregoing courses of action can be used to circumnavigate the cause of the
problem.
TEAM PROJECT Thermal
Transfer Plant
A detailed schedule is now required for the project. Major milestones
suggested by Total Manufacturing Solutions, Inc.’s (TMS) contract
department follow:
Milestones Time from project start(weeks)
Initial drawing  2
Order parts and materials  3
Initial drawing approval or revisions  4
Drawings revised and approved  5
Schedule production  5
Begin production  6
Document final testing procedures  6
Finish assembly/begin testing  9
Documentation, maintenance, and user
manuals  9
Ship tested unit to site 11
Install on site 13
Final testing and operator personnel
training 14
Customer satisfaction check 16
Your assignment is to prepare a list of activities and a detailed schedule (on a

daily basis) for the project team and an upper level schedule for TMS
management. The detailed schedule should consist of up to 50 activities; the
upper level schedule should contain approximately 20 activities.
In your report, explain each task and activity and its corresponding WBS and
OBS units, the type of precedence relations among activities, the way activity
duration was estimated, and your confidence in these estimates. Use a
network model to develop the schedule and a LRC to identify its relationship
to OBS units. Present the schedule as a Gantt chart and as a table of activities
and events with their corresponding times and slacks.
Discuss the range of schedules that can be adopted for this project, and
explain the methodology by which your team has selected the most
appropriate schedule. Present a “what if” analysis for your final choice,
testing its sensitivity to important sources of uncertainty.

Discussion Questions
1. What objectives, variables, and constraints should be considered in
developing a project schedule?
2. If a project, by definition, is something that is not performed on a
regular basis, then how can activity times be estimated?
3. What are the advantages and disadvantages of the five project activity-
duration estimation techniques presented in Section 9.2?
4. What are the major characteristics that must be present in a project to
use network techniques?
5. The “finish to start” precedence relation is the most common found in
projects. Give some examples in which “start to start,” “end to end,” and
“start to finish” precedence relations arise.
6. Identify some projects where PERT and CPM are inappropriate.
Explain.
7. How can the LP model in Section 9.9 be expanded to include resource
constraints that might arise as a result of, say, the limited availability of
equipment or technical personnel?
8. Discuss a project in which scheduling is not important. Explain why this
project is not sensitive to scheduling decisions.
9. Compare and list the relative advantages of (a) the Gantt chart, (b) CPM
analysis, and (c) the basic PERT approach to scheduling.
10. Is it possible for a project team to achieve high efficiency without
scheduling tasks and activities? Discuss.
11. “To excel in time-based competition, the early-start schedule should
always be implemented.” Discuss.

12. “To maximize the net present value of a project, all cash-generating
activities should begin on their early start, whereas all cost-generating
activities should begin on their late start.” Discuss.

Exercises
1. 9.1 A project is defined by the list of activities in Table 9.13
TABLE 9.13
Activity Immediate predecessors Duration (days)
A –  3
B –  4
C –  3
D C  2
E B  1
F A  5
G B  2
H B  3
I C 11
J D, E  3
K F, G  1
L K  4
M J, H  4
1. Draw the AOA network.
2. Draw the AON network.
3. Find the critical path.
4. Find the total slack and free slack of each activity.
5. Suppose that activities A, C, I are subject to uncertainty and that
only the following time estimates are available:

Activity a m b
A 2  4  5
C 1  3  4
I 8 11 15
Calculate the probability that the project will be completed in d days, for
d=10, 12, 14, 16, 18, 20. Plot the probability as a function of d.
2. 9.2 Estimate the time that it will take you to learn a new computer
software package that combines a spreadsheet with statistical analysis.
Explain how the estimate was made and what accuracy you think it has.
3. 9.3 Use the modular technique to estimate the time required to prepare a
proposal or business plan for manufacturing a new medical device that
analyzes blood enzymes.
4. 9.4 Use the benchmark job technique to estimate the time required to
type a 50-page paper and prepare figures using a computer graphics
package.
5. 9.5 Develop a linear regression model to estimate the dependent variable
“time to type a paper” as a function of two or more independent
variables.
6. 9.6 Develop a list of activities for the project “designing a new house.”
Estimate the duration of each activity, and define the precedence
relations among them. How much uncertainty exists in each activity?
The project ends when the plans and documents have been finalized.
7. 9.7 Develop an early-start and a late-start schedule for the project in
Exercise 9.6 using a Gantt chart. Identify the critical path, and calculate
the slack of noncritical activities.
8. 9.8 Develop the AOA network for the project in Exercise 9.6 . Calculate
the early time and the late time of each event and the early start, early
finish, late start, and late finish of each activity.

9. 9.9 Develop an AON network model for the project in Exercise 9.6 .
10. 9.10 Develop a linear program that generates the schedule for the project
in Exercise 9.6 .
11. 9.11 Develop a high-level AOA model for the project “designing and
building a new house.”
12. 9.12 Suppose that the project mentioned in Exercise 9.11 must be
finished 2 months before the early finish time. How would you solve
this scheduling conflict?
13. 9.13 Caryn Johnson is in charge of relocating (“reconductoring”) 1,700
ft of 13.8-kilovolt overhead primary line as a result of the widening of
the road section in which the line is presently installed. Table 9.14
summarizes the activities for the project. Draw the network model for
her, and carry out the critical path computations.
TABLE 9.14
Activity Description Immediatepredecessors
Duration
(days)
A Job review –  1
B Advise customers oftemporary outage A 0.5
C Requisition stores A  1
D Scout job A 0.5
E Secure poles andmaterials C, D  3
F Distribute poles E 3.5
G Pole locationcoordination D 0.5
H Re-stake G 0.5
I Dig holes H  3

J Frame and set poles F, I  4
K Cover old conductors F, I  1
L Pull new conductors J, K  2
M Install remainingmaterial L  2
N Sag conductor L  2
O Trim trees D  2
P De-energize and switchlines B, M, N, O 0.1
Q Energize and phase newline P 0.5
R Clean up Q  1
S Remove old conductor Q  1
T Remove old poles S  2
U Return material to stores R, T  2
14. 9.14 Thomas Cruise wants to buy a new motorboat and has summarized
the associated activities in Table 9.15 . Draw the AOA network model
and carry out the critical path computations for him.
15. 9.15 For Exercise 9.14 , compute the total slacks and free slacks, and
summarize the critical path calculations using the format in Table 9.5 .
16. 9.16 Determine the critical path(s) for projects (a) and (b) in the AOA
networks in Figure 9.43 .
17. 9.17 For Exercise 9.16 , compute the total slacks and free slacks, and
summarize the critical path calculations in a tabular format.
TABLE 9.15
Activity Description Immediatepredecessors
Duration
(days)
A Conduct feasibility study –  3

B Find potential customer for
present boat
A 14
C List possible models A  1
D Research all possiblemodels C  3
E Conduct interviews withmechanics C  1
F Collect dealer propaganda C  2
G Compile and organize allpertinent information D, E, F  1
H Choose top three models G  1
I Test-drive all three choices H  3
J Gather warranty andfinancing information H  2
K Choose one boat I,J  2
L Compare dealers andchoose dealer K  2
M Search for desired colorand options L  4
N Test-drive chosen modelonce again L  1
O Purchase new boat B, M, N  3

Figure 9.43
Networks for Exercise 9.16 .
Figure 9.43 Full Alternative Text
18. 9.18 In Exercise 9.16 , suppose that the estimates (a, b, m) are given in
Table 9.16 and that activity times follow a beta distribution. Use the data
in the table to calculate the expected activity times, d ^ ij , and then
compute the critical path for each event using d ^ ij as the completion
time for activity (i, j). Assume that the solution you obtain is the
“planned” time to complete each event, and then find the probabilities
that the events will occur without delay.

TABLE 9.16
Project (a)
Activity (a, b, m) Activity (a, b, m)
1, 2 (5, 8, 6) 3, 6 (3, 5, 4)
1, 4 (1, 4, 3) 4, 6 (4, 10, 8)
1, 5 (2, 5, 4) 4, 7 (5, 8, 6)
2, 3 (4, 6, 5) 5, 6 (9, 15, 10)
2, 5 (7, 10, 8) 5, 7 (4, 8, 6)
2, 6 (8, 13, 9) 6, 7 (3, 5, 4)
3, 4 (5, 10, 9 )
Project (b)
Activity (a, b, m) Activity (a, b, m)
1, 2 (1, 4, 3) 3, 7 (12, 14, 13)
1, 3 (5, 8, 7) 4, 5 (10, 15, 12)
1, 4 (6, 9, 7) 4, 7 (8, 12, 10)
1, 6 (1, 3, 2) 5, 6 (7, 11, 8)
2, 3 (3, 5, 4) 5, 7 (2, 8, 4)
2, 5 (7, 9, 8) 6, 7 (5, 7, 6)
3, 4 (10, 20, 15)
19. 9.19 Product Development. Consider the simplified set of activities in
Table 9.17 for the development of a consumer product from initiation
through the market test phase.
TABLE 9.17
Activity Symbol Immediatepredecessors
Time
estimate
(weeks)
Investigate demand A – 3

Develop pricing
strategy
B – 1
Design product C – 5
Conduct promotional
cost analysis D A 1
Manufacture
prototype models E C 6
Perform product cost
analysis F E 1
Perform final pricing
analysis G B, D, F 2
Conduct market test H G 8
1. Draw the AOA network for this project.
2. Calculate total slacks and free slacks, and interpret their meaning.
3. Determine the critical path and interpret its meaning.
4. Construct a Gantt chart and mark the latest start times for each
activity.
20. 9.20 For the product development project in Exercise 9.19 , consider the
detailed time estimates given in Table 9.18 . Note that the time estimates
in Exercise 9.19 are equivalent to modal time estimates in this exercise.
TABLE 9.18
Time estimate (weeks)
Activity Optimistic Most likely Pessimistic
A 1 3  4
B 1 1  2
C 4 5  9
D 1 1  1

E 4 6 12
F 1 1  2
G 1 2  3
H 6 8 10
1. Relabel your network in Exercise 9.19 to include d ^ ij (in place of
d ij and s ^ ij . Use Eqs. 9.1 () and (9.2).
2. Compare total slacks and free slacks to Exercise 9.19 .
3. Has the critical path changed?
4. Determine the following probabilities:
1. That the project will be completed in 22 weeks or less
2. That the project will be completed by the date obtained from
the critical path calculations using d ^ ij as the activity
durations
3. That the project takes more than 30 weeks to complete
21. 9.21 Criticism of the traditional PERT equations in Section 9.2.1 for
estimating the means and standard deviations of activities has led to the
development of alternative formulas by Perry and Greig (1975):
d ^ ij = a ij +0.95 m ij + b ij 2.95 (9.14)
s ^ ij = b ij − a ij 3.25 (9.15)
where a ij and b ij are estimates for the 5 and 95 percentiles of the
probability distribution of activity (i, j), and m ij is the mode. Use these
equations to recalculate d ^ ij and s ^ ij and answer the same questions
as in Exercise 9.19 . Compare the results.
22. 9.22 Space Module Assembly. An aerospace company has received a
contract from NASA for the final assembly of a space module for an
upcoming mission. A team of engineers has determined the activities,
precedence constraints, and time estimates as given in Table 9.19 .

TABLE 9.19
Activity Symbol Immediatepredecessors
Time
estimate
(days)
Construct shell of module A – 30
Order life support system and
scientific experimentation
package from same supplier
B – 15
Order components of control
and navigational system C – 25
Wire module D A  3
Assemble control and
navigational system E C  7
Preliminary test of life
support system F B  1
Install life support in module G D, F  5
Install scientific
experimentation package in
module
H D, F  2
Preliminary test of control
and navigational system I E, F  4
Install control and
navigational system in
module
J H, I 10
Final testing and debugging K G, J  8
1. Draw the AOA network for this project. (Hint: You should have 10
events and two dummy activities.)
2. Calculate total slacks and free slacks, and interpret their meaning.
3. Determine the critical path and interpret its meaning.

4. Construct a Gantt chart and identify scheduling flexibilities.
23. 9.23 A more careful analysis of time estimates for the space module
assembly of the preceding exercise is given in Table 9.20 . Note that the
“most likely estimates” are identical to the “time estimates” in Exercise
9.22 .
TABLE 9.20
Time estimate (weeks)
Activity Optimistic Most likely Pessimistic
A 25 30 45
B 10 15 20
C 20 25 35
D  3  3  5
E  5  7 12
F  1  1  1
G  4  5  7
H  2  2  3
I  4  4  6
J  8 10 14
K  6  8 15
1. Relabel your network in Exercise 9.22 to include d ^ ij (in place of
d ij ) and s ^ ij . Use Eqs. 9.1 () and (9.2).
2. Compare total slacks and free slacks to Exercise 9.22 .
3. Has the critical path changed?
4. Determine the following probabilities:
1. That the project will be completed in 54 days or less.

2. That the project will be completed by the date obtained from
the critical path calculations using d ^ ij as the activity
durations.
3. That the project takes more than 70 days to complete.
24. 9.24 Use Eqs. 9.14 () and (9.15) to recalculate d ^ ij and s ^ ij and
answer the same questions as in Exercise 9.23 . Compare the results.
25. 9.25 As part of an R&D project, it is required to produce 60 circuit
boards using a specific piece of equipment. According to the equipment
specification, its design capacity is 0.4 board per hour. However, past
experience indicates that significantly more time will be required. In
particular, the following frequency data were collected over a 1-week
period when the machine was working on other jobs.
Activity Frequency
Machine is working on a job 67
Parts are being fed to the machine  6
Maintenance is being performed  9
Machine is waiting for parts 22
1. Estimate the actual machine capacity.
2. How long will it take to complete the 60 boards?
3. If you want the capacity estimate to be within ±5% of the true value
with a 95% level of confidence, then what should the sample size
be? Assume that the capacity estimate is normally distributed.
26. 9.26 The project manager did not accept the approach that you proposed
in Exercise 9.25 and suggested the use of a parametric equation to
estimate the machine’s capacity.
1. Give an example of the type of data that should be collected to
develop such an equation.

2. Furnish an example of such an equation and demonstrate how to
use it.
3. State the assumptions used in employing this approach.
27. 9.27 Consider the precedence relations given in Table 9.21 .
TABLE 9.21
Activity Immediate predecessors Weeks
A – 1
B A 4
C A 3
D A 7
E B 6
F C, D 2
G E, F 7
H D 9
I G, H 4
1. Draw an early-start Gantt chart.
2. Draw the AON network for this project.
3. Draw the AOA network.
4. Generate all possible paths for the AOA network, calculate their
duration, and analyze the findings.
5. Calculate ES, EF, LF, and LS for each activity.
6. Calculate the slacks for the activities.
28. 9.28 There is uncertainty regarding the duration of activities D and E in
the project described in Exercise 9.27 expressed by the following data:

Time (weeks)
Activity Optimistic Most likely Pessimistic
D 6 7 8
E 5 6 9
1. Using an early-start approach, calculate the probability of
completing the project within 22 weeks or less.
2. Repeat part (a) using a late-start approach. State your assumptions
in both cases.

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Appendix 9A Least-Squares
Regression Analysis
In the least squares method, we define the residual, ei, or deviation from the
estimated line, Y ^ = b ^ 0 + b ^ 1 X, for each point i as follows:
e i = Y i − Y ^ i
These residuals will be positive or negative depending on whether the actual
point lies above or below the line. If they are squared and summed, the
resultant quantity must be nonnegative and will vary directly with the spread
of the points from the line. Different pairs of values for b ^ 0 and b ^ 1 will
give different lines and hence different values for the sum of the squared
residuals about the line. Thus, we have
∑ i=1 n e i 2 =f( b ^ 0 , b ^ 1 )
where the function f( ⋅,⋅ ) depends on the model being considered.
The principle of least squares is that the parameter estimates b ^ 0 and b ^ 1
should be chosen to make ∑ i=1 n e i 2 as small as possible; that is,
min ∑ i=1 n e i 2 =min ∑ i=1 n ( Y i − Y ^ i ) 2 =min ∑ i=1 n ( Y i − b ^ 0
− b ^ 1 X i ) 2
From calculus we know that the first-order necessary (and sufficient, in this
case) condition for optimality is that the partial derivatives with respect to b ^
0 and b ^ 1 must be zero. Taking partial derivatives, setting the results to zero
and solving yields
b ^ 1 = ∑ i=1 n ( X i − X ¯ )( Y i − Y ¯ ) ∑ i=1 n ( X i − X ¯ ) 2 and b ^ 0 =
Y ¯ − b ^ 1 X ¯
where

X ¯ = 1 n ∑ i=1 n X i and Y ¯ = 1 n ∑ i=1 n Y i
Given these estimates, an important question is: How good are they?
Elementary treatment of the relationship between two variables usually
emphasizes their correlation coefficient, R, which is computed as follows:
R= ∑ i=1 n ( X i − X ¯ )( Y i − Y ¯ ) ∑ i=1 n ( X i − X ¯ ) 2 ∑ i=1 n ( Y i − Y
¯ 2 )
This value can vary between −1 and +1. The closer it is to either extreme, the
better the fit. A related value is R 2 , sometimes known as the coefficient of
determination, which can be calculated variously as
R 2 = ∑ i=1 n ( Y ^ i − Y ¯ ) 2 ∑ i=1 n ( Y i − Y ¯ ) 2 =1− ∑ i=1 n e i 2 ∑ i=1
n ( Y i − Y ¯ ) 2
From the right-hand expression it should be clear that the maximum value of
R 2 is unity. This can occur only when ∑ i=1 n e i 2 =0; that is, when every e i
is zero so that all of the points on the scatter diagram line lie on a straight
line. The minimum value of R 2 is zero, which occurs when ∑ i=1 n e i 2 = ∑
i=1 n ( Y i − Y ¯ ) 2 ; that is, when each point on the regression line Y ^ i = Y
¯ so the explained variation is zero.
The coefficient of determination is equivalent to the proportion of the Y
variance explained by the linear influence of X. An R value of 0.9 therefore
indicates that the least-squares regression of Y on X accounts for 81% of the
variance in Y.

Appendix 9B Learning Curve
Tables
TABLE 9B.1 Learning Curve
Values for n β
Percent learning curve
Repetitions 60% 65% 70% 75% 80% 85% 90% 95%
1   1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
2   0.6000 0.6500 0.7000 0.7500 0.8000 0.8500 0.9000 0.9500
3   0.4450 0.5052 0.5682 0.6338 0.7021 0.7729 0.8462 0.9219
4   0.3600 0.4225 0.4900 0.5625 0.6400 0.7225 0.8100 0.9025
5   0.3054 0.3678 0.4368 0.5127 0.5956 0.6857 0.7830 0.8877
6   0.2670 0.3284 0.3977 0.4754 0.5617 0.6570 0.7616 0.8758
7   0.2383 0.2984 0.3674 0.4459 0.5345 0.6337 0.7439 0.8659
8   0.2160 0.2746 0.3430 0.4219 0.5120 0.6141 0.7290 0.8574
9   0.1980 0.2552 0.3228 0.4017 0.4930 0.5974 0.7161 0.8499
10   0.1832 0.2391 0.3058 0.3846 0.4765 0.5828 0.7047 0.8433
12   0.1602 0.2135 0.2784 0.3565 0.4493 0.5584 0.6854 0.8320
14   0.1430 0.1940 0.2572 0.3344 0.4276 0.5386 0.6696 0.8226
16   0.1296 0.1785 0.2401 0.3164 0.4096 0.5220 0.6561 0.8145
18   0.1188 0.1659 0.2260 0.3013 0.3944 0.5078 0.6445 0.8074
20   0.1099 0.1554 0.2141 0.2884 0.3812 0.4954 0.6342 0.8012
22   0.1025 0.1465 0.2038 0.2772 0.3697 0.4844 0.6251 0.7955
24   0.0961 0.1387 0.1949 0.2674 0.3595 0.4747 0.6169 0.7904
25   0.0933 0.1353 0.1908 0.2629 0.3548 0.4701 0.6131 0.7880
30   0.0815 0.1208 0.1737 0.2437 0.3346 0.4505 0.5963 0.7775

35   0.0728 0.1097 0.1605 0.2286 0.3184 0.4345 0.5825 0.7687
40   0.0660 0.1010 0.1498 0.2163 0.3050 0.4211 0.5708 0.7611
45   0.0605 0.0939 0.1410 0.2060 0.2936 0.4096 0.5607 0.7545
50   0.0560 0.0879 0.1336 0.1972 0.2838 0.3996 0.5518 0.7486
60   0.0489 0.0785 0.1216 0.1828 0.2676 0.3829 0.5367 0.7386
70   0.0437 0.0713 0.1123 0.1715 0.2547 0.3693 0.5243 0.7302
80   0.0396 0.0657 0.1049 0.1622 0.2440 0.3579 0.5137 0.7231
90   0.0363 0.0610 0.0987 0.1545 0.2349 0.3482 0.5046 0.7168
100   0.0336 0.0572 0.0935 0.1479 0.2271 0.3397 0.4966 0.7112
120   0.0294 0.0510 0.0851 0.1371 0.2141 0.3255 0.4830 0.7017
140   0.0262 0.0464 0.0786 0.1287 0.2038 0.3139 0.4718 0.6937
160   0.0237 0.0427 0.0734 0.1217 0.1952 0.3042 0.4623 0.6869
180   0.0218 0.0397 0.0691 0.1159 0.1879 0.2959 0.4541 0.6809
200   0.0201 0.0371 0.0655 0.1109 0.1816 0.2887 0.4469 0.6757
250   0.0171 0.0323 0.0584 0.1011 0.1691 0.2740 0.4320 0.6646
300   0.0149 0.0289 0.0531 0.0937 0.1594 0.2625 0.4202 0.6557
350   0.0133 0.0262 0.0491 0.0879 0.1517 0.2532 0.4105 0.6482
400   0.0121 0.0241 0.0458 0.0832 0.1453 0.2454 0.4022 0.6419
450   0.0111 0.0224 0.0431 0.0792 0.1399 0.2387 0.3951 0.6363
500   0.0103 0.0210 0.0408 0.0758 0.1352 0.2329 0.3888 0.6314
600   0.0090 0.0188 0.0372 0.0703 0.1275 0.2232 0.3782 0.6229
700   0.0080 0.0171 0.0344 0.0659 0.1214 0.2152 0.3694 0.6158
800   0.0073 0.0157 0.0321 0.0624 0.1163 0.2086 0.3620 0.6098
900   0.0067 0.0146 0.0302 0.0594 0.1119 0.2029 0.3556 0.6045
1,000 0.0062 0.0137 0.0286 0.0569 0.1082 0.1980 0.3499 0.5998
1,200 0.0054 0.0122 0.0260 0.0527 0.1020 0.1897 0.3404 0.5918
1,400 0.0048 0.0111 0.0240 0.0495 0.0971 0.1830 0.3325 0.5850
1,600 0.0044 0.0102 0.0225 0.0468 0.0930 0.1773 0.3258 0.5793
1,800 0.0040 0.0095 0.0211 0.0446 0.0895 0.1725 0.3200 0.5743
2,000 0.0037 0.0089 0.0200 0.0427 0.0866 0.1683 0.3149 0.5698
2,500 0.0031 0.0077 0.0178 0.0389 0.0806 0.1597 0.3044 0.5605
3,000 0.0027 0.0069 0.0162 0.0360 0.0760 0.1530 0.2961 0.5530

TABLE 9B.2 Cumulative
Learning Curve Values for n β
Percent learning curve
Repetitions 60% 65% 70% 75% 80% 85% 90% 95%
1   1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
2   1.600 1.650 1.700 1.750 1.800 1.850 1.900 1.950
3   2.045 2.155 2.268 2.384 2.502 2.623 2.746 2.872
4   2.405 2.578 2.758 2.946 3.142 3.345 3.556 3.774
5   2.710 2.946 3.195 3.459 3.738 4.031 4.339 4.662
6   2.977 3.274 3.593 3.934 4.299 4.688 5.101 5.538
7   3.216 3.572 3.960 4.380 4.834 5.322 5.845 6.404
8   3.432 3.847 4.303 4.802 5.346 5.936 6.574 7.261
9   3.630 4.102 4.626 5.204 5.839 6.533 7.290 8.111
10   3.813 4.341 4.931 5.589 6.315 7.116 7.994 8.955
12   4.144 4.780 5.501 6.315 7.227 8.244 9.374 10.62
14   4.438 5.177 6.026 6.994 8.092 9.331 10.72 12.27
16   4.704 5.541 6.514 7.635 8.920 10.38 12.04 13.91
18   4.946 5.879 6.972 8.245 9.716 11.41 13.33 15.52
20   5.171 6.195 7.407 8.828 10.48 12.40 14.61 17.13
22   5.379 6.492 7.819 9.388 11.23 13.38 15.86 18.72
24   5.574 6.773 8.213 9.928 11.95 14.33 17.10 20.31
25   5.668 6.909 8.404 10.19 12.31 14.80 17.71 21.10
30   6.097 7.540 9.305 11.45 14.02 17.09 20.73 25.00
35   6.478 8.109 10.13 12.72 15.64 19.29 23.67 28.86
40   6.821 8.631 10.90 13.72 17.19 21.43 26.54 32.68
45   7.134 9.114 11.62 14.77 18.68 23.50 29.37 36.47
50   7.422 9.565 12.31 15.78 20.12 25.51 32.14 40.22
60   7.941 10.39 13.57 17.67 22.87 29.41 37.57 47.65
70   8.401 11.13 14.74 19.43 25.47 33.17 42.87 54.99

80   8.814 11.82 15.82 21.09 27.96 36.80 48.05 62.25
90   9.191 12.45 16.83 22.67 30.35 40.32 53.14 69.45
100   9.539 13.03 17.79 24.18 32.65 43.75 58.14 76.59
120   10.16 14.11 19.57 27.02 37.05 50.39 67.93 90.71
140   10.72 15.08 21.20 29.67 41.22 56.78 77.46 104.7
160   11.21 15.97 22.72 32.17 45.20 62.95 86.80 118.5
180   11.67 16.79 24.14 34.54 49.03 68.95 95.96 132.1
200   12.09 17.55 25.48 36.80 52.72 74.79 105.0 145.7
250   13.01 19.28 28.56 42.08 61.47 88.83 126.9 179.2
300   13.81 20.81 31.34 46.94 69.66 102.2 148.2 212.2
350   14.51 22.18 33.89 51.48 77.43 115.1 169.0 244.8
400   15.14 23.44 36.26 55.75 84.85 127.6 189.3 277.0
450   15.72 24.60 38.48 59.80 91.97 139.7 209.2 309.0
500   16.26 25.68 40.58 63.68 98.85 151.5 228.8 340.6
600   17.21 27.67 44.47 70.97 112.0 174.2 267.1 403.3
700   18.06 29.45 48.04 77.77 124.4 196.1 304.5 465.3
800   18.82 31.09 51.36 84.18 136.3 217.3 341.0 526.5
900   19.51 32.60 54.46 90.26 147.7 237.9 376.9 587.2
1,000 20.15 34.01 57.40 96.07 158.7 257.9 412.2 647.4
1,200 21.30 36.59 62.85 107.0 179.7 296.6 481.2 766.6
1,400 22.32 38.92 67.85 117.2 199.6 333.9 548.4 884.2
1,600 23.23 41.04 72.49 126.8 218.6 369.9 614.2 1001.0
1,800 24.06 43.00 76.85 135.9 236.8 404.9 678.8 1116.0
2,000 24.83 44.84 80.96 144.7 254.4 438.9 742.3 1230.0
2,500 26.53 48.97 90.39 165.0 296.1 520.8 897.0 1513.0
3,000 27.99 52.62 98.90 183.7 335.2 598.9 1047.0 1791.0

Appendix 9C Normal Distribution
Function
TABLE 9C.1 Cumulative
Probabilities of the Normal
Distribution (areas under the
standardized normalized curve
from −∞ to z)
z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359
0.1 0.5389 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753
0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141
0.3 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517
0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879
0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224
0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549
0.7 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852
0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133
0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389
1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621
1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830
1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015

1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177
1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633
1.8 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706
1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990
3.1 0.9990 0.9991 0.9991 0.9991 0.9992 0.9992 0.9992 0.9992 0.9993 0.9993
3.2 0.9993 0.9993 0.9994 0.9994 0.9994 0.9994 0.9994 0.9995 0.9995 0.9995
3.3 0.9995 0.9995 0.9995 0.9996 0.9996 0.9996 0.9996 0.9996 0.9996 0.9997
3.4 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997 0.9998
Table 9C.1 Full Alternative Text

Chapter 10 Resource Management
10.1 Effect of Resources on Project
Planning
In project scheduling as discussed in Chapter 9, we assumed that the
precedence relations among activities are the sole constraints. On the basis of
this assumption, each activity could start as soon as all of its predecessors
were completed (assuming finish-to-start precedence relations). This type of
analysis is based on the implicit assumption that there are enough resources
available to permit any number of activities to be scheduled simultaneously.
In practice, management and deployment of resources is a key priority for a
project manager. A PERT/CPM schedule is almost always infeasible in
practice, once resources are considered. Since all large-scale projects involve
scarce resources of various types, including personnel and equipment, a
project manager must be prepared to deal with deviations from the
“unconstrained” PERT/CPM proscribed schedule.
Resource planning is the process by which a project manager decides which
resources to obtain, from which sources, when to obtain them, how to use
them, and when and how to release them. Project resource planning is mainly
concerned with the tradeoff analysis between (1) the cost of alternative
schedules designed to accommodate resource shortages, and (2) the cost of
using alternative resources; for example, overtime to meet a schedule or
subcontracting to accommodate a schedule change. Tradeoffs between
project completion and deployment of resources may be subject to constraints
on resource availability, budget allocations, and task deadlines. For different
projects, this tradeoff will yield different decisions. For example, a high-
priority project with a visible and tight due date may require the deployment
of additional, high-priced subject-matter experts. In this case, the objective of
project completion is paramount. On the other hand, when a project
completion date is more flexible, fewer and more standard resources may be

utilized.
An important function of the project manager is to monitor and control
resource use and performance during project execution. If scarce resources
are deployed efficiently, a project manager can effectively reduce the cost
and makespan of a project. A project manager—by definition—is someone
who is hired to deal with the inevitable variability that arises on projects. As
part of coping with unplanned events, a project manager must be adept at
repositioning and redeploying resources. For example, weather events can
significantly disrupt an airline schedule. The schedule management
department of a major airline carrier—functioning as project managers—
must be prepared to redeploy aircraft, crews, and passengers in an efficient
manner while satisfying customer service, government, and labor union
requirements.
Project resources are aggregated through the budget and expended over time.
The relationship between the project budget and schedule will be discussed in
Chapter 11.

10.2 Classification of Resources
Used in Projects
Project resources can be classified in several ways. One approach is based on
accounting principles, which distinguish between labor costs (human
resources), material costs, and other costs, such as subcontracting and
borrowing. This classification scheme is very useful for budgeting and
accounting. Its major drawbacks are that it does not specifically include the
cost of the less tangible resources such as information (blueprints, databases),
and it does not capture a critical aspect of project resource management—the
availability of resources.
A second approach is based on resource availability. Some resources are
available at the same level in every time period (e.g., a fixed workforce).
These are renewable resources. Other resources are allocated in a lump sum
at the beginning of the project and are used up over time. These are
depletable resources such as material. A third class of resources is available
in limited quantities each period. However, their total availability throughout
the project is also circumscribed. These are called doubly constrained
resources. The cash available for a project is a typical example of a doubly
constrained resource. Based on this classification, one objective in using
renewable resources is to minimize idle time or to maximize utilization. An
objective in using depletable resources is to maximize “effectiveness”—the
ratio between output and input.
A third classification scheme is similarly based on resource availability. One
set of resources includes all “unconstrained” resources—those that are
available in unlimited quantities for a fixed cost. A typical example is
untrained labor or general-purpose equipment such as a copying machine.
Alternatively, certain resources may be very expensive or impossible to
obtain for the complete duration of the project. Special facilities, such as the
use of a supercomputer and technical experts, who work on many projects,
are two such examples. This group of resources also includes those for which
a given quantity is available for the entire project, such as a rare type of

material that has a long lead time or a high-powered consultant. The quantity
ordered at the beginning of the project must last throughout, because of its
limited supply (in the case of the consultant, a project may purchase a
specified number of consulting hours or days).
Resources may be managed by using an ABC scheme, similar to what is used
in inventory management. Resources in the C category are readily available
and do not require continuous monitoring. In contrast, resources in the A
category have high priority and should be monitored closely because
shortages might significantly affect the project schedule and success. In
general, depletable resources and those limited by periodic availability are
critical resources, and tight controls should be placed on their consumption.
In addition to availability, the cost of resources should be considered when
developing project schedules. The decision of which resources to assign to a
particular activity is critical whenever an activity can be performed by
various resources. The combination of resources (often called the “mode”)
assigned to an activity affects both the duration and the cost of the activity
and may affect the schedule and the cost of the entire project.
Often, it is not possible to accurately allocate resources to activities at the
early stages of a project due to underlying uncertainty that initially shrouds
resource requirements. Therefore, resource planning monitoring and control
is a continuous process that takes place throughout the life cycle of a project.
In a multiproject environment, the assignment of resources to a particular
project has implications for other projects. It is common sense to start the
planning process by assuming that each activity is performed by the
minimum cost resource alternative. This mode of operation is known as the
“normal” mode, and it is associated with the “normal” time and “normal”
cost of the activity. To identify this alternative, the following points should
be considered:
The selection of resources should be designed for maximum flexibility
so that resources that are not essential for one project can be used
simultaneously on other projects. This flexibility can be achieved by
buying general-purpose equipment and by broadly training employees.

Up to a certain point, the more of a particular resource used, the less
expensive it is per unit time (as a result of savings in setup cost, greater
learning, and economies of scale).
The marginal contribution of a resource decreases with usage.
Frequently, when increasing the quantity of a resource type assigned to
an activity, a point at which additional resources do not shorten the
activity’s duration is reached. That is, inefficiencies and diminishing
returns set in.
Some resources are discrete. When this is the case, decreasing resource
levels, necessarily in integer quantities, could result in a sharp decline in
productivity and efficiency.
Resources are organizational assets. Resource planning should take into
consideration not only what is best for an individual project but also
what is best for the organization as a whole.
The organization has better control over its own resources. When the
choice of acquiring or subcontracting for a resource exists, the degree of
availability and control should be weighed against cost considerations.
The output of each resource is measured by its capacity, which is commonly
defined in two ways:
1. Nominal capacity: maximum output achieved under ideal conditions.
The nominal capacity of equipment is usually contained in its technical
manual. Nominal capacity of labor can be estimated with standard work
measurement techniques commonly used by industrial engineers.
2. Effective capacity: maximum output taking into account the mixture of
activities assigned, scheduling and sequencing constraints, maintenance
aspects, the operating environment, and other resources used in
combination.
Resource planning is relatively easy when a single resource is used in a single
project. When the coordinated use of multiple resources in multiple projects
is called for, planning and scheduling become more complicated, especially

when dependencies exist among several projects. In some cases, it is justified
to use excessive levels of inexpensive, readily available resources in order to
defer utilization of resources that are expensive or in limited supply.
The life cycle of a project affects its resource requirements. In the early
stages, the focus is on design. Thus, highly trained personnel, such as system
analysts, design engineers, and financial planners, are needed. In subsequent
stages, execution becomes dominant, and machines and material
requirements increase. A graph of resource requirements as a function of time
is called a resource profile. An example of labor and material profiles as a
function of a project’s life-cycle stages is presented in Figure 10.1. Curve (a)
depicts the requirements for engineers as a function of time. As can be seen,
demand peaks during the advanced development phase of the project. Curve
(b) displays the requirements for technicians. In this case, the maximum is
reached during the detailed design and production phases. This is also true for
material requirements, as shown in curve (c).

Figure 10.1
Typical resource requirement profiles.
Figure 10.1 Full Alternative Text
The general shape of the profiles depicted in Figure 10.1 can be modified
somewhat by careful planning and control. Slack management is one way to
reshape resource requirements. Because it is always possible to start an
activity within the range defined by its early- and late-start schedules, it may
be possible to achieve higher resource utilization and lower costs by
exploring different assignment patterns. In some projects, limited resource
availability forces the delay of activities beyond their unconstrained
PERT/CPM latest start time. When this happens, project delays are inevitable
unless corrective action can be taken immediately.

10.3 Resource Leveling Subject to
Project Due-Date Constraints
To discuss the relationship between resource requirements and the scheduling
of activities, consider the example project that was introduced in Table 9.2.
Assuming that only a single resource is used (unskilled labor) in the project,
Table 10.1 lists the resource requirements for each of the seven activities.
TABLE 10.1 Resource
Requirements for the Example
Project
Activity
Duration
(weeks)
Required labor
(days per week)
Total labor
(days required)
A 5 8 40
B 3 4 12
C 8 3 24
D 7 2 14
E 7 5 35
F 4 9 36
G 5 7 35
The data in Table 10.1 are based on the assumption that performing an
activity requires that the resource be used at a constant rate. Thus, activity A
requires 8 unskilled labor-days in each of its 5 weeks. When the usage rate is
not constant, resource requirements should be specified for each time period
(a week in our example).

The Gantt chart for the early-start schedule is shown in Figure 10.2a; the
corresponding resource requirement profile is depicted in Figure 10.2b. As
can be seen, the early-start schedule requires a high level of resource usage in
the early stages of the project. During the first 3 weeks, there is a need for 17
labor-days each week. Assuming 5 working days per week, the requirement
during the first 3 weeks is 17/5=3.4 unskilled workers per day. The fractional
component of demand can be met with overtime, second-shift, or part-time
workers. The lowest resource requirements occur in week 13, when only 3
labor-days are needed. Thus, the early-start schedule generates a widely
varying profile, with a high of 17 labor-days per week and a low of 3 labor-
days per week; the range is 17−3=14.
The Gantt chart and resource requirement profile associated with the late-start
schedule are illustrated in Figure 10.3. Because of the effect that scheduling
decisions

Figure 10.2
(a) Gantt chart and (b) resource profile for the early-start schedule.

Figure 10.2 Full Alternative Text
Figure 10.3

(a) Gantt chart and (b) resource profile for the late-start schedule.
Figure 10.3 Full Alternative Text
have on resource requirements, there is a difference between the profiles
associated with the late-start and early-start schedules. In the example, the
late-start schedule moves the maximum resource usage from weeks 1 through
3 to weeks 3 through 5. Furthermore, maximum usage is reduced from 17
labor-days per week to 12 labor-days per week, giving a range of 12−3=9. It
is important to note that the reduction in range while moving from the early-
start to the late-start schedule is not necessarily uniform over the intermediate
cases.
Resource leveling can be defined as the reallocation of total or free slack in
activities to minimize fluctuations in the resource requirement profile. It is
assumed that a more steady usage rate leads to lower resource costs. For
labor, this assumption is based on the proposition that costs increase with the
need to hire, fire, and train personnel. For materials, it is assumed that
fluctuating consumption rates imply an increase in storage requirements
(perhaps to accommodate the maximum expected inventory) and more effort
invested in material planning and control. In practice, project managers strive
to smooth resource requirements over time as a tactic for minimizing
disruption and uncertainty that fluctuations inevitably trigger.
Resource leveling can be performed in a variety of ways, some of which are
described in the references listed at the end of the chapter. A generic
resource-leveling procedure is illustrated next and used to solve the example
project.
1. Calculate the average number of resource-days per period (e.g., week).
In the example, a total of 196 resource-days or labor-days are required.
Because the project duration is 22 weeks, 196/22=8.9 or approximately
9 labor-days per week are required on the average.
2. With reference to the early-start schedule and noncritical activities,
gradually delay activities one at a time, starting with those activities that
have the largest free slack. Check the emerging resource requirement
profile after each delay. Select the schedule that minimizes resource

fluctuations by generating daily resource requirements close to the
calculated average.
Continuing with the example, we see from Table 9.5 that activity E has the
largest free slack (6 weeks). The first step is to delay the start of E by 3 weeks
until the end of activity B. This reduces resource requirements in weeks 1
through 3 by 5 units. The emerging resource profile is:
Week  1  2  3  4  5  6  7  8  9 10 11
Load 12 12 12 13 13 10 10 10 10 10  5
Week 12 13 14 15 16 17 18 19 20 21 22
Load  5  3  9  9  9  9  7  7  7  7  7
This profile has a maximum of 13 and a minimum of 3 labor-days per week.
Because the maximum occurs in weeks 4 and 5 and activity E can be delayed
further, consider a schedule in which E starts after A is finished (after week
5). The resource requirements profile in this case is:
Week  1  2  3  4  5  6  7  8  9 10 11
Load 12 12 12  8  8 10 10 10 10 10 10
Week 12 13 14 15 16 17 18 19 20 21 22
Load 10  3  9  9  9  9  7  7  7  7  7
The maximum resource requirement is now 12 and occurs in weeks 1 through
3. The minimum is still 3, giving a range of 12−3=9. The next candidate for
adjustment is activity B with a free slack of 2 weeks. However, delaying B by
1 or 2 weeks will only increase the load in weeks 4 and 5 from 8 to 12,
yielding a net gain of zero. Therefore, we turn to the last activity with a
positive free slack—activity D, which is scheduled to start at week 5.
Delaying D by 1 week results in the following resource requirement profile:
Week  1  2  3  4  5  6  7  8  9 10 11
Load 12 12 12  8  8  8 10 10 10 10 10

Week 12 13 14 15 16 17 18 19 20 21 22
Load 10  5  9  9  9  9  7  7  7  7  7
The corresponding graph and Gantt chart are depicted in Figure 10.4. Note
that this profile has a range of 12−5=7, which is smaller than that associated
with any of the other candidates, including the early-start and late-start
schedules. This is as far as we can go in minimizing fluctuations without
causing a delay in the entire project.
For small projects, the foregoing procedure works well but cannot always be
relied on to find the optimal profile. To improve the results, a similar
procedure can be executed by starting with the late-start schedule and
checking the effect of moving activities with slack toward the start of the
project. In some projects, the objective may be to keep the maximum
resource utilization below a certain ceiling rather than merely leveling the
resources. If this objective cannot be met by rescheduling the critical
activities, then one or more of them would have to be expanded to reduce the
daily resource requirements.
The analysis is more complicated when several types of resources are used,
the number of activities is large, and several projects share the same
resources. Sophisticated heuristic procedures have been developed for these
cases, some of which are listed in the references. Most project management
software packages use such procedures for resource leveling.

Figure 10.4
(a) Gantt chart and (b) leveled resource profile for the example
project.

Figure 10.4 Full Alternative Text

10.4 Resource Allocation Subject to
Resource Availability Constraints
Most projects are subject to resource availability constraints. This is common
when resources are limited, and suitable substitutes cannot be found. As a
consequence, any delay or disruption in an activity may render the original
project schedule infeasible. Cash flow difficulties may limit the availability
of all resource types. Some resources may be available in unlimited
quantities, but, as a result of cash flow problems, their use may have to be cut
back in a specific project or over a specific period of time.
Under resource availability constraints, the project completion date calculated
using PERT/CPM may not be achieved in practice. This is the case when the
resources required exceed the available resources in one or more time periods
and the slack of noncritical activities is not sufficient to close the gap.
Resource availability constraints are not always binding on the schedule. This
can be illustrated with the example project. If 17 or more labor-days are
available every week, then either an early-start or a late-start schedule can be
used to complete the project within 22 weeks. The leveled resource profile
derived above requires at most 12 labor-days per week. Therefore, as long as
this number is available, no delays will be experienced. If fewer resources are
available in some weeks, however, then the project may have to be extended
beyond its earliest completion date. Activities A and B require a total of 12
labor-days per week when performed in parallel. Despite low resource
availability, the project manager can try using one or more of the following
strategies to avoid an extension:
1. Performing activities at a lower rate using available resource levels. This
technique is effective only when the duration of an activity can be
extended by performing it with fewer resources. Consider activity B in
the example. Assuming that only 11 labor-days are available each week
and activity A (which is critical) is scheduled to be performed using 8 of
those days, only 3 labor-days a week are left for activity B. Because B

requires a total of ( 3 weeks )×( 4 labor-days per week )=12 labor-days
of the resource, it may be possible to schedule B for 4 weeks, each week
utilizing 3 labor-days.
This technique may not be applicable if a minimum level of resources
are required each period (week) in which the activity is performed. Such
a requirement might result from technological or safety considerations.
For example, a labor union may require a certain minimum crew size in
order to perform a particular activity.
2. Activity splitting. It might be possible to split some activities into
subactivities without significantly altering the original precedence
relations. For example, consider splitting activity A into two
subactivities: A 1 , which is performed during weeks 1 and 2, and A 2 ,
which is performed after a break of 4 weeks. It is possible then to
complete the project within 22 weeks, using only 11 labor-days each
week. This technique is attractive whenever an activity can be split, the
setup time after the break is relatively short, and the activities that
succeed the first subactivity can be performed in accordance with the
original plan; that is, the second subactivity has no effect on the original
precedence relations.
Activity splitting also has an additional benefit of potentially minimizing
the inherent variability associated with an activity. For example,
consider an activity that is performed by one (large) machine and
requires 60 minutes of processing. Let’s assume that this activity can be
divided into 10 equal subactivities where each subactivity is performed
by a (small) machine, each requiring 6 minutes of processing time.
Further, let’s assume that the coefficient of variation squared is 0.5 for
the process time of the activity on the single, large machine. Then, the
variance of the duration time is given by 60×60/2=1800. Now, assume
that the coefficient of variation squared of each of the ten subactivities
on each of the small machines is also 0.5. The variance of the duration
time on a single machine is given by 6×6/2=18. If we assume that the 10
subactivities are independent, we can sum the variances in duration
times over each of the 10 machines to obtain a total variance of 180.
Thus, by splitting the single, large activity into 10 subactivities, we

reduced the variance in duration time by a factor of 10. Variance
reduction is a technique that a savvy project manager utilizes in
managing a project, as it effectively controls a project’s schedule and
cost.
3. Use of alternative resources. This option is available for some resources.
Subcontractors or personnel agencies, for example, are possible sources
of additional labor. However, the corresponding costs may be relatively
high, so a cost overrun versus a schedule overrun tradeoff analysis may
be appropriate.
Frequently, in practice, the lack of availability of resources will cause one or
more activities of a project to be delayed beyond their total slack, causing a
delay in the completion of the project. To illustrate, consider the example
project under a resource constraint of 11 labor-days per week. Because
activity A requires 8 of these 11 days, activity B can start only when A
finishes. The precedence relations force a delay of activity D—the successor
of B, as well as F and G. The new schedule and resource profile are depicted
in Figure 10.5.
It is interesting to note that the maximum level of resources used in the new
schedule is 10 labor-days. Thus, in the example project, a reduction of the
available resource level from 11 to 10 labor-days per week does not result in
a change in the schedule. A further reduction to 9 labor-days each week will
cause a further delay of the project because the concurrent scheduling of
activities C, D, and E requires a total of 10 resource-days. A feasible schedule
in this case and the accompanying resource profile are shown in Figure 10.6.
It is impossible to reduce the resource level below 9 labor-days per week
because activity F must be performed at that level. Table 10.2 summarizes the
relationship between the resource level available and the project duration.
TABLE 10.2 Implications of
Resource Availability

Resource availability (work
days/week)
Project duration
(weeks)
Resource
utilization
12 22 0.74
11 24 0.74
10 24 0.82
 9 29 0.75
In his book Critical Chain, Goldratt (1997) called the resource that is
responsible for a project delay the critical resource or bottleneck. The
activities that are performed by this resource are part of a sequence of
activities that connect the start of the project to its end and constitute the
“critical chain.”
Resource utilization is defined as the proportion of time that a renewable
resource is used. For example, if 12 labor-days are available each week and
the project duration is 22 weeks, a total of 12×22=254 resource days are
available. Because only 196 days

Figure 10.5
Scheduling under the 11 resource days/week constraint: (a) Gantt
chart; (b) resource profile.
Figure 10.5 Full Alternative Text

Figure 10.6
Scheduling under the 9 resource-days/week constraint: (a) Gantt
chart; (b) resource profile.

Figure 10.6 Full Alternative Text
are used to perform all of the project’s activities, the utilization of this
resource is 196/254=0.74. Resource utilization is an important performance
measure, particularly for renewable resources in a multiproject environment.
Resource leveling and resource allocation techniques can be used to achieve
high levels of utilization over all projects and resources. Matrix
organizational structures help organizations achieve high utilization by taking
advantage of pooled resources. Although many organizations, in practice,
strive to efficiently utilize their resources and achieve close to 100% resource
utilization, attainment of this goal is unlikely.
The analysis of multiple projects in which several types of resources are used
in each is a complicated scheduling problem. In most real-life applications,
the problem is solved with heuristics using priority rules to make the
allocations among activities. Some of these rules are discussed in the
following section.

10.5 Priority Rules for Resource
Allocation
A common approach to resource allocation is to begin with a simple critical
path analysis assuming unlimited resources. Next, a check is made to
determine whether the resultant schedule is infeasible. This would be the case
whenever a resource requirement exceeds its availability. Infeasibilities are
addressed one at a time starting with the first activity in the precedence graph
and making a forward pass toward the last. A priority measure is calculated
for each activity competing for a scarce resource. The activity with the lowest
priority is delayed until sufficient resources are available. This procedure is
used to resolve each infeasibility.
Examples of common priority rules are as follows:
Activity with the smallest slack
Activity with minimum late finish time (as determined by critical path
analysis)
Activity that requires the greatest number of resource units (or the
smallest number of resource units)
Shorter activities (or longer activities)
A priority rule based on the late start of the activity and the project duration
calculated by a critical path analysis is also possible. For example, define
CPT=earliest completion time of the project (based on critical path analysis)
LS(i)=late start of activity i (based on critical path analysis) PT(i)=priority of
activity i,where PT(i)=CPT−LS(i)
This rule gives high priority to activities that should start early in the project
life cycle. In the case of multiple-project scheduling, the value of CPT is
calculated for each project.

Next, we look at a priority rule that is based on each activity’s resource
requirements. Let
AT( i )=duration of activity i R( i, k )=level of resource k required per unit of
time for activity i PR( i, k )=priority of activity i with respect to resource
type k, where PR(i, k)=AT(i)×R(i, k)
In this rule, high priority is given to the activity that requires the maximum
use of resource k.
A rule that is based on aggregated resources is used when some activities
require more than a single resource. Define
PSUMR( i )=priority of activity i based on all its required resources
=AT(i)×∑ kR(i, k)
To operationalize this rule, it is necessary to define a common resource unit
such as a resource-day.
A weighted time-resource requirement priority rule can be fashioned from
two of the previous rules; for example, let
ω=weight between 0 and 1 PTR ( i ) = the weighted priority of activity i ,
where PTR ( i ) = ω PT ( i ) + ( 1 – ω ) PSUMR ( i )
By controlling the value of ω, emphasis can be shifted from the time
dimension, PT(i), to the resource dimension, PSUMR(i).
Many of the priority rules above can be modified to take into account a
variety of additional factors, including:
Slack of the activity (total slack, free slack)
Early start, late start, early finish, and late finish of the activity
Duration of the activity
Number of succeeding/preceding activities

Length of the longest sequence of activities that contain the activity
Maximum resource requirement sequence of activities that contain the
activity
We illustrate some common heuristics. The longest-duration first heuristic
chooses the feasible activity with the longest duration time at each iteration.
Consider the example in Table 10.3.
TABLE 10.3 Longest
Duration First Heuristic
PERT Estimates
Activity Predecessor Optimistic MostLikely Pessimistic Duration Resource
A 1 2 4 2.17 3
B 5 6 7 6.00 5
C 2 4 5 3.83 4
D A 1 3 4 2.83 2
E C 4 5 7 5.17 4
F A 3 4 5 4.00 2
G B, D, E 1 2 3 2.00 6
The activities are ranked in descending order of their PERT durations—B, E,
F, C, D, A, G. Further, let’s assume that 10 resource units are available for
the project. At time 0, assuming only precedence constraints and no resource
constraints, we can start activities A, B, and C. However, these three
activities together require 12 resource units. Since B and C have priority over
A (their respective duration times are greater than A’s), B and C are started at
time 0, and activity A is delayed. Once C finishes at time 3.83, activity E is
started next since E has priority over A, and E has only C as a predecessor.
Activity A is started at time 6, upon the completion of B. Activities F and D
can start at time 8.17, upon completion of A. Finally, G is started at time 11,

upon completion of D. The makespan is 13, as G has an expected duration of
2. If resource availability was not constrained in this example, the
PERT/CPM critical path—based on precedence constraints only—would be
11 (activities C, E, and G).
The following example illustrates two classic scheduling heuristics. In the
Activity Time (ACTIM) algorithm, we calculate the difference between the
critical path completion time and an activity’s latest start time, for each
project activity. Consider the following example in Table 10.4.
TABLE 10.4 ACTIM
Example Data
Activity Duration Resource LS ACTIM
1-2  2  2  0 20
2-3  8  1  2 18
1-3  3  1  7 13
3-5 10  1 10 10
1-4  8  1 11  9
2-4  2  1 17  3
4-5  1  3 19  1
Let’s assume that 3 workers are available for the project’s duration. The
unconstrained critical path (assuming precedence constraints only) consists of
activities 1-2-3-5 and has a makespan of 20. At time 0, activities (1,2) and
(1,3) are started. Insufficient resources are available to also start activity
(1,4). When activity (1,2) finishes at time period 2, we have sufficient
resources to start both (2,3) and (1,4). Activity (2,4) can be started in period
3, upon the completion of (1,3). Activity (3,5) is started in period 10, upon
the completion of (2,3). Once (3,5) completes in period 20, activity (4,5),
which requires all of the available resources, can be started. The resource-
constrained makespan is 21. Intuitively, this heuristic emphasizes an
activity’s duration time and seeks to initially complete those activities that

have longer duration times, assuming precedence constraints are satisfied. It
does not explicitly address the level of resource required by each activity. An
activity’s resource requirement is addressed by the next heuristic.
The Activity Resource (ACTRES) heuristic is a scheduling heuristic that
considers a combination of both activity time and resource requirements. For
each project activity, we compute the product of its duration time and
resource requirement. It is illustrated with the same project network data that
was used to illustrate the ACTIM heuristic. Notice, however, that the
ordering of the activities differs, as resource requirements are taken into
account as illustrated in Table 10.5.
TABLE 10.5 Actres Heuristic
Activity Duration Resource Actres
3-5 10 1 10
2-3  8 1  8
1-4  8 1  8
1-2  2 2  4
1-3  3 1  3
4-5  1 3  3
2-4  2 1  2
Again, assume that 3 resources are available. Activities (1,2) and (1,4) are
started at the outset. Upon completion of (1,2), activities (2,3) and (1,3) are
started at time period 2. When (1,3) completes in period 5, activity (2,4) may
be started. Activity (3,5) may be started in period 10, once activity (2,3) is
completed. This activity completes in period 20. At that time, activity (4,5)
may be started since this activity requires all of the available resources. The
makespan, in this example, is 21 which is identical to the makespan that was
found with the ACTIM heuristic. For larger, more realistic projects, the two
heuristics will not generally arrive at the same makespan.
The Minimum Total Slack Time heuristic is the last procedure that we

illustrate. As its name implies, the algorithm selects activities with minimum
slack at each iteration. The example, in Table 10.6, assumes that three
resource units are available.
TABLE 10.6 Data for
Minimum Total Slack Heuristic
Task Pred Resource Duration ES LF TS
A 2 2  0  2 0
B A 2 6  2 10 2
C A 2 4  2  6 0
D A 1 2  2 10 6
E C 1 2  6 10 2
F C 1 4  6 10 0
G B, D, E, F 1 2 10 12 0
Initially, we solve for the unconstrained solution (i.e., the schedule that only
considers precedence constraints and ignores resource constraints). In this
example, we see, in Table 10.7, that time periods 3 and 4 are infeasible, since
activities B, C, and D together require 5 resource units. Activity D has the
largest slack among these three activities competing for resources in periods
3 and 4. However, delaying activity D will not resolve the schedule
infeasibility. Therefore, we choose to delay activity B, since its slack is
greater than C’s slack, and delaying this activity will enable a feasible
schedule.
TABLE 10.7 Minimum Total
Slack Heuristic
Period

Task 1 2 3 4 5 6 7 8 9 10 11 12 13 14
A 2 2
B 2 2 2 2 2 2
C 2 2 2 2
D 1 1
E 1 1
F 1 1 1 1
G 1 1
The heuristic proceeds by delaying activity B by four time periods, resulting
in the schedule shown in Table 10.8.
TABLE 10.8 Minimum Total
Slack Heuristic
Period
Task 1 2 3 4 5 6 7 8 9 10 11 12 13 14
A 2 2
B X X X X 2 2 2 2 2 2
C 2 2 2 2
D 1 1
E 1 1
F 1 1 1 1
G X X 1 1
Activity B now has a slack of −2, relative to the original schedule. By
pushing out activity B, we delayed activity G and increased the project’s
makespan by 2 time periods. Notice that the schedule is still not feasible—
time periods 7 and 8 call for the use of four resources among activities B, E,
and F. Now, we choose to delay activity E by 4 time periods, as it has the
longest total slack among the activities B, E, and F (we adjusted B’s total
slack to −2 ). In this case, delaying activity E does not further push back

completion of the project. Activity G completes in time period 14 (see Table
10.9) which is 2 time periods after the unconstrained schedule’s completion
time.
TABLE 10.9
Period
Task 1 2 3 4 5 6 7 8 9 10 11 12 13 14
A 2 2
B X X X X 2 2 2 2 2 2
C 2 2 2 2
D 1 1
E X X X X 1 1
F 1 1 1 1
G X X 1 1

10.6 Critical Chain: Project
Management by Constraints
Goldratt (1997) extended the notion of bottlenecks used in job-shop and
flow-shop scheduling to project resource management. Critical resources or
bottlenecks delay activities on the critical chain as a result of their limited
availability. Furthermore, limited resources required for non-critical activities
may cause delays in those activities; in some cases, the delays may be
significantly large enough to cause a (resource unconstrained) non-critical
chain to become the longest chain.
In a multiresource project, bottlenecks whose capacity is relatively
inexpensive to increase may cause low utilization of expensive or scarce
resources. For example, a leased crane is an expensive resource that might be
idle if an operator is not available because both resources are required
simultaneously to perform an activity. From an economic point of view, it is
preferable to maximize the utilization of the expensive resource at the risk of
underutilizing the inexpensive one. Therefore, if the leased crane is available
and needed 14 hours each day but an operator can work only between 8 and
10 hours a day, then it would be advisable to hire two operators for a total of
16 hours a day, allowing for 2 hours of operator idle time.
Of course, idle resources signal inefficiencies that should be brought to the
attention of management to determine whether they can be put to alternative
use. Resource utilization is a key factor, sharing center stage with cost and
on-time performance during project evaluation. Each of these factors figures
prominently in the planning and review process.
Because the critical chain is the longest sequence of activities that connect the
start of the project to its end under resource constraints and because any delay
in the critical chain will cause a delay of the entire project, Goldratt suggested
using buffers to hedge against uncertainty. In particular, a time buffer can be
used to protect the critical resource and the critical chain.

10.7 Mathematical Models for
Resource Allocation
Project scheduling under resource availability constraints has been the subject
of much research (e.g., see Demeulemeester and Herroelen 1997, Herroelen
et al. 1999, Tavares 1990). Most of the related studies assume that the
scheduling objective is to complete the project as early as possible (the
scheduling approach) or to maximize the net present value (NPV) (minimize
the net present cost) of the project (the budgeting approach). An early model
proposed by Patterson et al. (1989, 1990) can handle both objectives.
The following notation is used to describe the model; an activity-on-node
(AON) network is assumed:
Indices and sets
d=index for number of time periods that an activity is in progress
j=index for project activity ( j=1, 2, …, J )
k=index identifying resources that are available in a fixed quantity each
period [i.e., renewable resources ( k=1, 2, …, K )
m=index for mode of an activity; that is, the combination of resources
assigned to perform a particular project activity
t=index for time periods ( t=0, 1, 2, …, T )
P=set of all pairs of immediate predecessor relations; ( a, b )∈P denotes
that activity a is an immediate predecessor of activity b
Parameters

C=cash flow of activity j if performed in mode m during its d th period
in progress ( d=1, 2, …, D jm ); if C jmd <0, then there is a cash withdrawal; if C jmd >0, then there is a cash inflow
C jmv * =nonnegative cash inflow v periods after the completion of
activity j ( v≥1 ) (completion of a payment milestone)
C t =net cash position in period t; C 0 is the cash available at the start of
the project
D jm =Duration of activity j if performed in mode m
E j ( L j )=Earliest (latest) completion time for activity j determined
from critical path analysis based on shortest (longest) completion time
mode for activities in the network
J=unique terminal activity (may be a dummy) that has only one mode (
m=1; J also represents the number of activities in the project)
M j =number of modes associated with activity j ( m=1, 2, …, M j )
R kt =amount of resource k available in period t
r jmk =per period amount of renewable resource k required to perform
activity j in mode m
T=due date for project
α t =single payment, present value discount factor for t periods at
interest rate i; α t = ( 1 1+i ) t−1
Decision variables
x jmt ={ 1 if activity j in mode m is completed in period t 0 otherwise
The problem formulation for the case in which project duration is
minimized follows:

Minimize  ∑ t= E J L J t x J1t (10.1a)
subject to  ∑ m=1 M j ∑ t= E j L j x jmt =1, j=1, …, J (10.1b)
− ∑ m=1 M a ∑ t= E a L a x amt + ∑ m=1 M b ∑ t= E b L b ( t− D
bm ) x bmt ≥0, for all ( a, b )∈P (10.1c)
∑ j=1 J ∑ m=1 M j ∑ q=t t+ D jm −1 r jmk x jmq ≤ R kt
, k=1, …, K; t=1, …, T (10.1d)
C t−1 + ∑ j=1 J ∑ m=1 M j ∑ q=t t+ D jm −1 ( C jm( D jm +t−q) x
jmq + ∑ j=1 t=1 C jim * x jm(t−v) )=Ct, t=1,…,T (10.1e)
x jmt =0 or 1, for all j, m, t (10.1f)
In the model, the objective [Eq. (10.1a)] of minimizing the duration of the
project is achieved by scheduling the unique terminal activity J as early as
possible subject to the following constraints:
Ensuring that each activity will be completed in exactly one time period
using only one activity mode [Eq. (10.1b)]
Maintaining the precedence relations among activities [Eq. (10.1c)]
Imposing resource restrictions [Eq. (10.1d)]
Ensuring that an activity mode is selected only if sufficient cash is
available during each period of its duration [Eq. (10.1e)]
To maximize the NPV of the project, the objective function Eq. (10.1a) is
replaced by
Maximize ∑ t=1 T α t ( C t − C t−1 )+ C 0 (10.2)
The mathematical program given by (10.1.a)-(10.1.f) (or, alternatively, (10.2)
and (10.1.b)-(10.1.f)) is Eq. (10.1) (10.2) formally known as a zero-one
integer program. In practice, it is not realistic to try to solve this type of
problem to optimality when projects with several hundred activities are
considered or when several projects that share the same resources are
scheduled in parallel. Nevertheless, good solutions can be obtained with a
variety of heuristics. For example, Patterson et al. (1990) developed a
backtracking algorithm that makes initial allocations and then tries to
improve on the solution by shifting around resources, starting with the last

node and working backward.

10.8 Projects Performed in Parallel
The resource allocation and resource leveling techniques discussed so far are
based on the assumption that each project undertaken by an organization is
managed separately. This assumption is problematic if one or more of the
following conditions exist:
Technological dependency between projects
Resource dependency between projects
Budget dependency between projects
1. Technological dependency. Technological dependencies arise when
precedence relations among projects are present. Consider, for example,
an electronics firm that is involved in two projects: (1) the development
of a new microprocessor and (2) the development of a notebook
computer. If a decision is made to use the new microprocessor in the
notebook, then the success of the computer project is dependent on the
completion of the microprocessor. If this seems too risky, then the new
computer might be designed alternatively with an existing
microprocessor as well as with the new one. This reduces the degree of
dependency between the two original parallel projects.
2. Resource dependency. Resource dependencies occur when two or more
projects compete for the same resources. In the previous example, an
electrical engineer might be involved in both projects so management
must decide how best to allocate his or her time. One way to make this
decision is to examine the priority rules discussed earlier. Other factors
that should be considered are technological dependencies, the due date
of each project, and the economic consequences attending late
completion.
3. Budget dependency. Budget dependencies exist when several projects
compete for the same dollars or when the income from one group of

projects is expected to cover the costs of some other group. In this case,
coordination between the various projects is required.
The techniques developed for single-project scheduling can usually be used
when dealing with parallel projects. A single network constructed by
connecting all projects according to the precedence relations among them or
by assuming that all projects have the same start node and the same end node
may be used as a single project model for the multiproject situation. Once all
projects are combined into a single network, the techniques developed for
resource management in a single project are applicable.
Goldratt suggested buffer management as a tool for managing projects that
are performed in parallel. In this approach, the time buffers that protect the
critical chain of each project are used as the basis for the allocation of scarce
resources among the projects. Higher priority is given to the project that
consumed the highest proportion of its time buffer (with respect to the actual
progress made). For example, assume that two projects are performed
simultaneously, each having an initial time buffer of two weeks and a critical
chain of 10 weeks. At a given point in time, half of the work content of the
first project has been completed and one week of its time buffer has been
consumed. At the same point in time, 60% of the second project has been
completed (four weeks to go) and one week of its time buffer has also been
consumed. On the basis of this information, priority will be given to the first
project.
TEAM PROJECT Thermal
Transfer Plant
With the approved schedule, it is time to assemble the resources needed to
execute the rotary combustor project. Your team has been requested to
submit a detailed plan indicating all of the resources required and an initial
schedule for each. Be sure to define the different resources (e.g., electrical
engineers, mechanical engineers, material, a crane and operator).
Assume that resources are available but that management’s policy is to level

their use throughout the life cycle of each project. Develop a leveled resource
plan. Explain the differences between your initial resource plan and the new
one. In particular, discuss the benefits and costs associated with the leveled
plan.

Discussion Questions
1. Consider a project with which you are familiar and describe it briefly.
For each classification scheme discussed in Section 10.2, classify each
resource used in the project.
2. Discuss an example of a project that is not subject to resource
constraints. Is this project subject to other constraints?
3. Discuss the importance of information as a resource in a technological
project. Give an example in which availability of information is a major
constraint.
4. Select a classification scheme and classify the resource “information”
required by a technologically advanced country that is trying to develop
a manned space program.
5. Develop a flow diagram for a resource leveling procedure that can be
translated into a computer program. What are your objectives, and what
are the input, output, and data processing requirements?
6. Modify the flow diagram developed in Question 5 so that it can handle
resource allocation problems.
7. Give an example of a bottleneck resource in a project. Under what
conditions should this constraint be removed?
8. In the fall of 2002, a coalition force under the auspices of the United
States moved massive amounts of equipment, matériel, and troops into
the Persian Gulf area in a prelude to the war with Iraq to remove
Saddam Hussein from power. This logistical operation was followed in
the spring of 2003 with a large-scale military operation. Discuss the
dependencies between these two projects.
9. What are the difficulties involved in leveling a schedule, particularly
when the activities consume multiple resources?

10. How much does a project manager need to know about a scheduling or
resource leveling computer program to use the output intelligently?
11. Why is the impact of scheduling and resource allocation generally more
significant in multiproject organizations? How do large fluctuations in
demand affect the situation?
12. What difficulties do you foresee in assigning technical personnel, such
as software engineers, to multiple projects?

Exercises
1. 10.1 The following project is performed with a single type of resource
(labor), which is assumed to be available in unlimited quantities. The
resource usage rate is constant throughout the duration of each activity.
Thus, if the duration of an activity is 5 days and it requires 60 hours of
the resource, then 60/5=12 hours of the resource are required each day
that the activity is performed. The project data are shown in Table 10.10
. Develop a schedule that minimizes resource fluctuations.
TABLE 10.10
Activity Duration(days)
Immediate
predecessors
Resource
requirements (hours)
A  3 – 12
B  4 – 16
C  3 –  9
D  2 C 10
E  1 B  6
F  5 A 15
G  2 B 16
H  3 B 12
I 11 C 44
J  3 D,E 30
K  1 F,G 10
L  4 K 16
M  4 J,H  8
2. 10.2 Assume that daily resource availability is 2 hours less than the daily
resource requirement indicated by the schedule derived in Exercise 10.1
.

1. Use two different priority rules to allocate the available resources to
activities.
2. Comment on the performance of the rules selected.
3. 10.3 Each activity in a project can be performed by two different
resource combinations (Table 10.11 ). Assume that the usage rate of
each resource is constant throughout the duration of each activity. Now
find a schedule that minimizes the time required to complete the project.
Resources I and II both are available at a level of 12 hours each day.
TABLE 10.11
Activity
A B C D E F
Immediate predecessors  —  A  A
 
— B,C D,E
Mode 1
Duration (days)   2
  
3
  
5
  
3 2 1
Resource I required
(hours/activity)  0  9
 
10  6 8 4
Resource II required
(hours/activity)  5  6  5  9 6 3
Mode 2
Duration (days)  1  2   4
  
2 1 1
Resource I required
(hours/activity) 12 12
  
8
  
6 9 4
Resource II required
(hours/activity)  7  8
 
16
 
12 5 3
4. 10.4 Develop a resource plan and a schedule for the project “cleaning
and resupplying a passenger plane between flight legs.” Which resource

is the bottleneck?
5. 10.5 The precedence relations and crew size required to complete a
project are given in Table 10.12 . For example, activity E, which comes
after activity C, requires 10 weeks for its completion by a crew of six
people.
TABLE 10.12
Activity Immediate predecessors Time (weeks) Crew size
A –  4 4
B A  2 5
C A  6 3
D B  3 7
E C 10 6
F –  2 5
G D  5 6
H F  7 2
I D,E,G  1 8
J H 10 2
1. Construct an early-start Gantt chart and identify the critical path.
2. Calculate and chart the labor profile required to complete the
project for both an early-start and a late-start schedule.
3. Level the required labor as much as possible with the goal of
completing the project within the time period specified in part (a).
6. 10.6
1. Referring to Exercise 10.5 , assume that 10 people are assigned to
work on the project until it is finished. In light of the following
assumptions, schedule the project and calculate labor utilization:

1. No activities are allowed to be interrupted.
2. The crew size that performs an activity cannot be reduced, but
it is possible to increase the project’s completion time.
3. It is impossible to change the network.
4. It is not possible to increase the size of the crew and reduce
the time to complete an activity. The durations stated in the
table are the lower bounds; extra resources will just be wasted.
2. Repeat part (a) now assuming that you can reduce the crew size and
increase its duration (the number of person-weeks required for each
activity is constant).
3. Repeat part (b) now assuming that you may interrupt each activity
before it is completed and reschedule the remaining tasks at a later
time.
7. 10.7 The required labor profile for Exercise 10.5 is not of constant rate
but resembles a symmetric trapezoid, with the peak lasting 1 week. As
an example, consider activity G. Because a crew of six must work for a
period of 5 weeks to complete this activity, a total of 30 person-weeks is
required. To calculate the labor requirement during the peak period,
assuming a trapezoid profile, one should substitute the proper values
into the following equation.
lbrq=peak×( dur 2 +0.5 )
where
lbrq=total labor required to perform the activity
peak=peak labor required during the one week peak time
dur=activity duration.
Solving the equation for activity G, we obtain

peak= lbrq ( dur/2 )+0.5 = 30 5/2+0.5 =10
That is, during the 1-week peak period, there is need for a crew of 10
employees. Moreover, the required labor profile for the first 2 weeks is
linear starting from zero and ending at 10. The labor profile for the last 2
weeks is in the opposite direction; it starts at 10 and ends at zero at the
end of the fifth week. Assuming a symmetric trapezoid profile for each
activity, generate an early-start resource profile for the project.
8. 10.8 A trapezoid profile is a common shape used to describe labor
requirements over time. Assuming that the permanent crew size is equal
to the peak requirement, develop a model to calculate the crew
utilization for an activity as a function of the peak duration. In so doing,
assume that each activity i should be completed within a prespecified
duration, say D i days, and requires L i labor-days.
9. 10.9 A second project, identical to the one described in Exercise 10.5 , is
planned to start one week after the first. That is, the company intends to
work on the two projects at the same time.
1. Generate the early-start resource profile for the two projects.
2. Schedule the two projects so that the required labor profile will be
as level as possible.
3. Discuss the significance of the differences observed in the
schedules found in parts (a) and (b).
10. 10.10 The following data concern an activity that has to be performed
as part of a project:
Expected duration (days) 10
Standard deviation of the duration  2
Expected labor-days 30
Standard deviation of labor-days  3
1. What is the probability that completing the activity on time will

require at least a 10% addition to the expected labor-days?
2. A crew of three workers is assigned to this activity. What is the
probability that it will be completed in fewer than 11 days?
State your assumptions for both parts.
11. 10.11 Suppose that in Exercise 9.16 personnel requirements are
specified for the various activities in projects (a) and (b) as shown in
Table 10.13 .
TABLE 10.13
Activity
(i, j)
Number of
workers
Activity
(i, j)
Number of
workers
Project
(a): (1,2)  5 (3,6)  9
(1,4)  4 (4,6)  1
(1,5)  3 (4,7) 10
(2,3)  1 (5,6)  4
(2,5)  2 (5,7)  5
(2,6)  3 (6,7)  2
(3,4)  7
Project
(b): (1,2)  1 (3,7)  9
(1,3)  2 (4,5)  8
(1,4)  5 (4,7)  7
(1,6)  3 (5,6)  2
(2,3)  1 (5,7)  5
(2,5)  4 (6,7)  3
(3,4) 10
1. Draw the early-start Gantt chart for projects (a) and (b), and plot

the required number of workers as a function of time.
2. Level the resources for projects (a) and (b) as much as possible
without extending their durations. Plot the corresponding
manpower requirements over time.
12. 10.12 Table 10.14 below gives the results of a critical path analysis;
Table 10.15 lists worker requirements for each of the project’s activities.
TABLE 10.14
Earliest Latest
Activity Duration, Start, Finish, Start, Finish, Totalslack,
Free
slack,
(i,j) L ij ES ij EF ij LS ij LF ij TS ij FS ij
(0,1)  2  0  2  2  4  2  0
(0,2)  3  0  3  0  3  0  0
(1,3)  2  2  4  4  6  2  2
(2,3)  3  3  6  3  6  0  0
(2,4)  2  3  5  4  6  1  1
(3,4)  0  6  6  6  6  0  0
(3,5)  3  6  9 10 13  4  4
(3,6)  2  6  8 17 19 11 11
(4,5)  7  6 13  6 13  0  0
(4,6)  5  6 11 14 19  8  8
(5,6)  6 13 19 13 19  0  0
TABLE 10.15
Activity Number of workers Activity Number of workers

(0,1) 0 (3,5) 2
(0,2) 5 (3,6) 1
(1,3) 0 (4,5) 2
(2,3) 7 (4,6) 5
(2,4) 3 (5,6) 6
1. Draw the precedence graph for the project.
2. Draw the Gantt charts for the early- and late-start schedules. What
is the maximum number of workers required?
3. Draw the resource requirement profiles for the early- and late-start
schedules.
4. Try to level the resource requirements (workers needed) as much as
possible by applying the leveling procedure discussed in the text.
[Note that activities (0,1) and (1,3) require no manual labor, which
is indicated by assigning zero workers to each activity. As a result,
the scheduling of (0,1) and (1,3) can be made independent of the
resource leveling procedure.]
5. Suppose that activities (0,1) and (1,3) require eight and two
workers, respectively. Perform resource leveling and redraw the
Gantt chart and profile graph.
13. 10.13 A project has 11 activities that can be accomplished either by one
person working alone or by several people working together. The
activities, precedence constraints, and time estimates are given in Table
10.16 . Suppose that you have up to five people who can be assigned on
any given day. A person must work full days on each activity, but the
number of people working on an activity can vary from day to day.
TABLE 10.16
Activity Immediate predecessors Person-days required

A – 10
B A  8
C A  5
D B  6
E D  8
F C  7
G E,F  4
H F  2
I F  3
J H,I  3
K J,G  2
1. Prepare an AOA network diagram, and calculate the critical path,
total slacks, and free slacks assuming that one person
(independently) is working on each task.
2. Prepare an early-start Gantt chart.
3. Prepare a daily assignment sheet for personnel with the goal of
finishing the project in the minimum amount of time.
4. Prepare a daily assignment sheet to “best” balance the workforce
assigned to the project.
5. By how many days could the project be compressed if unlimited
personnel resources were available?

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Chapter 11 Project Budget
11.1 Introduction
An organization’s budget represents management’s long-range, midrange,
and short-range plans. A budget should contain a statement of prospective
investments, management goals, and resources necessary to achieve those
goals, phased over time. A budget and the budgeting process mirror an
organization’s structure. For example, in a functional organizational
structure, a budget aggregates an organization’s investments and expenditures
in three ways: (1) development of new products (engineering), (2) production
of existing products (manufacturing); and (3) campaigns for new or existing
products (advertising, marketing). In a project-oriented organizational
structure, a budget reveals the organization’s planned costs and expected
revenues for each project. Finally, in a matrix organization structure, a budget
reflects both functional and project-based components, as explained below.
The budget of any specific project is tied to the sponsoring organization’s
budget. In some organizations, a project budget includes only expenditures
(e.g., government agencies such as the Department of Defense are engaged in
projects strictly as clients). In other organizations, the project budget includes
both income and expenditures (e.g., contractors whose expenditures for labor,
materials, and subcontracting are covered by their clients). When an
organization is involved in several projects, the budgets of these projects are
coordinated centrally. For example, a portfolio of a pharmaceutical
manufacturer will ideally contain a mix of established products—“cash
cows”—that require fewer sales and marketing resources to generate sales,
and some new product introductions—“future blockbusters”—that require
heavy investment in marketing and sales tactics in order to establish the new
products in the marketplace.
In a matrix organization, a budget links the functional units to the projects.
On a specific project, the cost of resources invested by the functional unit is
charged against the project’s budget. This link is one of the interfaces

between the functional structure and the project aspect of the matrix
organization. In this chapter, we discuss the principles used in developing,
presenting, and using the budget in a project environment.
A well-designed budget is an efficient communication channel for
management. Through this instrument, managers (at all levels) are advised of
their organization’s goals and the resources allocated to their units. A detailed
budget defines expected costs and expenditures, thus setting the framework
of constraints within which each manager is expected to operate. These
constraints represent organizational policy and goals. A well-structured
budget is a yardstick that can be used to measure the performance of
organizational units and their managers. Managers who participate in the
budget development process commit themselves, their subordinates, and their
unit’s resources to the goals specified in the budget as well as the constraints
implied by the negotiated funding levels. A successful manager is one who
can drive required results on schedule, without exceeding budget. A well-
structured budget is also a useful tool for identifying deviations from plans,
the magnitude of these deviations, and their source. Therefore, it is part of the
baseline for cost and schedule control systems.
A budget’s level of detail depends on the planning horizon for which it was
prepared. A long-range, or strategic, budget defines an aggregate level of
activity for an organization over a period of, say, 3–10 years. For example, in
a functional organization, this budget might define a goal of selling 100,000
units in the coming year with a 15% increase in sales in each of the following
4 years. The expected marketing cost in the budget is $50,000 for the first
year with 8% increases in each subsequent year. In an organization with a
project structure, the strategic budget will define the total budget for each
project. For example, assume that for project X, the design stage has a one-
year completion due date and a $500,000 budget. A critical design review is
scheduled accordingly. In two years, a prototype will be tested in the lab. The
associated budget is $600,000. The final product will be tested in the third
year for a cost of $550,000. A long-range budget is typically updated
annually.
By using the budgeting process, management establishes long-range goals,
schedules to achieve these goals, and the available resources. When the actual

expenditures, income, and results are compared to the original budget,
management can monitor the organization’s performance. Also, when
necessary, management can change the budget to control both goal setting
and resource allocation.
A midrange, or tactical, budget is a detailed presentation of the long-range
budget and covers 12 to 36 months. It is typically updated quarterly. The
tasks to be performed within each work package (WP) provide the basis of
the entries. A rolling planning horizon is used so that every time (e.g.,
quarterly) the midrange budget is updated, a budget for the ensuing quarter is
added while the budget for the recently completed quarter is deleted. The
tactical budget details the expected monthly costs of resources, including
labor and materials, as well as overhead. In a functional organization, the
tactical budget forecasts the expected costs and revenues of each product
family and the expected costs of each functional department.
A short-range, or operational, budget lists specific activities, the resources
assigned to them, and their costs. This budget spans a period up to one year
and covers the detailed costs of resources (e.g., labor and material) required
to perform each activity. For example, the short-range budget of a project
might specify that the design of a prototype be done on a $10,000 computer-
aided design system that runs on a $5,000 piece of equipment. Lead times are
three and two weeks, respectively, for the hardware and software. Installation
starts as soon as both items are delivered. The expected cost of installation
and training is $2,000. This short-term (operational) budget relates project
costs to project activities through the work breakdown structure (WBS),
organizational breakdown structure (OBS), and the project’s lower level
network model.
A project’s budget contains several dimensions. The first relates to the tasks
and activities to be performed. The primary effort is to establish the
relationship among cost, resources, and time for scheduled tasks and
activities. The second dimension is based on the OBS. Each task is assigned
to an organizational unit in the OBS. The third dimension is the WBS. Each
task is assigned to a WBS element in the lowest level of the hierarchy. Over
time, however, they are distributed among the WBS elements at their
corresponding levels.

As each organization develops its own budgeting procedures, several points
can help make the budget an efficient vehicle for planning, as well as a
standard channel of communication:
A budget presents management’s objectives stated in terms of
measurable outputs: for example, the successful completion of a test or
the development of a new software module. These outputs should be
presented with their budgetary constraints. Thus, the budget presents
available resources and the goals to be achieved using these resources.
The presentation can be based on a functional structure, a project’s
organizational structure, or a combination of the two if a matrix structure
is assumed.
A budget presents costs and revenues, phased over time. The
presentation should facilitate a periodic and cumulative comparison
between actual and planned performance levels.
A budget should be divided into long-range (strategic), midrange
(tactical), and short-range (operational) levels. Each level should contain
a detailed breakdown of the budget at the preceding level for the
planning horizon. A rolling horizon approach should be used in
developing the budgets of new periods and in updating the budgets of
previous periods.
A management reserve may be included at strategic and tactical levels. This
reserve acts as a buffer against uncertainty and should be consumed by
transforming it into specific line items in the mid- and short-range budgets.

11.2 Project Budget and
Organizational Goals
The budget of an organization reflects management’s goals. These goals and
organizational constraints determine decisions on project selection, resource
allocation, modes of operation, and the desired rate of progress for each
project. The budget depends on the perceived organizational mission and the
sector to which the organization belongs (private, government, or nonprofit).
It also depends on internal and external environmental factors. The following
are seven common factors that affect project selection and budget structure:
1. Competition. Most organizations in the private sector need a competitive
edge to survive. External challenges force continued improvement
within the organization and occur in various ways, such as the
following:
Time-based competition. Spurs the implementation of concurrent
engineering with the goal of shortening new product development
cycles and improving customer service. It is also instrumental in
reducing customer lead times. A major emphasis is on achieving
project milestones and goals in a timely manner.
Cost-based competition. In a cost-based environment, the project
budget includes smaller, tightly controlled reserves; an effort is
made to perform activities in the normal (least expensive) mode
and to trim overhead cost.
Quality-based competition. Quality management including quality
planning, quality assurance, and quality control is the focus of
competition and is budgeted accordingly.
2. Profit. The ability to generate profits in the short and long run is
essential to most organizations in the private sector. Project selection
decisions are frequently based on a project’s expected profits. A project

can be tentatively evaluated by any of the techniques discussed in
Chapter 3, including net present value (NPV), internal rate of return, and
payback period.
3. Cash flow. The organizational cash flow represents an aggregate of all
routine activities combined with all ongoing projects. When unexpected
cash flow problems arise, projects that generate quick cash become
high-priority items in the budget allocation process. In some cases, an
organization may prefer projects that begin to produce revenues
immediately, albeit small, rather than projects that generate a slow cash
flow and higher profits in the distant future. In the short run, to improve
the cash position of the firm, activities that generate income (e.g.,
payment milestones) may be budgeted earlier than other activities that
have the same or an even shorter slack.
4. Risk. Uncertainty and risk may influence budgetary decisions. An
organization that tries to avoid the risk of delays may budget its projects
according to an early-start schedule. This, in turn, may lead to early
expenditures and cash flow problems. Organizations that try to minimize
the risk of cost overruns sometimes budget each activity at its lowest
level (normal mode of operation). If longer activity duration occurs, then
the lowered risk of a cost overrun can translate into an increased risk of
delays. The selection of new projects may also be influenced by risk
assessment. In this case, an organization considers a particular project
within the context of the organization’s portfolio of projects.
5. Technological ability. Some organizations in the public sector are
willing to budget high-tech projects to acquire new, more advanced
technologies. In the private sector (including such industries as
computers, microelectronics, nanotechnology, genetic engineering, and
aerospace), an organization’s technological ability is an important aspect
of its competitive edge. To outdistance competitors, technologically
advanced projects are selected and budgeted to assure progress.
6. Resources. A project budget includes the value of resources allocated to
that project. However, if adequate resources are not available to the
project team, the project cannot be executed in a timely fashion,
regardless of whether sufficient funds were allocated. Therefore, it is

important to classify and track resources according to their availability
on the basis of the detailed classification scheme presented in Chapter
10. In the long- and midrange budgets, organizational plans for
acquiring new resources are put forth. The short-range budget addresses
plans to use these resources. Nevertheless, some resources may not be
available, even if budgeted adequately. Therefore, in preparing the
budget, resource availability (both internal and external to the
organization) and resource lead time (i.e., time required to procure the
needed resources) need to be coordinated with the planned costs of these
resources.
7. Perceived needs. Project selection and budgeting depend largely on
organizational goals. In the government sector, especially in defense,
perceived needs (or new threats) are a driving force. Cost and risk
considerations might be secondary when national security or public
health is considered.
These seven factors link organizational goals and the internal and external
aspects of the operational environment with each project’s budget. Clearly,
developing an organizational budget and a budget for each project requires a
coordinated effort among management, accounting, marketing, and other
functional areas. This issue is the subject of the following section.

11.3 Preparing the Budget
Budget preparation is the process by which organizational goals are
translated into a plan that specifies the allocated resources, the selected
processes, and the desired schedule for achieving these goals. The budget
must integrate information and objectives from all functional areas of the
organization with information and objectives from the various project leaders.
Although upper management sets the long-range (strategic) objectives, lower
level management is responsible for establishing the detailed (operational)
plans and must clearly articulate and understand the short-range objectives
before executing the budget.
In a project or a matrix organization, lower-level managers, who are
concerned primarily with the daily operations, should be most knowledgeable
in the technical details regarding the most appropriate way to perform each
project. They should also be up to date on expected activity durations and
costs. Thus, it is important to integrate upper level management vision with
the knowledge and experience of functional and project managers.
An organizational budget consists of both ongoing activities, such as the
production and marketing of existing products, and one-time efforts or
projects. It is easier to budget ongoing activities, because past budgets for
these activities can serve as a reference point for planning. By adjusting for
anticipated demand, the expected inflation rate, and the effect of learning,
financial planners can develop a new budget based on past information.
Project budgeting is more difficult, though, because previous budgets are
often unavailable. Cost estimation (Chapter 4), the project schedule (Chapter
9), and the effect of resource availability (Chapter 10) are considered in
developing a project budget.
The building blocks of a project’s budget are the WPs in which tasks
performed on the lowest level WBS elements are assigned to organizational
units at the lowest level of the OBS. A budget is developed for each WP.
Budgets are then developed for each WBS element at each level in the
hierarchy and for each organizational unit at each OBS level.

The process of integrating one-time project budgets and budgets of ongoing
activities into an acceptable organizational budget requires planning and
coordination. The final budget should embody sound, workable programs for
each functional area and coordinate the efforts of functional units and project
managers to achieve their goals. Three procedures are commonly used in
budgeting: the top-down approach, the bottom-up approach, and the iterative-
mixed approach.
11.3.1 Top-Down Budgeting
The trigger for the budgeting process is the strategic long-range plan that is
developed by top management on the basis of its experience and perception
of the organization’s goals and constraints. The long-range plan is then
passed to the functional unit managers and the project managers who develop
the tactical (midrange) and detailed operational (short-range) budgets.
One problem with top-down budgeting is the translation of long-range
budgets into short-range budgets. The former can be spread in any number of
ways over the budgets of projects and functional units. Top management has
limited knowledge of the specifics of each project, task, and activity—
knowledge that is unavailable when preparing the long-range budget using
the top-down approach.
A second problem with this approach is the competition for funds among
lower level managers who try to secure adequate funding for their operations.
Since top management fixes the total budget, lower level managers, in
essence, are in competition for scarce resources. Organizational politics can
lead to a lack of cooperation among different business units and a sub-
optimal allocation of resources.
Table 11.1 illustrates the top-down budgeting process.
TABLE 11.1 The Top-Down
Approach to Budget

Preparation
Step Organizationallevel Prepared budget at each step
1 Topmanagement
Strategic budget based on organizational
goals, constraints, and policies
2 Functionalmanagement Tactical budget for each functional unit
3 Projectmanagers
Detailed budgets for each project, including
the cost of labor, material, subcontracting,
overhead, etc.
11.3.2 Bottom-Up Budgeting
In contrast with top-down budgeting, many organizations adopt a bottoms-up
approach. Each project manager prepares a budget proposal that supports
efficient and on-schedule project execution. On the basis of this input,
functional managers prepare the budgets for their units, considering the
resources required in each period. Finally, top management streamlines and
integrates the individual project and functional unit budgets into a strategic
long-range organizational budget.
The advantages of this approach are the clear flow of information and the use
of detailed data available at the project management level as the basic source
of cost, schedule, and resource requirement information. The disadvantage is
that resources required across individual projects and functional area budgets
may exceed the total amount of resources available to an organization at any
given time. Top management can influence the bottom-up budgeting process
by issuing high-level goals and budget constraints to middle and lower level
managers as they prepare short- and midrange budgets.
Table 11.2 illustrates the bottom-up budgeting process.

TABLE 11.2 Bottom-Up
Approach to Budget
Preparation
Step Organizationallevel Budget prepared at each step
1 Topmanagement
Setting goals and selection of projects (a
framework for budget)
2 Projectmanagement
Detailed budget proposals for projects
including costs of material, labor,
subcontracting, etc.
3 Functionalmanagement Midrange budget for each functional unit
4 Topmanagement
Adjustments and approval of the aggregate
long-range budget resulting from the process
Since the aggregate budget is developed on the basis of input obtained from
the project and functional managers, the gap between strategic and
operational objectives may be wide. This creates a need to fine-tune the
organizational budget. The process is carried out iteratively through
adjustment and review until a satisfactory compromise is achieved.
11.3.3 Iterative Budgeting
The two budgeting approaches presented above are “pure” in that the process
flows in one direction, either bottom-up or top-down. In practice, budgeting
is a hybrid process where information flows in an iterative fashion between
the various levels of management. A typical iterative approach starts with top
management setting a budget framework for each year in its strategic plan.
This framework directs the selection of new projects and serves as a guideline
for project managers as they prepare their budgets. Detailed project budgets

are aggregated into functional unit budgets and, finally, into an organizational
budget that top management reviews and, if necessary, modifies. Based on
the approved budget, functional units and project managers modify their
respective budgets. The process may undergo several iterations until
convergence takes place at the strategic, tactical, and operational levels.
This process is based on input from all levels of management and usually
produces better coordination between the different budgets (functional versus
project and long-range versus short- and midrange). Major disadvantages
center on the relatively long duration needed for agreement and the excessive
use of management time. Nevertheless, iterative budgeting is widespread in
practice.
Although an organization’s budget is typically fixed at the beginning of a
fiscal year, it is continuously adjusted throughout the year, based on market
events and conditions; for example, additional advertising money is budgeted
if a key competitor unexpectedly launches a major television campaign. A
project manager must, therefore, be attuned to budgeting and changes in the
overall budget and must continuously champion and advocate for funding
and resources.

11.4 Techniques for Managing the
Project Budget
A project budget represents scheduled expenditures and scheduled revenue as
a function of time. The simplest approach to budgeting is to estimate the
expected costs and income associated with each activity, task, and milestone.
Based on the project schedule, these costs are assigned specific dates, and a
budget is generated. In addition, indirect costs—not related to a specific
activity—such as those associated with management, facility operations, and
quality control must be considered. Development of project budgets based on
schedule and resource considerations is the first step in an iterative approach.
The next step is to integrate the individual project budgets into an acceptable
organizational budget.
11.4.1 Slack Management
One approach to integrating individual project budgets is to change activity
timing and the associated expenditure or income, an approach known as slack
management. Noncritical activities that have free slack are usually the first
candidates for this type of rescheduling. Activities with (positive) total slack
are the next choices, and the final choices are critical activities that can be
delayed only at the cost of delays in project completion time. Rescheduling
activities facilitates integration of single-project budgets into an acceptable
organizational budget.
To illustrate the relationship between a project’s cash flow and its schedule,
let us return to the example project. The length of the critical path in the
project is 22 weeks. Critical activities are A, C, F, and G, whereas activities
B, E, and D have either free or total slack that offer flexibility in budget
planning. Table 11.3 depicts the costs and durations of the project’s activities.

TABLE 11.3 Project Activity
Durations and Costs
Activity Duration (weeks) Cost ($1,000)
A 5 1.5
B 3 3.0
C 8 3.3
D 7 4.2
E 7 5.7
F 4 6.1
G 5 7.2
31.0
An early-start schedule results in relatively high expenditures in the project’s
earlier stages, while a late-start schedule results in relatively high
expenditures in the later stages. Table 11.4 presents the project’s cash flow
for the early-start schedule assuming, for budgeting purposes, that the cost of
each activity is evenly distributed throughout its duration. Table 11.5
enumerates the cash flow of the project for the late-start case.
TABLE 11.4 Cash Flow of an
Early-Start Schedule
Activity
Week A B C D E F G Weeklycost, $
Cumulative
cost, $
 1 300 1,000 814.3 2,114  2,114
 2 300 1,000 814.3 2,114  4,229
 3 300 1,000 814.3 2,114  6,343
 4 300 814.3 1,114  7,457

 5 300 814.3 1,114  8,571
 6 412.5 600 814.3 1,827 10,398
 7 412.5 600 814.3  827 12,225
 8 412.5 600 1,013 13,238
 9 412.5 600 1,013 14,250
10 412.5 600 1,013 15,263
11 412.5 600 1,013 16,275
12 412.5 600 1,013 17,288
13 412.5  412 17,700
14 1,525 1,525 19,225
15 1,525 1,525 20,750
16 1,525 1,525 22,275
17 1,525 1,525 23,800
18 1,440 1,440 25,240
19 1,440 1,440 26,680
20 1,440 1,440 28,120
21 1,440 1,440 29,560
22 1,440 1,440 31,000
Total 1,500 3,000 3,300 4,200 5,700 6,100 7,200 31,000
TABLE 11.5 Cash Flow of the
Late-Start Schedule
Activity
Week A B C D E F G Weeklycost, $
Cumulative
cost, $
 1 300  300  300
 2 300  300  600

 3 300 1,000 1,300 1,900
 4 300 1,000 1,300 3,200
 5 300 1,000 1,300 4,500
 6 412.5  412 4,913
 7 412.5 600 814.3 1,827 6,739
 8 412.5 600 814.3 1,827 8,566
 9 412.5 600 814.3 1,827 10,393
10 412.5 600 814.3 1,827 12,220
11 412.5 600 814.3 1,827 14,046
12 412.5 600 814.3 1,827 15,873
13 412.5 600 814.3 1,827 17,700
14 1,525 1,525 19,225
15 1,525 1,525 20,750
16 1,525 1,525 22,275
17 1,525 1,525 23,800
18 1,440 1,440 25,240
19 1,440 1,440 26,680
20 1,440 1,440 28,120
21 1,440 1,440 29,560
22 1,440 1,440 31,000
Total 1,500 3,000 3,300 4,200 5,700 6,100 7,200 31,000
Figure 11.1 depicts the cash flows for the early- and late-start schedules;
Figure 11.2 depicts their cumulative cash flows. From Figure 11.2, we see
that if the strategic long-range organizational budget allocates only $4,913 to
the project for weeks 1 through 5, then during this period, only a late-start
schedule is feasible. Also, increasing the project’s budget over $10,398 for
the first 5 weeks makes an early-start schedule feasible. Any budget in-
between will force a delay of noncritical activities.
The choice between an early- and a late-start schedule affects the risk level
associated with the project’s on-time completion. Using a late-start schedule
implies that all activities are started as late as possible without any slack to
buffer against uncertainty, increasing the probability of delays. The budgeting

process must often tradeoff between delaying the start of certain activities—
and, thus, defraying costs to a later time period—versus starting activities as
early as possible, thus reducing the risk of a schedule overrun.
Projects with large numbers of activities tend to have a large choice of
schedules with associated budgets. For example, in Figure 11.2, any schedule
that falls between
Figure 11.1
Cash flow for early-start and late-start schedules.
Figure 11.1 Full Alternative Text

Figure 11.2
Cumulative cash flow for early-start and late-start schedules.
Figure 11.2 Full Alternative Text
the early- and late-start budget lines would be feasible from the point of view
of meeting the critical milestones on time.
11.4.2 Crashing
In addition to using slack management as part of the budgeting process, a
project manager may change the mode and duration of an activity by
changing the technologies and/or the resources used to perform it. We
assumed, in Chapter 10, that each activity is performed in the most
economical way, defined as the normal mode. That is, the combination of

resources assigned to each activity was selected to minimize the cost of
completing it. However, in many cases, it is possible to reduce an activity’s
duration by spending more money. This implies that tradeoffs exist between
the minimum cost longest duration (normal time normal cost) option at one
extreme and any other option that reduces an activity’s duration at a higher
cost.
This is the essence of the original version of the critical path method (CPM),
which places equal emphasis on time and cost. The emphasis is achieved by
constructing a time–cost curve for each activity, such as the one shown in
Figure 11.3. This curve plots the relationship between the direct cost for the
activity and its resulting duration. In its simplest form, the plot is typically
based on two points: the normal point and the crash point. The former gives
the cost and time involved when the activity is performed in the normal way
without extra resources, such as overtime, special materials, or improved
equipment. In contrast, the crash point gives the time and cost when the
activity is fully expedited; that is, no cost is spared to reduce its duration as
much as possible. As an approximation, it is then assumed that all
intermediate time–cost tradeoffs are possible and lie on the line segment
between these two points (see the line segment in Figure 11.3). Thus, the
only estimates needed are the cost and time for normal and crash points.

Figure 11.3
Typical time-cost tradeoff curve.
Consider, for example, a manual painting operation that requires 4 days at
$400 per day. With a special compressed airflow system, however, two
workers can complete the job in 2 days for $1,000 per day. Thus, the activity
can be performed in 4 days for $400×4=$1,600 or in 2 days for
$1,000×2=$2,000. The normal duration is associated with the lowest cost
option for the activity. This value is used in a CPM analysis and in the
preparation of the initial budget.
More formally, the normal duration of an activity is the duration that
minimizes the direct cost. In some instances, a schedule that is based on
normal durations may produce high indirect costs, for example, when a
project due date is given and a penalty is charged for completion after the due
date. Even when the due date can initially be met by a normal schedule,
uncertainty during project execution may cause schedule overruns. The
resultant penalties must be traded off with the cost of shortening the duration
of some activities to minimize (or avoid completely) late charges.
A similar situation occurs when a fixed overhead is charged for a project’s
duration, for example, rental of facilities. In this case, management might
consider shortening some activities to reduce the project’s duration and save
on indirect costs.
Crashing is the procedure whereby an activity’s duration is shortened by
adding resources and paying extra direct costs. A crashed program includes
activities performed more quickly than they normally would be as a result of
the allocation of additional resources. A project manager must decide which
activities to crash and by how much. To illustrate this point, consider the
crashing costs and durations listed in Table 11.6 for the example project.
TABLE 11.6 Duration and Cost

for Normal and Crashed
Activities
Normal Crashing activitythe first time
Crashing activity a
second time
Activity Cost Duration(weeks)
Additional
cost
Duration
(weeks)
Additional
cost
Duration
(weeks)
A $1,500 5 $2,000 4 $1,000 3
B  3,000 3  2,000 2 – –
C  3,300 8  2,000 7  1,000 6
D  4,200 7  2,000 6  2,000 5
E  5,700 7  1,000 6 – –
F  6,100 4  1,000 3 2,000 2
G  7,200 5  1,000 4  1,000 3
In Table 11.6, the normal duration and the normal cost of each activity are
those used in the basic schedule. Each activity can be crashed at least once.
Five of the activities (A, C, D, F, and G) can be crashed twice, as the table
shows.
It is possible to construct the relationship between the project’s duration and
its direct cost, starting with an all-normal schedule in which each activity is
performed at its lowest direct cost and at a normal duration. To reduce the
project’s length, the critical path must be shortened. Thus, at each step, the
critical paths are examined and the activity that is least expensive to crash is
selected for crashing on each critical path. These activities are crashed, and
the process continues with the new critical paths being examined.
To illustrate this heuristic process, consider the data in Table 11.6. The
project’s normal duration is 22 weeks and the critical activities are A, C, F
and G. Reducing the project’s length requires crashing one critical activity.
At this stage, the cost of crashing each critical activity is as follows:

Activity Cost to crash
A $2,000
C 2,000
F 1,000
G 1,000
Activities F and G are the least expensive to crash. In particular, the cost of
activity F crashed from 4 to 3 weeks is $7,100, as illustrated in Table 11.7.
The first column in the table represents the project with a normal duration (22
weeks). The second column represents the project after crashing F from 4 to
3 weeks; the project’s duration is now 21 weeks and the crashed activity (F)
is marked by an asterisk (*). The crashing procedure continues until a 14-
week makespan is obtained. At this point, two critical paths emerge: A-C-F-
G, which lasts 14 weeks and contains no activities that can be crashed
further; and B-D-F-G, which also lasts 14 weeks but contains two activities,
B and D, that can be crashed for $2,000 and $3,000, respectively. Because
the length of sequence A-C-F-G cannot be reduced, the project’s minimum
duration is 14 weeks. Table 11.7 summarizes the results.
Budgeting decisions are easier when the time–cost relationship for a project
is known. The following example analyzes the tradeoff between direct and
indirect costs. Suppose that a fixed overhead of $500 per week is charged for
a project’s duration. Furthermore, assume that the project is due in 18 weeks
and that a penalty of $1,000 per week is imposed starting in the 19th week.
The budget problem in this case translates into a tradeoff between the cost of
crashing and the cost of additional overhead plus penalty. Table 11.8
summarizes these cost components, accompanied by the project’s total costs
as a function of its length. The minimum cost occurs at a project length of 19
weeks; that is, it is more economical to pay a $1,000 penalty and $500 in
overhead for the 19th week than to crash activity F for $2,000.
Total project cost may not be the only criterion for budget planning. If, for
example, customer satisfaction depends on project completion within 18
weeks, then the $500 savings should be evaluated against customer goodwill
that might be lost.
Figure 11.4 graphically depicts the different cost components and the total

cost of the project as a function of its duration. The crashing problem can be
modeled as either a linear or mixed-integer linear program, depending on
whether a continuous tradeoff exists between an activity’s duration and cost
(as assumed in Figure 11.3), or whether only certain combinations are
possible. The formulation presented below reflects the latter case, whereby
only a finite number of time–cost combinations are available for each
activity. Assuming an activities-on-arrow (AOA) network, the following
notation is used:
Figure 11.4
Example project cost as a function of its duration.
Figure 11.4 Full Alternative Text
TABLE 11.7 Crashing the
Project

(Cost in $1,000, Duration in Weeks)
22
Weeks
21
Weeks
20
Weeks
19
Weeks
18
Weeks
17
Weeks Weeks
Activity Cost Dur Cost Dur Cost Dur Cost Dur Cost Dur Cost Dur Cost
A 1.5 5 1.5 5 1.5 5 1.5 5 1.5 5 3.5* 4 4.5
B 3.0 3 3.0 3 3.0 3 3.0 3 3.0 3 3.0 3 3.0
C 3.3 8 3.3 8 3.3 8 3.3 8 3.3 8 3.3 8 3.3
D 4.2 7 4.2 7 4.2 7 4.2 7 4.2 7 4.2 7 4.2
E 5.7 7 5.7 7 5.7 7 5.7 7 5.7 7 5.7 7 5.7
F 6.1 4 7.1* 3 7.1 3 7.1 3 9.1* 2 9.1 2 9.1
G 7.2 5 7.2 5 8.2* 3 9.2* 3 9.2 3 9.2 3 9.2
Total
cost of
activities
31 32 33 34 36 38 39
* Crashed activity
TABLE 11.8 Project Costs as a
Function of its Duration
Project
length
(weeks)
Direct cost of
activities
Late
completion
penalty
Overhead
cost
Total
project
cost
22 $31,000 $4,000 $11,000 $46,000
21 32,000 3,000 10,500 45,500
20 33,000 2,000 10,000 45,000
19 34,000 1,000  9,500 44,500
18 36,000   0  9,000 45,000
17 38,000   0 8,500 46,500

16 39,000   0  8,000 47,000
15 41,000   0  7,500 48,500
14 44,000   0  7,000 51,000
A=set of activities
i=index for events on the AOA network model; i∈N={ 1, …, n }, where
i=1 is the unique “start” event that has no predecessors and i=n is the
unique “finish” event that has no successors
( i, j )=project activity that starts at event i and ends at event j; ( i, j )∈A
k=index for a particular time–cost combination
K ( ij ) =index set of possible time–cost combinations for activity (i, j)
L ijk =duration of activity (i, j) when it is performed at time–cost
combination k
C ijk =direct cost of activity (i, j) if performed at time–cost combination
k
C o =overhead cost per period of time
Decision Variables
t i =time event i takes place
y ijk =( binary ) equal to 1 if time–cost combination k∈K( ij ) is selected
for activity (i, j); 0 otherwise
The problem of minimizing total cost is:
Minimize  C o t n + ∑ ( i,j )∈A   ∑ k ∈K ( i,j ) C ijk   y ijk (11.1a)
subject to  t j − t i ≥ ∑ k∈K( ij ) L ijk   y ijk =1  for all ( i, j )∈A
(11.1b)
∑ k∈K( ij ) y ijk =1,   for all ( i, j )∈A (11.1c)
t 1 =0, t i ≥0, y ijk =0 or 1,  for all i,j,k (11.1d)

The objective function (11.1a) represents the project’s total cost, which is
composed of a direct and an indirect component. The first set of constraints
(11.1b) maintains the precedence relations in the network; the second set
(11.1c) ensures that each activity is performed at one of its time–cost
combinations. Constraint (11.1d) defines the variables.
As an example of the model, consider the following project:
Time–cost combination 1 Time–cost combination 2
Activity (i, j) Time (weeks) Cost Time (weeks) Cost
(1,2) 5 $100 3 $150
(1,3) 4 $70 3 $100
(2,4) 4 $200 3 $300
(3,4) 6 $500 3 $900
The effect of the overhead cost per period ( C o ) on the optimal schedule can
be analyzed by solving (11.1) for the example and varying the value of C o .
The specific model for this example follows:
Minimize C o t 4 +100 y 121 +150 y 122 +70 y 131 +100 y 132 +200 y
241 +300y242+500y341+900y342
subject to t 2 − t 1 ≥5 y 121 +3 y 122
t 3 − t 1 ≥4 y 131 +3 y 132
t 4 − t 2 ≥4 y 241 +3 y 242
t 4 − t 3 ≥6 y 341 +3 y 342
y 121 + y 122 =1
y 131 + y 132 =1
y 241 + y 242 =1
y 341 + y 342 =1

t 1 = 0 jc , t i ≥0, i=2, …, 4
y ijk =0 or 1 for ( i, j )∈A={ ( 1, 2 ), ( 1, 3 ), ( 2, 4 ), ( 3, 4 ) }, k=1, 2
Solving the model for different values of the overhead cost, C o , gives the
solutions presented in Table 11.9. The tradeoff between the overhead cost
and the cost of crashing activities is clear from these results. It is not justified
to crash any activity if the overhead cost is less than $20 per period. The first
activity to be crashed is (1, 3). Only when the overhead per period is between
$180 and $190 is it justified to crash all four activities. A linear programming
model may also be stated to model the situation where the cost to crash an
activity is linear and each activity’s duration may vary between a minimum
possible activity duration t jC and a normal activity duration t jN . Let’s
assume that a project is depicted with an activity-on-node (AON) network.
Also, let C jN and C jC denote the normal direct cost and the direct cost at
the maximum crash point, respectively.
TABLE 11.9 Parametric
Solution to Time–Cost Tradeoff
Example
(Cost in $, Duration in Weeks)
Overhead
cost, C o
Activity (1, 2) Activity (1, 3) Activity (2, 4) Activity (3, 4)
Cost Duration Cost Duration Cost Duration Cost Duration
 10 100 5  70 4 200 4 500 6
 20 100 5  70 4 200 4 500 6
 30 100 5 100 3 200 4 500 6
 40 100 5 100 3 200 4 500 6
 50 100 5 100 3 200 4 500 6
 60 100 5 100 3 200 4 500 6
 70 100 5 100 3 200 4 500 6

 80 100 5 100 3 200 4 500 6
 90 100 5 100 3 200 4 500 6
100 100 5 100 3 200 4 500 6
110 100 5 100 3 200 4 500 6
120 100 5 100 3 200 4 500 6
130 100 5 100 3 200 4 500 6
140 100 5 100 3 200 4 500 6
150 100 5 100 3 200 4 500 6
160 100 5 100 3 200 4 500 6
170 100 5 100 3 200 4 500 6
180 100 5 100 3 200 4 500 6
190 150 3 100 3 300 3 900 3
200 150 3 100 3 300 3 900 3
We define b j =( C jC − C jN )/( t jN − t jC ) as the marginal cost of crashing
activity j by one time period. Also, let P(j) denote the set of predecessor
activities for activity j. The decision variables in the linear program (LP)
include s j and t j which are the start time and duration time of activity j,
respectively (activity n is assumed to be the last activity of the project). The
model is given as follows:
Min ∑ j b j t j + C o s n
Subject to:
For all tasks j€P( j ), s j ≥ s i + t i (precedence constraints) and t jC ≤ t j ≤ t
jN (upper and lower bounds on the activity duration times).
In addition, non-negativity conditions are needed for the decision variables t j
and s j .
An Excel implementation of a model to optimize the time–cost tradeoff is
given in Appendix 11A.
Budgeting decisions are influenced by external factors such as the time value
of money. If the minimum acceptable rate of return is high, then the NPV of a

project may become an important criterion in budgeting. The reference list at
the end of the chapter contains several papers dealing with this subject. The
intuitive approach to project budgeting under the NPV criterion is to delay
activities that require a capital outlay and to start, as early as possible,
activities that generate cash. Because some activities lead to customer
payment (i.e., cash generation) but require a capital outlay, a tradeoff analysis
is required to schedule these activities in the best possible way from the
perspective of the budget.

11.5 Presenting the Budget
The project budget is a communications channel that must serve both project-
related and organizational planning and control needs. Two dimensions are
used to measure the quality of a project’s budget: its ability (1) to advance
organizational goals within imposed constraints and (2) to communicate the
proposed plan to the project team and organization and sometimes to
subcontractors and the client.
The budget is easier to understand and use if it is presented clearly and
concisely. Consider the following recommendations when preparing and
presenting a project’s budget:
1. Incorporate a schedule indicating the time that expenditures and
revenues are expected to be realized.
2. Make an effort to define milestones that correspond to the achievement
of measurable goals. Typical milestones for research and development
(R&D) projects are system design review, preliminary design review,
critical design review, and the passing of prototype performance tests. In
contractor/client projects, the achievement of such milestones can serve
as the basis for client payments. It is important to budget milestones
according to the costs of activities that lead up to them. In the example
project (see Figure 9.23), if event 3 is defined as a milestone that
represents the completion of activities A and B, then its budget is based
on the costs of activities A and B ($4,500). Assuming an early-start
schedule, these activities are scheduled to terminate 5 weeks after the
project initiation. Assuming a $2,500 overhead (or $500 per week), the
total payment of this milestone is likely to be above $4,500 and close to
$7,000.
3. Use the budget as a baseline for progress monitoring and control. If a
weekly progress report is required, then plan the budget at the weekly
level. However, if weekly progress reports are issued but the budget is
prepared on a monthly basis, a meaningful comparison between planned

progress and actual progress is possible only once every four progress
reports. Similarly, break down the budget to enable a direct comparison
with the progress reports. The cost breakdown used in preparing the
budget should be the same as the breakdown used to collect and analyze
data for both the project and the organizational control systems.
4. The budget should translate short-range objectives into work orders,
purchasing orders, and so on. This links the design and development
phases to the production phase through the budgeting and work
authorization processes.
5. Break down the budget by the organizational units responsible for its
execution and the work content assigned to such units. For example,
Table 11.10 itemizes the activities by assigned departments of the
example project.
TABLE 11.10 Breakdown of
the Budget by
Organizational Units
Activity Department 1 Department 2
A  – $1,500
B  –  3,000
C  $3,300  –
D  4,200  –
E  –  5,700
F  6,100  –
G  7,200   –  
Department total $20,800 $10,200
6. Whenever you use a specific standard in budgeting, reference it. For
example, suppose that activity C is welding the pressure tank of a

submarine and is budgeted at $3,300. This figure might have been
derived from the company standard, which says that it costs $300 per
inch to perform a weld. The estimated welding length is 11 inches. Such
information should be referenced (i.e., footnoted) in the budget. By
referencing the standard used, you can later trace any deviations in
actual cost to the deviation’s source (i.e., the cost per inch or the length
of the welding) and, if necessary, update the standard.
7. Include five components in the short-term (operational) budget:
1. WPs of discrete effort. Each WP defines the organizational element
responsible for a task and the task’s WBS element. Identifying the
WP this way allows to present the budget along WBS and OBS
lines. It also serves as a baseline for a control system capable of
tracing the sources of deviations between planned and actual
progress, as explained in Chapter 12.
2. Level of effort. This category, which includes the cost of efforts
related to more than one WP, occurs as activities progress over
time.
3. Apportioned effort. This category includes the cost of efforts based
on a factor of a discrete effort (WP) as exemplified by such
activities as inspection and quality control.
4. Cost of material. These costs include the WBS element for which it
will be used and the OBS element that will use it.
5. Other costs. Costs, such as those associated with subcontracting,
must be included.
8. Budget planners should try to define most of a project’s effort in discrete
terms as part of the WPs. These packages present units of work at levels
at which the work is performed and at which the effort is assignable to a
single organizational element.
9. Budget overhead costs for each organizational element with a clear
definition of the procedures used for allocating these costs. One option

is to include a management reserve in the long- and midrange budgets as
a buffer against uncertainty. The level of management reserve depends
on the amount of uncertainty involved in estimating the actual cost,
timing, and technological maturity of the effort required. This reserve
should be factored into the budget, once again in discrete terms, as work
progresses and information becomes available.
10. Define a target budget at completion as the total budget costs plus
management reserve and undistributed money.
These recommendations need to be fine-tuned and customized in order to
mesh with an organization’s budget cycle and “culture.”

11.6 Project Execution: Consuming
the Budget
During the project’s production phase, three processes occur simultaneously:
1. The short-range budget is translated into work authorizations and
purchasing orders. This process generates work orders, purchase orders,
and contracts with suppliers and subcontractors. It requires a feedback
system that facilitates a comparison between actual progress and the
original plans and compares the actual cost of the effort performed with
the budgeted cost. The exact structure of a feedback system used for
project control depends on the project’s structure and the organization’s
needs. This is explained in Chapter 12.
2. The tactical (midrange) budget is translated into a short-range budget
through a rolling horizon mechanism. Cost estimates and schedules are
accumulated into cost accounts as well as into apportioned effort and
level of effort. This is a multistage process because the tactical budget
for each period contains several short-range (operational) budget
periods. Developing a new, realistic short-range budget requires detailed
planning involving the integration of original project plans with reports
on actual progress. The short-range budget should detail the midrange
budget and, in case of cost or schedule deviations, present a detailed
plan for corrective action. Thus, development of the operational budget
is based on knowledge regarding the planned execution of activities and
the project’s actual status.
3. The long-range budget is gradually converted into the midrange budget.
This process involves the distribution of accumulated funds, the
allocation of management reserves to specific WPs, and the handling of
engineering changes. Such changes are frequent in long projects. During
the project execution phase, new market requirements (stakeholders
needs) or new technological developments may call for modifications in
the project’s technological aspects. The configuration management

system handles all of these change requests. This system keeps track of
change requests and the steps followed that lead to approval or rejection.
An approved technological change may have both cost and schedule
consequences. Thus, the process of translating long-range budgets into
midrange budgets should address all approved technological changes
and their impact on the project.
Management reserves, designed to buffer uncertainty, should be consumed as
soon as the results of tests and studies are available. Such results provide the
basis for developing a detailed project plan that translates management
reserve budgets into WPs, thus reducing the level of uncertainty. For the
project manager, budgeting is not static, but an ongoing process. Long-term
plans are translated into detailed short-term budgets, and short-term budgets
are translated into work orders, purchase orders, and contracts with
subcontractors and suppliers.

11.7 The Budgeting Process:
Concluding Remarks
The budgeting process provides an interface between organizational goals as
perceived by top management and the project manager’s actions to achieve
those goals. A budget links a project’s schedule, required resources, and NPV
and provides an action framework for each organizational element. Budgets
of individual functional units and projects, as well as those of routine,
ongoing activities, are integrated into an overall, organizational budget.
Each project’s budget is important in transforming goals into both plans and
actions while providing guidelines for integration across the OBSs and
WBSs. Management uses the budget as a communications channel to inform
organizational elements of resource allocation decisions and the level of
performance that is expected over time. An organization’s budgeting process
should be sufficiently flexible to respond to proposed changes and deviations
from the plan, as internal and external environmental factors inevitably
evolve. Not only is the quality of the budget important, but also the
communication that goes into its presentation and dissemination to the
broader organization is critical. A transparent and open budgeting process
enables management to win over resistant elements in an organization (e.g.,
explain budget cuts and unapproved expenditures). Saying all that experience
suggests that, for many organizations, transparency in the budgeting process
remains an unrealized ideal.
TEAM PROJECT Thermal
Transfer Plant
Your proposed schedule has been reviewed by the contract department at
Total Manufacturing Solutions, Inc. (TMS) and has been given tentative
approval. However, the vice president of finance has requested a detailed

budget for the project of designing and manufacturing the rotary combustor.
The budget should tie the OBS and the WBS to the project’s activities. Use
the following format:
Direct cost Indirect cost
Week Labor Material Other Labor Material Other
1
2
.
.
.
For each line item in the budget, identify its OBS and WBS relationship, and
specify the expected cost and corresponding variance. Along with the budget,
discuss the effect of an early-start schedule and a late-start schedule on cash
flow, and explain why the selected schedule is the best from the cash flow
point of view. (Is it?)

Discussion Questions
1. Develop a budgeting procedure for a university. Explain the role of each
management level together with its input and output.
2. Develop a budgeting procedure for a contractor who works on small
housing projects.
3. Develop a budget for the project “getting an undergraduate degree.”
Explain your assumptions and your analysis.
4. Assume that you are in charge of developing your state’s department of
transportation budget. Write specific instructions for project managers in
your department to facilitate a bottom-up budgeting process.
5. What kind of logic is used in the budgeting process of the federal
government?
6. Give an example of a project in which a late-start schedule is used
because of budgeting and cash flow considerations.
7. Give a detailed example of an activity that can be performed in several
modes. Describe each mode, the technology required, and the associated
cost.
8. Develop a flowchart for a computerized project budgeting program.
Explain the input and output of each element and the data processing
required.
9. Identify two projects for which the top-down budgeting approach would
be most appropriate. What advantages does it provide?
10. Assume that you have crashed a project as much as possible but that the
length of the critical path is still not acceptable. What other options are
available?

11. Most computer codes that have been developed to solve the crashing
problem assume a linear relationship between the time and cost for an
activity. This leads to a linear program. What does this assumption say
in terms of resource allocation, and when might it be acceptable?
12. When a project leader tries to perform slack management, what
difficulties might he or she encounter?
13. Read the article by Herroelen and Leus (2001) on the merits and pitfalls
of critical chain scheduling listed in the Reference section of Chapter 10.
Explain the relationship between the critical chain and the critical path
and the relationship between the critical chain and resource allocation.
14. Discuss the pros and cons of the critical chain and explain under what
conditions it might be a good approach to scheduling resources.

Exercises
1. 11.1 Develop a budget for the project described in Table 11.11 assuming
that the cost of each activity is linearly distributed over its duration.
TABLE 11.11
Activity Duration (days) Immediate predecessors Cost
A 3 – $3,000
B 4 –  2,000
C 3 –  6,000
D 2 C  2,000
E 1 B  1,000
F 5 A 10,000
G 2 B  4,000
H 3 B  9,000
I 11 C 11,000
J 3 D, E  3,000
K 1 F, G  1,000
L 4 K  2,000
M 4 J, H  8,000
1. Assume an early-start schedule.
2. Assume a late-start schedule.
3. Assume that a “leveled budget” is desired (i.e., the same daily cost
is desired for each day of the project).
2. 11.2 Using the data in Exercise 11.1 , assume that the activities can be
crashed as shown in Table 11.12 :

TABLE 11.12
Activity Normal time Crash time Cost of crashing per day
A 3 2 $1,000
B 4 2  500
C 3 2  500
D 2 1 1,000
E 1 1 –
F 5 4  500
G 2 1 1,500
H 3 2 1,000
I 11 8 1,500
J 3 1 1,000
K 1 1 –
L 4 3 1,000
M 4 4 –
Develop the functional relationship between the direct cost of this
project and its duration.
3. 11.3 Using the data in Exercise 11.2 , assume that the overhead for the
project is given by
Overhead= $2,000+α×$1,000 per day
What is the project duration that minimizes its total cost if:
1. α=1?
2. α=3?
4. 11.4 Assume that a continuous time–cost tradeoff exists for each
activity, as shown in Figure 11.3 . Write out the corresponding linear
program for minimizing the total project cost, defining all notation used.

What constraint would you add to ensure that the project is completed
within T time periods?
5. 11.5 For the project data given in Table 11.13 , assuming an overhead of
$350 per period, find the minimum cost schedule. What are the critical
activities? How much total slack and free slack exist for the noncritical
activities? Find the cost of the early-start and late-start schedules.
Resolve the problem with a deadline of 9 weeks using the linear
program developed in Exercise 11.4 .
6. 11.6 Given the data shown in Table 11.14 for the direct costs of the
normal and crash durations, find the different minimum cost schedules
between the normal and crash points for the project defined in Exercise
9.16 .
7. 11.7 Consider the time–cost estimates for the product development
project in Exercise 9.19 as given in Table 11.15 . Indirect costs are made
up of two components: a fixed cost of $5,000 and a variable cost of
$1,000 per week of elapsed time. Also, for each week that the project
exceeds 17 weeks, an opportunity cost of $2,000 per week is assessed.
1. Construct a table that enumerates the critical path and
corresponding direct cost and duration for each possible funding
strategy. The first two entries should be the “normal” and “all
crash” strategies. Then either crash or compress (one week at a
time) all activities on the critical path, and calculate the
corresponding direct cost and duration for the resulting strategies.
Use the data in the table to construct a bar graph of completion time
versus total cost ( direct+indirect+opportunity ).
TABLE 11.13
Normal Crash
Activity Immediatepredecessors Duration Cost Duration Cost
A – 4 $100 2 $300

B – 3 $200 1 $200
C A, B 2 $50 1 $100
D A, B 3 $100 2 $300
E A 4 $150 1 $400
F C, D 4 $250 1 $100
G D, E 2 $300 1 $200
H F, G 3 $200 2 $100
TABLE 11.14
Project (a)
Activity (i, j) Normal Crash
Duration Cost Duration Cost
(1, 2) 5 $100 2 $200
(1, 4)  2  $50 1  $80
(1, 5)  2 $150 1 $180
(2, 3)  7 $200 5 $250
(2, 5)  5  $20 2  $40
(2, 6)  4  $20 2  $40
(3, 4)  3  $60 1  $80
(3, 6) 10  $30 6  $60
(4, 6)  5  $10 2  $20
(4, 7)  9  $70 5  $90
(5, 6)  4 $100 1 $130
(5, 7)  3 $140 1 $160
(6, 7)  3 $200 1 $240
Project (b)
Activity (i, j) Normal Crash
Duration Cost Duration Cost
(1, 2)  4 $100  1 $400

(1, 3)  8 $400  5 $640
(1, 4)  9 $120  6 $180
(1, 6)  3 $20  1 $60
(2, 3)  5 $60  3 $100
(2, 5)  9 $210  7 $270
(3, 4) 12 $400  8 $800
(3, 7) 14 $120 12 $140
(4, 5) 15 $500 10 $750
(4, 7) 10 $200  6 $220
(5, 6) 11 $160  8 $240
(5, 7)  8 $70  5 $110
(6, 7) 10 $100  2 $180
2. Construct the Gantt chart for the minimum total cost schedule.
3. Construct a two-part schedule of direct costs (of the type illustrated
in Figures 9.10 and 9.11) based on the time schedule in part (b). Of
the two, which schedule yields the lowest peak cost? Also, on the
bases of variance, which of the two levels costs the most?
TABLE 11.15
Time estimates
(weeks)
Direct cost estimates
($1,000)
Activity Normal Crash Normal Crash
A 3 1.0  3.5  10.0
B 1 0.5  1.2  2.0
C 5 3.0  9.0  18.0
D 1 0.7  1.0  2.0
E 6 3.0  20.0  50.0
F 1 0.5  2.2  3.0
G 2 1.0  4.0  9.0

H 8 6.0 100.0 150.0
8. 11.8 Develop a mathematical programming formulation for the problem
of minimizing the total cost of completing the project discussed in
Exercise 11.7 . Use a commercial optimization package to find the
solution.
9. 11.9 Planmatics is undertaking a modernization program. The set of
activities in Table 11.16 has been defined for refurbishing one of its
wave soldering machines. The AOA network is given in Figure 11.5 .
Figure 11.5
AOA network for Exercise 11.9 .
TABLE 11.16
Crash date
Activity d ^ ij(days)
s ^ ij
(days)
Maximum possible
compression (days)
Expediting
cost per day
($)
A  6 2 0   —
B  2 0 1   50
C 12 3 2   80

D  8 1 2  175
E  7 2 1  100
F 16 4 0   —
G 23 2 1  100
H 25 5 3  300
I  4 1 1 1,000
1. Find the critical path, total slacks, and free slacks.
2. Find the probability of completion within 45 days.
3. Find the minimum cost increase to reduce the expected project
duration by 1 day.
4. Find the minimum cost increase to reduce the expected project
duration by 2 days.
5. Find the minimum project duration and the expected cost increase.
10. 11.10 Consider the project information given in Table 11.17 .
TABLE 11.17
Activity Immediatepredecessors
Expected
time
(weeks)
Normal
cost ($)
Expediting
cost per
week
($/week)
Minimum
time
(weeks)
A – 3 3,000 1,500 2
B – 6 7,200 1,000 4
C A 2 2,000 2,000 1
D A 7 7,000 2,000 3
E C, B 1 4,000 – 1
F B 3 3,000 1,500 2

1. Calculate the project cost based just on the costs of the activities.
2. Generate the weekly and cumulative cash flow charts, once for an
early-start schedule and once for a late-start schedule.
3. Discuss the implications of the charts generated in part (b).
11. 11.11 For the project described in Exercise 11.10 :
1. Generate the time–cost chart.
2. What is the shortest completion time for the project, and what
bottleneck activities prevent further time reduction?
12. 11.12 A managerial fee of $1,400 per week is to be paid as long as the
project in Exercise 11.10 has not been completed.
1. Calculate the optimal project duration.
2. You have been offered a bonus of $5,000 if you complete the
project within 8 weeks. Will you make it? Explain.
13. 11.13 Each activity in the project described in Exercise 11.10 has a
duration variance of 1 week. For example, the expected time for activity
A is 3 weeks, with a variance of 1 week. Assuming that the normal cost
of each activity is to be used, discuss the possible impact of the activity
variance on the project cash flow.
14. 11.14 You have signed a contract to complete the project described in
Exercise 11.10 within 10 weeks. The weekly managerial fee is $2,000.
1. Generate the schedule that will delay expenses to the last possible
moment and indicate its associated cash flow.
2. Generate the cash flow requirement resulting from the objective to
increase the probability that the project will be completed on
schedule.
15. 11.15 Find a schedule for Exercise 10.3 that minimizes the cost of the

project assuming that resource I costs $10/hour, resource II costs
$15/hour, and there is an overhead of $150/day for the project.
16. 11.16 Assuming that the weekly labor cost per employee is $1,200 and
that the fringe benefit rate is 25%, determine the cumulative cash flow
requirement for the project described in Exercise 10.5 .
1. For an early-start schedule.
2. For a latest start schedule.
3. What if the allocated budget is below the late-start cash flow line?
4. What if the allocated budget is above the early-start cash flow line?

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Appendix 11A Time–Cost Tradeoff
With Excel
An optimization tool, known as Solver, exists within Excel that may be used
to solve the time/cost tradeoff, described in Section 11.4.2. To invoke the
Excel Solver, a user selects Solver from Excel’s Data ribbon. The Solver may
need to be installed as follows. After selecting the Office button, a user then
selects Excel Options, Add-Ins, and Go, making sure that Solver is checked
off in the list of add-ins.
Once Solver is invoked and launched by a user, the dialog box in Figure
11A.1 appears. The dialog box contains three sections that must be
completed: Set Objective, By Changing Variable Cells, and Subject to the
Constraints. Set Objective is the objective function value. It refers to the cell
in the Excel spreadsheet that will either be minimized or maximized in the
optimization. By Changing Variable Cells refers to the cells in the Excel
spreadsheet that contain the decision variables. Subject to the Constraints
refers to the cells in the Excel spreadsheet that represent the optimization
model’s constraint set.

Figure 11A.1
Figure 11A.2 contains the Excel spreadsheet that models the preceding
example. It may be used to assess the tradeoff between finishing the project
early through crashing versus finishing the project at its regularly scheduled
time at lower cost.

Figure 11A.2
Figure 11A.2 Full Alternative Text
Rows 2–5 contain information regarding the four tasks. For each task, the
start time, finish time, and duration are shown. The start times, cells B2:B5,
are decision variables that are computed by executing Solver. The duration
times, cells C2:C5, are dependent on the binary variables, contained in
column F, cells F13:F16. For example, the duration time for the first task is
computed by the Excel formula:
( B13∗F13 )+( D13∗( 1−F13 ) ).

The Excel cells B13 and D13 refer to the regular duration time and crash
duration time for task (1, 2), respectively. The Excel cell F13 is a binary
decision variable where D13=1 , if the regular duration time is selected by
Solver, and D13=0 , otherwise. The finish times for each task, cells E2:E5, are
computed in Excel by respectively summing a task’s start time and its
duration; that is, they are dependent on the decision variables.
Additional information, regarding the four tasks, is contained in rows 13–16.
The problem data for each task—its regular and crash duration times and
regular and crash costs—are given cells B13:E17. The cells F13:F17, as
described above, contain the values of the binary, decision variables,
associated with each task. Finally, cells G13:G17 contain the direct costs
associated with each task, based on the formula:
( C13∗F13 )+( E13∗( 1−F13 ) ).
That is, cells G13:G17 are populated after Solver is executed, and the time–
cost tradeoff is optimized. If a task’s associated binary variable is set to 1,
then the regular cost associated with that task is populated in column G.
Otherwise, the crash cost associated with that task appears in Column G.
The overhead cost per period—problem data—is given in cell B8. The total
direct cost and the total overhead cost are given in cells B18 and B19,
respectively. The former is computed by summing the direct cost for each
task, cells G17:G20. The latter is computed by multiplying the overhead cost
per period times the makespan where the makespan is the latest finish among
all of the tasks (in this case, cell E5 which contains the finish time of task (3,
4)). The objective function value to be minimized is total cost, which is
contained in cell B21.
The Solver dialog box must be completed by a user, as shown in Figure
11A.3. In the Set Objective box, the user will enter the cell B21. It is
important to check off Min (rather than Max) since the model is minimizing
total cost. In the By Changing Variable Cells box, the user should list the cell
ranges of the decision variables. In this example, cells B2:B5 contain the
starting times of each task, and cells F13:F17 contain the binary variables
associated with each task, indicating whether regular or crash mode is
optimal.

Figure 11A.3
Figure 11A.3 Full Alternative Text
In order to set up the constraints, a user should select the Add button to the
right of the Subject to the Constraints box. Once Add is selected, a user will

be presented with another dialog box that enables the user to specify a
constraint to Solver; see Figure 11A.4. In Solver, a cell, a range of cells, or a
scalar value may be provided by a user in formulating a constraint. That is,
both the Cell Reference and Constraint boxes, that is, the “left-hand side” and
“right-hand side” of the constraint, may be populated by one or more cell
references and/or scalar values. Solver will not accept an arithmetic formula
in the Add Constraint dialog box. A user may also constrain certain decision
variables to be binary.
Figure 11A.4
Once the objective function value, decision variables, and constraints are
represented, a user may execute the Solver. Prior to running the optimization,
a user may select a Solving Method, for example, Simplex LP. In addition, a
user may wish to review the Options when solving an integer model, such as
the one in this example. In particular, a user may specify a maximum run
time, a maximum number of branch and bound iterations, or a stopping
criterion (e.g., feasible solution is no more than 5% away from optimality).
The model in Figure 11A.2 may be run with different values for overhead
cost in order to perform sensitivity analysis on the time–cost tradeoff. For
example, by setting the overhead cost per period to $100 (modifying cell B8
only) and re-executing Solver, the results in Figure 11A.5 are obtained. In
this case, the optimal solution is to crash task (1, 3) and achieve a minimum
total cost of $1,800.

Figure 11A.5

Chapter 12 Project Control
12.1 Introduction
Planning is a fundamental component of project management. A project
manager prepares a detailed plan that covers the technological, budgetary,
scheduling, organizational, and risk-related aspects of a project and is
essential to facilitate coordination across multi-disciplinary functions and
outside contractors. Unfortunately, even the best of plans cannot guarantee
success. Uncertainty and changing environmental conditions are bound to
intervene in unforeseen ways, sometimes positively, sometimes negatively.
Plans are based on assessments of needs and the estimation of such factors as
activity durations, resource availability, labor efficiency, and cost, each of
which may be subject to a high degree of variability. Furthermore, needs and
goals are dynamic, changing over time. New technologies developed during
the life cycle of the project, a rethinking of corporate strategy, the
replacement of key personnel, and new market or legal circumstances all may
conspire to make the original plans obsolete. Thus, it is essential to monitor
actual progress and to update the original plans as needed. Project monitoring
and control are most essential in complex, technically advanced projects in
which the likelihood of technological, environmental, and economic changes
occurring during a project’s life-cycle is greatest.
The design and implementation of a project control system is therefore an
important part of project management. The basis of any control system is a
statement of project goals and their relative importance. For each goal, one or
more performance measures are needed. For example, a common goal is to
keep a project on schedule. Appropriate performance measures can be based
on the actual start or finish times of critical activities, completion of
milestones, or timing of acceptance tests. Selection of a performance measure
depends on the corresponding goal and the level of management to which
actual values of the performance measure will be reported. Thus, a low-level
manager who is responsible for a specific set of activities needs detailed
information on those activities. A project manager monitors the actual

completion time of critical activities, whereas upper management needs to be
informed on the completion time of major milestones.
Once performance measures are selected, information required to report the
actual value of each performance measure must be defined. For example,
completion of a milestone may be reported by successful completion of an
acceptance test and issuance of an appropriate report by quality control. The
same milestone may be reported as completed only after customer payment is
received. Selection and use of performance criteria require collection of
specific data, which may not be trivial. If data are available in an existing
reporting system, the cost of data collection is reduced and the likelihood of
conflicting data in the project control system and other management
information systems is minimized.
Data collected are used as a basis for estimating performance measures at any
point during a project’s duration and to forecast future values on the basis of
past performance. Estimates of current values are the basis of “real-time”
control, that is, a comparison of the actual value of a performance measure
with its planned value. Control limits are set to assess the severity of
deviations. Deviations that are larger than a predetermined value are used to
trigger corrective action. “Triggers” form the basis of management by
exception, whereby actual deviations from plans alert management to a
particular problem that needs attention.
A second mode of control is trend control, which is based on forecasts of
future performance measures. Actual values of performance measures are
extrapolated into the future in an effort to detect deviations before they occur.
Forecasts of future deviations trigger preventive actions designed to minimize
future problems. Trend control is important because information, based on
existing values of performance measures, may not reveal irregularities.
However, data trends over the last few control periods may indicate a high
likelihood of future problems.
The designer of a project control system therefore should address the
following questions:
1. What performance measures should be selected?

2. What data should be used to estimate the current value of each
performance measure?
3. How should raw data be collected, from which sources, and in what
frequency?
4. How should data be analyzed to detect current and future deviations?
5. How should results be reported, in what format, to whom, and how
often?
Answers to these questions underlie the design of the control system’s data
collection, data processing, information distribution, and response processes.
Management should exercise project control throughout a project life cycle.
Information provided by a control system is essential for the ongoing
decision making aimed at keeping a project on track.
Several measurements can be taken in support of project control. These can
be classified into four major categories: schedule, cost, resources, and
performance. Table 12.1 elaborates on each with an eye toward
understanding the difficulties that may arise.
Some of the measurements in the table are commonly used by industrial and
service organizations to manage routine functions, such as inventory tracking,
accounting/auditing, quality control, production scheduling, and data
processing. There are issues, however, that are unique to the project
environment that require customized control systems to handle the one-time,
non-repetitive effort that is typical of projects.
TABLE 12.1 Measurements
for Project Control
Measurement Category affected
Delay in starting critical tasks Schedule
Delay in finishing critical tasks Schedule

Noncritical tasks becoming critical Schedule
Milestones missed Schedule
Due date changes Schedule
Price changes Cost
Cost overruns Cost
Insufficient cash flow Cost
High overhead rates Cost
Long supply lead time for required
material Resources, schedule
Low utilization of resources Resources, cost
Resources availability problems Resources, schedule, cost
Changes in labor cost Resources, cost
Changes in scope of project Performance, cost, schedule,resources
Lack of technical information Performance, cost, schedule
Failure in tests Performance, cost, schedule
Delays in approvals of
configuration changes Performance, schedule
Errors in records (inventories, etc.) Performance, cost, schedule
Control systems are part of an organization’s management information
system (MIS). Each organization tends to develop or adopt an MIS that fits
its needs, its structure, and the environment in which it operates.
Organizations that fund research and development projects and say, because
of technological uncertainties, agree to pay the actual cost of the project plus
a predetermined contractor fee (cost plus fixed fee contract), face the problem
of controlling activities of different contractors, each employing a different
control system. Major organizations such as the U.S. Department of Defense
(DOD), the U. S. Department of Energy (DOE), and the National Aeronautics
and Space Administration (NASA) developed guidelines or requirements that
their contractors must incorporate in their respective control systems. The
common approach is to let contractors choose the MIS and control system
that best suit their needs, subject to a set of criteria called cost/schedule
control systems criteria (C/SCSC). Rules are given for the following five

aspects of the project: (1) organization, (2) planning and budgeting, (3)
accounting, (4) analysis, and (5) revisions and access to data. Appendix 12B
lists the criteria used by the DOE. Similar criteria are used by the DOD and
NASA.
In this chapter, we concentrate on techniques specifically developed for cost
and schedule control. We also discuss methods used for quality control and
control of technological changes, that is, configuration management.

12.2 Common Forms of Project
Control
Project control can be exercised through formal or informal mechanisms.
Small, technically simple, short-range projects, performed by collaborative,
highly motivated teams that are physically co-located under a single
organizational unit, may not need a formal control system. The decision to
introduce a formal control system and the selection of a specific system
should be primarily based on two aspects of a project: (1) risk involved and
(2) cost of the control system and its expected benefits. A high-risk project is
one in which the probability of undesired outcomes is significant as a result
of environmental conditions, complexity of the project, or other factors. If the
cost associated with undesired outcomes is high, then it behooves an
organization to invest in a formal, well-designed control system.
Selection of a control system depends on many factors, such as the
characteristics of the project structures [organizational breakdown structure
(OBS) and work breakdown structure (WBS)], the technological nature of the
project, the schedule, the budget, and the personality of the members of the
project team. Control systems can be very simple, taking the form of weekly
team meetings to discuss current status, or can be very sophisticated,
comprising a battery of hardware, software, and personnel.
Schedule control in its simplest form is based on a comparison between a
planned schedule, as depicted by a Gantt chart or results of a critical path
analysis, and actual performance. Data on actual progress are collected
periodically (every week, every month, etc.) or continuously (as soon as an
activity is completed or a milestone is achieved) and are used as input to the
control system. By comparing the initial schedule (the baseline) with the
current updated schedule, deviations are detected. These deviations trigger
corrective action, such as reallocation of resources to expedite late activities.
Simple cost control is achieved by comparing the actual cost of project
activities to the planned budget. Although most organizations have some

form of a cost monitoring and control system, data required for project cost
control may be challenging to extract from these systems. For example, direct
labor and material cost of specific project activities may not be available
because the department in which those activities are performed does not keep
records for each activity separately. Actual cost may be accumulated by
department or by work orders. To facilitate cost control by project activities
or WBS elements, a customized cost control system is required. Once the
information on actual costs of project activities is available, cost overruns can
be detected, trends can be analyzed, and management’s attention can be
brought to bear when current or future costs are considered out of control.
An important assumption that is frequently made when identifying deviations
from the original plan is that the amount of resources allocated to an activity
per period is constant over its duration and that output is proportional to
input; that is, there is no learning. For example, if activity A is expected to
cost $3,600 and is scheduled over a 6-week period, then the budget is
$600/wk. Also, if 2 man-weeks produce a certain level of output, then 4 man-
weeks will produce twice that level. A project manager should ensure that no
special circumstances exist to void the assumption of uniform resource usage
over time.
Cost and schedule control, based on a simple comparison between planned
and actual performance, is illustrated using the example project introduced in
Section 9.4. Suppose that a weekly report detailing cost and schedule
performance is issued. Referring to the example, three activities (A, B and E)
are scheduled to start the first week of the project, assuming an early-start
schedule. The duration and cost of these activities are summarized in Table
12.2. Actual performance for the first month of the project (weeks 1 through
4) is summarized in Table 12.3. On the basis of the information in these
tables, the following observations can be made:
TABLE 12.2 Duration and
Cost for Activities Performed
in Month 1

Activity Duration (weeks) Cost Cost per week
A 5 $1,500 $300
B 3 $3,000 $1,000
E 7 $5,700 $814
TABLE 12.3 Actual
Performances in Month 1
Week 1 Week 2 Week 3 Week 4
Activity Activitystatus
Actual
cost
Activity
status
Actual
cost
Activity
status
Actual
cost
Activity
status
Actual
cost
A Started $500 Inprocess $1,000
In
process $1,300 Completed $1,500
B Started $1,000 Inprocess $2,000
In
process $2,500 Completed $3,000
E Started $814 Inprocess $1,500
In
process $2,500 In process $2,900
Week 1. All three activities started on schedule. Assuming, for
simplicity, that the budget of each activity is uniform over time, the
weekly budget of activity A is $1,500/5=$300, as shown in Table 12.2.
Similarly, activity B is budgeted at $3,000/3=$1,000 per week, and
activity E at approximately $5,700/7=$814 per week. The budget for the
first week therefore is $300+$1,000+$814=$2,114. According to the
plan, the amount that should have been spent on activity A is $300.
Activity A shows a cost overrun of $200 ( $500−$300 ); activities B and
E are exactly on target.
Week 2. All three activities are in process, as scheduled. Activity A has
a cumulative cost overrun of $400 ( $1,000−2×$300=$400 ), whereas the
overrun for week 2 is $200 ( $500−$300 ). The actual cumulative cost of
activity B is as planned and the actual cumulative cost of activity E for
the period is $128 below the planned budget ( $1,500−2×$814=$128 ).

Week 3. Activity B is late because it was scheduled to be completed by
the end of week 3. Activities A and E are in process as scheduled. The
cumulative actual cost of activity A is $1,300, whereas its planned cost
for the three weeks was only 3×$300=$900, the difference of
$1,300−$900=$400 is the same as in week 2. The actual cost of activity
B is only $2,500 compared with a budget of $3,000, while the actual
cost of E is $2,500 compared with a budget of 3×$814=$2,442.
Week 4. Activity A is completed 1 week earlier than planned, activity B
is completed 1 week late, and the total cost of both activities is exactly
as planned. Activity E is in process, and its total cost of $2,900 is below
the budget of $3,256 ( 4×$814 ).
Analysis. Depending on when the information on actual activity start and
end times becomes available, schedule delays may not be detected in a timely
manner. For example, activity B was not completed on time, but only at the
end of week 3 did this become known.
Information on actual dollars spent is not a sufficient measure for cost
control. Actual cost must be compared with actual project accomplishments
and progress. For example, the cost overrun associated with activity A was
due to the fact that it was ahead of schedule (completed in 4 weeks instead of
5). This situation could not be observed from the cost and schedule
information above.
In addition to cost and schedule control, performance control with respect to
the technical aspects of the project is the third aspect common to all control
systems. An organization’s quality control and quality assurance system
serves to control performance. A major problem with performance control
stems from the one-time nature of projects. Engineering changes throughout a
project life cycle make quality control a difficult task, primarily because it is
not possible to use past data as a basis for statistical process control. In
addition, quality control is dependent on the availability of an updated project
configuration, something that is difficult to achieve in a timely manner. To
monitor and control engineering changes, a configuration management
system is needed.
Although “stand-alone” independent control systems for cost, schedule, and

performance are common, these three dimensions are not entirely
independent in most projects. To integrate the three control systems, project
review meetings should be held frequently. In such meetings, representatives
from the various groups and organizations that are participating in a project
discuss progress and decide on necessary corrective action. Review meetings
can be scheduled periodically, upon a request as a result of an exceptional
event, or when a predetermined milestone is reached. Typical examples are
the preliminary design review and the critical design review, which are major
milestones in the project design phase.
Milestone-related review meetings are typically scheduled to demonstrate and
analyze major subsystems and prototypes. The integrative nature of a project
review meeting in which progress is assessed and problems are aired is the
essential advantage of this form of control. However, the need to bring
together experts from different functional areas (and sometimes from
different organizations) for such meetings makes this form of control
expensive and difficult to organize. There is a need for a project control
system that integrates information on cost, schedule, and performance to help
management monitor and control projects performed by several
organizational units.

12.3 Integrating the OBS and WBS
with Cost and Schedule Control
A project control system is designed to give management assurance that a
project is proceeding according to plan. Its major function is to monitor
progress, detect deviations between the original plan and actual conditions,
identify trends that may impact successful project completion, and initiate
corrective actions. Control limits are established for critical parameters, and
deviations outside these limits are flagged. Corrective action is taken when
deviations are considered significant. A major problem in project control is a
lack of standards deriving from past performance. The ad hoc nature of
projects motivates the adoption of control limits that are based on intuition
and risk analysis rather than on historical data, as in statistical process
control.
The idea of control limits is depicted in Figure 12.1. In Figure 12.1a, the
cumulative budget for activity A in the example project is plotted along with
actual cost as a function of time for weeks 1 through 4. The control limits for
actual cost are set at ±10% of the cumulative budget. The need for an upper
control limit is obvious, as a project manager must guard against budget
overruns. Actual expenditures below budget are also monitored because they
might signal a delay in performing some activities.

Figure 12.1
Control limits and actual cost for activity A, weeks 1 through 4.
Figure 12.1 Full Alternative Text
In Figure 12.1b, the weekly budget for activity A, in the example project, is

plotted as a function of time for weeks 1 through 4. Again, the control limits
for actual cost are set at ±10% of the weekly budget. Corrective actions may
be undertaken if the deviation between actual cost and planned cost is
considered too high.
A similar report for several activities or for an entire project can be
constructed. Each report should be designed according to the needs of the
management level for which it is produced. By introducing cost deviation and
control limits, automatic detection of problematic deviations is possible. On
the basis of predetermined control limits, management can be informed of
activities whose periodic or cumulative deviation from plan exceed an
acceptable range and, therefore, may require attention.
An important effectiveness measure for a project control system is average
response time; that is, the average time between the occurrence of a deviation
outside control limits and its detection. Another important performance
measure is traceability; the ability of a control system to identify the source
of a problem, causing the deviations. It is important to establish a relationship
between the source of the problem and the affected project components and
to inform the responsible organizational units. The time when the problem
occurred should also be recorded as a third dimension of this measure.
An appropriate data structure is required to achieve traceability. This
structure must relate plans and corresponding progress reports to the relevant
time periods, to the appropriate segments of the project work content, and to
the organizational units that are responsible for these segments. Two
hierarchical structures are commonly used in an integrated manner to
facilitate traceability: (1) the OBS and (2) the WBS.
12.3.1 Hierarchical Structures
As discussed in Chapter 7, the OBS is a model of a project’s organizational
structure. Each entity that is responsible for one or more project tasks is
represented. At the lowest level of the OBS, the operational units engaged in
execution of project activities are represented. Higher levels represent various
management layers such as foremen and department managers, up to the vice

president of operations and the chief executive officer. Along with the OBS,
authority and responsibility have to be clearly defined, as well as policies and
procedures promulgated for reporting and authorizing work. The OBS
defines communication lines used for reporting progress (from the bottom
up), and for issuing work orders and technical instructions (from the top
down). An OBS for the example project is illustrated in Figure 12.2. Work
packages (WPs), or activities, are assigned to organizational units as follows:
Organizational unit Activities performed
Department 1 C, D, F, G
Department 2 A, B, E
Figure 12.2
OBS for example project.
The OBS is integrated with the WBS, which is typically a hierarchy of
hardware, software, data and services, of tasks required to complete a project.
The WBS organizes, defines, and displays the product to be produced as well
as the work to be accomplished in a project. At the lowest level of the WBS,
specific WPs, or tasks, are listed. These tasks are integrated through the
higher levels into subsystems, into systems, and, at the top level, into a
complete project. A simplified WBS that consists of three elements is
illustrated in Figure 12.3. The upper level in the figure represents the entire
project, whereas the lower level comprises the three major elements of the
WBS. For the example project, the following relationships exist between
project activities and the WBS elements:
WBS element Activities related to WBS element

Element I A, C, D
Element II B, F
Element III E, G
Figure 12.3
Simple WBS.
The same principles apply to larger projects. For example, the upper three
levels of a WBS for an electronic system are presented in Appendix 12A
(based on MIL-STD-881A).
By integrating the OBS and the WBS, each activity in a project is linked to
both structures at their lowest levels, as illustrated in Figure 12.4. Department
1 performs activities C and D required for element I in the WBS. As defined
by the linear responsibility chart (see Section 7.5), there should be one
responsible organizational unit for each WP. The cost associated with each
WP is accumulated and controlled by the corresponding cost account. WPs
and cost accounts form the basic building blocks of a project control system
that supports traceability in both the OBS and WBS dimensions.

Figure 12.4
Linking the OBS and the WBS.
Figure 12.4 Full Alternative Text
Design of a control system is initiated during the conceptual design phase of
a project, as goals and performance measures are defined and the risks
associated with the project are identified. Later, the OBS and WBS are
developed together with activities to be performed, related costs, durations,
and precedence relations. By the end of the planning phase, the detailed OBS,
WBS, schedule, and budget serve as baseline parameters for a control system.
During project implementation, at the end of each control period,
comparisons are made between work content completed and work content

scheduled for that period. An effort is made to detect schedule overruns and,
if present, to reduce them to a minimum by adjusting the original plan.
Simultaneously, at the end of each control period, the control system
compares budgeted costs and actual costs. Monitoring schedule and cost is
typically performed at the end of each period (e.g., week, month) and
cumulative reports are prepared for management review.
Based on the original schedule for the example project, activity A should be
completed 5 weeks after the start date, activity B after 3 weeks, and activity E
after 7 weeks. Figure 12.5 presents a Gantt chart for the original plan. A
summary report of actual progress after 4 weeks, together with the planned
and actual costs, is presented in Table 12.4. The Gantt chart in Figure 12.5
illustrates the early-start schedule for activities A, B, and E. The summary
report in Table 12.4 indicates actual progress measured by work content
performed, actual cost as reported by the accounting system, and the original
budget for these activities.
Figure 12.5

Gantt chart for an early start.
Figure 12.5 Full Alternative Text
TABLE 12.4 Summary
Report for Weeks 1-4
Activity Actualcost Budgeted cost
Work performed as % of
work content
A $1,500 $300×4=$1,200 100
B $3,000 $3,000 100
E $2,900 $814×4=$3,256 2 7 =28.6
Total $7,400 $7,456
Actual progress made can be estimated by several methods. In many
instances, it is a simple matter of measuring output. For example, assuming
that activity E involves assembling 70 platforms for a batch of
telecommunication systems and that by the end of the fourth week only 20
have been finished, then 2/7, or 28.6%, of the work content has been
accomplished. Here the estimate of actual work completed is unbiased and
exact. In other cases, it may be more subjective, based on the opinion or
observation of an expert such as a foreman, an engineer, the client
representative, or the quality control group. A rough estimate can be used
when the duration of activities is about the same as the length of the control
period. In this case an activity can be assumed 50% completed when it starts
and 100% completed at its finish. This estimate is easy to compute and
eliminates the need for a subjective measure.
Continuing with the example, a simple analysis of the costs for the first
month does not identify any problems because actual costs ($7,400) are a bit
less than budgeted costs ($7,456). Furthermore, a critical path analysis that is
based on actual progress reveals that the free slack of activity E (6 weeks) is
shortened by 2 weeks as a result of delays, but that activity E is still not on
the critical path. Nevertheless, none of the analytic techniques discussed thus

far is capable of detecting the deviations between the project plan and actual
progress. More detail is needed to assess the situation accurately. In
particular, an exhaustive cost/schedule control analysis that integrates cost
data with information on actual progress reveals that the project is not only
behind schedule but also over budget. This is because the actual progress on
activity E in 4 weeks is equal to the work content planned for just the first 2
weeks. Thus, activity E is subject to a 50% delay. Furthermore, the budgeted
cost of 2/7 of E is only 2×$814=$1,628, whereas its actual cost is $2,900 for
the first 4 weeks.
This example illustrates the close relationship among cost, schedule, work
content, and the need for an integrative measure that ties all three components
together in a control system.
In practice, for certain types of activities, for example, software development,
estimating the percent of work completed at an intermediate stage is more
problematic. Software development is not simply measured by number of
lines of code that are written. Rather, a piece of software cannot typically be
marked as “completed” until it has been integrated with other software
modules and tested. Consequently, less formal control systems are often used
by project managers in practice for projects where the output is not tangible
and is not easily measured.
12.3.2 Earned Value Approach
The earned value (EV) concept integrates cost, schedule, and work
performed by ascribing monetary values to each. In EV-based control
systems, only three variables are used as the basic building blocks. Each is
discussed below.
1. Budgeted cost of work scheduled (BCWS), or planned value (PV), is
defined as the value (in monetary units) of work scheduled to be
accomplished in a given period of time (a single control period, or an
ordered sequence beginning with the first period). The BCWS values of
activities A, B, and E in the example project for the first month are as
follows:

Activity BCWS
A 4×$300=$1,200
B           $3,000
E 4×$814=$3,256̲
Total           $7,456
Thus, the work content scheduled to be accomplished during the first 4
weeks of the project is budgeted at $7,456.
2. Actual cost of work performed (ACWP), or actual cost (AC), is defined
as the cost actually incurred and recorded in accomplishing the work
performed within the control period. In the example, these costs are
Activity ACWP
A $1,500
B $3,000
E $2,900
Total $7,400
As can be seen, a total of $7,400 was spent during the first 4 weeks to
accomplish the work performed.
3. Budgeted cost of work performed (BCWP), or EV, is defined as the
monetary value of work actually accomplished within a control period.
In the example, 100% of activity A is accomplished. Therefore, its
BCWP is equal to the total budget of activity A, which is $1,500.
Similarly, for activity B, BCWP= $ 3,000. However, for activity E, the
work performed is only 2/7 of the activity’s estimated work content.
Therefore, its BCWP= $ 5,700× 2 7 =$1,628. The BCWP values are
summarized as follows:
Activity BCWP
A $1,500
B $3,000
E $1,628

Total $6,128
When it is not possible to estimate accurately the percentage of work
completed, the stage approach can be applied. Each life-cycle stage of a WP
represents a specific percentage of the total value of the WP. Of course, the
percentages differ from one type of WP to another, depending on their type
of business. In the analysis, the EV of a WP is a function of its life-cycle
stage. The completion of each stage is considered a milestone. As an
example, consider the following stages:
Stage Stage value Cumulative value (%)
End of planning 15 15
End of execution 45 60
End of testing 20 80
First submission 10 90
Final submission 10 100
If a certain stage has been completed but the next stage has not yet been
started, then the EV equals the value stated. If, for example, the execution
stage has been completed and the WP is waiting for the testing stage, then the
current EV is 60% of the original budget. If testing is under way, then it is
assumed that half (50%) has been completed. Therefore, given that the value
of testing is 20%, half of 20% is 10%, so the cumulative EV of the WP is
70%. If the total PV of the WP was $10,000, then the present EV is
BCWP=10,000×0.7=$7,000.
In practice, estimating the stage value associated with a particular WP is not
often well defined, limiting the implementation of the EV approach as a
practical control system for project management.
The three measures BCWS, ACWP, and BCWP are the basis of the control
analysis which detects deviations in time, schedule, and, especially, cost. In
particular, we are concerned with the following.
1. Schedule deviations. The difference between the BCWP and the
budgeted cost of work scheduled (BCWS) indicates (in monetary units)
the deviation between work content performed and work content

scheduled for the control period. If the absolute value of the difference is
very small, then, in terms of work content, the proper volume of work
was completed. A positive difference indicates that a project is ahead of
schedule, and a negative difference implies that a project is late with
regard to work volume. Defining the schedule variance (SV) as the
difference between BCWP and BCWS, we get
Activity BCWP−BCWS=SV
A     $1,500−$1,200=$300
B     $3,000−$3,000=$0
E   $1,628−$3,256= −$1,628 _
Cumulative variance= −$ 1,328
On the basis of the SV values, we conclude that for activity A, the work
performed is worth $300 more than what was planned for the control
period; in activity B, the work performed is exactly equal to what was
planned; and in activity E, the work performed is worth $1,628 less than
what was planned for the period.
The cumulative variance indicates that the project is already late 4
weeks after its start. This measure, together with a CPM analysis,
enables a project manager to track critical activities and detect overall
trends in schedule performance. Although a delay in noncritical
activities may not cause immediate project delays, resources required to
perform them will be needed in a later period. This shift in resource
requirements may cause a problem if the load on resources exceeds
available capacity.
Schedule delays detected by the EV analysis should be monitored
closely. When a delay extends beyond a control level, an analysis of
resource requirements tests whether, as a result of resource limits, the
entire project may be delayed. By combining CPM analysis to detect
delays in critical activities with EV analysis, the two major sources of
schedule delays are monitored (delays in critical activities and delays
caused by resource shortages).
2. Cost deviations. Deviations in cost are calculated on the basis of the

work content actually performed during the control period. Therefore,
the cost variance (CV) is defined as the difference between the BCWP
and the ACWP. A positive CV indicates a lower actual cost than
budgeted for work performed during the control period, whereas a
negative CV indicates a cost overrun. The CV of activities A, B, and E
is presented for the first 4 weeks of the example project:
Activity BCWP−ACWP=CV
A     $1,500−$1,500=$0
B      $3,000−$3,000=$0
E   $1,628−$2,900= −$1,272 _
Cumulative variance= −$ 1,272
Activities A and B are exactly on budget; the actual cost of performing these
activities is equal to the budgeted cost for the accomplished work content.
Activity E, however, shows a cost overrun of $1,272 because the work
performed on this activity was budgeted at $1,628, whereas the actual cost
turned out to be $2,900.
SV and CV are absolute measures indicating deviations between planned
performance and actual progress, in monetary units. Based on these
measures, however, it is difficult to judge the relative schedule or cost
deviation. A relative measure is important because a $1,000 cost overrun of
an activity that was originally budgeted for $500 is clearly more troublesome
than the same overrun on an activity that was originally budgeted for
$50,000. A schedule index (SI) and a cost index (CI) are designed to be
proportional measures of schedule and cost performance, respectively.
The SI is defined as the ratio of BCWP/BCWS. Thus, an SI value equal to 1
indicates that the associated activity is on schedule. Values larger than 1
suggest that the activity is ahead of schedule, and values smaller than 1
indicate a schedule overrun.
The CI is defined as the ratio of BCWP/ACWP, implying that when CI
equals 1 the activity is on budget. CI values larger than 1 indicate better-than-
planned cost performance, and values smaller than 1 indicate cost overruns.
CI may be considered a cost effectiveness index because it specifies the value

of work obtained from each dollar spent. For example, CI=1.05 means that for
every dollar spent, $1.05 was obtained.
Following are CI and SI values for the example project after 4 weeks:
Activity BCWP BCWS =SI BCWP ACWP =CI
A $1,500 $1,200 =1.25 $1,500 $1,500 =1
B $3,000 $3,000 =1 $3,000 $3,000 =1
E $1,628 $3,256 =0.5 $1,628 $2,900 =0.56
These values indicate that, during the control period, 25% more work was
performed for activity A than planned ( SI=1.25 ) but at the exact cost
budgeted for that work content ( CI=1 ). For activity B, the planned work
content was performed at the planned cost, and for activity E only half of the
planned work content was performed ( SI=0.5 ). The cost effectiveness of
performing that work content was only 56% ( CI=0.56 ).
The SI and the CI can be calculated for a single activity, for a group of
activities, or for the whole project. This is done by accumulating the values of
BCWS, BCWP, and ACWP, for the appropriate activities, and calculating the
values of SI and CI on the bases of these totals. For our example, the project
schedule index after 4 weeks is
SI = $1,500 + $3,000 + $1,628 $1,200 + $3,000 + $3,256 = 0.82
and the CI is
CI = $1,500 + $3,000 + $1,628 $1,500 + $3,000 + $2,900 = 0.83
The above ratios can be interpreted as follows. For scheduling, on average,
only 82% of the scheduled work was completed, which suggests that the
project may be late. The amount of delay is not clear because the duration of
the project is dictated by the critical path and not by the average work content
already completed. If a late-start strategy is being used, however, then there is
a one-to-one relationship between the SI and delay in project completion. For
cost, the index value of 83% means that for every dollar spent, the value of
the work completed, on average, was just $0.83. In other words, we can

expect a cost overrun for the project.
The EV analysis can be performed on a periodic or on a cumulative basis.
Table 12.5 summarizes the three values (BCWS, BCWP, and ACWP) for
activities A, B, and E for weeks 1 through 4. This information can also be
presented graphically for each activity or for the entire project. Figure 12.6
depicts the cumulative values of BCWS, BCWP, and ACWP for each
activity, and Figure 12.7 presents these values for the entire project.
TABLE 12.5 The Values of
BCWS, BCWP, and ACWP for
Weeks 1–4
Week 1 Week 2
Activity BCWS BCWP ACWP BCWS BCWP
A $300   $500   $500   $300   $500  
B $1,000   $1,000   $1,000   $1,000   $1,000   $1,000  
E $814   $300   $814   $814   $400  
Total $2,114   $1,800   $2,314   $2,114   $1,900   $2,186  

Figure 12.6
EV analysis: (a) activity A; (b) activity B; (c) activity E.
Figure 12.6 Full Alternative Text
Figure 12.7
EV analysis for the project.
Depending on the activity, Figure 12.6 illustrates three different situations:
1. Activity A. The EV (BCWP) and the actual cost (ACWP) are the same,
and both are above BCWS. This implies that activity A is performed at
cost and ahead of schedule.
2. Activity B. BCWP and ACWP are the same. During weeks 1 and 2 they
are equal to BCWP (i.e., activity B is on budget and on schedule). In
week 3, BCWP and ACWP are below BCWS, indicating a delay that

causes activity B to finish in week 4 instead of week 3.
3. Activity E. The value of BCWP is consistently below BCWS and
ACWP. Therefore, activity E is late and experiences a budget overrun.
Figure 12.7 illustrates the project cost and schedule situation. BCWP is below
BCWS and ACWP, thus the entire project is late and over budget. The SI and
the CI of the project for the first 4 weeks are summarized in Table 12.6.
TABLE 12.6 Values of SI and
CI for Weeks 1–4
Week BCWS BCWP ACWP CI= BCWPACWP
SI= BCWP
BCWS
1 $2,114 $1,800 $2,314 0.78 0.85
2 $4,228 $3,700 $4,500 0.82 0.88
3 $6,342 $5,000 $6,300 0.79 0.79
4 $7,456 $6,128 $7,400 0.83 0.82
An alternative view of the data in Figure 12.7 is presented in Figures 12.8
and 12.9, where the values of SI and CI are plotted as a function of time.
Both SI and CI are below 1, which means that the project is late and suffers
from budget overruns. Furthermore, there is no clear trend of improvement in
SI and CI.

Figure 12.8
SI for the project.

Figure 12.9
CI for the project.
To integrate schedule and cost information, the values of SI and CI are
plotted together in Figure 12.10. Each point on the graph corresponds to a
control period. By observing the time associated with each point, it is
possible to see the trend in the CI and SI.
Figure 12.10
Integrating CI and SI.

Figure 12.10 Full Alternative Text
In project management, the goal is to maintain values of CI and SI that are
greater than or equal to 1, which would place them in the upper right
quadrant of Figure 12.10. This is not the case in this example. Nevertheless,
we see in Figure 12.10 that in week 4, both CI and SI show improvement
over week 3. This is after a similar improvement in week 2, which was
followed by poor performance in week 3.

12.4 Reporting Progress
The values of BCWP and ACWP for each activity are the building blocks in
a progress report. The OBS-WBS matrix that relates each activity to a
bottom-level OBS unit and to a bottom-level WBS element facilitates
analysis at any OBS level, WBS level, or combination of the two. For
example, from Figure 12.4, we see that activities A, B, and E are performed
by department 2. None of the activities assigned to department 1 is scheduled
for the first month. Thus, the OBS-based progress report given in Table 12.7
for the first month shows no activity for department 1, and a summary of
activities A, B, and E for department 2.
TABLE 12.7 Cumulative Cost
and Schedule Control Report
by OBS Element (Weeks 1-4)
Organizational
unit BCWS BCWP ACWP SV CV SI CI
Department 1 0 0 0 0 0 – –
Department 2 $7,456 $6,128 $7,400 −$1,328_
−
$1,272
_
0.82 0.83
Total project $7,456 $6,128 $7,400 −$1,328 −$1,272 0.82 0.83
Based on the data in Table 12.7, it is clear that department 1 was not
scheduled to work on the project during the first month and, indeed, did not
perform any activities. Department 2 was scheduled to perform work content
budgeted at $7,456 but completed only $6,128 worth of work, whereas the
actual cost for the period was $7,400. Department 2 experienced a setback in
performing its work content, precipitating a budget overrun of $1,328 during

the first month. This amount is considered “sunk cost” because it cannot be
retrieved.
The WBS report for the example project is contained in Table 12.8. This
report and the OBS report are similar since both are based on the same data––
the project plan and the same three measures: BCWS, BCWP, and ACWP.
The WBS report reveals that element III should be monitored carefully
because it is experiencing both a schedule delay and a budget overrun.
TABLE 12.8 Cost and
Schedule Control Report by
WBS Element
WBS
element BCWS BCWP ACWP SV  CV  SI  CI 
I $1,200 $1,500 $1,500 $300 0 1.25 1
II $3,000 $3,000 $3,000 0 0 1 1
III $3,256 $1,628 $2,900 −$1,628_
−$1,272
_ 0.5 0.56
$7,456 $6,128 $7,400 −$1,328 −$1,272 0.82 0.83
The reports in Tables 12.6 and 12.7 can be produced for each control period
or on a cumulative basis from the start of the project. Many computer
packages that support EV calculations provide this information. The totals in
Tables 12.6 and 12.7 are identical. This is because both represent the total
project performance for the first 4 weeks. The accumulation of information
from lower level OBS or WBS elements to the project level (or any other
higher level) is called roll-up and can be applied to both the OBS and the
WBS because of their hierarchical nature. At each level of the OBS or the
WBS, the values of ACWP, BCWS, and BCWP associated with each
organizational unit or WBS element are calculated as the sum of the
corresponding values of the organizational units or WBS elements under it.
Using the roll-up mechanism, it is possible to generate reports at different

OBS and WBS levels according to management needs. On the basis of the
cumulative values of BCWS, BCWP, and ACWP, the CV and SV can be
calculated. Thus, the integration of the two hierarchical structures (OBS and
WBS) with the EV concept provides the foundation for an information
system that supports cost and schedule control at each managerial level.

12.5 Updating Cost and Schedule
Estimates
When data that reflect the current status of tasks and actual costs are
collected, it is only logical to update previous estimates of the project’s
completion time and budget requirements. Estimates tend to improve as
actual progress is made. This is due to the completion of activities for which
actual duration and cost become known, as well as to better information on
workforce productivity and the availability and cost of resources. Original
estimates are usually based on historical records of similar projects and may
be problematic. When new data become available, the critical path analysis
should be updated, using actual duration times of completed activities and
updated estimates for the duration of future activities.
A project manager constantly updates activity duration times and costs, as
events associated with a project unfold. If, for example, a recent estimate
indicates that the expected total cost of the project is (much) higher than the
original budget, then a management decision may be needed. The revised
estimate may cause a change in project specifications and requirements, or, in
the extreme case, abandonment of the project. A control system focuses
management’s attention on potential problems as soon as the likelihood of
such problems actually arising is deemed high.
To re-estimate the cost of the project, acceptable accounting procedures must
be defined together with the necessary data elements. The following notation
is used for this purpose:
BAC
Budget at completion: total budget of the project activities, based on
the original project plan
=sum of BCWS values over lower level OBS elements, or
=sum of BCWS values over lower level WBS elements

WR Work remaining: budgeted cost of the work not yet accomplished bythe end of the reporting period; WR=BAC − BCWP
ETC Estimate to complete: updated estimate of the cost of the WR
EAC Estimate at completion: updated estimate of the total project cost;EAC=ACWP+ETC
Because the value of ACWP is known, only a revised estimate of ETC is
required to update the EAC estimate.
Estimating EAC: Original Estimate
Approach
This approach is based on the assumption that the original estimate of the
cost of WR is valid and therefore only the original estimate of the work that
was already performed should be replaced by the actual cost of that work
content.
EAC=ACWP+ETC, and because
ETC=BAC−BCWP, we get
EAC=ACWP+( BAC−BCWP )=BAC−( BCWP−ACWP )=BAC−CV
Thus in the revised budget, the EAC is equal to the original BAC adjusted by
the CV.
Estimating EAC: Revised Estimate
Approach
The updated estimate of WR is based on the assumption that the relative
deviation in the cost of the work completed is a good estimate for the relative
deviation of the cost of WR.

The relative deviation of cost of work completed is defined as follows:
ACWP BCWP = 1 CI
Assuming the same deviation factor for WR, we get
ETC = WR × 1 CI = ( BAC−BCWP ) × 1 CI
Therefore, we can write
EAC = ACWP + ( BAC-BCWP ) × 1 CI = ACWP + BAC CI − BCWP CI
Substituting
ACWP = BCWP CI
we get
EAC = BAC CI = BAC × ACWP BCWP
The two estimation procedures can be applied at each OBS level, at each
WBS level, or at the total project level. For the example project with BAC= $
31,000 (see Table 11.3), the report after 1 month shows the following results:
BCWS= $ 7,456 CV= −$ 1,272 CI=0.83
BCWP= $ 6,128 SV= −$ 1,328 SI=0.82
ACWP= $ 7,400
Thus, revised costs using (1) the original estimate approach and (2) the
revised estimate approach are
1. EAC=ACWP+BAC−ACWP=BAC−CV=$31,000−( −$1,272 )=$32,272
2. EAC=BAC× 1 CI =$31,000× 1 0.83 =$37,349
The difference between the two values stems from the fact that in the first
approach, we assume that past cost performance is not a predictor of future
performance. In the second approach, we assume that past deviations are a

good predictor of cost deviations in the WR.
The two estimation procedures may be used. The important point is for a
project manager to be consistent and use the same estimation procedure for
the entire project throughout its life cycle.
Selection of an estimation procedure is a management decision that should be
made in the conceptual design phase of the project. Consistency in predicting
total costs results in the ability to show, at each control period, the current
cost status together with the trend of cost predictions from the start of the
project. Such consistency enables comparisons of performance between OBS
and WBS elements at different time periods, as well as monitoring of cost
trends that foreshadow future problems. In a similar way consistency in
predicting the project duration results in the ability to show, at each control
period, the current schedule status together with the trend from the start of the
project.
Rather than continually monitor performance, threshold values may be used
to trigger management-by-exception activities. For example, by specifying
threshold values of 5% and 10% for CI and SI, respectively, any negative
deviation from the original plan (100%) for either one of these values would
be reported to upper management along with a plan for corrective action.
Specific threshold values and procedures for reporting and reacting to
deviations are organization-dependent and must be worked out on a project-
by-project basis. The more important measure is CI because it is a strong
indicator of ongoing budgetary requirements. In contrast, SI does not provide
the same level of information about delays and hence is not as strong an
indicator. Delays are determined by the critical path, whereas SI is
determined from all delays regardless of whether the corresponding activities
are critical.

12.6 Technological Control: Quality
and Configuration
Cost and schedule control are important management responsibilities.
Technological control is required to detect any deviations from technical
specifications and standards that may change during the life cycle of the
project. To achieve a satisfactory level of performance, an integrated quality
control and quality assurance program with well-established procedures must
be designed and implemented.
The concept of total quality control is relevant for the success of a project.
Quality should be a focal point of any organizational unit (OBS element) that
is performing work on any element of a project (WBS element) at any point
in the project life cycle. In the early stages of a project, systems engineers
evaluate various design alternatives based on performance, quality, and
reliability measures, as well as cost and schedule. It is important to remember
that the bitter taste of a low-quality, unreliable product lingers long after the
sweet taste of low cost and fast delivery.
The alternative selected in the initial stages of a project is designated as the
“baseline” for purposes of configuration management and control. Recall that
configuration management (CM) is a system designed to ensure that the
product delivered at the end of a project is built according to specifications
laid out in the baseline and all approved subsequent engineering change
requests (ECRs).The components, procedures, and logic of a CM system are
discussed in Chapter 8.
The next component––configuration control––is integrated with quality
control by a mechanism called configuration test and audit. This component
of the CM system is designed to guarantee that quality control is based on the
most recent configuration composed of the baseline design and all approved
ECRs. The integration of configuration management with cost and schedule
control is done at the configuration control board (CCB). The CCB is the
focal point of configuration control, as explained in Chapter 8. Members of

the CCB are representatives of the project and the functional areas that might
be affected by proposed design changes. The CCB evaluates ECRs on the
basis of their impact on cost, schedule, and performance. By linking all four
control systems together, deviations in cost, schedule, quality, or design can
be detected and addressed in a timely manner.
The four basic control systems––cost, schedule, quality, and configuration—
operate throughout the project life cycle within the framework of the OBS-
WBS matrix. Together they are used to detect deviations, to identify their
organizational source and their effect on various elements of the WBS, and to
assist in developing solutions to problems caused by such deviations.
In the previous sections, we presented a generic approach to project control.
We now focus on several specific techniques that are applicable under
limited, but prevalent, circumstances.

12.7 Line of Balance
Project management techniques discussed so far are designed for the one-
time effort in which a specific, unique set of goals related to a single project
have to be met. There are, however, projects that involve repetition of
activities. Examples might include the construction of a highway or the
construction of a pipeline divided into several segments. Each segment is
managed as a project and the same activities are performed in each segment.
In these cases, it is possible to view each segment as a project (although no
longer unique), or to define the total construction effort as a single project
with repetitive activities. Because such projects are not uncommon, a special
technique called the line of balance (LOB) has been developed to support
their management and control.
The LOB technique is based on control points or milestones in the project life
cycle. These control points are related to critical activities and resources that
are identified during the planning phase. A typical control point is the
successful completion (including test and inspection) of an activity on the
critical path. The elapsed time between consecutive control points is
estimated, and a milestone schedule for each segment is developed.
The master production schedule (MPS) in such projects specifies the planned
completion time of each segment based on the contractual agreement with the
client. As the project starts, control is exercised by comparing the number of
segments that pass each control point with the number that should have
passed that point according to the MPS. Any deviations trigger a detailed
analysis aimed at identifying the cause of the deviation and the appropriate
corrective action.
To illustrate the LOB approach, consider a manufacturer of communication
systems. Each system is tailor-made for the customer who may place an order
for one or several identical units. Suppose that a customer orders a total of
110 systems in a specific configuration. It is estimated that 6 weeks are
required to complete one unit. Four milestones are selected as control points
(see Table 12.9):

TABLE 12.9 Schedule of
Milestones or Control Points
Control
point Description
Week after start of
unit production
Lead time to
delivery (weeks)
A Rackinstallation    2 6−2=4
B Subsystemsinstallation 3 6−3=3
C Subsystemsintegration 5 6−5=1
D Acceptancetest    6 6−6=0
1. End of rack installation: 2 weeks after the start of work on a system
2. End of subsystems (modules) installation: 3 weeks after the start of work
on a system
3. End of subsystems integration: 5 weeks after the start of work on a
system
4. End of acceptance tests and delivery: 6 weeks after the start of work on
a system
The MPS specifies delivery dates for the 110 systems in accordance with the
data in Table 12.10. On the basis of the MPS and the list of control points (or
milestones), it is possible to forecast the number of systems expected to pass
through each milestone at the end of each week. For example, the number of
systems expected to pass each milestone by the end of the fifth week is as
follows:
TABLE 12.10 Delivery

Schedule for the 110 Systems
Delivery date as
of week
Systems scheduled for
delivery
Cumulative number
of systems
6 30 30
7 20 50
8 10 60
9 30 90
10 20 110
The 20 systems scheduled for delivery on week 10 should be 5 weeks
from delivery. Because it takes 6 weeks to complete a system, these
systems should be 1 week in process not having passed any milestone
yet.
The 30 systems scheduled for delivery on week 9 should be 4 weeks
from delivery or 2 weeks into the process and should have completed
milestone A only.
The 10 systems scheduled for delivery on week 8 should be 3 weeks
from delivery or 3 weeks into the process and should have completed
milestones A and B.
The 20 systems scheduled for delivery on week 7 should be 2 weeks
from delivery or 4 weeks into the process and should have finished
milestones A and B.
The 30 systems scheduled for delivery on week 6 should be 1 week from
delivery or 5 weeks into the process and should have finished milestones
A, B, and C.
These results are summarized in Table 12.11. Thus, 90, 60, and 30 systems
should have completed milestones A, B, and C, respectively. Figure 12.11
displays this information graphically.

TABLE 12.11 Scheduled
Milestones at the End of Week
5
Deliveries
scheduled in
week
Number
of
systems
Time to
delivery
(weeks)
Number of systems
scheduled to finish at
milestone:
A B C D
5 – – – – – –
6 30 1 30 30 30 –
7 20 2 20 20 – –
8 10 3 10 10 – –
9 30 4 30 – – –
10 20 5 – – – –
Total=90      60 30 0
Figure 12.11

Planned number of systems to finish each milestone after 5 weeks.
It is possible to use a graphical procedure to control a repetitive project by
combining the milestone information with the MPS. To construct the control
chart, first plot the cumulative number of systems versus time to depict the
MPS. On this graph, start at the current control period (week 5), and for each
milestone, add its corresponding lead time. For milestone A, the lead time is
4 weeks. Adding 4 weeks to the current control period (week 5), we get 9
weeks. The cumulative number of units corresponding to 9 weeks on the
MPS is 90 units, as illustrated in Fig. 12.12. Thus, the expected number of
systems to complete milestone A is 90 systems. In a similar way, the
expected number of systems can be constructed for each milestone.
Figure 12.12

Constructing the planned status from the MPS.
Figure 12.12 Full Alternative Text
The LOB displays the work that should be accomplished to ensure delivery
according to the MPS. Suppose that after 5 weeks, 80 systems completed
milestone A, 60 completed milestone B, 40 completed milestone C, and 20
systems completed milestone D. The deviations between the plan (LOB) and
actual achievement are as follows:
Milestone LOB Actual Deviation
A 90 80 −10
B 60 60 0
C 30 40 −10
D 0 20 20
Thus, milestones A and C are late with respect to the MPS. At A, 10 systems
late corresponds to a 10 90 ×100%=11% delay. Milestone B is exactly on
schedule, whereas at C and D, actual performance is ahead of schedule.
A detailed analysis of the activities performed before milestone A should be
initiated. In case an increase in the workforce is required to catch up with the
MPS, the necessary resources may be obtained from some of the activities
that precede milestone D, which is ahead of schedule.
A graphical display of the LOB and the actual performance gives a clear
indication of the project’s status. Figure 12.13 depicts the situation for week
5 in the example.

Figure 12.13
LOB and actual performance.

12.8 Overhead Control
Project execution costs can be divided into the following categories:
Direct costs resulting from expenses tied explicitly to the performance
of WPs, which have a tangible deliverable at the end of their execution
Direct overhead costs (DOH) resulting from infrastructure expenses
required for all stages of the project
Organizational overhead resulting from the overall support that a project
obtains from other organizational units
Total cost is computed by summing the cost for each cost category. Unlike
the first two categories of costs, general overhead cost is beyond the control
of a project manager since it is “spread” by the company’s accounting system
over all company activities. In the previous sections, we discussed control
mechanisms for direct costs. We now introduce control mechanisms for
DOHs.
Estimation of the DOH budget typically assumes that infrastructure support
for a project remains constant during the duration of its execution. Therefore,
a fixed amount of DOH dollars will be required per unit time until a project is
completed. Examples of infrastructure activities include project management,
quality assurance, and data processing. DOH is also called the level of effort
because certain levels of resources are required per unit time. Performance,
associated with those efforts, is difficult to quantify since specific
deliverables or milestones are not typically tied to overhead resources.
A common method of estimating the amount of resources required for
infrastructure support is based on a designated percentage of the direct costs
associated with the WPs. The actual percentage is somewhat arbitrary and
depends on company policy, judgment, and experience. A typical range
might be from 10% to 25%, depending on the nature of the project and its
duration. For example, standard projects, such as introducing an “off-the-

shelf” software package for salary administration, will require less DOH
compared with designing and building a package from scratch.
Project managers often believe, incorrectly, that in matrix organizations that
outsource their WPs, a project’s rate of progress does not affect total cost,
especially when the contractual vehicle is based on a fixed fee. They fail to
account for a continued need for infrastructure support throughout a project’s
life cycle. This need explains the high correlation between cost overruns and
late completions in such organizations. Without a project control system that
evaluates the effectiveness of resources used for infrastructure support, the
total cost of a project may increase significantly without early detection.
Example 12-1
Consider a project whose direct costs are estimated to be $4 million. Using
historical data on similar projects, coupled with the fact that many
technological risks exist, it was decided to add 25% for DOH; that is,
DOH=0.25×$4M= $ 1M. This means that the total direct budget is $4M+ $
1M= $ 5M. To simplify, let us assume that there are no organizational
overhead costs that need to be included.
Suppose that the customer for the project has agreed to pay $5.75M upon
completion. In percentage terms, the expected profit is ( $5.75M−$5M
)/$5M×100%=15%. If the planned execution period is 20 months, then the
DOH budget is $50,000 per month.
Suppose that a 12-month delay was experienced as a result of critical
resource shortages, so the project was not completed until 32 months after it
began. During project execution, the project manager was under the illusion
that there were no cost overruns because the EV analyses, which were
performed on each WP periodically, never showed any significant deviations.
Second, the appropriateness of the DOH estimates was never verified.
As a result of the delay, an additional $600,000(=$50,000×12) was required
to cover infrastructure costs, thus dropping the profit to
$750,000−$600,000=$150,000. That is, the actual profit was 3%, rather than

the planned 15%.
Let us now demonstrate the use and effectiveness of two EV approaches to
control (1) the classical method and (2) the adjusted method. To begin, let us
assume that the status of the project after five months of activity is as follows.
Actual cost of WPs to date: ACWP= $ 650,000
Value of work scheduled to be completed: BCWS= $ 1,000,000
EV of completed work: BCWP= $ 625,000
Overhead cost of infrastructure to date: DOH= $ 250,000
Assuming a linear effort over time, BCWS was calculated by multiplying the
total direct cost by the portion of work that was scheduled to be completed
within the 5-month period. That is, BCWP= $ 4M×( 5/20 )=$1M. Similarly,
the planned DOH budget for the first 5 months was calculated based on
proportional outlays; that is, DOH = 5 × $50,000 = $250,000. Actual DOH
expenditures were as originally planned for the first 5 months.
Using the Classical Analysis
Approach
The first step is to calculate the CI, which enables us to determine whether
the project is on budget. For this purpose, we use actual costs and the EVs of
the work completed thus far. Actual cost for both the execution of the WPs
and the DOH is
ACWP + DOH = $650,000 + $250,000 = $900,000
The EV of work completed should consider work performed and the value of
the infrastructure work. The first component is calculated in the manner
demonstrated in the previous sections. In calculating the value of the work
associated with DOH, the assumption made in the classical approach is that
the value of the work performed equals the actual cost, in this case $250,000.
Therefore, the total EV is

EV = $625,000 + $250,000 = $875,000
and the CI is
CI = $875,000 / $900,000 = 0.972
This value, which is just below 1, indicates that the budget overrun after five
months of activity is not very significant, at least in percentage terms. On the
basis of the above calculation, the revised budget for the project is
$5,000,000/0.972 = $5,144,000 implying that an additional $144,000 is
required at this time to complete all work. As the project progresses, this
estimate might change.
Using the Adjusted Approach
In this method, the EV of the DOH expenses is adjusted to reflect actual
progress rather than proportional progress. If all of the WPs that are
scheduled to be completed during the control period have been completed,
then the DOH EV of work is equal to the DOH budget planned for that
period. In the analysis, the SI is used to calculate the extent of work progress
realized. Recall that SI measures the portion of work completed compared
with the portion planned to be completed during the control period. In this
case,
SI = BCWP/BCWS = $625,000/$1,000,000 = 0.625
Therefore, the DOH EV is
BCWP × SI = $250,000 × 0.625 = $156,000
Using these data, the adjusted value of the CI is
CI = ($625,000+$156,000)/($650,000+$250,000) = 0.868
giving a revised total budget of

$5,000,000/0.868 = $5,760,000
Thus, the forecasted budget overrun is now $760,000 rather than $144,000, as
calculated by the classical method. The difference is due to a combination of
additional execution and especially overhead expenses that are expected to
result during the remainder of the project.
Calculating the cost efficiency index for just the infrastructure support (DOH)
during the first five months using the adjusted approach, we obtain
CI(DOH) = BCWP/ACWP = $156,000/$250,000 = 0.625
This value is identical to the SI because the $250,000 actually spent on DOH
is equal to the amount originally budgeted for the first 5 months. A value of
0.625 for CI(DOH) indicates clearly that the infrastructure resources were not
used effectively during this time.
A project manager should continually verify that DOH expenses are in line
with work performed on the WPs during the control period. When progress is
less than planned, DOH should be reduced accordingly. Of course, it will not
always be possible to achieve a linear reduction because overhead is not
necessarily proportional to effort. For example, assume that three machine
tools were leased for the purpose of building prototypes. Now, if a change is
approved to redesign a subcomponent, then the rate of progress may be
slowed considerably. Depending on the leasing arrangements, it may not be
possible to reduce the DOH; however, the project manager should at least
consider terminating the lease on one of the machine tools and reducing the
number of prototypes to compensate for delays and expected cost overruns.
TEAM PROJECT Thermal
Transfer Plant
With the approval of the rotary combustor project, a detailed plan for project
control is required. In developing the plan, your team should address the
following issues:

1. Which aspects of the project should be monitored (e.g., cost, schedule)?
2. Where will the data come from?
3. What is the original source of data?
4. How often should data be collected?
5. How should the data be processed? (Distinguish between trend analysis
and identification of exceptions.)
6. What kind of reports will be issued? Who should get the reports? How
often?
7. What kind of ad hoc questions should the control system support?
Be as specific as possible. Present a flow diagram for data processing and a
format of each report that you suggest. Be careful not to produce too many
reports or to collect data that will not be used later. Explain and justify your
approach to the control of the project.

Discussion Questions
1. Describe the control systems used in one organization with which you
are familiar.
2. Referring to Question 1, explain how the control system that you
identified deals with uncertainty.
3. Give an example of an organization that does not use any control
systems. Is this justified?
4. Suppose that you have decided to build a new house. Explain what kind
of project control you will consider and why.
5. Why is it important to integrate cost and schedule control? Give an
example for which separate cost and schedule control systems may not
function properly.
6. Explain how you would measure the EV of the following activities:
1. Writing a term paper
2. Building a nuclear power plant
3. Designing a new car
4. Developing a new training program
7. Is there a need for “technological control” in developing a new
insurance policy? Explain.
8. Explain what the responsibilities of “quality control” are in a project
associated with making a Hollywood-style movie. How would these
responsibilities differ if the movie were a documentary on, say, the
search to identify the human genome?

9. Is there a need for a control system in projects performed by nonprofit
organizations? Explain and give examples.
10. Explain the advantages and disadvantages of the LOB technique as
opposed to using several PERT networks.
11. How would you build total quality management principles into a project
control system?
12. Why will a delay in the completion of a project probably cause a budget
overrun?

Exercises
1. 12.1 The National Institutes of Health supports research and
development of new treatments for AIDS (acquired immune deficiency
syndrome). Develop a project control system by which the agency will
be able to control projects that it supports.
1. What are the objectives of the control system?
2. What are the performance measures?
3. What data are required?
4. How should raw data be collected?
5. How should the data be analyzed?
6. How should the results be reported, and how often?
2. 12.2 Consider the project plan defined in Table 12.12 .
TABLE 12.12
Activity Scheduled startday
Scheduled finish
day Cost/day
A 1 3 $1,000
B 1 5 $5,000
C 3 7 $3,000
D 5 15 $1,000
E 7 22 $2,000
F 7 25 $4,000
A cost schedule control system produces weekly reports. The reports for

weeks 1, 2, and 3 (assume 5 working days each week) are shown Table
12.13 .
TABLE 12.13
Week 1 Week 2 Week 3
Activity  Status Cost 
 
Status Cost 
 
Status Cost 
A Inprocess $1,500 Finished $3,000 Finished $3,000
B Inprocess $25,000 Finished $30,000 Finished $30,000
C Inprocess $7,000 Finished $10,000 Finished $10,000
D Notstarted 0
In
process $5,000
In
process $7,000
E Notstarted 0
Not
started 0
In
process $10,000
F Notstarted 0
In
process $10,000
In
process $20,000
1. Write a weekly progress report for each activity based on the above
information.
2. Comment on the level of control that can be achieved based on the
given information.
3. 12.3 In Exercise 12.2 , an estimate of the “percent complete” for each
activity each week is reported in Table 12.14 . Redo parts (a) and (b).
TABLE 12.14
Percent complete

Activity Week 1 Week 2 Week 3
A 50 100 100
B 30 100 100
C 10 100 100
D 0 20 60
E 0 0 25
F 0 30 40
4. 12.4 An activity on the critical path of a project was scheduled to be
completed within 12 weeks, with a budget of $8,000. During a
performance review, which took place 7 weeks after the activity was
initiated, it was found that 50% of the work had already been completed
and that the actual cost was $4,500.
1. Calculate the EV of the activity.
2. Calculate the CI and SI for the activity.
3. Calculate the expected BAC using the original estimate approach.
4. Calculate the expected BAC using the revised estimate approach.
5. Compare and discuss the results obtained in parts (c) and (d).
5. 12.5 The performance of a project was evaluated 10 weeks after its start.
Table 12.15 gives the relevant information.
TABLE 12.15
Activity Immediatepredecessors
Normal
time Budget
Organizational
unit
Percent
complete
Money
A — 4 $90 U1 100
B A 2 $35 U2 100
C A 6 $75 U2 40

D B 3 $60 U1 80
E C 10 $80 U1 0
F — 2 $40 U2 100
G F 5 $55 U1 50
H F 7 $80 U2 100
I D, E, G 1 $40 U2 0
J H 10 $100 U1 0
1. On the same Gantt chart, show the project plan and the project
progress, and discuss the two.
2. Calculate the SI for each organizational unit U1 and U2 and for the
project as a whole. Discuss.
3. Repeat part (b) for the CI.
4. On the basis of past performance, update the expected completion
time and budget. State your assumptions.
6. 12.6 For the project described in Exercise 12.5 , calculate and chart the
following values: BCWS, BCWP, and ACWP. Assume linearity of cost
versus time. State any additional assumptions that you believe are
needed.
7. 12.7 Big State University has decided to start a new program for
executives called “Management of Technology.” Your task is to design
the control system for this project. Discuss the following issues:
1. The performance measure that should be used.
2. Ways to collect the relevant data for evaluating the current
situation.
3. How should raw data be selected for evaluating the project?
4. How should the data be analyzed?

5. How should the results be reported?
8. 12.8 In designing the new program outlined in Exercise 12.7 , identify
the WPs and the organizational units that will be responsible for their
implementation.
9. 12.9 Explain CM and control within the curriculum of your school. Give
three examples that demonstrate a good configuration control process
and three that identify poor CM.

Bibliography
Burke, R., Project Management: Planning and Control Techniques,
Fourth Edition, Halsted Press, New York, 2003.
Fleming, Q. and J. Koppelman, Earned Value Project Management,
Second Edition, Project Management Institute, Newtown Square, PA,
2000.
Globerson, S. and J. Riggs, “Multi-Performance Measures for Better
Operational Control,” International Journal of Production Research,
Vol. 27, No. 1, pp. 187–194, 1989.
Globerson, S. and A. Shtub, “Effective Measurement of Project
Progress,” Proceedings of the Project Management Institute Conference,
New Orleans, pp. 381–387, October 1995.
Kogan, K., T. Raz, and R. Elitzur, “Optimal Control in Project
Management: Analytically Solvable Cases,” IIE Transactions on Design
& Manufacturing, Vol. 34, No. 1, pp. 63–75, 2002.
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Management Institute, Newtown Square, PA, 1999.
Pryor, S., “Project Control: Part 1: Planning and Budgeting,”
Management Accounting, Vol. 66, No. 5, pp. 16–17, 1988.
Pryor, S., “Project Control: Part 2: Measuring, Analyzing and
Reporting,” Management Accounting, Vol. 66, No. 6, pp. 18–19, 1988.
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European Journal of Operational Research, Vol. 127, No. 2, pp. 252–
261, 2000.
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Multidimensional, Strategic Concept,” Long Range Planning, Vol. 34,

pp. 699–725, 2001.
Shenhar, A., A. Tishler, D. Dvir, S. Lipovetski, and T. Lechler,
“Refining the Search for Project Success Factors: A Multidimensional
Typological Approach,” R&D Management, Vol. 32, No. 2, pp. 111–
124, 2002.
Shtub, A., “Evaluation of Two Schedule Control Techniques for the
Development and Implementation of New Technologies: A Simulation
Study,” R&D Management, Vol. 22, No. 1, pp. 81–87, 1992.
U.S. Department of Defense, Performance Measurement for Selected
Acquisitions, DOD 7000.2, Washington, DC, June 1977.
U.S. Department of Defense, Cost/Schedule Control Systems Criteria
Joint Implementation Guide, Washington, DC, Several publication
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Contract Performance Measurement, DOE/2250.1A, Office of Project
and Facilities Management, Washington, DC, September 1982.
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Contract Performance Measurement: Work Breakdown Structure Guide,
Office of Project and Facilities Management, Washington, DC, 1981.
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Forecasting the Final Cost of a Project,” Project Management Journal,
Vol. 31, No. 1, pp. 53–57, 2000.

Appendix 12A Example of a Work
Breakdown Structure
The WBS is an important building block of the project management system.
Organizations that are frequently engaged in engineering projects have
developed guidelines for designing the WBS. The following is a summary
WBS for an electronic system. This is one of several WBSs presented in
MIL-STD-881-A, “Work Breakdown Structures for Defense Material Items,”
April 25, 1975.
Level
1      
Level
2       Level 3      
Electronic
system
Prime mission
equipment Integration and assembly
Sensors
Communications
Automatic data processing
equipment
Computer programs
Data displays
Auxiliary equipment
Training Equipment
Services
Facilities
Peculiar support
equipment
Organizational/intermediate
(including
equipment common to depot)
Depot (only)
Systems test
and evaluation Development test and evaluation
Operational test and evaluation

Mockups
Test and evaluation support
Test facilities
System/program
management Systems engineering
Project management
Data Technical publications
Engineering data
Management data
Support data
Data depository
Operational/site
activation Contractor
Technical Support
Site/construction
Site/ship/vehicle
Conversion
System assembly
Installation and checkout on site
Common
support
equipment
Organizational/intermediate
(including
equipment common to depot)
Depot (only)
Industrial
facilities Construction/conversion/expansion
Equipment acquisition or
modernization
Maintenance
Initial spares
and initial repair
parts
(Specify by allowance list,
grouping, or hardware element)

Appendix 12B Department of
Energy Cost/Schedule Control
Systems Criteria
1. General
1. The management control systems used by the contractor in
planning and controlling the performance of the contract shall meet
the criteria set forth in paragraph 2 below. Nothing in these criteria
is intended to affect the basis on which costs are reimbursed and
progress payments are made, and nothing herein will be construed
as requiring the use of any single system, or specific method of
management control or evaluation of performance. The contractor’s
systems need not be changed, provided they satisfy the criteria.
2. An element in the evaluation of proposals will be proposer’s
systems for planning and controlling contract performance. The
proposer will fully describe the system to be used. The prospective
contractor’s cost and schedule control system proposal will be
evaluated to determine whether it meets the criteria. The
prospective contractor will agree to operate compliant systems
throughout the period of contract performance if awarded the
contract. DOE will rely on the contractor’s compliant systems and,
therefore, will not impose separate management control systems.
2. The Criteria The contractor’s management control systems will include
policies, procedures, and methods that are designed to ensure that they
will accomplish the following:
1. Organization
1. Define all authorized work and related resources to meet the
requirements of the contract, using the framework of the

contract WBS.
2. Identify the internal organizational elements and the major
subcontractors responsible for accomplishing the authorized
work.
3. Provide for integration of the contractor’s planning,
scheduling, budgeting, estimating, work authorization, and
cost accumulation systems with each other, the contract WBS,
and the OBS.
4. Identify the managerial positions responsible for controlling
overhead (indirect costs).
5. Provide for integration of the contract WBS with the
contractor’s functional organizational structure in a manner
that permits cost and schedule performance measurement for
contractor WBS and organizational elements.
2. Planning and budgeting
1. Schedule the authorized work in a manner that describes the
sequence of work and identifies the significant task
interdependencies required to meet the development,
production, construction, installation, and delivery
requirements of the contract.
2. Identify physical products, milestones, technical performance
goals, or other indicators that will be used to measure output.
3. Establish and maintain a time-phased budget baseline at the
cost account level against which contract performance can be
measured. Initial budgets established for this purpose will be
based on the negotiated target cost. Any other account used for
performance measurement purposes must be formally
recognized by both the contractor and the Government.
4. Establish budgets for all authorized work with separate

identification of cost elements (labor, material, etc.).
5. To the extent the authorized work can be identified in discrete,
short-span WPs, establish budgets for this work in terms of
dollars, hours, and other measurable units. When the entire
cost account cannot be subdivided into detailed WPs, identify
the long-term effort in larger planning packages for budget
and scheduling purposes.
6. Provide that the sum of all WPs budgets, plus planning
package budgets within a cost account equals the cost account
budget.
7. Identify relationships of budgets or standards in underlying
work authorization systems to budgets for WPs.
8. Identify and control level-of-effort activity by time-phased
budgets established for this purpose. Only that effort which
cannot be identified as discrete, short-span WPs or as
apportioned effort will be classed as level of effort.
9. Establish overhead budgets for the total costs of each
significant organizational component whose expenses will
become indirect costs. Reflect in the contract budgets at the
appropriate level the amounts in overhead pools that will be
allocated to the contract as indirect costs.
10. Identify management reserve and undistributed budget.
11. Provide that the contract target cost plus estimated cost of
authorized but unpriced work is reconciled with the sum of all
internal contract budgets and management reserve.
3. Accounting
1. Record direct costs on an applied or other acceptable basis in a
formal system that is controlled by the general books of
account.

2. Summarize direct costs from cost accounts into the WBS
without allocation of a single cost account to two or more
WBS elements.
3. Summarize direct costs from the cost accounts into the
contractor’s functional organizational elements without
allocation of a single cost account to two or more
organizational elements.
4. Record all indirect costs that will be allocated to the contract.
5. Identify the bases for allocating the cost of apportioned effort.
6. Identify unit costs, equivalent unit costs, or lot costs as
applicable.
7. The contractor’s material accounting system shall provide for:
1. Accurate cost accumulation and assignment of costs to
cost accounts in a manner consistent with the budgets,
using recognized, acceptable costing techniques.
2. Determination of price variances by comparing planned
versus actual commitments.
3. Cost performance measurement at the point in time most
suitable for the category of material involved but no
earlier than the time of actual receipt of material.
4. Determination of CVs attributable to the excess usage of
material.
5. Determination of unit or lot costs when applicable.
6. Full accountability for all material purchased for the
contract, including the residual inventory.
4. Analysis

1. Identify at the cost account level on a monthly basis using data
from, or reconcilable with, the accounting and budgeting
systems:
1. BCWS and BCWP.
2. BCWP and applied (actual when appropriate) direct costs
for the same work.
3. EACs and BACs.
4. Variances resulting from the above comparisons
classified in terms of labor, material, or other appropriate
elements together with the reasons for significant
variances, including technical problems.
2. Identify on a monthly basis, in the detail needed by
management for effective control, budgeted indirect costs,
actual indirect costs, and variances along with the reasons.
3. Summarize the data elements and associated variances listed
in paragraph 2d(1) and (2) above through the contractor
organization and contract WBS to the reporting level specified
in the contract.
4. Identify significant differences on a monthly basis between
planned and actual schedule accomplishment together with the
reasons.
5. Identify managerial actions taken as a result of paragraph
2d(1) through (4) above.
6. Based on performance to date and on estimates of future
conditions, develop revised estimates of cost at completion for
WBS elements identified in the contract and compare these
with the contract budget base and the latest statement of funds
requirements reported to the government.

5. Revisions and access to data
1. Incorporate contractual changes in a timely manner recording
the effects of such changes in budgets and schedules. In the
directed effort before negotiation of a change, base such
revisions on the amount estimated and budgeted to the
functional organizations.
2. Reconcile original budgets for those elements of the WBS
identified as priced line items in the contract and for those
elements at the lowest level of the project summary WBS,
with current performance measurement budgets in terms of
changes to the authorized work and internal replanning in the
detail needed by management for effective control.
3. Prohibit retroactive changes to records that pertain to work
performed and that will change previously reported amounts
for direct costs, indirect costs, or budgets, except for
correction of errors and routine accounting adjustments.
4. Prevent revisions to the contract budget base except for
government-directed changes to contractual effort.
5. Document, internally, changes to the performance
measurement baseline, and on a timely basis, notify the
government project management through prescribed
procedures.
6. Provide the contracting officer and his or her duly authorized
representatives access to all of the foregoing information and
supporting documents.

Chapter 13 Research and
Development Projects
13.1 Introduction
Over the past decade, about 40% of the Fortune 500 have dropped off this
select list, victims of complacency, poor financial management, and a failure
to keep pace with the competition. It should come as no surprise that today’s
organizations, especially the behemoths, are not designed for innovation.
They are the by-products of a more orderly and regulated environment.
Courting change, acting opportunistically, and shifting direction at a
moment’s notice were not, until recently, required for survival, never mind
excellence. Not only were such traits not required, but to have emphasized
them would have detracted from performance! Doing yesterday’s job just a
little better––at most––was the prescription for success. Indeed, this is the
saga of the post-World War II U.S. automobile industry, steel industry,
chemical industry, and even the first two decades of the computer industry.
In the field of high technology, the key to staying competitive is product
innovation supported by a strong commitment to research and development
(R&D). But how to do this? One school of thought says that we have to be
much faster at developing new products. Proponents provide airtight schemes
for reducing production cycle times and filling market niches as they appear.
The complexity of these schemes is often stunning, but who could argue? As
Peters (1990) said, “It’s a complex world.” New approaches are required to
slash product development cycles by at least an order of magnitude in many
industries. However, the rigidity of several of the most popular approaches
with their “one size fits all” character leaves something to be desired.
The alternative to airtight formulas, some say, is a “ten-man band of lunatics
cast adrift.” Although this idea, realized in what are sometimes called
“skunkworks,” has worked well for Lockheed in its development of high-
altitude reconnaissance aircraft and for Data General when it developed the

Eagle line of super minicomputers (Kidder 1981), there is a wealth of
evidence suggesting that the significant breakthroughs of the 1990s, as
exemplified by the products and services of Nokia, Yahoo!, Nintendo, eBay,
and Cisco, will not come from orderly plans alone or the right company at the
right time. Formulas are questionable, and ten-man bands alone are not up to
the innovation task. These forms of isolated incubation rarely produce
continuous results because they operate too far outside the existing
organization. Most established businesses are convergent thinking and
survive on order, measurement, and predictability. In contrast, innovation
most often arises from divergent thinking environments that thrive on
disorder, imagination, and ambiguity. What is needed to foster new ideas is a
strategic plan with R&D prominently featured.
Companies vary in the degree of sophistication with which they accomplish
planning. Gluck et al. (1980) presented a four-stage evolutionary model for
carrying out this task:
Stage I companies have the basic financial planning system in which
everything is reduced to a financial problem and the value standard is to
meet the budget.
Stage II companies extend basic financial planning by means of long-
range forecasts.
In Stage III companies, planners try to understand the market
phenomena that are forcing change, look for opportunities that may lead
to a more attractive portfolio, and devise alternative strategies for top
management consideration.
In Stage IV companies, management is involved in strategic planning
that stimulates entrepreneurial thinking and promotes all-around
commitment to the corporate plan.
This type of classification offers an easy way to segregate and evaluate where
companies are in the planning process. Top management should give
deliberate thought to the degree of sophistication that they have reached and
how R&D fits into the overall plan. In general, planning is a two-pronged
effort. The first prong centers on development of a strategic plan that defines

and communicates longer term business directions; the second involves
development of an operating plan that specifically identifies tasks or projects
to be undertaken in pursuit of corporate goals. At this point, a distinction
needs to be drawn between traditional capital budgeting and R&D planning.
R&D, along with new product development, is a low-probability game, no
matter how much you plan, survey, consult with customers, or align yourself
with the competition. There are literally thousands of variables that must be
juggled at once. There are variables that deal with technology (design,
engineering, manufacturability, quality, serviceability, operability), variables
that deal with distribution (who, through which channels, level of interest in
the product, when), and there are variables that deal with customer use (the
lag time between development of a new product and its routine adoption,
even when dramatic and unmistakable benefits are evident from the outset,
often runs decades—and almost always occurs via a convoluted, totally
unpredictable path); not to mention variables that involve competitors (big,
small, domestic, foreign) and new entries into the marketplace.
In the remainder of this chapter, we present some of the unique aspects of an
R&D project. In so doing, we reflect and extend many of the ideas discussed
previously. To be successful, it is necessary for an organization to instill a
project orientation everywhere. To be speedy, to practice innovation on every
product and process, and to develop new and scintillating products quickly
require that all functional boundaries between design, engineering,
manufacturing, operations, purchasing, sales, marketing, and distribution be
destroyed—not broken down or softened, but destroyed. A second guideline
is for virtually every person in the company to spend a fair amount of his or
her day on project teams with people from other functions. The essence of
perpetual quality improvement, service improvement, rapid product
development, and increased operational efficiency is getting people from
multiple, warring factions to working together on output-oriented activities
that generally go unmanaged in traditional “vertical” organizations.

13.2 New Product Development
13.2.1 Evaluation and Assessment of
Innovations
Every business—large or small—must constantly evolve and renew itself.
For example, it must bring new offerings to the market (products and
services), redesign business processes such as production, marketing,
distribution, and, perhaps, even reconfigure its underlying business structure
and model. Regardless of the business context, whether in a traditional
industry such as hard-rock mining or in Internet services, when a new idea
comes across the bow of an organization, how does it decide whether to
inject that new idea into its development funnel and invest in its
development, or pass on it? The New Product Development (NPD) or New
Product Introduction (NPI) process is inherently risky, as an organization
does not know a priori whether the new product (or service) will be
successful. However, the eight assessment criteria below can guide an
organization in making a “go/no go” decision relative to going to market (i.e.,
commercializing) any new idea.
Assessment 1: Benefits
An innovative product or service should be superior to existing solutions
(products or services) used by consumers. If an idea satisfies a market’s
unmet need, then consumers will want to switch to and adopt or buy it. A
new product that offers new features, a new service that improves
convenience, or a new product or process that lowers cost are all examples of
a successful innovation. In short, an organization must be able to sell the
value proposition of what is new and why it is better.
Although a product, based on new technology, may deliver benefits beyond

what is available in the market, its benefit-to-cost ratio may be too low to
justify adoption. Initial market penetration is likely to be slow and greater
investment may be required to convert technical success into commercial
success.
For example, when automated welding systems were becoming more widely
available (Meldrum and Millman 1991), some customers were forced into
using them for safety or manufacturing reasons. Others adopted them because
their customers were placing quality demands that could be met only by
automated systems. For the vast majority of companies, welding was a low-
profile activity, and the new, technologically advanced welding systems were
not widely adopted. A similar example is found in large-area displays for
public information systems where LEDs (light-emitting diodes) have proved
to be acceptable technologically and have resisted replacement by liquid
crystal alternatives.
The continued embrace of familiar techniques has long been recognized by
those doing research in diffusion of innovation theory (Rogers 1976).
Marketers of advanced technology must judge how fast and how far their
product will be received as an acceptable substitute by the various sectors of
the market. The degree and speed of market penetration depend on how well
existing technology solves customers’ problems and how far the extra
benefits supplied by the new technology are perceived to offer competitive
advantages.
Assessment 2: Marketing
“In the modern world of business, it is useless to be a creative, original
thinker unless you can also sell what you create.” —David Ogilvy, co-
founder of Ogilvy & Mather
From a marketing perspective, a successful innovation requires that an
effective channel to the customer be established and that demand for the
innovation materialize. The product, process, or service being considered
must have a physical or electronic channel of distribution to customers that is
appealing and cost-effective.

Perhaps the biggest risk area for new or enhanced innovative offerings is best
paraphrased by the line from a movie (Field of Dreams, starring Kevin
Costner): “If we build it, will they come?” Some inventors are infatuated with
their invention, and they assume that consumers will also be taken by its
appeal. Market research, usually comprising focus groups, pilot studies, or
surveys, is often used to reduce the uncertainty about demand and consumer
adoption. Yet, despite market research, many new products are
unsuccessfully launched or substantially fail to reach expected demand
levels. Examples include Apple’s Newton and Lisa products, Sony’s
Minidisc, wristband TV, and Coca Cola’s reformulated New Coke attempt of
the 1980s.
Assessment 3: Scale up
The creation of a new idea is often followed by a prototype mock-up or the
manufacture of a small number of trial sample items. A pilot plant could be
established for low-scale production to determine whether an innovation can
successfully and robustly be mass-produced, with correct specification and
quality. If it is a new technology, will it be able to be sold and used by others
such that it performs to specification? If it is a new product or service, will it
work ‘in the field’ under a variety of conditions?
An example of a very promising new product that became a disaster because
it could not be scaled-up was the Pulsar car battery, invented and developed
by a former leading manufacturing company in Australia, Pacific Dunlop.
The new features were compelling, in that it was lighter, better electrically,
more reliable, and had a separate cell that made it impossible to flatten the
battery. The prototype worked well. Yet, when a factory was set up in
Geelong to produce some 300,000 batteries per year, mass production proved
to be impossible without significant quality problems. When it was mass-
produced and installed in cars, a significant proportion of batteries leaked
acid. This led to large losses of investment, brand value, and consumer
claims. The company’s factories were eventually closed, and the product was
withdrawn, with large amounts of money being written off. Ultimately,
Pacific Dunlop closed down.

Support technology must be adequate to make a proposed product a
worthwhile investment. Often, a product concept must await an “enabling”
technology. For example, nearly three decades elapsed before the concepts of
“factory of the future” and the “paperless office” were realized in practice.
Molins System 24 is generally regarded as the forerunner of modern flexible
manufacturing systems. Although several were installed in the late 1960s and
1970s, the concept was ahead of its time. Computing power and software
development were inadequate, and the step was too great for users to take.
Islands of automation, rather than fully integrated systems, resulted.
Similarly, for office automation, extensive displacement of manual/paper-
based tasks had little chance of acceptance without electronic integration. As
sophisticated communications technology and workstations such as
multiplexing, networking products, and protocols became widespread in
practice, market opportunities for the adoption of new technology increased.
A similar problem was noted by Meldrum and Millman regarding optical
storage medium, which is an inexpensive plastic film that stores vast amounts
of optical information and can be formed into a sheet, disk, tape, or cylinder.
One tape of 500 meters can store one terabyte (one billion bytes) of
information. Full commercialization and the opportunities that this
technology can address are not yet realizable. Development of suitable
hardware systems and some further advances in laser technology are required
before market potential is realized.
Assessment 4: Leadership team
This criterion assesses whether leadership and managerial skills are in place
to take an innovation from idea to commercial success. Effective leadership is
required from inception through market launch and beyond. Resilient
managers are required who will overcome many obstacles and deploy
creativity and business acumen. Innovations need to be shepherded through
many development stages, requiring energetic and highly capable people at
the helm.
Assessment 5: Intellectual property

The intellectual property (IP) of any innovation must be protected. Also, an
innovation should not infringe on the IP of others. As part of the process in
bringing an innovation to market, an organization may need to licence or buy
some technology that is owned by others. It is important to acquire the legal
rights to—or control of—all the IP involved in an innovation. Otherwise,
legal difficulties may ensue. A classic dispute over ownership and rights to IP
was between Apple and Samsung, who sued each other in a few countries
over ownership and control of certain technologies contained within mobile
phones. Each side claimed damages and even attempted to block sales of
each other’s phones, based on alleged infringement of IP rights.
Intellectual property can be protected by legal instruments such as patents,
trademarks, or copyrights. Although a patent seeks to achieve market
exclusivity using legal mechanisms, acquiring a patent is a slow and
expensive process. A second IP protection strategy is to “run fast” and stay
ahead of competitors who will try to emulate “your” innovation. Third, it
may be possible to hide IP, in some way to keep it a secret, whether it is the
core of a software package, or the formula for Coca Cola or KFC’s spices.
A fourth IP protection strategy is to create a joint venture between two or
more entities so that IP is shared or pooled. A “teaming up” strategy for
protecting IP is common in the biotechnology and IT sectors.
Assessment 6: Financial return on
investment (ROI)
Quoting from the movie Jerry McGuire, a key criterion for success in NPD or
NPI is: “Show me the money!” Any innovation should provide an ROI.
Compared with a mainstream product or service, an organization that brings
an innovation to market will require a higher ROI due to the higher level of
risk. Innovation is not an end in itself but must contain tangible
organizational and stakeholder benefits. For example, a new pharmaceutical
product may employ a novel mechanism of action. However, if it is not
effective and safe for patients, it will not be adopted by physicians and health
care professionals.

Assessment 7: Corporate social
responsibility
This criterion can also be interpreted as a test of the sustainability of the
innovation. An innovation must be acceptable in terms of its environmental
impact. Innovations, aimed at changing how people work or what they
consume, should be at least environmentally neutral (for example, not
increasing greenhouse gases emission) and, ideally, environmentally “net
positive.” Increasingly, stakeholders, including consumers, government
agencies, shareholders, and special interest groups, will not allow innovations
to damage the environment.
Similarly, the social and community outcomes of an innovation need to be
considered. If any segment of a community is disadvantaged by an
innovation, then resistance is likely to be encountered. Therefore, an
innovation should be designed with a goal of creating win-win outcomes for
as many stakeholder groups as possible. An innovation must also be neutral-
to-positive with respect to all matters concerned with legal compliance and
ethical standards of behavior.
Assessment 8: Fit with
organizational strategy
An organization that develops an innovative product or service must decide
whether to scale-up the innovation and attempt to commercialize the idea or
sell or license the idea to another entity. The innovating organization must
consider the ROIs of each of these options, its capital budget, and the various
assessment criteria outlined above. There are often a range of alternative
options, such as joint ventures and outsourcing or subcontracting an ancillary
function such as production/operations/distribution.
The organization that develops an innovation may not necessarily

commercialize it or may share the effort in bringing the innovation to market.
If the developer does not have infrastructure in place to capably scale-up the
innovation, then that capability must be acquired. Furthermore, the developer
must consider whether the innovation is aligned with the overall strategic
thrust of the organization. If an invention is unrelated to existing products and
services of an organization, then commercializing a particular innovation may
not fit with the organization’s direction. For example, a large bank developed
a technical advance embodied in some software that improved its service
offering and which also seemed to have potential well beyond that bank’s
applications. It could have commercialized this development on its own and
sought to sell it to others, kept it secret and buried within its own source code,
or licensed or sold it or the rights to it to a third party for development and
distribution into markets. The bank, in this case, chose to keep its banking
focus and not develop and commercialize the innovation in-house. The bank
spun off the software capability to a third party business to sell and distribute
under its own brand and license.
The principle of “Stick to your knitting” should not always be followed.
Nokia is an example of a company that developed innovative new technology
in mobile phones and applications, while its core business was in timber and
forestry.
These assessment criteria are offered as a set of filters for evaluating
innovation with respect to go-to-market decisions. At the early stages of an
NPD or NPI funnel, there is significant uncertainty and fuzziness, and
evaluation of these assessment criteria is challenging. As a project proceeds,
more data, evidence, and precision can be gathered. The assessment criteria
bring clarity and structure to the go/no go decision making process that
characterizes NPD/NPI.
In addition to these eight criteria that may be used to assess innovation,
introduction of new and breakthrough technology has some additional risk
factors that need to be considered.
13.2.2 Changing Expectations

Some high-tech products are designed and developed against a customer
specification, but specifications are prone to change during the development
cycle, causing costs and schedules to deviate measurably from the original
plan. Government-linked contracts have attracted media attention— none
more so in Great Britain than GEC-Marconi’s efforts to win the Nimrod
contract with the U.K. Ministry of Defense. Although there were numerous
problems surrounding this project, the performance requirements of the
ministry shifted continually over its life, thwarting GEC’s ability to come up
with a suitable product on time.
Turning again to the example of automated welding systems, a similar story
can be found. One prospective customer who manufactured components for
the automobile industry returned to the developer four times to request a
redesign of the system. The problem was that each time a new specification
was suggested, the customer realized a little better the potential of the product
in other areas where the system might have an application. Unwilling to lose
the development opportunity, unable to charge for quotation services, and
desperate for customers, the supplier found himself involved in a significant
amount of redesign work, for little reward in the long run. The customer
eventually went back to a system close to the original specification, being
unable to afford the more complex system that had taken his fancy.
13.2.3 Technology Leapfrogging
Substitute technologies or new generations of products that are based on
existing technologies may appear just as a company is pushing its existing
range of products into the marketplace. This is a particular problem in the
high-tech field, where rapid innovation can turn obsolete overnight products
that required large investments in time and cost to develop. As a
consequence, sustained investment is necessary to stay in the race.
Engines for wide-bodied passenger aircraft provide a useful illustration: The
first generation of engines such as the Pratt & Whitney JT9, Rolls-Royce
RB211, and General Electric CF6 represented high-risk “discontinuous”
product innovation of the make-or-break variety. Indeed, without U.K.
government intervention after placing the Lockheed Tri-Star contract, Rolls-

Royce would not have survived. Later generations of these large turbo-fan
engines have been based on incremental innovation, typically offering higher
thrust ratings, improved fuel consumption, and lower noise levels. Similar
patterns of discontinuous innovation and subsequent leapfrogging via
incremental innovation are to be found in other industries.
13.2.4 Standards
Both the existence and the nonexistence of performance and quality standards
for technology-based ventures can be a challenge in marketing innovative
products. If formal standards do not exist, then customers have nothing
against which to evaluate their potential purchase. This has the effect of
making the product difficult to sell because it will be a higher risk purchase
and the process of writing specifications will take a lot longer. Conversely, in
the absence of formal standards, informal or de facto standards may lead to a
mismatch between the proposed technology and customer requirements.
Airship Industries, a U.K. company that has led efforts to reintroduce airships
as a mode of transport and surveillance, provides a classic example of the risk
associated with the nonexistence of standards. In their efforts to establish
airships as a credible mode of transport, they believed that it would be
essential to obtain U.K. Civil Aviation Authority certification, which would
have worldwide acceptability. Their problem was that no standards existed,
and certification, in any event, proved very hard to come by. As the
commercial manager of the company noted, the first production model flew
in 1981 but did not gain U.K. certification until 1984. Full U.S. Federal
Aviation Administration certification was granted in 1989.
Another example of informal standards or industry-established norms
preventing a technology from becoming a profitable commercial venture is
the experience of JVC during their early attempts to establish a position in the
video recorder market. The first commercial videotape recorder (VTR) was
marketed in 1955 by the U.S. firm Ampex for use in film and television
productions (Nayak and Ketteringham 1993). The first Japanese version of
the Ampex system arrived in 1958. JVC, later to become the world leader in
home video with the VHS system, produced their version of a similar VTR in

1959. This was a better and simpler product, but it failed commercially, as
their machine was incompatible with the standards then established, which
were derived from the Ampex and Sony technology. Although this provided
an important lesson for JVC that proved useful in the now famous battle for
home video standards, the failure nearly resulted in JVC’s premature
withdrawal from this market (Rosenbloom and Cusumano 1987).
A related example is the development of standards for high-definition
television (HDTV). Since the early 1980s, a battle has been raging between
the U.S. Federal Communications Commission and its Japanese and
European counterparts. The contentious issues revolve around picture format
and compatibility with existing systems. As a result, the introduction of
HDTV into the United States was delayed by at least 5 years, giving the
Japanese electronics industry an opportunity to perfect the technology and
guaranteeing its dominance of the market.
If standards exist, they provide the supplier and the customer with a reference
against which to manufacture and evaluate. However, they often vary among
industries and countries, making it difficult for a competitive company to
expand its sphere of operation. Instances in which formal standards have
created problems for entry and expansion in export markets are not hard to
find. For example, a company that sells connectors for optical fiber cabling in
the telecommunications market, having developed a good business in the
United Kingdom based on British Telecom standards, found it hard to sell in
Germany where DIN standards operate. To gain approval, the company
sought a collaborative arrangement with another connector company but
ended up supplying components only, thereby deriving reduced added value
from this market.
13.2.5 Cost and Time Overruns
It has been argued that cost overruns will have less impact than schedule
delays. As noted by John Doyle, former vice president of Hewlett-Packard:
“If we over-spend by 50% on our engineering budget but deliver on time, it
impacts 10% on revenue. If we are late, it can impact up to 30% on
revenues.”

13.3 Managing Technology
Technology may be defined as an ability to create a reproducible way to
generate improved products, processes, and services. A modern
manufacturing business must have a substantial portfolio of individual
technologies. Management of technology should ensure that a firm maintains
command of technologies relevant to its purposes and that these technologies
support the firm’s business strategy and shareholder value.
Technology management for strategic advantage is difficult and often
frustrating. As Erickson et al. (1990) pointed out, the central issue is the need
to reconcile risk and the unpredictability of discovery with the desire to fit
technical programs into orderly management of the business. The traditional
approach to managing technology has been largely intuitive. R&D is treated
as an overhead item, with budgets set in relation to some business measure
(e.g., sales) and at a level deemed reasonable by industry practice. Budgets
may be projected several years ahead but are usually set annually. Within this
budget framework, decisions about areas of concentration and project
continuations may be left largely to R&D management. There is no assurance
that the R&D organization, left to its own devices, will pursue programs
related to corporate strategy, either in focus or in degree of innovation and
risk.
In response to this unsatisfactory situation, many firms have become
somewhat more sophisticated. Managers outside the technology area
participate in suggesting or reviewing projects. Some firms subject R&D
programs to a rigorous financial justification process on the basis of net
present value. Arguing that R&D projects are investments—as in a sense they
are—corporate management seeks justification on the basis of rate of return
or payout. It is difficult, though, to predict financial returns for an R&D
project, especially if it is focused on achieving a significant innovation. As a
consequence, new activities may be limited to conservative, incremental
projects; results will be more predictable but will have marginal strategic
impact.

Clearly, then, there is a need for a measured, sophisticated approach to R&D
management. Interest in a better approach has been stimulated by various
developments (Erickson et al. 1990, Jain and Triandis 1996, Roussel et al.
1991). First, many corporate leaders have moved beyond financially driven
planning, a characteristic of the 1970s and 1980s. Second, the success of
entrepreneurial, high-technology companies has excited interest in the
potential of technology to build company value. Third, firms have seen that
industry leaders give high priority to technology management. Fourth, quality
and manufacturing capability are now considered strategic business assets.
Together, these developments have helped to create a desire to manage
technology in a way that is congruent with business strategy.
The first step in the strategic management of technology is to determine the
mix of products and markets that will best sustain and enhance cash flow.
The next step is to test how well the firm’s technologies support the ideal
product and market mix. The third step is to focus technology investments so
that they better support the firm’s strategy.
It is often useful to examine a firm’s technologies in light of two questions:
1. What is the significance of the technologies in the firm’s portfolio, as
measured by their competitive impact and maturity?
2. In each product area or business, how strong is the firm’s technological
competitive position?
13.3.1 Classification of Technologies
In general, it is possible to identify three broad classes of technologies in a
typical firm’s technological portfolio.
1. Base technologies. These are technologies that a firm must master to be
an effective competitor in its chosen product-market mix. They are
necessary—but not sufficient—to achieve competitive advantage. These
technologies are widely known and readily available. Electronic ignition
systems for automobiles is an example.

The trick for R&D management here is to invest enough, but only
enough, effort to maintain competence. The danger is that inertia will
sustain programs in these base technologies longer and at greater scale
than they deserve, perhaps because these are the traditional areas where
the R&D organizations feel at home. The U.S. auto industry in the 1960s
and 1970s, for example, invested too heavily in familiar areas of product
technology rather than in new, less comfortable areas where
opportunities to develop new process technology existed.
2. Key technologies. These technologies provide competitive advantage.
They may permit the producer to embed differentiating features or
functions in a product or to attain greater efficiencies in the production
process. An example is food-packaging technology that enables the
purchaser to use microwave cooking.
The primary focus of industrial R&D is on extending and applying the
key technologies at the firm’s disposal; they should be given the highest
priority when contemplating investment opportunities. Unwilling to
invest in key process technologies in the 1950s and 1960s, the U.S. steel
industry paid the price in the 1970s; foreign competitors, whose entry
into the U.S. market had been encouraged by consumer goods
manufacturers, far outstripped their domestic counterparts in
productivity.
3. Pacing technologies. These technologies could become tomorrow’s key
technologies. Not every participant in an industry can afford to invest in
pacing technologies; this is typically what differentiates the leaders (who
do) from the followers (who do not). The critical issue in technology
management is balancing support of key technologies to sustain current
competitive position and support of pacing technologies to create future
vitality. Commitments to pacing technologies or potential breakthroughs
are hard to justify in conventional, ROI terms. Indeed, these
commitments can be thought of more accurately as buying options on
opportunity. Relatively modest commitments—and thus modest
downside risk—can give the potential for large upside reward. Realizing
that potential depends on still-unresolved technical and market
contingencies. If the option is not pursued, then the potential does not

exist. Smith, Kline & French supported pursuit of receptor modeling in
the 1960s, a pacing technology in the pharmaceutical industry at that
time. This work led ultimately to the development of TAGAMET and
the establishment of the company as an industrial leader.
An effective R&D program must include some investment to build a core of
competence in pacing technologies and some effort to gain intelligence from
sources such as customers, universities, and scientific literature to help
identify and evaluate these technologies. At the same time, disciplined
judgments about commitments to pacing technologies are necessary;
enthusiastic overspending on advanced technology can undercut essential
support of key technologies.
13.3.2 Exploiting Mature
Technologies
Technologies mature, just as industries and product lines do. The younger the
technology is, the greater will be the potential for further development, but
less certain the benefits. However, a mature technology can often be a key
technology. Many Japanese firms use mature technologies as a major
competitive weapon. The Sony Walkman, for example, was a wildly
successful new product that was based on comparatively mature
technologies. The Walkman fortuitously combined Sony’s work on the
miniaturization of its tape recorder line and its work on lightweight
headphones. Company engineers were trying to make a miniature stereo tape
player-recorder, but they could not fit the recording mechanism into the target
package size. A senior officer realized that combining headphones with a
nonrecording tape “player” would eliminate the need for speakers, reduce
battery requirements, and result in a small stereo tape player with outstanding
sound (Nayak and Ketteringham 1993).
Sometimes a mature technology becomes a key technology when it is applied
in a new context. Empire Pencil gained a major cost and quality advantage by
using mature plastic extrusion technology as the basis of a new way to
manufacture lead pencils. Conventional lead pencil manufacturing requires

the use of fine-grained, high-quality wood, such as cedar, and a good deal of
hand labor for assembly. Materials are becoming more expensive, and
damage to the graphite core during the assembly process causes quality
problems. A development team was confronted with this question: How can
we improve quality and cut costs? The team realized that wood powder in a
plastic binder could simulate the fine-grained wood. From there it was a
straightforward step to produce pencil stock in a continuous extrusion
process, with wood powder and a core of graphite powder in a plastic binder.
Other mature technologies may be protected (e.g., by patents or proprietary
treatment) and thus give their owners a key competitive advantage. A
Japanese grinding machine manufacturer successfully diversified into the
manufacture of integrated-circuit wafer equipment. A critical factor in its
success was its proprietary mature machine technology. Examples, such as
this, may tempt a firm in a mature line of business to diversify into new
products and markets where its proprietary but mature technology could have
a key competitive impact, but this strategy is risky. The better alternative is to
look, as Empire Pencil did, for new technology to invigorate a mature or
aging product line.
A business or product line whose key technologies are mature faces a serious
threat of being blindsided by a competitor using new key technologies. This
is what Xerox did to the established copier manufacturers and what word
processing did to the typewriter industry.
As an industry or product sector matures, the key technologies often become
manufacturing process technologies rather than product feature technologies.
This is the case in many mature industries, including chemicals, machine
tools, consumer appliances, and food products.
13.3.3 Relationship between
Technology and Projects
Defining projects by type provides useful information on the role of existing
technology in their development and how resources should be allocated.

Wheelwright and Clark (1992) suggested a two-dimensional qualitative scale
for classifying projects: (1) the degree of change in the product and (2) the
degree of change in the underlying manufacturing process. The greater the
change along either dimension is, the more will be the resources that are
needed. They also identified five project types. The first three—derivative,
breakthrough, and platform—are associated with the marketplace; the
remaining two—research and development—precede commercialization.
Each of these five project types requires a unique combination of
development resources and management styles. Understanding how the
categories differ helps managers predict the distribution of resources
accurately and allows for better planning and sequencing of projects over
time. A brief description of the first three categories follows.
Derivative projects range from less expensive versions of existing products to
add-ons or enhancements to established production processes. For example,
Kodak’s wide-angle, single-use 35mm camera, the Stretch, was derived from
the no-frills Fun Saver introduced in 1990. Designing the Stretch was
primarily a matter of modifying the lens.
Development work on derivative projects typically falls into three categories:
incremental product changes, say, new packaging or a new feature, with little
or no manufacturing process change; incremental process changes, such as a
lower-cost assembly technique, improved reliability, or a minor change in
materials used, with little or no product change; and incremental changes on
both dimensions. Because design changes are usually minor, incremental
projects are more clearly bounded and require substantially fewer resources
than do the other categories. Because derivative projects are completed in a
few months, ongoing management involvement is minimal.
Breakthrough projects are at the other end of the development spectrum
because they involve significant changes to existing products and processes.
Successful breakthrough projects establish core products and processes that
differ fundamentally from previous generations. Like smartphones, compact
disks, and superconducting ceramics, they create an entirely new product area
that can define a new market.
Breakthrough products often incorporate revolutionary technologies or

materials and hence usually require revolutionary manufacturing processes.
Management should give development teams considerable latitude in
designing new processes, rather than force them to work with outdated or
marginally efficient equipment, operating techniques, or supplier networks.
Platform projects are the middle of the development spectrum and thus are
harder to define. They entail more product or process changes than do
derivatives, but they do not introduce the untried technologies or materials
that are found in breakthrough products. Honda’s 1990 Accord line is an
example of a new platform in the auto industry. Computer-integrated
manufacturing techniques were successfully exploited to improve assembly
operations, but no fundamentally new technologies were introduced. In the
computer market, in consumer products, Proctor & Gamble’s Liquid Tide is
the platform for a full line of Tide brand products.
Well-planned and well-executed platform products typically offer
fundamental improvements in cost, quality, and performance over preceding
generations. They introduce improvements across a range of dimensions:
speed, functionality, size, and weight. (Derivatives, conversely, usually
introduce changes along only one or two dimensions.) Platforms also
represent a significantly better system solution for the customer. Because of
the extent of changes involved, successful platforms require considerable up-
front planning and the participation of marketing, manufacturing, and senior
management, as well as engineering.
Companies target new platforms to meet the needs of a core group of
customers but design them for easy modification into derivatives through the
addition, subtraction, or removal of features. Well-designed platforms also
provide a smooth migration path between generations so that neither the
customer nor the distribution channel is disrupted. Consider Intel’s family of
Pentium microprocessors. This family was aimed at a core customer group—
the high-end desktop/workstation user—but variations addressed the needs of
most other users. Moreover, software compatibility with predecessors, such
as the Celeron, permitted existing customers to make the transition to the
Pentium family with minimal effort. Over the life of this platform, Intel
introduced a host of derivative products, each offering some variation on
speed, cost, and performance and each able to leverage the process and

product innovations of the original platform.
Platforms offer considerable competitive leverage and the potential to
increase market penetration, yet many companies underinvest in them
systematically. The reasons vary, but the most common is that management
lacks an awareness of the strategic value of platforms and fails to conceive
projects that exploit their capabilities.

13.4 Strategic R&D Planning
All corporate departments, operating divisions, and companies develop plans.
In each division, R&D, engineering, manufacturing, marketing, sales, and the
various support groups participate to produce the division’s strategic plan.
The purpose of this plan is to define how each unit will carry out relevant
corporate goals. The relationship between corporate planning with R&D
planning is shown in Figure 13.1.
Figure 13.1
Relationship between corporate and R&D planning.
Figure 13.1 Full Alternative Text
It may seem obvious that R&D portfolios should be aligned with corporate
goals, but, too often, R&D groups are not given support and guidelines by top

management. Successful planning depends on a dialogue between top
management and the R&D leader regarding mission, goals, strategies, and
means of implementation. These are important aspects of participative R&D
management.
13.4.1 Role of R&D Manager
An R&D manager fulfills corporate strategy by planning for change
throughout the planning exercise. He or she must consider uncertainties of
innovation (probabilities of technical and market success) and uncertainties
of the environment (effects of public policy, consumer mood, actions by the
competition). The manager must recognize technology push (the brilliant idea
seeking a market), market pull (a market need seeking a product) and the
general corporate climate or attitude towards various project proposals and
strategic directions.
If you are a manager of an operational R&D group, you must recognize the
needs of the parent business unit; if you are in a central R&D group, then you
must recognize the needs of the corporation as a whole. In either case, you
must have the means and the ability to monitor technology and to forecast
change. A key requirement is to keep your eyes on horizons well beyond
current technology. Also, you must recognize where and who the
entrepreneurs and project champions are in your company.
Finally, top management must understand the sources and effects of
uncertainty, be receptive to innovation, and be the stimulus for strategic
planning and the agitator for an innovative environment. If they are not, then
strategic technical planning will never evoke empathy and the group will
flounder and fail.
13.4.2 Planning Team
The head of the R&D group in a business unit (a unit may be a section,
department, division, combinations of these, or a company) and the managers

of the various R&D areas in the group should be the planning team members.
How deeply the team draws its members from the organization depends on
company size, commonality of interests among business units, and questions
such as “what is a reasonable team size?” Ideally, senior professionals,
managers of various functions, and planners in the business unit will assist.
Research Managers Form The
Planning Team
To simplify the discussion, consider a corporate R&D planning group. In this
example, the vice president of R&D is the team leader, with other members
being managers of the group’s various R&D areas. Managers of relevant
operations will assist or be asked for assistance. Corporate officers and staff
from selected functions may be asked to review critical points in the
developing plan.
If you are the head of R&D and thereby the team leader, then you cannot
delegate the thought processes required for the planning process and the
derivation of results. You can, of course, use every fact-finding function
available, but the team does the actual manipulation of inputs and produces
the output results. This may seem like a lot of work, but once accomplished,
you will know more about your operation than ever before and be equipped
to manage your assigned functions.
Good Managers Do Not Delegate
The Planning Process
When the planning team is assembled, the leader should remind members of
the unit’s mission (also called charter or definition of business) and the
mission of the R&D group. If the team is in an operations unit, then the
mission statement emphasizes upgrades and means to advance market share
of present products; if the team is in central R&D, then emphasis is on new

products, new technologies, and new opportunities. These mission statements
are important because they define the business and give its scope in clear,
concise language. Two typical mission statements would be:
Division X designs, manufactures, and sells sensors and monitoring
equipment to meet the severe environments within the mining industry.
The mission of the R&D group is to enhance the performance of current
products and to discover and develop new products that will aid in
maintaining and advancing market leadership of the division. In so
doing, the R&D group will provide technical surveillance over current
and emerging relevant technologies, monitor competitors’ products and
services, maintain and advance market share through upgrades and
extensions to the product line, and develop selected new products within
the scope of the business.
Company Y designs, manufactures, and sells hardware and services to
energy producers. The mission of the R&D group is to discover and
develop products that will give the company commanding leadership in
its selected business areas. In so doing, the corporate R&D group will
provide surveillance over current and emerging relevant technologies,
conceive and develop new products to meet future change, and provide
problem-solving research and services to operations as needed.
Such mission statements focus the team on the issues that are important to the
business of its parent unit. The team leader presents to the team the needs of
top management and discusses specifically the goals that management wants
R&D to meet. (These needs should reflect, in part, the inputs from R&D.)
The goals of top management may be cast as general statements, such as:
Look at area X over the next few months and see if you can conceive an
advanced method.
Create a new generation of products in the near future from emerging
technology A.
Alternatively, the goals may be
Provide division Y with an upgraded product Z using your materials

technology, and let’s see where you are in six months.
Reduce materials costs of product B this year.
The planning team reviews any goals previously set by the R&D group to
determine their compatibility with current goals. The team identifies what can
be done within available resources, which employee and equipment resources
are needed, and so on. Goals are reviewed, refined, and revised at the end of
the planning phase. (A goal is usually defined as something to be
accomplished within a specified period.) Finally, the team discusses how to
accomplish the six stages of planning enumerated in Table 13.1 and sets out
tasks and schedules. The team leader also discusses the methods to be used in
fulfilling the assigned tasks.
TABLE 13.1 Stages of the
Strategic Technical Planning
Process
1. Information-gathering stage
Determine status of the business unit
Ascertain needs of operations
Determine status of competition
Conduct technical planning studies
Consider key concerns and issues
2. Consolidation stage
Derive scenarios of possible futures

List needs, opportunities, threats, and impacts
List key concerns and issues
List strengths and weaknesses
3. Strategy formulation stage
Analyze and evaluate lists of needs against lists of key
concerns and issues
Evaluate maturity of present technologies and possible
use of new technologies
Match lists with strengths and weaknesses
Develop preliminary alternative strategies
Develop candidate tactics
Evaluate and suggest priority of strategies
4. Selection stage
Select one set of strategies, or
Look again at some new technologies and then decide
5. Implementation stage
Consider project candidates (tactics) in depth
Test tactics against best and worst scenarios
Consider funding limitations
Suggest priorities of specific projects
Describe the group’s R&D areas

Set goals
Draft strategic and operational plans
6. Review stage
Submit plans for review
Adjust plans as necessary
Planning Is A Multistage Process
The strategic technical planning task required of the team can be facilitated
by use of the six planning stages. In the first stage, the team collects
information. In the second stage, the team consolidates (categorizes, digests,
and assimilates) this information into various lists. These lists are used in the
succeeding stages of planning, so their comprehensiveness is critical to the
overall effort. The next three stages are progressive refinements of the current
findings.

13.5 Parallel Funding: Dealing with
Uncertainty
A primary role of the R&D project manager is to narrow the range of
technological choices that the organization faces without sacrificing market
or performance goals. Because of the inherent uncertainty at each stage of
project development, it is common to identify and explore several alternatives
to facilitate selection of the most promising candidates. During the
development of the Airborne Warning and Control System by the U.S. Air
Force, for example, both Hughes and Westinghouse were awarded
multimillion-dollar contracts to design and build prototype radars for the
Boeing aircraft. Considering the extent of the technological unknowns, the
Air Force believed that the additional money spent in a runoff competition
was justified given the rigorous technical requirements and tight timetables
surrounding the program. This approach has become standard for virtually all
U.S. government agencies, whether the system involved is an unmanned
combat air vehicle or a multiline optical character reader.
The use of parallel strategies is one means by which experienced managers
cope with the uncertain nature of the R&D environment (Abernathy and
Rosenbloom 1969). Such an approach has the threefold advantage of
avoiding the difficulty of trying to predetermine which ideas or technologies
will succeed, hedging against the risk of outright failure, and building a
broader technological base. The decision to fund more than one alternative at
each juncture, though, must be tempered by the potential tradeoffs between
increased probability of success and increased cost, as well as the behavioral
issues associated with parallel choice (Balthasar et al. 1978). When a
particular alternative evidences clear superiority, however, a sequential
strategy may be called for wherein other candidates are pursued only if the
preferred candidate fails to meet expectations.
A stream of technical choices, made by project managers, group leaders, and
their clients, determines the cost of an R&D project and the value of its
outcome. Choices between competing approaches to the solution of technical

problems must be made in the face of substantial uncertainty in situations in
which time and resources are limited.
By a “parallel strategy” we mean the simultaneous pursuit of two or more
distinct approaches to a single objective, when successful completion of any
one would satisfy the stated requirements. Nevertheless, the sequential
strategy, that is, commitment to the best evident approach, is most common
in practice. In a majority of situations, the benefits of a parallel strategy may
seem obscure, whereas its additional costs are quite real.
13.5.1 Categorizing Strategies
One can generalize two broad categories for the use of parallel strategies
(Abernathy and Rosenbloom 1969). In the first category, called a parallel
synthesis strategy, uncertainty is broad, cost of information is relatively low,
and there may be only a limited commitment to further work. In the
contrasting case, the parallel engineering strategy, the bounds of uncertainty
are more definite, information cost is relatively high, and there is a strong
commitment to satisfy developmental objectives.
The parallel synthesis strategy is most often found in the first phase of a
program. At that point, substantial uncertainty exists concerning the types of
needs that the developmental product is to satisfy, the potential of each
alternative to satisfy those needs, and the probable cost of each alternative.
Information that can reduce those uncertainties can be obtained by means of
analytical studies, special tests, and limited development of the several
prototypes. This sort of activity frequently serves to synthesize an approach
to the larger problem and defines many of the outcome characteristics. A
parallel synthesis strategy typically is a means of gaining information and
maintaining options so that the best path may be selected for subsequent
development.
In the synthesis phase, definition of the program is incomplete. A manager
may still be ignorant of factors that will prove to be the most significant
sources of later uncertainty. For example, Admiral Rickover said, in
reference to the nuclear submarine program, “In the beginning of naval

development, neither the technical problems nor their solutions were well
understood. Many of the problems were not even known.” The various
approaches to development are seldom independent, and a new approach may
be synthesized from elements of those initially defined. In general, in R&D
projects, decision makers can only accurately estimate expected benefits and
costs in the later stages of the project, once many of the initial technical
uncertainties have been clarified.
With the parallel engineering strategy, in contrast to the synthesis strategy,
the decision maker usually is committed to bringing the development project
to successful completion. If he or she chooses only the preferred alternative
and it does not prove acceptable, then the decision maker must seek a new
solution. This implies time delays and higher costs, however, because the
development will continue at its high expenditure rate until a solution is
found. Thus, the basic cost structure of engineering development work
influences the characteristics of a parallel engineering strategy. Additional
costs stem from loss of reputation, penalty charges, and out-of-pocket and
opportunity costs that result from not having the product available when it is
needed or can be sold. Studies have shown that the cost of late completion is
often the major component of the cost of following a single, unsuccessful
approach. In the contrasting case of the synthesis strategy, the consequences
are somewhat different. An incorrect choice may mean that the program is
discontinued, because the benefits that would be offered by a different
alternative may never be demonstrated.
13.5.2 Analytic Framework
For complex situations in which many technological alternatives exist, an
analytic methodology can be helpful in selecting among those project tasks
whose outcomes can be described only in probabilistic terms. What makes
the underlying problem exceptionally difficult is that both systemic and
statistical dependencies are likely to exist among these tasks. Typical
dependencies include an overlap in resource use, technical interrelationships
among task outcomes, and externalities for which the value contributions or
joint performance of several tasks may be non-additive.

To address the combination of uncertain outcomes and task dependencies,
analysts have relied on Monte Carlo-based simulation models such as
SIMRAND developed by the Jet Propulsion Laboratory (Miles 1984), and Q-
GERT developed by Pritsker (1979). In a similar vein, Bard (1985)
formulated the decision problem as a probabilistic network and used a
heuristic embodying simulation within a dynamic program as a solution
methodology. In particular, he divided each R&D project into a number of
different parts or stages, such that it was possible to complete each stage by
undertaking one or more competing tasks. The corresponding problem can be
represented diagrammatically as a directed network that comprises sets of
parallel arcs linked in series. An example of such a network is depicted in
Figure 13.2, where each arc represents a specific task whose outcome is
characterized by an empirical probability distribution or random variable.
Typical outcomes or performance measures might be eventual unit
production costs, mean time to failure, or technical probability of success. In
the model,
Figure 13.2
Network representation of parallel funding problem.

Bard assumed that each task is defined by an algebraic expression that
consists of one or more input (random) variables. As a consequence, outcome
distributions are difficult to obtain in closed form, hence the need for
simulation.
13.5.3 Q-GERT
The most heralded use of the program evaluation and review technique
(PERT) and the critical path method (CPM) network techniques since their
inception has consistently been in R&D planning and control. These
techniques, however, are somewhat limited in that they are unable to reflect
many of the real complexities associated with R&D projects. Many
situations, such as multiple branching (e.g., the success or failure of a task),
probabilistic branching, and repeating activities via feedback loops, that are
frequently part of the R&D process cannot be modeled in a PERT/CPM
network. These limitations gave rise to the graphical evaluation and review
technique (GERT), a simulation methodology designed to accommodate the
interdependencies and uncertain nature of project tasks (Moore and Clayton
1976).
An additional aspect of R&D management that occasions even greater
complexity and difficulty is the scheduling and planning of several projects
when more than one research team is involved. This problem has also been
explored with GERT, and promising results have been reported for
applications of modest scope. Nevertheless, a limiting factor in GERT is that
as the number of R&D teams and projects increases, the accompanying
network becomes impossible to construct and decipher, thus defeating the
value of the methodology. In response, Q-GERT was developed to provide
even greater potential for planning and scheduling in a multi-team, multi-
project environment.
Q-GERT is an extension of the GERT modeling procedure and, as such,
contains most of the capabilities and features of the latter, including
probabilistic branching, network looping, multiple sink nodes, multiple node
realizations, and multiple probability distributions. Q-GERT derives its name
from the special queue nodes that it has available for modeling situations in

which queues build up before service activities. However, Q-GERT contains
other unique and innovative features for handling specific and complex
networks that are particularly applicable in R&D planning. The most
outstanding of these features is the ability to assign unique network attributes
such as activity time and node branching probabilities to each individual
project, and then process each project through a single generalized network.
In addition to the relative advantages that Q-GERT offers with respect to
other simulation and network techniques, Taylor and Moore (1980) attested
to its ease of use. The methodology requires only that the R&D projects
under consideration be diagrammed in network form and then converted into
a standard input format for the Q-GERT simulation package. To demonstrate
the power of the approach, Taylor and Moore presented two case studies that
centered on an R&D subsidiary of a large textile manufacturer in the
southeastern United States.

13.6 Managing the R&D Portfolio
R&D is an investment that must compete for corporate support with other
investment opportunities, such as plant modernization, advertising, and
market expansion. Program and laboratory directors must continually defend
the value of their research to top management as well as decide what mix of
projects is best for the firm. Project managers must determine whether their
projects are on schedule and whether expected payoffs outweigh costs.
As part of their normal functions, upper management periodically reviews
research programs, projects, and staff to assess progress and determine the
contribution that each is making to the corporation’s goals. The R&D
management and review process, shown in Figure 13.3, is nearly identical to
that discussed in Chapter 5 for more conventional projects. The information
gathered from the four basic reviews identified can be used to justify research
expenditures, assist in budget and program planning, and provide a means of
evaluating individual performance. The consequences of continuing to fund
an R&D project when failure is imminent go beyond the actual dollars lost.
The additional waste in human and material resources may have far-reaching
effects: Marginal projects may fail to receive the extra boost needed to move
them beyond a critical stage, apparently healthy projects may begin to
deteriorate when additional resources are not forthcoming, and promising
new projects may have to be deferred as the competition moves ahead.
These points are underscored by Liberatore (1987), who attributed the
importance of the R&D project management decision to two factors. First,
R&D spending represents a sizable investment for many firms and may have
a significant impact on their current and future financial position as well as
on their ability to compete technologically. Second, projects often entail
companywide commitments that translate into large opportunity costs if
managed improperly.
Most projects do not begin until an in-depth assessment of their probability of
success is made and the outcome seems favorable. As the project evolves,
uncertainties

Figure 13.3
Stages of the industrial R&D process.
Figure 13.3 Full Alternative Text
that jeopardize completion may develop. In some instances, the market for
the end product may change, falling below acceptable levels and calling into
question overall profitability. Alternatively, technological problems that

become either too expensive or too difficult to solve may arise. This is most
critical during the early stages of development, when quality and cost
decisions are made and research directions are forged.
There has been much work in project selection and resource allocation (e.g.,
see Martino 1995, Schmidt and Freeland 1992, Chen and Askin 2009) and
the examination of decisions involved in project termination (Balachandra
1984, Balachandra and Raelin 1980). To help isolate the causal factors, Baker
et al. (1986) analyzed 211 R&D projects that were carried out between 1975
and 1982 by 21 companies and found that a positive answer to the following
four questions is a likely sign of success:
Has a relevant business need, problem, or opportunity been identified?
Has an appropriate scientific need, problem, or opportunity been
identified?
Can the project results be transferred effectively to the internal user?
How well can the internal user produce, market, distribute, and sell the
resulting product or process?
Conversely, they found that a project has less of a chance to succeed if R&D
personnel are unsure about its commercial potential, if the match between its
technical and commercial aspects is vague, or the level of uncertainty on how
the results are to be brought to the marketplace is high.
Much of this work corroborated the earlier findings of Balachandra (1984),
who identified a set of 14 key variables shown to be highly correlated with
project failure. The implied conclusion was that by evaluating changes in
these variables periodically, the R&D manager would be better able to make
the crucial decisions related to project initiation and termination.
In light of this research, Bard et al. (1988) developed a decision support tool
to be used by the R&D manager to help update his or her portfolio at review
time. In the remainder of this chapter we highlight their methodology and the
ideas surrounding its implementation. Appendix 13A presents the results of a
case study that centered on a small computer firm that specializes in

peripheral equipment. Specific issues related to terminating a project once the
decision has been made are detailed in Chapter 15.
13.6.1 Evaluating an Ongoing
Project
To be useful to managers, quantitative methods must provide reliable results
and fit within the existing decision-making framework. At a minimum,
models should include those variables that managers believe are most
important and for which they can provide hard data or firm opinions. As
mentioned, Balachandra (1984) identified two groups of factors that strongly
influence project outcomes. His work was based on a discriminant analysis of
114 R&D projects gleaned from 41 firms spanning heavy manufacturing, oil
and gas, electro-mechanics and instrumentation, utilities, chemicals, and
electronics. Table 13.2 summarizes the characteristics of the database. Each
group is discussed below.
Critical factors
The successful completion of an ongoing R&D project is closely linked to a
number of critical factors. If it is determined that any one of the following has
deteriorated significantly since the last review, then immediate termination is
implied.
TABLE 13.2 Characteristics
of Database for Determining
Critical Factors
Item Range

Number of employees 50–2,000
Sales $50M–$2B
R&D budget $1M–$50M
Number of employees in R&D 10–50
Number of R&D projects 1–50
Project duration (years) 0.5–8
1. Government regulations
2. Raw material availability
3. Market conditions
4. Probability of technical success
The first three, termed “exogenous critical factors,” are generally outside the
control of the firm. The fourth is assumed to be a function of the resources
allocated to the project.
As an example of a negative change in government regulations (1), recall that
the development of many diet foods based on saccharine had to be
discontinued when the U.S. Food and Drug Administration affirmed their
cancer-causing properties. With regard to raw material availability (2), we
note that shortages are likely to have a damaging effect on market potential.
In the 1970s, many Mexican pharmaceuticals had to discontinue research into

the development of synthetic hormones from the barbasco root when the
export market abruptly changed and the price of the plant soared.
Similarly, markets (3) may suddenly vanish as consumer tastes change or
when substitutes seem to offer more immediate benefits. A good example of
this was Polaroid’s attempt to introduce instant movies (Polarvision).
Unfortunately, the onslaught from videocassette recorders was too great to
contend with, and the product met a quick demise. A more recent example is
the failure of Nokia and its conventional cellular phones as a result of the
introduction of smartphones by Apple. The last critical factor is the
probability of technical success (4)—a measure that is extremely difficult to
assess (Rubenstein and Schroder 1977). In any event, if it is perceived to fall
below some acceptable level, then dependent projects must be set aside until
the necessary technology materializes. In the early 1970s, a number of
computer firms had to shelve various bubble memory projects because
CMOS chips did not become available on schedule. Later, however, many
products that use this memory device have found a niche as a result of
belated technological advances. If none of these critical factors has
deteriorated significantly since the last review, then the project would then be
evaluated with respect to the key variables described below.
Key Variables
Variables in the second group are more volatile than those in the first but are
not as strongly critical. A significant deterioration in a minority of them may
not measurably affect outcomes. Thus, project termination is implied only
when a substantial majority has declined since the last review.
The key variables can be broadly categorized as environment related, project
related, and organization related. Each subgroup is outlined below. An in-
depth discussion is given by Balachandra (1984).
1. Environment-related variables
1. Positive chance event

2. Product-life-cycle stage
These two variables are outside the control of the organization but are
very much influenced by the environment. A positive chance event (1)
might be associated with the introduction of a complementary product
into the marketplace that would enhance the desirability of a product
currently in R&D.
When a product is in the initial stages of its life cycle (2), the probability
of false starts is greatest. Unfortunately, this is largely a function of the
technological environment and is beyond the control of the R&D team.
If a product quickly moves out of its infancy stage into its growth stage,
then R&D projects that pertain to the product are more likely to be
successful.
2. Project-related variables
3. Pressure on project leader
4. R&D manager is project champion
5. Probability of commercial success
6. Support of top management
7. Project personnel commitment
8. Smoothness of technological route
9. End user market
10. Emergence, toward the end of a project, of project champion from
outside of R&D
The eight variables in this subgroup are directly related to the project. A
fraction of these depend on the subjective perceptions of the team
managers and personnel. Specifically, it was found that positive
feedback (3) from top management, as evidenced by the enthusiasm that
they show toward the project team, smoothes the route to completion. If

a project champion emerges (10), then this can also strongly influence
the chances for success. Without such a person, most desirably in the
form of the R&D manager (4), organizational as well as technical
barriers may become very difficult to overcome. The timing of when a
project champion emerges also seems to make a difference.
The probability of commercial success (5) is the single most important
variable in the group. To assess this measure, a solid knowledge of the
market and costs associated with production and distribution are
required. As the project evolves, these factors become clearer to
management. A product whose costs will be higher because of
unanticipated technical and production problems is a serious candidate
for termination. The probability of commercial success should increase
or at least remain the same from one review period to the next.
The support of top management (6) and the commitment of project
workers (7) are also highly correlated with success. The latter may
decrease if problems such as poor leadership or snags in technology are
perceived but not acknowledged.
The smoothness of the technological route (8), as viewed by the project
leader and evidenced by delays in meeting deadlines, is another
important variable. So is a limit on the number of end users (9). An
increase in possible applications for a new product during its
development may dilute the effort, resulting in delays and indecision.
This, in turn, may lead to complicated redesign and subversion of the
original goals.
3. Organization-related variables
11. Company profitability
12. Anticipated competition
13. Presence of internal competition
14. Number of projects in R&D portfolio

Each of the four variables in this subgroup is affected by conditions
throughout the firm. In particular, it seems that the more profitable a
company (11), the greater the chances of completing the project. This may be
attributed to better managerial controls and better screening of new product
ideas. If a product has no competition in the market (12), however, then it is
likely that the R&D team will take a more relaxed attitude toward its mission.
This is a prelude to failure. Conversely, if the competition is known to be
working on a similar project, then both pressure and motivation intensify.
In many cases, emergence of internal competition (13) for common resources
can act as a catalyst. The existence of multiple demands for technicians and
equipment enhances the motivation of the project team. Nevertheless, as the
size of the portfolio grows (14), there is a greater chance of individual
failures as a result of less management oversight and a proportional reduction
in funding.
Monitoring Scheme
During the review process, if significant shortcomings in any of the four
critical factors (regulations, raw materials, markets, and technology) are
observed, then the project is marked as a good candidate for scrapping.
(Further investigation may be required before a final decision is made.) If no
serious problems are found, the project is reviewed for negative changes in
the key variables. A project score is computed by adding one point for each
variable that has not deteriorated since the last review. A total of nine or more
points indicates a high probability of success. Projects with scores between
six and eight are deemed to be on the verge of failing and hence require an
immediate and detailed evaluation. A score of six or less indicates a high
probability of failure.
At any stage in the evaluation process, it may be possible to save a
marginally failing project by allocating additional resources to alter (5), (7),
and (13) or by influencing the qualitatively controllable variables (3), (8), (9),
and (14). For example, if the technological route is problematic or perceived
support of the project leader has declined, then a commitment on the part of
management may be all that is needed to bring a project score up to the

desired level. A model that addresses this situation and accounts for
competition for resources among ongoing projects is developed in the next
section. Because of the qualitative nature of most of the factors, an interactive
approach is prescribed. This facilitates a timely assessment of the portfolio by
allowing for on-line updates of performance data and the immediate
disposition of marginal projects.
13.6.2 Analytic Methodology
At the beginning of a review period, each project is evaluated individually
and collectively in accordance with the monitoring scheme outlined above.
The first stage of this two-stage process involves the critical factors. If one or
more of these are strongly negative, then the project is terminated and its
remaining resources are redistributed. Next, the 14 key variables are
evaluated. If the resulting score for a specific project equals or exceeds the
threshold, T, then it remains in the portfolio; if not, then a judgment is made
to determine whether the score can be raised to the desired level by altering
one or more of the controllable factors. If this is not possible, the project is
terminated and its resources are reallocated.
These ideas are formalized in a three-step procedure using the following
notation.
i=index for projects
j=index for key variables
n=number of projects in the active portfolio
n ^ =total number of projects in the portfolio and on the candidate list
n max =maximum number of projects to be included in the portfolio
B=total budget
B i =current budget for project i

b i =maximum funding allowable for project i
p i =probability of technical success for project i
P i =threshold value of p i
f ij ( t )=value of key variable j for project i during review period t
a ij =dependent zero-one scoring variable, indicating whether key
variable j for project i is at an acceptable level
T=threshold value for project score
1. Step 1
1. Screen each project separately with respect to the three exogenous
critical factors; terminate those with strong negative indicators.
2. Screen remaining projects in portfolio with respect to probability of
technical success using threshold P i ; terminate those that cannot be
improved sufficiently within budgetary guidelines.
2. Step 2
1. Compute total score a i for project i as follows. Let
a ij ={ 1, if f ij ( t )− f ij ( t−1 )≤0 0, otherwise   for all i and j
a i = ∑ j=1 14 a ij , i=1,…, n
[&*AS*a_{i}*AP*|=|~SA~[C]*sum*{14}
{j|=|1}a_{ij},|em|i|=|1,|elip|, n&]
2. Compare the score obtained with threshold value T, and define a
zero-one indicator variable a ^ i as follows:
a ^ i ={ 1, if a i ≥T 0, otherwise   i=1,…, n
3. Determine the disposition of project i. If a ^ i =1, then place project
in portfolio; if not, then evaluate the feasibility of increasing a ^ i

from zero to one by increasing those a ij ′s associated with the
controllable key variables currently at zero. Terminate if not
possible; otherwise, indicate whether or not additional effort will
raise the score. Include the project in the portfolio if the response is
positive.
3. Step 3
Compute the amount of free resources, R, where
R=B− ∑ i=1 n B i a ^ i
These three steps constitute the updating and qualitative evaluation of the
current portfolio at the beginning of review period t. Some projects will be
canceled outright; others will be further scrutinized by the decision maker to
determine whether their condition can be improved.
To operationalize steps 1 and 2 in a manner that promotes consistency across
projects and managers, two procedures are recommended. The first is to
provide benchmarks for the interviewees in terms of background and
reference data. With respect to market conditions, for example, the
benchmark associated with a negative change might be determined by
comparing sales figures for similar products over the last two quarters. The
second procedure is aimed at building a consensus by soliciting responses
from both the project leader and a subset of team members. Discrepancies
can be fed back for reconsideration.
Model formulation
At this point, we need to allocate the remaining funds, R, to the active
projects, including those on the candidate list. A decision model is formulated
for this purpose using the following additional notation:
V i =present value of returns attributed to project i
y i =additional amount of resources allocated to project i

x i =total amount of resources allocated to project i
u i =zero -one decision variable for continuing project i
u ^ i =zero -one decision variable for selecting project i from set of
candidate projects to be in the portfolio
A project that is performing well at the beginning of a review period will not
necessarily have its funding continued. Although normally this is not the
case, it may be determined that the resources currently allocated to that
project should be reduced and the difference reallocated to projects whose
payoffs are potentially higher. Under such circumstance termination will
occur if the probability of technical success, p i , drops below its threshold, P i
. In general, p i is assumed to be a function of the total budget, x i , assigned to
project i ( x i = y i + B i ) and will be defined by one of the relationships
shown in Figure 13.4. Now, if the probability of commercial success is
denoted by f i5

Figure 13.4
Relationships between probability of technical success and funding:
(a) nonlinear; (b) piecewise linear; (c) discrete; (d) linear.
Figure 13.4 Full Alternative Text
(for simplicity the dependence of the critical factors on t will be dropped
from the notation), we solve the following problem:
Maximize ∑ i=1 n V i p i ( x i ) f i5 u i + ∑ i=n+1 n ^ V i p i ( x i ) f i5 u ^ i
(13.1a)
subject to ∑ i=1 n y i u i + ∑ i=n+1 n ^ x i u ^ i ≤R (13.1b)
x i = y i + B i ≤ b i ,  i=1,…, n (13.1c)
x i ≤ b i ,  i=n+1,…, n ^ (13.1d)
∑ i=1 n u i + ∑ i=n+1 n ^ u ^ i ≤ n max (13.1e)
∑ j=1 14 a ij ≥T u i ,  i=1,…, n (13.1f)
p i ( x i )≥ P i u i ,  i=1,…, n (13.1g)
p i ( x i )≥P u ^ i ,  i=n+1,…, n ^ (13.1h)
x i ≥0,  y i ≥− B i ,  u i ∈{ 0, 1 },  u ^ i ∈{ 0, 1 }, for all i (13.1i)
The objective function (13.1a) represents the expected return from the
portfolio for both active and candidate projects. Constraint (13.1b) restricts
the funding in period t to the remaining budget R. Constraints (13.1c) and
(13.1d) place a limit on the amount allocated to a given project, whereas
(13.1e) controls the maximum number of projects in the portfolio. The
remaining structural constraints (13.1f) through (13.1h) ensure that if a
project is selected, then its key variable score is at least equal to the threshold
value T( =9 ), and its probability of technical success is at an acceptable level.
This formulation permits resources to be removed from an active project as
long as p i ( x i ) does not fall below P i .
Implicit in the construction of problem (13.1) is the assumption that
additional resources allocated to project i will affect p i as well as the three
other quantitatively controllable key variables (5), (7), and (13). The
functional relationships between x i and these key variables must be worked

out on an individual basis. For example, the probability of technical success
might be increased by the acquisition of better or more advanced laboratory
equipment, while increasing worker commitment might be accomplished
through the installation of a minicomputer to facilitate the project’s data
processing. The formulation above does not treat the key variables (5), (7),
and (13) explicitly.
Implementation
Problem (13.1) is a mixed nonlinear integer program whose degree of
difficulty depends in part on the functional forms chosen to represent p i . In
the implementation, Bard et al. (1988) used the discrete model in Figure 13.4,
so the problem reduces to a pure nonlinear integer program whose terms are
at most quadratic in the decision variables x, u, and u ^ . Such problems may
be converted to integer linear programs by adding one variable and two
constraints for each quadratic term (see Bard 1986). Because most R&D
portfolios usually contain fewer than 30 projects, this type of transformation
will yield a problem whose dimensions are well within the reach of current
codes. If any of the other three models in Figure 13.4 are used, then different
techniques may be required.
To put the problem into a more manageable form, let us redefine the decision
variables x i such that x ik equals 1 if project i is funded at level k, and 0
otherwise. Also, let p ik be the probability of technical success associated
with allocation b ik and K i be the number of permissible funding levels for
project i. This leads to the following pure zero-one linear formulation:
Maximize ∑ i=1 N V i f i5 ( ∑ k=1 K i p ik x ik ) (13.2a)
subject to ∑ i=1 N ∑ k=1 K i b ik x ik ≤B (13.2b)
∑ k=1 K i b ik x ik ≤ b i ,   i=1,…,N (13.2c)
∑ i=1 N u i ≤ n max (13.2d)
∑ k=1 K i p ik x ik − P i u i ≥0, i=1,…,N (13.2e)
∑ k=1 K i x ik ≤ u i ,   i=1,…,N (13.2e)
x ik ∈{ 0, 1 },  u i ∈{ 0, 1 },  for all i and k (13.2f)
where N≡ n ^ . Problem (13.2) assumes that if the score of project i is not at

least at the threshold, T, then u i =0. This eliminates the need for constraint
(13.1g). Also, the vector u has been redefined to include u ^ .
In solving problems such as (13.2), it is important for the analyst to be able to
enter data in a simple format and to be able to change parameters easily while
investigating various scenarios. Here, the complete methodology was
embodied in three separate modules: (1) a front-end, menu-driven routine for
input and control; (2) a model generator for data formatting; and (3) a zero-
one integer program solver. Use of the methodology, along with the
computations, is demonstrated in Appendix 13A.
TEAM PROJECT Thermal
Transfer Plant
Your team was invited to the CEO’s office at Total Manufacturing Solutions,
Inc. (TMS). At the meeting, the CEO told you how impressed he was by the
prototype rotary combustor project and expressed his confidence in your
team leading the new waste management and recycling division. He expects
this division to master the leading technology in waste disposal. To begin,
you are asked to search the literature and to propose related high-tech R&D
projects. The CEO would like you to present your proposal for the most
appropriate such project at the next TMS board meeting.
It is clear that a detailed proposal addressing all aspects of the R&D project
will be supported by the CEO. You are also aware of the once-in-a-lifetime
opportunity for recognition and advancement that has been presented to you.
Prepare a proposal for the new R&D project explaining the following:
Why the project is the most appropriate for TMS
What technology will be used
The nature of the expected risks

The proposed schedule and budget
The approach you will take to maximize the probability that the project
will succeed

Discussion Questions
1. What characteristics distinguish R&D project management from
conventional project management? What additional skills does an R&D
project manager need?
2. Identify a few breakthrough technologies and the products that they
spawned.
3. Pick a major U.S. industry, such as automobiles or computers, and
discuss the lapses in technology and innovation on the domestic front
that permitted foreign competitors to get a foothold and, in some cases, a
dominant share of the market. Who or what do you think was to blame
for this situation?
4. In the mid-1980s, General Motors undertook a $5 billion program to
introduce robotics and computer-integrated manufacturing techniques
into many of its assembly plants. The results were disappointing, to say
the least. Enormous technical problems dogged the program from the
beginning, and the ultimate gains in productivity were decidedly modest.
What do you think went wrong? Why? From the long-term perspective,
was the automation program a good idea?
5. Give a few examples for which commercial success did not follow
technical success with regard to new product introduction. What were
the reasons for market failure?
6. Can you think of any example for which lack of standards retarded the
introduction of a new product or technology? Give some details.
7. Pick an industry and identify its base, key, and pacing technologies.
8. What are the differences between a strategic technical plan and an
operational plan for an R&D project?
9. As the head of an R&D group that is contemplating the development of

a notebook computer with a built-in fax machine, who would you like to
have on your strategic planning team? Why?
10. Consider a new technology, such as superconductivity or magnetic
levitation, and identify several (parallel) ways of realizing it on a
commercial rather than a laboratory scale.
11. Identify a new technology for which you believe that parallel
development is not warranted. Explain your choice.
12. Why has simulation modeling, which is a descriptive technique, been
preferred to mathematical programming, which is a prescriptive
technique, for analyzing and providing help in the management of R&D
projects?
13. Which of the critical factors and key variables described in Section 13.6
do you think would apply to conventional projects?
14. What are some of the shortcomings of the mathematical programming
model presented in Section 13.6.2 to manage an R&D portfolio? Can
you suggest ways of correcting or accommodating them?
15. What data are needed to run the mathematical programming model
(13.2)? How would you go about collecting these data?

Exercises
1. 13.1 Identify a new product that is based on an innovation in
technology, and draw up a strategic technical plan for its development.
Be sure to discuss the risk factors at each stage, and indicate how you
would deal with each.
2. 13.2 Assume that you are in charge of a round-trip mission to Mars. The
goal is to spend 3 months on the planet’s surface performing
experiments and collecting data that will be used to help set up a future
colony. Construct a strategic plan for this mission.
3. 13.3 The transonic airplane, now on the drawing board, is intended to be
a commercial transport operating between continents at supersonic
speeds. It will fly a ballistic trajectory and be able to reach Japan from
the United States in only a few hours. Identify the base, key, and pacing
technologies for this vehicle. Discuss the economic, political, social, and
technical issues surrounding its development.
4. 13.4 Select an industry such as semiconductors or consumer electronics,
and go through the six stages of the strategic technical planning process
listed in Table 13.1 .
5. 13.5 Consider the problem of trying to decide which tasks to fund in
parallel to achieve a given technical objective. For example, the
objective might be to develop a low-cost rechargeable battery to power
an electric vehicle. The funding options might be the various types of
battery technologies available (see Bard and Feinberg 1989). What are
the decision variables and functional relationships associated with the
problem? What data are required? Be specific with respect to probability
functions and any other relationships that might exist.
6. 13.6 Construct a mathematical programming model for the parallel
funding problem discussed in Exercise 13.5 and Section 13.5 .

7. 13.7 Choose a new technology, describe its major features, and explain
how you would apply the total quality management principles discussed
in Chapter 8 to an R&D project aimed at commercialization. What is
different about total quality management applied to an R&D project
versus a conventional project?
8. 13.8 For each of the 14 key variables presented in Section 13.6.1 ,
identify the internal and external data sources that can be used to
ascertain their status.
9. 13.9 Computer Assignment. Write an interactive computer program
implementing the three-step procedure discussed in Section 13.6.2 for
screening projects and computing project scores.
10. 13.10 Using a commercial integer programming package, solve model
(13.2a)–(13.2f) initialized with the case study data presented in
Appendix 13A .

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Appendix 13A Portfolio
Management Case Study
Portable Solutions is a Texas-based company that has been selling personal
computers and peripherals since 1982. In May 1985, it expanded its
operations and began to produce its own brand of tape backup units for PCs.
Since that time, the company has introduced three different but related
products to the market, including a 10-gigabyte self-threading backup system,
a 20-gigabyte streaming tape backup system, and a 40-gigabyte streaming
tape backup.
To maintain profitability and ensure its survival, Portable Solutions has been
exploring two new ventures. The first involves vertical integration of its
current line; the second centers on the development of new products to
compete in complementary areas. To put these ideas into motion, the
company has established an R&D portfolio that includes the following six
projects.
1. A signature verification system that has the capacity to store 145,000
signature files on an optical, nonerasable medium and the ability to
access each within 3 seconds. The system is intended for use by banks
and would replace current methods, which typically rely on microfiche
as the storage medium. With respect to performance, optical technology
offers far greater speed and reliability than do any of its competitors. In
addition, the permanent nature of the files offers built-in security.
2. A signature verification system that has the capacity to store 85,000 files
on a hard drive and the ability to access each in 3 seconds or less. This
system would incorporate most of the features of (1) but would use
magnetic tape as the storage medium. The advantage here is lower
development cost, while the disadvantage concerns record security (i.e.,
information can be altered easily).
3. A portable laser drive with the capacity to store 115 megabytes of

information. This system should be fully portable without the hint of
compatibility or installation problems. To date, speed and capacity have
been limited by mechanical hard-drive technology. Optical technology,
however, now permits storage of vast amounts of data in compact form,
free of maintenance, and without the data integrity problems that have
plagued magnetic media.
4. A small computer standard interface (SCSI) for existing and future
product lines. This interface will be compatible with most mainframes as
well as the Apple Macintosh and various workstations. At this time, the
company’s market is limited to PCs. A SCSI will open up new
opportunities throughout the industry.
5. A port extender interface for personal computers. This device permits
the addition of peripherals when no extra slots for interface cards are
available. It plugs directly into a floppy port, leaving the bus slots
available for other applications. The proliferation of add-ons makes this
an especially attractive product.
6. A combination 20-gigabyte hard disk/20-gigabyte portable backup
system for military applications. This unit must pass stringent military
quality and durability standards. The marketing department indicates
that the demand for this product is high and that many channels exist for
its promotion. Nevertheless, engineering has expressed serious doubts
about achieving the required levels of performance within the target cost
range.
In the course of marketing, management has recently identified four
additional projects as potentially lucrative and is considering several
funding alternatives. The new projects include:
7. A downloading peripheral that transfers data from a 9-inch mainframe
magnetic tape to a 3.5-inch optical cartridge for use with personal
computers. At the time of the study, the mainframe standard for
information archiving over the past 20 years had been the 9-inch tape.
This medium requires constant maintenance to avoid data loss and
consumes expensive central processing unit (CPU) time during retrieval.
Downloading tapes to optical media not only creates a maintenance-free

environment but also permits easy access through personal computers.
8. A smart system that is capable of downloading 9-inch magnetic tapes to
3.5-inch optical cartridges. This system would be self-contained, not
needing additional equipment to operate. It would differ from the system
described in (7) by the incorporation of a CPU.
9. A record management system that is capable of storing up to 2 terabytes
of information in a 12-inch optical cartridge. As envisioned, a
microcomputer would serve as processor, and up to 1,000 cartridges
could be managed at once using a jukebox principle.
10. An image-scanning device that is capable of tracking pavement
conditions on highways and determining when to record a damaged
sector. In addition, it must be capable of classifying the damage and
deriving a repair schedule that is based on severity of damage and
equipment availability. The goal is to develop a system that will reduce
highway repair costs by approximately 50%.
As is usually the case, Portable Solutions does not have the resources to fund
all of these projects. Model (13.2) presented in Section 13.6 will be used to
determine the best allocation of materials and personnel and to decide the
level of activity for each project accepted.
Given the demand for resources, management believes that at most seven
projects should be undertaken at one time, and has imposed a $250,000
ceiling on the R&D budget. Table 13A.1 lists the input data for the 10
projects, and Table 13A.2 specifies the relationships between probability of
technical success, p ik , and funding level, b ik . These data were derived from
extensive interaction with the firm’s four principal officers and represent the
consensus that emerged after two iterations of individual and joint
discussions.
TABLE 13A.1 Input Data
For R&D Case Study

Project
Probability of
commercial
success ( f i5 )
Threshold
probability
( P i )
Present
value of
return ( V i )
Maximum
budget ( B
i )
1 0.75 0.35 $3.7M $75K
2 0.82 0.40 $8.2M $105K
3 0.67 0.45 $7.5M $145K
4 0.92 0.35 $4.1M $110K
5 0.55 0.30 $5.1M $90K
6 0.88 0.35 $7.8M $145K
7 0.68 0.40 $3.5M $90K
8 0.75 0.35 $9.0M $100K
9 0.67 0.30 $7.5M $128K
10 0.94 0.45 $8.6M $129K
TABLE 13A.2 Relationship

Between Probability of
Technical Success and Funding
Level
Level 1 Level 2 Level 3 Level 4
Project p i 1 b i1 p i 2 b i 2 p i 3 b i 3 p i 4 b i 4
1 0.44 $22K 0.56 $34K 0.72 $54K 0.89 $72K
2 0.36 $18K 0.45 $26K 0.57 $47K 0.82 $64K
3 0.40 $25K 0.58 $52K 0.72 $90K 0.95 $130K
4 0.35 $20K 0.50 $38K 0.75 $84K 0.94 $100K
5 0.30 $15K 0.55 $40K 0.70 $60K 0.90 $90K
6 0.25 $25K 0.50 $50K 0.75 $65K 0.98 $120K
7 0.25 $15K 0.56 $40K 0.76 $60K 0.89 $82K
8 0.36 $20K 0.49 $40K 0.62 $81K 0.82 $94K

9 0.25 $25K 0.54 $50K 0.77 $98K 0.95 $125K
10 0.35 $25K 0.50 $48K 0.75 $89K 0.94 $129K
At the time of the study, five of the first six projects were actively being
pursued at a cost of $240,000. The third project was not in the portfolio but
was still considered a candidate. Table 13A.3 indicates the individual funding
levels, along with the total.
TABLE 13A.3 Funding for
Basic Portfolio
Project Level Funding
1 2 $34,000
2 4 $64,000
4 1 $20,000
5 3 $60,000
6 3 $62,000
    Total=$240,00

In the process of updating the portfolio, all current projects passed the critical
factors test at step 1, and all but project 5 passed the key variables test at step
2. Running the model with the four remaining projects, project 3, and the four
new ones, led to the selection of six projects, as shown in Table 13A.4. The
total budget allocation accompanying this solution is $249,000 and the
expected return is $19.56M. The specific funding levels are also shown in
Table 13A.4.
TABLE 13A.4 Results for
Updated Portfolio
Project Level Funding
2 2 $26,000
4 1 $20,000
6 3 $65,000
8 2 $40,000
9 2 $50,000
10 2 $48,000
Total=$249,00

The fact that projects 1, 3, and 7 were not chosen does not necessarily mean
that they will be discarded but simply that they will be shelved until the next
review or until additional funds become available. It is also possible that
project 5 could be resurrected at a future time.
Regarding the computations, the solution was obtained in 15.22 minutes on
an IBM-PC. This involved solving a 32-variable, zero-one integer program
with 11 constraints. With today’s technology, problems of this size can be
solved in fractions of a second. Of course, the computational effort grows
exponentially with the total number of levels and projects, so large problems
may still take some time. Note that constraints (13.2c) and (13.2d) can be
handled implicitly, and that the nine project variables, u i , can be eliminated
by appropriately redefining (13.2d) and (13.2f).
Assessment of Methodology
The above presentation demonstrates the facility with which an R&D
manager can update his or her portfolio provided that all of the pertinent data
are available and that an accurate assessment of the key variables can be
made. Updates can take place at any time but are commonly scheduled
around the budgetary cycle. As some projects reach their critical stages,
though, it may be desirable to increase the frequency with which the portfolio
is reviewed.
After testing the methodology with a number of high-tech firms, it was found
that most managers were less interested in the final results than in the process
itself. The value for them was in systematically stepping through each project
and assessing its status. The biggest stumbling block arose in the evaluation
of changes in the key variables. The lack of up-to-date information often led
to difficulties in making consistent judgments across the portfolio. In some
instances, for example, not all managers were aware of a lapse in worker
commitment or the critical need for a technological breakthrough.
Nevertheless, the information gathered at the interview sessions was prized as
much for the insight that it provided as for the confidence that it instilled in
the decision-making process. By isolating the major components of that

process, the interactive dynamics enabled the participants to gain a better
understanding of the forces at work.

Chapter 14 Computer Support for
Project Management
14.1 Introduction
Project management is the process of achieving multidimensional goals
related to on-time delivery, adherence to requirements, and cost minimization
in a unique environment that is subject to resource availability, cash flow,
and technological performance constraints, all in the presence of uncertainty.
The tools and techniques that have been developed to assist project managers
in their job were introduced earlier. Most of these tools are based on a model
that transforms input data into some form of output that facilitates decision
making. For example, scheduling by the critical path method (CPM)
transforms information about required activities, performance times, and
precedence relations into a list of critical activities, available slack for
noncritical activities, and an estimate of project completion time. Each tool is
designed to handle a specific aspect of the project management process.
However, a project manager frequently needs an integrated mechanism to
deal with several aspects of a project at once. This has led to the development
of software packages, many Web-based, that now make it possible for
different organizations to interact efficiently by standardizing procedures,
reports, and data files.
The new generation of software packages (or information systems) integrates
project management with other activities of the organization. For example,
enterprise resource planning (ERP) systems can simultaneously manage
projects and recurrent activities that share the same information and the same
resources (e.g., in a matrix organization). Furthermore, these information
systems support the definition, execution, monitoring, and control of project
management processes, as discussed in Chapter 2. Some of these packages
are available as a Software as a Service (SaaS) that are not installed on the
user’s computer but rather available on the cloud.

Early software packages typically concentrated on a limited set of tools and
techniques for scheduling and managing costs. Data input and processing
were batch-oriented, and only a prespecified set of output reports were
available. The introduction of the Web, coupled with rapid advances in
software engineering and reduced processing and computer memory costs,
led to the development of integrated software packages that are able to
address a multitude of functions simultaneously.
The current trend in the area of software development is toward interactive,
fully integrated systems that can handle multiple projects and use the Web for
communication. Many of these systems can handle all of the different aspects
of project management throughout a project’s life cycle, including:
Configuration management
Scheduling
Budgeting
Cost analysis
Resource management
Monitoring and control
Potential users face two issues: (1) how to select the most appropriate
software package for their needs and (2) how to introduce the chosen package
into their organization successfully. In the next section, guidance is offered to
those who are charged with the responsibility of resolving the first issue. We
use the software package Microsoft Project to illustrate concepts. This is
followed by a discussion of the major criteria that accompany a benefit-cost
analysis aimed at making the selection. The remainder of the chapter offers
insights into smoothing the implementation of project management software.

14.2 Use of Computers in Project
Management
Project management requires the deliberate treatment of organizational
processes, economic factors, and technological aspects, as well as the
implementation of methodologies for planning, scheduling, and control.
When choosing a project manager, it is important to consider (among many
other things) leadership abilities, verbal communication skills, and
motivation level. Today’s computers cannot replace a skilled project manager
because computers do not possess these attributes. However, they can support
a project manager in certain decision-making processes if the problems at
hand are well defined and amenable to quantitative or symbolic manipulation.
Even if this is only partially the case, the computer’s ability to store, retrieve,
and process large quantities of data, along with its powerful communication
capabilities, can help prepare information for the decision maker. In
particular, we rely on software to:
Supply needed information from the database
Support decisions with appropriate models and data
Support project monitoring and control
Support multiple project monitoring and control including portfolio
management
Support communication among stakeholders
Support project management processes with workflow models
Support the integration of projects and recurrent activities that share the
same resources
These functions were discussed throughout the book. We now elaborate on
several of them in the following subsections.

14.2.1 Supporting the Project
Management Process Approach
The software support in this case is integrated in the sense that the processes
are connected to each other as the output of one process serves as the input to
another process. Using the PMBOK framework as an example, the processes
in the nine knowledge areas form a complete project management
methodology. Each process is defined by its required input, the tools and
techniques used to manipulate the input data, and the output produced. The
software uses a workflow management module to route the required input to
the person who is responsible for each process, along with the tools and
techniques (models) needed to execute the process. This module monitors the
progress of each process and alerts the project manager to delays. The system
saves predefined data and process outputs in its database for future use.
Lessons learned are transformed into procedural updates, and the tools and
techniques discussed next are used to support both organizational and
individual learning.
14.2.2 Tools and Techniques for
Project Management
Some software packages are limited in scope and are mainly a collection of
tools and techniques (a model base) supported by a database, a user interface,
and a report generator. These systems are essentially a subset of those
described in Section 14.2.1 and support the following basic functions:
1. Scope of work and work breakdown structure (WBS). The initial step in
using most project management software is typically to define the
project’s work content in the form of a statement of work (SOW) or
scope of work and translating it into a WBS. A template for the SOW is
a handy tool for organizations that perform similar projects. The
development of a WBS is greatly facilitated by computer packages

whose input and presentation format reflect the underlying hierarchical
structure of the project, that can assign appropriate codes to WBS
elements at each level, and that can check for inconsistencies such as
disconnected or lower level WBS elements connected to more than one
higher level element. The division of the project/program into its basic
building blocks is easier using a module that automatically assigns WBS
codes and checks for inconsistencies as part of the data input process.
Figure 14.1 illustrates a WBS diagram that corresponds to the seven
tasks of the example project used throughout the book.
Figure 14.1
WBS for example project.
2. Organizational breakdown structure(OBS). The next step in project
management is to allocate the work content among participating
organizations; that is, to develop the project’s OBS. This structure
depicts the communication lines for reports, work authorization, and so
on. A module similar to the one that supports the WBS supports the
creation of a clearly defined organizational structure. Integrating the
WBS module with the OBS module generates a matrix that assigns each
lower level WBS element to a lower level OBS element creating work
packages assigned to work package managers.
The OBS and WBS hierarchies allow for information processing

through a roll-up mechanism. This mechanism transfers information
from lower to upper level elements through the connections defined in
the OBS-WBS matrix. The established relationships help to generate
reports at several managerial levels. This is also a good starting point for
the development of work flow when project management processes are
introduced and supported by the software.
One important aid to a project manager is a software package that
supports the development, maintenance, and integration of the WBS and
OBS. The process subdivides the project’s work and allocates it to the
participating organizations. This division capability is also important in
integrating individual efforts and helping update all groups on their
share of the total effort as it relates to the entire project. The WBS and
OBS modules not only generate reports at various managerial levels but
also keep these reports coherent and synchronous. By using the same
OBS-WBS hierarchy throughout a project’s life cycle, the plans
developed during startup are used to execute, monitor, and control each
stage of the project.
Once the organizational structure is defined and each participating
organizational unit is assigned tasks or a scope of work within a
specified work package (WP), it is possible to break down the project’s
work content further into activities and to estimate each activity’s
duration for each mode of the activity (a mode is a combination of
resources assigned to perform the activity). This breakdown forms the
basis for the following steps in the project’s planning cycle.
3. Scheduling. Defining the time frame or calendar for the project is the
first step in scheduling. In the calendar definition phase, the project
manager may select a current organizational calendar or develop one
that is project specific. The calendar defines working days per week,
daily working hours, scheduled holidays and vacations, and so on. One
important decision is to select the minimal time unit. Some projects
(e.g., the maintenance of electric power plants or airplanes) require a
detailed schedule at the level of minutes or hours. For long-term
construction projects, a minimum time unit of a day or a week may
suffice. Figure 14.2 illustrates a calendar for the example project, which

is scheduled to start in the beginning of March 2005. The figure shows
only the first four weeks because the rest of the calendar is defined
similarly.
Figure 14.2
Calendar for the example project.
Based on the calendar and the estimated activity durations, scheduling
can begin. In most software packages, this is done by defining
precedence relations among activities. The first step is usually to assume
finish-to-start precedence relations; that is, when an activity can start as
a function of its immediate predecessors. The CPM logic is then applied
and the early-start, early-finish, late-start, and late-finish dates are
calculated for each activity. These dates are based on the calendar
selected, with its predetermined holidays and vacations. The resulting
schedule can be a table of activities with the corresponding dates and
slacks, a Gantt chart, or a network model [activity on arrow (AOA),
activity on node (AON)]. Some software packages include all three
formats, whereas others include only a tabular report or a Gantt chart.
Figure 14.3 presents the early-start Gantt chart for the example project
with the critical activities in bold; Figure 14.4 depicts

Figure 14.3
Early-start Gantt chart for example project.
Figure 14.3 Full Alternative Text

Figure 14.4

AON network for example project.
Figure 14.4 Full Alternative Text
the same information in an AON diagram with the boxes identifying the
critical activities in bold. All dates are given in the following format:
day/month/year.
The basic schedule developed by the process described above is called
an “unconstrained” schedule. The precedence relations among activities
are assumed to be the only limiting factors. The next step is to introduce
the time constraints imposed on activities or events (project milestones).
Time constraints may require an activity to start or end on a given date,
and may produce an infeasible schedule as a result of conflicts between
the critical path’s length and the imposed milestones. For example, a
conflict occurs if the length of the critical path is 12 months but the
contract calls for a delivery date 11 months after kickoff.
In some projects, managers can resolve conflicts by introducing other
forms of precedence relations, such as start-to-start and finish-to-finish.
Modeling the real situation with these alternatives may alleviate the
problem. Some software packages support all types of precedence
relations mentioned in Chapter 9, including those with built-in delays or
lags. Understanding and controlling precedence relations is crucial in the
scheduling process and helps develop a more realistic model. If
managers cannot resolve conflicts, then they must modify the project
master plan until a feasible schedule is achieved.
In some projects, alternative modes can be defined where each mode
corresponds to the combination of resources assigned to perform the
activity and the resulting duration. Using multi-mode analysis, crashing
can be used to solve due dates or milestone time constraints. This is the
logic used by the Project Team Builder software.
In projects for which a major concern is uncertainty, as evidenced by
stochastic activity durations, program evaluation and review technique
(PERT) logic can be applied to analyze the effects of unanticipated
disruptions on overall project length. Most commercial software

packages do not handle stochastic activity durations. The few that do,
like the Project Team Builder software, usually rely on Monte Carlo
simulation, as explained in Chapter 9. Most commercial software
packages are limited to calculations of the total slack and free slack of
each activity, as depicted in Figure 14.5. As a rough estimate, it can be
assumed that the larger the slack of an activity, the smaller the risk that
it will become critical.
4. Hammocks and subnets. The scheduling process occurs at the activity
level, but it is desirable to be able to generate reports at any of the
various OBS or WBS levels. Many software packages that support the
OBS and WBS have this capability. In addition, many packages have
roll-up mechanisms, such as hammock activities and subnets. A
hammock activity replaces a group of activities. This type of
aggregation is suitable for high-level reports that do not require a single
activity level of detail. The subnet or sub-network facility is similar to
the hammock concept but represents activity groups by two or more
“aggregate” activities. Another possibility is to aggregate activities into
tasks and tasks into hammock tasks. Figure 14.6 summarizes the
schedule of the example project using both aggregated activities for
WBS elements and detailed project activities.
Charts that are designed for upper level management normally present
aggregated activities or tasks only; however, any mix of activities, tasks,
sub-networks, and hammocks is possible. Figure 14.7 summarizes the
schedule of the example project in a tabular format. The schedule reports
each activity’s duration, early start and finish, and late start and finish.

Figure 14.5
Slack report for example project.
Figure 14.6
Gantt chart with hammock activities for the example project.
Figure 14.6 Full Alternative Text
Figure 14.7
Schedule summary report for example project.

Figure 14.7 Full Alternative Text
5. Resource planning. In addition to time constraints, a project may be
circumscribed by the availability of resources. Thus, the next step in the
planning process is to add a resource dimension. The simplest approach
is to assign resources to each activity, assuming that the same resource
level is used throughout the activity’s duration. Based on the earlier
schedule, the required level for each resource type is calculated for each
period. This approach helps identify time periods when resource
requirements exceed resource availability. Project managers can
reschedule activities to avoid resource overload, or, if needed, they can
try to acquire more resources.
Some sophisticated software packages allow for the uneven distribution
of resource consumption over the activity’s duration. When using such a
package, a manager should specify each activity’s required resource
level during every period in which the activity is performed. Figure
14.8a gives the resource profile for the example project, accompanied by
a Gantt chart assuming an early-start schedule.

Figure 14.8a
Resource profile and Gantt chart for example project: early-
start schedule.
Figure 14.8b gives the resource profile and the Gantt chart for the late-
start schedule. As can be seen by examining the two figures, the
maximum of the resource profile is moved from the project’s early
phase in the early-start schedule toward the project’s middle phase in the
late-start schedule.
Figure 14.8b
Resource profile and Gantt chart for example project: late-start
schedule.
Software packages that support scheduling under resource availability
constraints offer a large variety of decision support applications. One

application is resource allocation, which specifies the availability level
of each resource type for each calendar period. If resource requirements
exceed resource availability for one or more resource types in a given
period, then the resource allocation procedure reschedules activities.
Rescheduling may be limited to each activity’s slack or be subject to a
constraint imposed by a given project termination date. Priorities may be
assigned to projects or to activities within projects so that high-priority
activities receive scarce resources first.
Some packages allow for several types of resource capacities, such as
overtime, second shifts, and subcontracting. The different types of
capacities eliminate infeasibilities while allowing for tighter control of
resource costs and usage. Moreover, some software packages offer the
option of activity preemption. If this option is available, then low-
priority activities that are already started may be stopped if high-priority
activities compete for the same resource. When one or more of the high-
priority activities terminate, the preempted activities are resumed. This
activity splitting option can add to the flexibility of planning and can be
used to solve problems during project execution.
Another application that is available for resource management is
resource leveling. Software packages with this option can reschedule
activities to achieve a relatively constant use of one or more resources.
As explained in Chapter 10, a leveled usage profile tends to decrease
resource costs and increase resource use.
Many resource allocation and resource leveling procedures are available.
The resource management module of each software package is based on
a specific algorithm. Because of the complexity of these scheduling
problems, most commercial packages apply a heuristic, an algorithmic
procedure that seeks a “good” feasible solution but does not guarantee
an optimum. As a result, the performance of the resource leveling or
resource allocation modules in different software packages will vary
with respect to computation times and quality of schedules produced.
6. Resource management. The resource management modules of
commercial software packages use a variety of approaches to model the
relationship between resource availability and a project’s schedule.

Some packages assume that all resources are renewable; that is, the same
resource capacity level is available during each period. This assumption
may be correct for a fixed workforce and equipment assigned full time
to the project, but not for subcontracting or materials, and typically not
in a matrix organization. Other packages assume that resources are
depleted with use, which is true for materials. Some packages assume
that an activity’s duration is a function of the resources available to
perform the activity (modes or the time-resource tradeoff). Other
packages assume that the duration of an activity is fixed. Consequently,
when selecting a software package for a specific project, a manager
should carefully examine the type of resources that the package can
handle as well as the quality of resource leveling and resource allocation
procedures.
The OBS-WBS matrix depicts the relationship between resources and
functional management. Each resource is assigned to an organizational
unit in the OBS and to activities related to WBS elements. This dual
resource link provides a trace ability during the project’s life cycle.
Software packages that include the OBS-WBS matrix may support
resource management by keeping track of the resources that each OBS
element uses to perform the activities on its assigned WBS components
or WPs. Resource use is calculated by recording the actual effort
associated with the activities performed by each resource in each period
and comparing this effort with the resource’s available capacity.
Functional management and the project manager commonly use this
calculation as a performance measure.
Software packages that support resource management are very helpful
during the planning phase where an important goal is to resolve
conflicts. These packages are also helpful in the implementation phase,
because uncertainty may cause changes in the original schedule, leading
to shifts in priorities. However, managing resources is important for
other reasons as well: budgeting and cost management.
The most sophisticated software packages of the ERP type can integrate
resource management of projects with the resource management of the
rest of the organization that deals with recurrent activities. The

advantage of this integrated approach is its early warning mechanism
that alerts management to overloaded resources that are needed
simultaneously both for projects and for recurrent activities.
7. Budget preparation. The WBS, OBS, resource allocation, and schedule
form the basis for project budgeting and cost estimation. Direct labor
and direct material costs are prepared at the activity level. Indirect costs
can be added at any OBS level. Each activity’s direct costs should
include its assigned resource costs. Therefore, managers should select a
software package that can correctly represent these various components.
Some resource costs are based on an hourly rate and calculated as the
rate times actual activity hours. Other resources, such as materials, have
a per-unit cost, for which the measurement unit might be a kilogram or a
cubic meter. Some resources may even have several rates; for example,
overtime labor costs might be different from labor costs on regular time
or a second shift. Overhead costs are charged against various baselines.
Level-of-effort costs, such as those for project management, accrue as a
passage of time, whereas apportioned-effort costs are based on a factor
of a discrete effort, such as inspection. The initial project budget should
specify other overhead costs, including facility operations and energy.
Therefore, a software package’s ability to communicate with the
databases that store information about rates and actual costs is an
important criterion for software selection. Figure 14.9 presents a cost-
schedule report for the example project, which includes each activity’s
total costs along with its scheduled start and finish times.

Figure 14.9
Cost-schedule report for example project.
Figure 14.9 Full Alternative Text
Contractors who respond to requests for proposals (RFPs), in an effort to
win contracts, must prepare cost estimates for each RFP. Thus, the
ability of a project management software package to support cost
estimation may be an important aspect for such contractors. Cost
estimates are based on a cost breakdown structure (CBS). (Refer to
Chapter 4 for a discussion of cost component classification.)
Information on the actual costs of previous projects stored in a user-
friendly database is very helpful in preparing cost estimates.
Some software packages support a CBS and life-cycle cost (LCC)
analyses. The need for these functions becomes obvious when a client
requires them in a contract or RFP. In some cases, the contractor may
want to use LCC analysis for his own benefit. By consistently updating
the CBS for each project, historical cost data are accumulated and bid
preparation for future projects is more accurate and less time consuming.
In addition to budgeting, managers may need to forecast and manage the
cash flow. Tying milestones and activities to cash flows makes it
possible to schedule a project to achieve a desired cash flow or to
schedule several projects simultaneously under a variety of cash flow
constraints. The schedule affects a project’s budget and in many projects
is subject to a host of monetary restrictions. The relationship between
costs and schedule can be analyzed by time-cost models, whereby each
activity’s duration is assumed to be a function of the activity’s costs or
the cost of the resources assigned to perform the activity. Recall that
crashing and modes are the terms used to describe time-cost tradeoffs.
Not all commercial packages support this function, though, so managers
with such needs should purchase a package that permits crashing or
accommodates user-written subroutines.

ERP-type systems support organizational cash flow management
combining the cash flow of projects with the cash flow of ongoing
activities. This allows the chief financial officer of the organization to
plan, monitor cash flows, and better control the cash position of the
entire organization.
8. Configuration management. In engineering projects, the technological
aspects should be coordinated with the project management support
system. The need to select a configuration baseline, to evaluate proposed
engineering changes and their performance, cost, and schedule impacts,
and to keep track of the current configuration translates into handling
and controlling large data sets and transactions. Although software
packages for configuration management are available on the market,
most do not support the other facets of project management. Thus, when
selecting a software package, a project manager should consider the
interaction between that package and the configuration management
system and define the required interfaces.
Some software packages contain product data management systems
(PDMSs). These systems combine configuration management with
workflow management and database management. The database is used
to keep records of parts and related files. The PDMS then facilitates the
design process providing the security, file storage, revision control,
classification, notification, and application integration.
9. Sensitivity analysis and project monitoring. Once a plan is established,
the project manager should examine its sensitivity to changing
conditions. Uncertainty plays a major role in project management.
Examples of sources of uncertainty are activity duration estimates,
resource availability, cost estimates, and lead time for material
deliveries. The basic project planning models do not consider these
aspects of uncertainty, so performing a sensitivity analysis in the form of
“what if” questions is recommended. This allows the team to study the
project’s plan under various conditions. The results may signal a need to
develop a risk management plan that includes mitigation and a
contingency plan, especially when a major failure is possible. A
software package’s ability to perform a “what if” analysis and to store

several plans for the same project is therefore essential when significant
levels of uncertainty are present.
An acceptable project plan (1) outlines how the schedule, costs, and
resource use fit within the imposed constraints and (2) allocates the
work in a feasible manner. The project is ready to begin when a feasible
plan, accepted by the project stakeholders, is constructed and approved.
During the execution phase, the process of issuing work orders and
managing resources can be automated with the help of proper hardware
and software. A schedule for each resource that includes all of its
assigned tasks and activities and their planned start and finish dates is a
valuable tool. Figure 14.10 presents a schedule for the example project.
To monitor progress, it is essential to keep track of all activity start and
end times. In some cases, estimates of the percentage of work completed
for ongoing activities are also provided to the software package.
Accompanying this information are accumulated data on resource use
and expenditures. The analysis can take many different forms. Progress
reports in the form of a Gantt chart, with completed activities marked,
are very popular. Also common are tabular reports that indicate, from
the project’s start, actual progress versus planned progress for each task
or activity during the current period or on a cumulative basis. Whereas
periodic reports are useful in exception identification, the cumulative
reports are important for trend analysis. Figure 14.11 depicts a progress
report based on a Gantt chart, giving each task’s status and original
schedule. Figure 14.12 displays a resource-oriented progress report. For
comparison, each task’s actual start, actual finish, status, and actual
hours are reported, along with scheduled finish times. This type of report
is helpful in monitoring resource use.
Information on actual resource use by various OBS elements can serve
as a basis for performance or management quality assessments, whereas
actual cost information is the basis for project cost performance
reevaluations. Although an ability to store

Figure 14.10
Detailed schedule for labor.
Figure 14.10 Full Alternative Text
Figure 14.11
Gantt chart-based progress report.
Figure 14.11 Full Alternative Text

Figure 14.12
Progress report for labor.
Figure 14.12 Full Alternative Text
information on actual resource use and project costs is important, not all
commercial packages can track both sets of data. Some packages can
track actual resource use and from these data, estimate actual
expenditures. Other packages can track actual costs but not actual
resource use. Therefore, selecting a software package for a specific
organization or project depends on the availability of other systems to
perform these functions.
10. Project control. Tracking progress, actual costs, and resource use forms
the basis of the project control system. Project control detects the
deviations between planned and actual performance and analyzes trends.
Deviations are examined to identify the source of a problem and to
forecast future trends. On the basis of the results, corrective measures
are implemented. The control system compares planned and actual
progress in several dimensions, including scope of work, schedule,
expenditures, resource use, and technological performance. When a
deviation is found in any of these dimensions, root cause analysis is

performed to determine the influence on elements of the OBS and WBS.
One major problem with a control system that is based on a simple
comparison between planned and actual values is the interaction
between different dimensions of a project. For example, under some
conditions, the interaction between costs and schedule makes it possible
to shorten an activity’s duration by changing the resources allocated to
perform it and increasing its direct costs. Similarly, technological
changes may affect a project’s costs, schedule, and resource
requirements which is based on the earned value (EV) concept,
discussed in Chapter 12. In the case of a DOD project, one major issue
when choosing a software package is compliance with the criteria. Even
if compliance is not required, the control system’s ability to detect
deviations, to trace their source(s), and to forecast future performance
from past accomplishments is an important consideration in selecting a
project management system. Figure 14.13 presents a report based on EV
logic. The budgeted cost of work performed (BCWP), actual cost of
work performed (ACWP), and budgeted cost of work scheduled
(BCWS) values are enumerated for each task.
Figure 14.13
Earned value-based progress report.
Figure 14.13 Full Alternative Text
Another important aspect of project control is technological change

control. This activity involves evaluating changes and deciding whether
to accept them. A software package that supports configuration
management activities is a useful tool in engineering project
management.
11. Software supporting multiple project management and portfolio
management. The allocation of resources among competing projects in
the same organization, the management of multiple project cash flows,
and the sharing of information and data among different projects
performed at different locations are very important for project-oriented
organizations. Special models support the management of several
projects, including decisions regarding project selection, prioritization,
and project termination.
12. Internet access and mobile applications. The widespread use of the
Internet and mobile devices makes it an attractive communication link
for project participants who have special information requirements. For
example, stakeholders who travel frequently and need access to the
current project data from different locations find Internet access and the
use of mobile devices invaluable. In a similar way, subcontractors and
suppliers in different cities or countries can share information and
communicate via the Web. As a result, some project management
software packages are available as a Software as a Service (SaaS) and
are web based. A possible arrangement is for the using organization to
purchase a license to use the software, which is installed on the vendor’s
server, rather than purchase it outright. The vendor allocates database
and model base resources to the users and provides them with all
information needed. This way, the users do not have to deal with
purchasing, installing, maintaining, and updating the software; they
simply enjoy the latest version. Multinational organizations find the
Internet extremely useful because it provides a common database for
projects that are performed all over the globe.
13. Software life-cycle support. A computer software package’s ability to
support decision making throughout a project’s life cycle is an important
factor in the software selection process. Analyzing a package’s decision-
making capabilities involves answering the following questions:

What does the package do?
How does the package do it?
What costs are involved in purchasing, using, maintaining, and
upgrading the package?
Related issues are the time and effort required to learn the software, the
human-machine interface, available logistics support for users, and
hardware requirements.
The human-machine interface is a crucial factor that affects
implementation costs and user acceptance. Team members who are
unfamiliar with a package will be more accepting if it can be learned
quickly. User friendliness and easy learning are achieved with
descriptive menus, on-line help screens, error-tolerant commands, and
windows with pointing devices. A package that can access data from
existing databases and can communicate with other management
information systems is easier to introduce because some of the input
formatting is already familiar.
14. Report generation. Report capabilities are another important aspect to
consider. Some packages contain only a standard set of tabular reports
and graphs that summarize the results of the CPM analysis and, if
applicable, resource allocation, resource leveling, and budgeting data.
The problem with standard reports is that they may not use the
organization’s terminology, and therefore, may not be able to provide a
specific answer to a specific question without additional work.
Furthermore, it may not even be possible to derive the answer by
scanning the output files.
Packages that are equipped with report generators are much more
flexible and can produce reports for a given activity, for a given WBS or
OBS element, and for a specific WBS or OBS level. Some generators
produce reports that integrate information from the project management
system with information from ERP systems, spreadsheets, and word
processors.

A user should also consider a package’s ability to design and produce
graphical reports. Various types of charts and diagrams can summarize
large amounts of data, including trends and correlations between
different aspects of the project, on a single graph. Some packages
contain a standard set of graphical reports, whereas more advanced
packages allow the user to produce them from any data set in the project
management system.
15. Vendor support. Apart from a software package’s functions, the
vendor’s logistic support is an important factor that affects the success
of the implementation effort. A software package requires logistic
support throughout its life cycle. In the early stages, importing data,
integrating with existing databases and software, and teaching the
package are crucial. Vendors can provide numerous training options,
including in-house training, training at the vendor’s facility, remote
learning through the Internet, tutorial programs, and manuals. A vendor
can offer assistance in the early implementation stages, such as help
with installation, data entry, and initial processing. Users may need
additional assistance during the operational phase, because they may
discover bugs or request tailoring for special needs. Finally, if the
software is to be integrated with existing or new information systems,
then the vendor’s services will be needed to establish interface protocols
and communication links.
16. Hardware requirements. Another issue to consider in the selection
process is hardware requirements. Hardware costs, especially for
personal computers, have decreased radically in recent years and mobile
devices are widely used. Software that can use larger amounts of
random access memory (RAM) will run more quickly and so might be
better than less-expensive packages that perform numerous disk access
operations to reduce RAM use. A software package’s ability to support a
variety of existing mobile devices is also important, as well as its ability
to work in a network.
Software that is available through the Web—installed on the vendor’s
server—offers a relatively inexpensive means of managing a project.
This type of application can use existing PCs and mobile devices, so

little if any new investment is needed.
Well designed and actively supported software packages can make
routine tasks, such as data collection, data processing, and data retrieval,
easier for a project manager. However, successful implementation
ultimately depends on how well the package fits the organization’s
needs. The following section lists helpful criteria for selecting the most
appropriate software package for a specific application.

14.3 Criteria for Software Selection
Some organizations purchase project management software as an addition to
existing information systems. When a new system is introduced, such as an
ERP system, it is important to make sure that it functions smoothly with the
project management software. Integration of the two allows the organization
to manage its resources simultaneously, assigning them to projects or to
ongoing operations with the same system.
An organization that purchases stand-alone project management software is
unlikely to find commercial packages that provide 100% of the support that it
needs to manage its projects. Even if such a package is found, its cost may be
prohibitive. Therefore, managers must systematically evaluate and select the
most appropriate package. In so doing, three sets of criteria should be
considered:
Operational criteria related to the software’s capabilities and
performance.
Information systems’ evaluation criteria applicable to any type of
software package, not just project management software. These criteria
are related to hardware requirements, software integrity, quality, and so
on.
LCC criteria.
The first set of criteria is based on the package’s intended use and includes
questions about the different functions, such as scheduling, budgeting, and
control. The second set is important in the selection of any management
information system and addresses questions related to the software’s ability
to function properly under different organizational and operational
conditions. The third set is concerned with the cost of purchasing, installing,
maintaining, and using a software package throughout its life cycle.
Although the specific criteria in each set depend on the package’s intended

applications and on the organization’s software needs, evaluators can develop
a “generic” list of criteria. Such lists frequently appear in articles that
evaluate and compare software packages, a sampling of which is included in
the reference section at the end of the chapter.
The level of sophistication of the various packages varies considerably. The
evaluation and selection criteria should reflect the level of support needed.
The set of functions that are available defines the scope of the package.
Unsophisticated users would be interested primarily in packages at the lowest
level, mainly supporting the planning phase, which includes scheduling and
budgeting. Most packages can also handle resources to some degree. In
addition, they often are capable of producing a prespecified set of reports.
Recall that a progress report is generated by re-planning the project on the
basis of updated data. A comparison between the updated and the original
plan forms the basis for project control.
Software packages at the next level support all of the functions performed by
low-level packages as well as resource leveling, resource allocation, and
project control. The corresponding modules identify cost and schedule
variances and predict the budget at completion. Flexible report generators are
also available at this level. These generators facilitate data presentation by
permitting the user to select the most appropriate output formats. Many of
these packages can integrate easily with popular tools such as Excel or word
processing tools to provide additional data processing and reporting
capabilities. Packages at this level support mobile applications on a variety of
mobile devices.
Packages at the high end support process management by workflow logic and
portfolio management in the form OBSs and WBSs for multiple projects.
They can handle several projects competing for the same resources and can
assign resources to projects that are based on predetermined priority rules. At
this level, software packages allow users to write their own applications using
a high-level programming language. Thus the packages can include
applications such as configuration management and inventory and material
management. Some of these packages have a graphical report generator and a
relational database so that they can retrieve any data set to which the software
has access and print it out using the report generator. This enables users to

construct special reports to specific needs.
The following criteria, suggested by articles, books, and the authors’
experience, have proved useful for selecting project management software
packages. An appropriate subset can be adopted for each situation.
Operational criteria
Multiproject management
Functionality for all phases of the project management process
Summarization capability for all projects in the organization
Strategic decision support
Process management logic
Process flowcharting
Ability to launch supporting software and reference material
Help customized to guide the user through the corporate
methodology
Scheduling activities
Number of activities per project
Number of projects that can be analyzed simultaneously
Types of precedence relations supported
Modeling of delays or lags within the precedence relations
Possible time units (hours, days, weeks)
Number of calendars that can be defined and saved

Critical path analysis
Computation of free and total slacks
External constraints on activity start and end dates
Support of milestones
External constraints on milestones
Support of hammock activities
Support of sub-networks
Network presentations as AOA
Network presentations as AON
Network drawings on screen, on a plotter, on a printer
Zooming capability on network drawings
Presentation of Gantt charts
Interactive editing of Gantt charts and of network drawings
Handling of stochastic activity duration: PERT or simulation
analysis
Activity duration presented as a function of resource
availability
Time-cost analysis
Automatic check of network logic for loops, disconnected
activities
“What if” analysis

Critical chain analysis
Budgeting, cost estimation, and cash flow
Support of several currencies
Handling of inflation rates
Connection between cost and activities, resources, milestones,
organizations, WBS elements
Communication with existing cost accumulation, cost control,
and cost estimation systems
Identification of direct versus indirect cost
Identification of cost categories, such as labor and material
Planning and budgeting the cost of materials and inventories
Support of CBS
Support of statistical analysis of cost estimating relationships
Development of budgets and cash flows for a given schedule
Scheduling subject to budget constraints
Scheduling to minimize direct and indirect costs (PERT/cost)
Support of LCC models and analysis
“What if” analysis
Resources
Number of different resources per activity
Number of different resources per project

Number of different resources for multiple projects
Handling of renewable resources (labor)
Handling of depleting resources (material)
Resource leveling
Resource allocation
Planning with alternative resources (e.g., subcontracting)
Preemption of activities
Definition of resource availability by dates, hours,
organization
Allocation of resources among competing projects
Variable rate of resources (e.g., regular time versus overtime)
Variable usage of resources during the execution of an activity
“What if” analysis
Project structure
Definition of OBS: number of levels
Logical checks on completeness of OBS
Definition of WBS: number of levels
Logical checks on completeness of WBS
Integration of the OBS and WBS to form WPs
Drawing of OBS and WBS on screen, plotter, printer
Definition of communication lines and work authorization

responsibility
Coding system for OBS-WBS matrix
Roll-up mechanism in OBS-WBS matrix for cost analysis
Limited access to data by passwords assigned to OBS units
Definition of a linear responsibility chart
Operation in a computer network
Configuration management
Definition of configuration items
Coding system for configuration items
Definition of baselines
Handling engineering change requests
Support of configuration identification
Support of configuration change control
Support of configuration status accounting
Support of configuration review and audits
Project control
Number of project baseline plans that can be handled and
stored
Ability to define cost accounts and WPs
Ability to construct the BCWS at all WBS and OBS levels
Ability to accumulate, store, and retrieve the BCWP (or EV)

at all WBS and OBS levels
Ability to accumulate, store, and retrieve the ACWP at all
WBS and OBS levels
Ability to calculate cost and schedule variances and indices at
all WBS and OBS levels for each period and on a cumulative
basis
Ability to forecast the estimated budget at completion based
on actual progress (estimated by the EV) and actual cost
Ability to compare actual progress with different baselines
Ability to signal cost and schedule deviations larger than
predetermined thresholds
Ability to analyze trends in cost and schedule performances
Compliance with C/CSCS
Ability to control use of material and actual cost of material
used
Ability to control use of resources and actual cost of these
resources
Buffer management integrated with critical chain
Reporting
Standard reports available
Report generator
Graphical reports
Integration with word processor

Output to plotters
General system characteristics criteria
Friendliness: time to learn, help facilities, use of a menu,
windows
Documentation: operations, maintenance, installation
Security: data input, output, editing
Integrity of database
Communication with other information systems
Applications for mobile devices
Hardware requirements
Support available from vendor
User base: recommendations of current users
LCC related criteria
Purchase cost (per unit, quantity discounts)
Cost of hardware, facilities, and so on
Estimated cost of operation and maintenance
Expected service life
Cost of updating and new versions
Estimated value at phaseout time

The list above is generic and should be modified according to the specific
needs of the project or organization. Appendix 14A presents an example of a
criteria set developed by the Project Management Institute (PMI). In the next
section, we demonstrate how a comprehensive list of criteria can be used to
guide the software selection process.

14.4 Software Selection Process
Effective project management is a direct function of the tools that are
available to support decision making at all levels of detail. An adequate
software package facilitates the project manager’s job by integrating different
aspects of the project and simplifying routine data processing tasks. A
software package that does not serve the project team’s needs is of little value
and may even prove to be a burden. Therefore, those who are responsible for
choosing the software should approach their task advisedly.
The selection process begins by identifying data processing needs. This
involves addressing the following questions:
How many projects will be managed in parallel?
What is the expected size of each project?
How many different resources are needed?
How many organizations will participate in each project?
The second step is to analyze the type of management decisions that the
software package will support. Should the package support integrated
resource and cash management across many projects or the whole
organization? Should the package support configuration management?
Should it support budgeting and cost estimates? Do existing systems already
perform these functions satisfactorily?
Third, a criteria list should be constructed. This list is used, along with one of
the selection/evaluation methodologies described in Chapters 5 and 6, to
select and identify the most appropriate package. Since evaluation techniques
are subjective (relative importance or scores are subjectively assigned to each
criterion), the selection decision should not be based on this analysis alone,
nor should a final decision be made at this point in the process.
In fact, now is the time to analyze data from past projects and perhaps to

construct one or more test projects with attributes that reflect the current
environment. The test projects can be simulated by “planning” them with the
help of the software. Information on “actual” performance is added, and
reports are generated to support project “control.” Because the simulation can
be performed quickly, a 10-year project, for example, can be studied in 1 to 2
days and the results analyzed immediately. Allowing future system users to
participate in the simulation helps them to better understand the software
package and to identify and adjust for various package weaknesses. In any
case, the package should be approved only for a trial period and only after all
intended users are satisfied with the simulated results.
We recommend trying out the package for a short period to test its suitability
for the organization and upcoming projects. The selection team can then
decide whether to adopt the package or to investigate another based on the
experience over the trial period.
To demonstrate the software package selection process using a scoring
model, consider an organization that wishes to compare two project
management software packages, A and B, using the generic criteria list
presented above. Table 14.1 contains the relative weights assigned to each
criteria type (set). Criteria related to software costs are not included, because
a cost-effectiveness measure will be used as part of the selection process.
TABLE 14.1 Relative
Weights Used in the Scoring
Model
Criteria set Weight
Activities and scheduling 20
Budgeting, cost estimation, and cash flow 15
Resources 15
Project structure 0
Configuration management 10

Project control 10
Reporting 10
General system characteristics 10
100
Next, a scale is developed for each criteria set that assigns a weight to each
member of the set. The sum of the weights associated with each criterion in a
set is 100. In the evaluation, each criterion is given a score between 0 and 10.
This number is translated into points by multiplying it by the corresponding
weight. Once the points are tallied for each criteria set and normalized by
dividing them by 100, the total score for the package is calculated. This is
done by forming the weighted sum using the weights in Table 14.1; the
maximum package score is 100.
The input data and the calculations for each package are summarized in Table
14.2. The cost data for the two options are contained in Table 14.3 and the
results of the analysis are presented in Table 14.4. The latter shows that
package B is better than package A in the total score but ranks lower in
scheduling, budgeting, and project structure. Package B’s purchase cost is
$6,000 higher than package A’s and requires $300 more per year to update.
Based on these results, management must now weigh B’s higher cost against
its superior performance and select the package that is more appropriate for
the organization. Computing an “effectiveness/cost” ratio indicates that
package A, with a score of 1.34 points per $1,000, is somewhat better than B,
whose score is 1.29.

14.5 Software Implementation
The successful implementation of project management software depends
largely on its ability to support the project team’s activities. If, for example,
the software can produce reports that were prepared manually in the past,
then the project team benefits from using the package. However, if top
management requests new reports that are based on increased expectations of
the software’s capabilities, then the extra work required to generate these
reports may produce unanticipated slippage in the schedule.
The software evaluation team should include all end users who are expected
to be involved in selection and implementation. Clearly, the end users know
which functions need support and which priorities should be assigned to
each. Incorporating the end users into the decision-making process (criteria
list development, assessment of each criterion’s weight, and evaluation scale
development) will go a long way toward smoothing acceptance of the
selected package. In addition, including future users in package testing and
project simulation allows them to contribute their insights and experiences to
the selection process and, later, during implementation. Thus, choosing the
software should be a joint effort among information systems experts, analytic
support personnel, and all potential system users.
Once the team selects a system, implementation starts with a training
program in which future system users learn operational procedures and gain
an understanding
TABLE 14.2 Calculations for
the Operational Criteria
Operational criteria Weight Package Ascore/points
Package B
score/points
Activities and Scheduling

Total weight: 20
Number of activities per project 5 8/40 6/30
Number of projects that can be
analyzed simultaneously 5 7/35 7/35
Types of precedence relations
supported 5 8/40 8/40
Modeling of delays or lags
within the precedence relations 3 9/27 6/18
Possible time units (hours,
days, weeks) 4 4/16 9/36
Number of calendars that can
be defined and saved 5 5/25 4/20
Critical path analysis 5 8/40 5/25
Computation of free and total
slacks 5 6/30 6/30
External constraints on activity
start or end dates 5 2/10 6/30
Support of milestones 3 0/0 5/15
External constraints on
milestones 2 0/0 3/6
Support of hammock activities 5 5/25 2/10
Support of subnetworks 1 3/3 5/5
Network presentation as AOA 5 6/30 0/0
Network presentation as AON 5 0/0 3/15
Network drawings on screen,
on a plotter, on a printer 5 7/35 3/15
Zooming capability on network
drawings 2 6/12 4/8
Presentation of Gantt charts 5 8/40 5/25
Interactive editing of Gantt
charts, network drawings 2 6/12 4/8
Handling of stochastic activity
duration PERT or simulation
analysis
3 0/0 0/0

Activity duration presented as a
function of resource availability 5 5/25 3/15
Time-cost analysis 5 7/35 6/30
Automatic check of network
logic for loops, disconnected
activities
5 10/50 5/25
“What if” analysis 5 7/35 5/25
Total 100 565 100=5.65
466 100
=4.66
Budgeting, Cost Estimation, and Cash Flow
Total weight: 15
Support of several currencies 7 3/21 5/35
Handling of inflation rates 5 5/25 0/0
Connection between cost and
activities, resources,
milestones, organizations, WBS
elements
10 7/70 5/50
Communication with the
current budgeting and cost
control systems
10 8/80 7/70
Identification of direct versus
indirect cost 7 6/42 8/56
Identification of cost categories
such as labor and material 5 7/35 5/25
Management of the cost of
materials and inventories 5 5/25 6/30
Support of CBS 10 8/80 7/70
Support of statistical analysis of
cost estimating relationships 5 5/25 0/0
Development of budgets and
cash flows for a given schedule 10 8/80 6/60
Scheduling subject to budget
constraints 8 6/48 6/48

Scheduling to minimize direct
and indirect costs (PERT/cost)
5 5/25 6/30
Support of LCC models and
analysis 8 5/40 6/48
“What if” analysis 5 6/30 7/35
Total 100 626 100=6.26
522 100
=5.22
Resources
Total weight: 15
Number of different resources
per activity 10 8/80 9/90
Number of different resources
per project 10 6/60 8/80
Number of different resources
for multiple projects 10 6/60 9/90
Handling of renewable
resources (labor) 5 5/25 6/30
Handling of depleting resources
(material) 8 6/48 6/48
Resource leveling 10 7/70 8/80
Resource allocation 10 6/60 6/60
Planning with alternative
resources (e.g., subcontracting) 5 3/15 6/30
Preemption of activities 3 0/0 2/6
Definition of resource
availability by dates, hours 8 5/40 4/32
Allocation of resources among
competing projects
8 6/48 5/40
Variable rate of resources (e.g.,
regular time versus overtime) 5 6/30 8/40
“What if” analysis 8 5/40 9/72
Total 100 576 100=5.76
698 100
=6.98

Project Structure
Total weight: 10
Definition of organizational
structures: number of levels 20 8/160 7/140
Definition of WBS: number of
levels 20 8/160 6/120
Integration of the OBS and
WBS to form WPs 20 9/180 7/140
Definition of communication
lines and work authorization
responsibility
15 6/90 5/75
Roll-up mechanism in OBS-
WBS matrix for cost analysis 15 8/120 6/90
Limited access to data by
passwords assigned to OBS
units
10 10/100 8/80
Total 100 810 100=8.10
645 100
=6.45
Configuration Management
Total weight: 10
Definition of configuration
items 10 10/100 10/100
Definition of baselines 10 8/80 10/100
Handling engineering change
requests 10 8/80 10/100
Support of configuration
identification 15 10/150 10/150
Support of configuration
change control 15 6/90 10/150
Support of configuration status
accounting 20 7/140 10/200
Support of configuration
review and audits 20 7/140 10/200

Total 100 780 100
=7.8
1000 100
=10.0
Project Control
Total weight: 10
Number of project baseline
plans that can be handled and
stored
10 5/50 6/60
Ability to define cost accounts
and WPs 10 4/40 10/100
Ability to construct the BCWS
at all WBS and OBS levels 10 5/50 7/70
Ability to accumulate, store,
and retrieve the BCWP (EV) at
all WBS and OBS levels
10 6/60 8/80
Ability to accumulate, store,
and retrieve the ACWP at all
WBS and OBS levels
10 5/50 9/90
Ability to calculate cost and
schedule variances and indices
at all WBS and OBS levels for
each period and on a
cumulative basis
5 6/30 8/40
Ability to forecast the estimated
budget to completion on the
basis of actual progress: EV
and actual cost
7 5/35 10/70
Ability to compare actual
progress with different
baselines
7 4/28 8/56
Ability to signal cost and
schedule deviations larger than
predetermined thresholds
7 5/35 6/42
Compliance with C/SCSC 8 6/48 8/64
Ability to control use of
material and actual cost of 8 9/72 8/64

material used
Ability to control use of
resources and actual cost of
these resources
8 6/48 8/64
Total 100 546 100=5.46
800 100
=8.0
Reporting
Total weight: 10
Standard reports available 20 8/160 9/180
Report generator 20 5/100 8/160
Graphical reports 20 3/60 8/160
Integration with word processor 20 5/100 5/100
Output to plotters 20 3/60 8/160
Total 100 480 100=4.8
760 100
=7.6
General System Characteristics
Total weight: 10
Friendliness: time to learn, help
facilities, menus, windows, etc. 20 5/100 7/140
Documentation for operation,
maintenance, and installation 5 8/120 6/90
Security, data input, output,
editing 15 3/45 8/120
Integrity of database 20 5/100 7/140
Communication with other
information systems
10 6/60 6/60
Hardware requirements 10 8/80 8/80
Support available from vendor 5 6/30 9/45
User base: recommendations of
current users 5 5/25 9/45
Total 100 560 100=5.6
720 100
=7.2

TABLE 14.3 Cost Data for
Selection Problem
LCC related criteria PackageA
Package
B
Purchase cost (per unit, quantity discounts) $6,000 $12,000
Cost of hardware, facilities, etc. $15,000 $15,000
Estimated cost of operation and
maintenance $2,500/yr $2,500/yr
Expected service life 5 yr 5 yr
Cost of updating and new versions $500/yr $800/yr
Estimated value at phaseout time 0 0
of each module’s basic logic. This training program should precede actual
system use to eliminate learning difficulties and unnecessary frustrations. We
all have a limited tolerance for failure.
Only trained personnel should use the system, and the vendor or internal
system experts should support initial applications because they can solve
startup problems quickly. Management should begin implementation by
focusing on functions that the users are currently performing and then expand
the reach until all desired functions are included. When introducing the
system, a manager should avoid assigning additional tasks to the project
team. During the initial stages, the system should help users perform routine
tasks efficiently. By performing routine tasks and alerting users early to
potential problems, the system can free users’ time to deal with exceptions
and uncertainty. This will increase the chances for acceptance.
TABLE 14.4 Weighted Scores
for Criteria Sets and Results

Criteria Weight Package A score/points Package B score/points
Activities and
scheduling 20 5.56             4.66            
Budgeting,
cost
estimation, and
cash flows
15 6.26             5.22            
Resources 15 5.76             6.98            
Project
structure 10 8.10             6.45            
Configuration
management 10 7.80             10.00            
Project control 10 5.46             8.00            
Reporting 10 4.80             7.60            
General
systems
characteristics
10 5.60             7.20            
Total points 100 48.40             56.10            
Total cost $36,000             $43,500            
Relative cost
(with respect
to the lowest-
cost package)
100%             121%            
Effectiveness
ratio
(points/$1,000)
1.34             1.29            
After a predetermined time, management should conduct a survey of users’
opinions regarding the performance of the software. At this point, final
procedures for system assessment, data updating, and data processing should
be established. These procedures should support management’s need for
information while simplifying routine data-handling tasks.
Once again, we recommend a deliberate process that involves potential users
in package selection, training, and phased implementation. Management

should implement the software in stages to avoid overwhelming users with
new and unfamiliar applications. Project management is greatly simplified
when appropriate software tools are selected intelligently and introduced into
the organization. Nevertheless, the best tools are useless if the project team
does not accept them.

14.6 Project Management Software
Vendors
The PMI in its publication “Project Management Software Survey” listed
some 80 vendors of project management software packages, and many of
these vendors have more than one product (PMI 1999). Selection of the right
software package for a specific organization is not a trivial or an inexpensive
task and is itself a project.
Appendix 14A provides a list of selection criteria developed by the PMI. The
weight for each criterion and the score of each software package with respect
to each criterion are application specific, however, and should be determined
by the team responsible for the selection process. For those who desire other
sources of information, we note that magazines such as OR/MS Today
published by the Institute for Operations Research and the Management
Sciences (INFORMS) and The Industrial Engineer published by the Institute
of Industrial Engineering (IIE), periodically review software packages and
survey vendors.
TEAM PROJECT Thermal
Transfer Plant
The research and development project that you have proposed in Chapter 13,
coupled with your experience with the prototype rotary combustor, has made
your team the project management experts at TMS. Your success has
motivated top management to introduce project management techniques
throughout the organization. The information technology (IT) department has
made a proposal to develop a project management application on a
spreadsheet to support Total Manufacturing Solutions (TMS’s) needs in this
area. The department chief argues that because most TMS engineers and
managers are familiar with Excel, it would be much easier for them to learn

and use an application that is based on this product. In addition, he foresees
that full integration with existing databases and software will be achieved
more quickly.
Because of your experience and expertise, TMS management has asked you
to compare Microsoft Project, the software used by your team, with the
proposed Excel application. In so doing, explain which aspects of project
management can be supported by a spreadsheet. Discuss the advantages and
disadvantages of the IT proposal.
Develop a software selection plan, including appropriate models that can be
used to help in the selection process. Analyze Microsoft Project’s ability to
satisfy the requirements and compare it with a tailor-made spreadsheet
program. Write a report and prepare a presentation that will summarize your
analysis.

Discussion Questions
1. For which aspects of project management are computers most useful?
Explain.
2. Are there aspects of project management for which computer support
cannot be used?
3. Develop a list of criteria for a project management software package to
be used in a project management course.
4. Which aspects of project management can be supported by a simple
spreadsheet application?
5. Write a description of one application from the list you developed in
your answer to Question 4.
6. Which aspects of project management can be supported by a database
system?
7. Write a description of one application from the list you developed in
your answer to Question 6.
8. You are in charge of the selection and implementation of a new project
management software package in your organization. Develop a project
plan and explain the details.
9. What risks are associated with the project discussed in Question 8?
10. Prepare a risk management plan for the project discussed in Question 8.
11. What improvements or changes in Microsoft Project would you
recommend?
12. To simplify Microsoft Project, which features would you eliminate?

Exercises
1. 14.1 Write a report on the software package Microsoft Project or another
one with which you are familiar. Identify the advantages and
disadvantages of the package as a tool for supporting the study of
project management.
2. 14.2 Develop a software selection methodology for the project “Design
of a New Space Laboratory for Crystal Manufacture.” Use the
methodology to assess Microsoft Project’s ability to support the
management of this project.
3. 14.3 Try to solve several of the exercises in Chapters 9 , 10, 11, and 12
using Microsoft Project or some other package. Rewrite your answer to
Exercise 14.1 based on your experience with the software.
4. 14.4 Obtain a project management software package with which you are
not familiar. For the example project in the book, determine how much
time is required to learn its basic functions, including data entry, critical
path analysis, and report generation.
5. 14.5 Develop a spreadsheet application for project scheduling that
calculates the early start, early finish, late start, late finish, total slack,
and free slack for each activity.
6. 14.6 Develop a spreadsheet application for resource management within
an OBS-WBS framework. The application should present planned use,
cost of resources, actual use and cost, and the deviations for each
organizational unit.
7. 14.7 Develop a PERT program on a spreadsheet that is based on the
three time estimates for each activity. Your program should be able to
calculate the probability of completing the project by a given date.
8. 14.8 You have been asked to choose the project management software to
be used as a teaching aid for a project management course.

1. Write the software specifications for such a package.
2. What might be the main differences in the software specifications
associated with the following needs?
1. A software package to be used as a teaching aid in a course
2. A software package to be used for planning and controlling
projects managed by your school
9. 14.9 Develop a benchmark project to be used to evaluate the suitability
of software packages as a teaching aid in project management studies.
10. 14.10 Discuss the following comment and develop a numerical example
to validate your remarks: “The purchasing price of a project
management software package is a negligible issue as long as the price
is no more than a few thousand dollars.”
11. 14.11 As part of your company’s effort to select a project management
software package, you have been asked to approach several other
companies that presently use such packages.
1. Develop a questionnaire to help collect the relevant information.
2. Fill out two questionnaires, each representing a different software
package.
3. Compare the responses of the companies and select the best
software of the two.
12. 14.12 Read a recent article evaluating project management software
packages. Such articles frequently appear in technical journals and
magazines such as Industrial Engineer, OR/MS Today, and PC World.
Discuss the following issues, based on the article:
1. Which features do most of the packages have in common?
2. What new features are starting to emerge?

3. Which seem to be the leading packages, and what are the major
reasons for this?
4. Specify some of the more important criteria used in evaluating the
packages.
5. Suggest criteria for software evaluation other than those used in the
article.

Bibliography
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Appendix 14A PMI Software
Evaluation Checklist
The following checklist was developed by the PMI (1999). Its purpose is to
guide the potential user of project management software through the time-
consuming process of evaluating the multitude of products on the market.
The weights to be assigned to the various criteria are applications dependent.
The criteria are listed according to the software category—there are seven
categories. Most software packages on the market fit into more than one
category.
14A.1 Category 1: Suites
Software packages that are designed to bring together all information
required to manage the project and to provide features such as:
Functionality for all phases of the project management process
Summarization capability for all projects in the enterprise
Strategic decision support
Executive information system type interface
14A.2 Category 2: Process
management
Software packages that are designed to make the corporate
methodologies and supporting processes available electronically and to

provide features such as:
Process flowcharting
Ability to launch supporting software and reference material
Help customized to guide the user through the corporate
methodology
Interfaces to project management software
14A.3 Category 3: Schedule
management
Software packages that are designed to support project or program
planning and control and to provide features such as:
Define the sequence of activities
Critical path calculation
Time analysis
Resource leveling
Schedule status
Reports
14A.4 Category 4: Cost
management
Software packages that provide features such as:

Proposal pricing
Budget management
Forecasting including rate escalation
Performance measurement
Variance analysis
14A.5 Category 5: Resource
management
Software packages that are designed to bring together all information
required to manage the project and to provide features such as:
Identifying the resource pool
Organizing resources by skill, department, or other meaningful
codes
Requesting resources from functional or departmental managers
Demand management based on current projects, future projects,
strategic initiatives, and growth
Summary views and reports across multiple projects
14A.6 Category 6: Communications
management
Software packages that are designed to provide features such as:

Electronic to-do lists for resources assigned to the project
Audit trail for changes to time sheets
Interfaces with popular project management software packages to
automate updates to the project schedule
Support for billable and nonbillable projects
Interface with financial systems
Customizable views for preparers and approvers
14A.7 Category 7: Risk
management
Software packages that are designed to provide features such as:
Documentation of project risk
Mathematical schedule simulation
Risk mitigation planning
14A.8 General (common) criteria
Document management
Version control
Document collaboration
Reporting
Report writer

Report wizard
Publishes as HTML
Number of user-defined fields
Drill-down/roll-up
Import/export
Automatic E-mail notification
Macro recorder/batch capable
Can “canned” reports be modified?
Sort, filter
Architecture
Databases supported
Supports distributed databases
Three-tier client/server
Client operating systems
Server operating systems
Network operating systems
Minimum client configuration
Minimum server configuration
Client runs under Web browser
Open architecture

Supports OLE
Documented object model
Documented application programming interface
Simultaneous edit of data file
Does product have a programming language?
Are years stored as four-digit numbers?
Online help
Right mouse click
Hover buttons
Interactive help
Help search feature
Web access to product knowledge base
Vendor information
Training
Computer-based training
Training materials available
Customized training materials
Online tutorial
Consulting available from vendor
Site license discounts

Enhancement requests
Modify source code, support through upgrades
Global presence
Global offices
Multilingual technical support
Language versions (list)
Audit software quality assurance process?
Security
Configuration access privileges
Passwords expire (forced update)
Electronic approvals
Password protect files
Category-Specific Criteria 14A.9
Category 1: Suites
Integrated components
Timesheets
Methodology/process
Cost
Estimating

Repository
Reporting module
Configuration management
Requirements management
Risk management
Issues management
Action items
Communications management
Document management
Additional components (list them)
Repository (enterprise database)
Multiproject Gantt charts
Multiproject resource utilization
Multiproject resource work graphs
Time period analysis
Trending analysis
Variance analysis
EV reporting
Ad-hoc query for reporting
New project estimating

New project definition
Number of projects
Resource management
Capacity analysis
Demand analysis
Unused availability analysis
Maps employees to resource type
Skills and proficiency levels
Standard role definitions as supported by the organization’s
methodology
Methodology integration
Product comes with methodologies (list)
Can input corporate methodologies
Suggests routes through methodologies
Captures and re-uses best practices (re-use successful project plans
as new models)
Attach guidelines
Attach reference documents, templates
Customized, context-sensitive guidelines (help for corporate
methodologies)
14A.10 Category 2: Process

Management
Estimating
Top-down
Bottom-up
Generated WBS for use in scheduling tool
List scheduling tools
Role/resource assignment
Work effort estimates by resource
Methodologies
Product comes with methodologies (list)
Can input corporate methodologies
Suggests routes through methodologies
Reference
Attach guidelines
Attach reference documents, templates
Customized, context sensitive guidelines (help)
Tasks in schedule linked to methodology guidelines
Miscellaneous
Navigate methods via hyperlinks

Complexity factor adjustments
What-if analysis
Cost-benefit analysis
Issues management
Action items
Change management
Requirements management
Risk management
14A.11 Category 3: Schedule
Management
Time analysis
Full critical path
Relationship types
SS, FS, SF, FF
Allow SS and FF on a set of tasks
Lags on relationships
Calendars on relationships
Mixed durations (minutes, hours, days, weeks, and months)
Time-limited schedule calculation

Resource-limited schedule calculation
Query over-allocations by
Skill
Resource type
Department
Other (user defined)
Resource calendars
Individual resource calendars
Variable availability
Scheduling/leveling features
Resource leveling
Resource smoothing
Leveling by date range
“Do not level” flag (bypass project during leveling)
User-defined resource profiles (spread curves)
Team/crew scheduling
Skill scheduling
Number of skills per resource
Alternate resource scheduling
Rolling wave scheduling

Activity splitting
Perishable resources
Consumable resources
Assign role (skill), software replaces with name at specific point in
process
Heterogeneous resources
Homogeneous resources
Hierarchical resources
Share resource pool across multiple projects
Number of resources to be included in resource scheduling
Resource costs
Rate escalation
Overtime
Top-down budgeting
Performance analysis/cost reporting
Calculates BCWS
Calculates BCWP
Calculates ACWP
Physical % complete (in addition to schedule % complete)
Reports
30-60-90 day and user-defined report windows

Predecessor/successor report
Updates out-of-sequence report
To-do list (turnaround report)
Number of structures per project
WBS, OBS, CBS
User defined
Maximum number of structures supported
Features
Outline view
Number of tasks
Number of resources
Multiproject
Number of projects scheduled simultaneously
Prioritize projects for scheduling
Dependency trace view
Charting
Early-start verses resource leveled-start Gantt chart
Highlight critical path in charts
Variable timescale Gantt charts
Variable timescale network logic diagrams (time-phased network
logic)

Zoned network diagrams
Structure drawings
PERT chart
Maximums
Number of tasks per project
Number of projects per multiproject
Number of layers of projects in program
Number of resources per task
Number of defined resources
Number of calendars per project
14A.12 Category 4: Cost
Management
Performance measurement calculation methods
Weighted milestones
Apportioned
50-50
Level of effort
Percentage complete
Units complete

50-50, 0-100, 100-0
User defined
EV calculations
BCWP
BCWS
ACWP
Proposal pricing
Top-down budgeting
Forecasting
Forecasting (what if budget increases by 10%; statistical methods)
Saves simultaneous forecasts
Budget management
Rate build-up
Customize budget elements
Number of WPs in a cost account
Direct costs
Indirect costs
Burden templates for indirect costs
Custom calculations
Multiple estimate-to-complete (ETC) calculations

Foreign currencies supported (list)
Reporting
Aggregate costs over multiple projects
Cumulative reporting
Fiscal calendars
Irregular reporting calendars
Cash flow
Periodic cost profile (cost during a time period)
By resource (summarize costs incurred by use of a resource)
Cash flow reports
Report writer
Report wizard
Publishes as HTML
Number of user-defined fields
Drill-down/roll-up
Import/export
Automatic E-mail notification
Macro recorder/batch capable A67
Can “canned” reports be modified?
Sort, filter

14A.13 Category 5: Resource
Management
Scheduling/leveling features
Resource leveling
Resource smoothing
Leveling by date range
“Do not level” flag (bypass project during leveling)
User-defined resource profiles (spread curves)
Team/crew scheduling
Skill scheduling
Number of skills per resource
Alternate resource scheduling
Rolling wave scheduling
Activity splitting
Perishable resources
Consumable resources
Assign role (skill), software assigns role later
Hierarchical resources
Heterogeneous resources

Homogeneous resources
Query over allocations by
Skill
Resource type
Department
User defined
Resource costs
Rate escalation
Overtime
Top-down budgeting
Bottom-up cost summarization
Calculate unit cost
Skills database
Resumes
Search by skill
Portfolio resource analysis
What-if scenarios
Project templates
Miscellaneous
Individual resource calendars

Share resource pool across multiple projects
Electronic resource requestor (send message to functional manager
asking for his or her people)
14A.14 Category 6:
Communications Management
Communications features
Team “push” communication channels
Threaded discussion
Bulletin board
Newsgroups
Team management
Creates and delivers action items
Creates and delivers task lists
Delegates work requests to team
Electronic resource requestor (send message to functional manager
asking for his or her people)
Document management
Version control
Document collaboration
Online project management methodology

Online deliverables templates
Features
Action items
Risk documentation
Issues management
Meeting minutes
Agendas
Project templates
Integrates with scheduling tools
Project templates
Task status updates
E-mail enabled
Workflow management
Graphics add-ons
Custom graphics
Gantt charts
Network logic diagrams
WBS
Other structure drawings
Gantt charts

Text wrapping
Multiple rows of text per activity
Zones (horizontal bands labeled based on a field value)
Multiple milestones
Highlight critical path in charts
Variable timescale Gantt charts
Variable timescale (time-phased network logic)
Network logic drawings
Zones (horizontal bands labeled based on a field value)
User-defined node positioning
Multiple milestones
Variable timescale
Breakdown structures
User-defined box styles
User-defined positioning
Mixes connecting line styles (dotted, solid, etc.)
Collapse/expand to any level
Number of levels supported
Management graphics
Pie charts

Trend charts
Bar charts
Scatter diagrams
Histograms
Horizontal bars
Vertical bars
3D effects
Mountain charts
Supported data sources (list)
Timesheet features
Support for project and nonproject time
Timesheets generated from scheduling software
Users can add tasks not on schedule
Supports rate escalation
Status reporting by task
Customizable user interface (view/suppress fields)
Number of user-defined fields
Incorporates business rules and data validation criteria
Can user retrieve approved timesheet for adjustments?
Can retrieve feature be turned off?

Timecard adjustments recorded in audit trail
ETC in effort
ETC in duration
Remaining duration
Reports
Creates a report identifying changes made to the schedule
Exception reports
Summary reports
Web enablement
Can timesheet be updated through a Web browser?
Which browsers/versions are supported?
Security
Approver security
Alternate approvers
Field level security: lock specific fields
Management validation: approve/reject electronically
Miscellaneous
Runs served (doesn’t have to be installed on each client)
Drill-down/roll-up

14A.15 Category 7: Risk
Management
Simulations
Monte Carlo simulation?
Custom sample size?
Performs schedule simulation
Performs cost simulation
Performs resource simulation
Analysis
Analyzes schedule risk
Standard deviation & variance
Other statistical coefficients (e.g., mean to complete, confidence
interval, median, mode, mean)
Based on project data (e.g., determine overloaded resources,
dependencies at risk)
By experiment, comparing runs
Analyze cost risk
Standard deviation & variance
Other statistical coefficients (e.g., mean to complete, % confidence
level, median, mode, mean)
Graphical representations

Histograms
Gantt chart
Comprehensive reports (i.e., tabular)
Features
Calculation of expected monetary value (risk event
probability×risk event value )
Track criticality index
Suggest and document mitigation strategies based on knowledge
database
Ability to enter assumptions and analysis defaults (e.g., time or
resource constraints)
Capability to import and export from/to other standard office
automation tools
Risk identification (e.g., checklist)
Identification of “hangers,” sources of risk
Tracks historic risk data (to be used as a baseline) to enable
comparisons with ongoing changes
Support of probability distribution curves
Uniform, triangular, normal, beta
Maximum and minimum duration
PERT
Input of low, most likely, and high duration

Chapter 15 Project Termination
15.1 Introduction
Project termination is an important, yet often mismanaged, phase in a
project’s life cycle. At some point, management must decide to terminate a
project. However, this can be a difficult and agonizing activity, because
projects tend to develop a life and constituency of their own. Team members,
subcontractors, and other support personnel often become effective advocates
for continuing a project long after its useful life has expired. Nevertheless, all
projects must end, and it is up to management to ensure that their concluding
phase is smooth, timely, and as painless as possible.
The reality is that team members frequently overlook or try to delay
termination to the last possible moment. Such delays can have serious
consequences because they create unnecessary stress and are costly for both
the organization and the project personnel. Therefore, a successful project
must include a well-planned and executed termination phase that saves time
and money and avoids unnecessary conflict.
Managing project termination revolves around two central questions
concerned with “when” and “how” to close down a project. The answer to the
first question seems obvious: Terminate the project when its goals are met.
Some projects, though, are canceled before this point is reached because of
changing market conditions, organizational shakeups, cost overruns, or
technical difficulties. However, if a manager is convinced that a project will
produce results, then he or she may be predisposed to slant cost and
performance data in the most favorable direction. Sometimes when managers
realize that a project is in real trouble, rather than accept failure, they may
choose to invest more resources. As a general rule, though, premature
termination should be considered only when the probability of success is
clearly too low to justify further investment in the project.
The PMBOK lists eight major activities that should be performed during

project termination (these activities may be performed when a phase of the
project is terminated as well):
1. Obtain acceptance by the customer or sponsor to formally close the
project.
2. Conduct post-project review.
3. Record impacts of tailoring to any process.
4. Document lessons learned.
5. Apply appropriate updates to organizational project assets.
6. Archive all relevant project documents in the project management
information system (PMIS) to be used as historical data.
7. Close out all procurement activities ensuring termination of all relevant
agreements.
8. Perform team members’ assessments and release project resources.
These activities accomplish the following objectives:
1. Project closure provides assurance that the project has met all customer
and other stakeholder requirements. The customer must formally accept
the project results and deliverables and confirm that the project has been
terminated to his or her satisfaction. The project closure documents may
include approval of regulations, approval of standards, internal and
external test results, and integration and final acceptance test results.
2. Lessons learned includes documents that analyze the causes of
variances, the reasoning behind corrective action taken, and other
inferences and conclusions regarding the project. This information
should be documented and stored so that it becomes part of the historical
database for both the current project and future projects that might be
undertaken by the performing organization. The cumulative record
provides a mechanism for understanding the consequences of
technological choices and a vehicle for knowledge management.

3. Project archives contain a complete set of indexed project records. All
information collected during a project life cycle should be saved in files
or electronic databases and any project-specific or programmable
historical databases that are relevant to the project should be updated.
When projects are performed under contract or when they involve
substantial procurement, it is especially important to maintain accurate
financial records. The central database for the project archives should be
designed to interface with other information systems, such as
procurement management, human resources management, and
accounting.
Project termination requires a clear set of procedures for reassigning
materials, equipment, personnel, and other resources. A project manager with
good leadership skills will carefully plan and execute a project’s termination.

15.2 When to Terminate a Project
Judging when a project’s goals are met is difficult because the degree of
success or failure at any given time may not be quantifiable in terms of the
performance measures agreed upon at the outset. In addition, success tends to
increase at a decreasing rate, implying that change is less visible with the
passage of time. As an example, the goals associated with the initial stages of
a project are often easier to accomplish than those associated with later
stages. Because detecting a partial success or failure is not a simple matter,
management tends to delay termination until the outcome is clearer or more
information is available. This “wait and see” attitude can be very expensive.
Project costs may escalate, and, in most failed projects, these costs cannot be
recovered. In many cases, the project manager is forced to act subjectively
without full confidence in the decision.
Conversely, a project’s termination costs may be a stumbling block to what
objectively looks like the best course of action. When the initial decision to
start a project is made, managers rarely know or even consider what the
closing costs and salvage value of the project will be if it is terminated
prematurely. New projects are supposed to succeed, not fail. It would be
psychologically disturbing to think or plan otherwise. Therefore, when
management is faced with a budget-busting bill for closing out a project
prematurely, the decision might be to continue spending money with the hope
that the situation will improve despite the evidence to the contrary. At the end
of the Cold War, the United States was faced with just such a dilemma. The
reality of canceling tens of billions of dollars in defense contracts meant
skyrocketing unemployment in the aerospace and shipbuilding industries and
huge financial penalties to buy out ongoing contracts. To cite one example, in
the early 1990s, the U.S. Congress decided to go ahead with a $3 billion
program to build a prototype of the next-generation nuclear attack submarine
to avoid closing down General Dynamic’s Electric Boat Division in Groton,
Connecticut. Politics and the severe short-term economic effects that the local
community would probably have experienced were determining factors.
Economics and politics alone, though, do not always drive the termination

decision. The L1011 Tri-Star program of Lockheed is a prime example. For
more than a decade, the aircraft accumulated enormous losses and, in fact,
was never really expected to earn a profit. However, the program was
Lockheed’s reentry into commercial aviation and became a symbol that
broadened the company’s image beyond simply being a defense contractor.
This suggests another difficulty in reaching consensus on the exact
termination point of a project; namely, defining the goals. For example,
consider a construction project in a residential neighborhood. The project
may accomplish its goals as soon as the houses are built, as soon as they are
sold and tenants move in, or, possibly, at the point at which the one-year
contractual warranty period expires. The situation may be even more difficult
when the project involves new or untested technologies, such as the
development of an Earth-orbiting space station. In this example, the design
team is likely to make engineering changes throughout the station’s
construction, assembly, and even operation. Members of the research and
development (R&D) team may be assigned to other parts of the organization
(National Aeronautics and Space Administration) or may continue as a team
involved in related projects and activities. Here, project termination is almost
impossible to define. A third example involves an engineering team that is
designing a new product intended for mass production, such as a new
generation of smartphones. When a prototype is successfully developed, the
team may be integrated into the parent company as a division to manufacture,
support, and improve the new product.
Meredith and Mantel (2003) proposed three approaches to project
termination: extinction, inclusion, and integration.
1. Termination by extinction occurs when the project stops because its
mission is either a success or a failure. In either case, all substantial
project activities cease at the time of assessment. The project team or a
special, project termination team conducts the phase-out. Its aim is to
reassign resources, close out the books, and write a final project report.
This is discussed in Section 15.5.
2. Termination by inclusion occurs when the project team is given a new
identity in the parent organization. Resources are transferred to the new
organizational unit, which is integrated into the parent organization. This

type of termination is typical for organizations with a project/product
structure.
3. Termination by integration occurs when the project’s resources, as well
as its deliverables, are integrated into the parent organization’s various
units. This approach is very common in a matrix organization because
most people involved in a project are also affiliated with one or more
functional units. When the project terminates, team members are
reintegrated into their corresponding units.
Many projects may not reach clear success or failure points. Therefore,
management should monitor each project vigilantly to look for signs that
suggest that the termination point has been reached. Monitoring is facilitated
by the project control system, which is operated by the project team as
discussed in Chapter 12. In addition, an external organizational unit, not
directly involved with the project, should conduct a termination audit at
predefined time periods (also known as “kill points”) to ensure a more
objective analysis. The client may also require formal evaluations and audits
as each phase ends (these are known as “gates” and the project life cycle that
includes such gates is known as the phase—gate approach. These gates
should be included as part of the initial project plan.
Financial audits commonly used in organizations concentrate on financial
well-being and economic status. By contrast, a project audit covers a large
number of aspects, including:
The project’s current status versus stated goals as related to schedule,
costs, technical performance, risk, human relations, resource use, and
information availability.
Future trends, that is, forecasts of total project costs, expected
completion time, and the likelihood that the project will achieve its
stated goals.
Recommendations to change the project’s plans or to terminate the
project if success seems unlikely.
When performed conscientiously, an audit report will be more objective than

the project control system reports. However, because of auditing costs, these
reports are not issued regularly. Termination decisions, then, frequently result
from information provided by the control system. If the cumulative
information indicates that success is unlikely, then an audit team may be
assembled to evaluate the situation more closely. We note here that a decision
against initiating the termination phase (i.e., the “do nothing” decision)
should be based on a project’s satisfactory performances, not on a lack of
alarm signals. For assistance in this matter, the project manager must rely on
the control system throughout the project life cycle. The information that it
provides can trigger an audit to support the termination decision.
Assuming that the control system functions well and that current information
is available, management needs a methodology for reaching a termination
decision. Project management researchers have developed lists of questions
designed to address this issue. Although most studies have focused on R&D
projects, the following list is appropriate in the majority of circumstances.
The questions may be difficult to answer, requiring a special audit to obtain
the necessary information.
Did the organization’s goals change sufficiently so that the original
project definition is inconsistent with the current goals?
Does management still support the project?
Is the project’s budget consistent with the organizational budget?
Are technological, cost, and schedule risks acceptable?
Is the project still innovative? Is it possible to achieve the same results
with current technology faster and at lower cost without completing the
project?
How is the project team’s morale? Can the team finish the project
successfully?
Is the project still profitable and cost-effective?
Can the project be integrated into the organization’s functional units?

Is the project still current? Do sufficient environmental or technological
changes make the project obsolete?
Are there opportunities to use the project’s resources elsewhere that
would prove more cost-effective or beneficial?
Based on the answers to these questions, perhaps obtained with the help of
the economic analysis and project evaluation/selection techniques discussed
in Chapters 3, 5 and 6, management should be able to decide whether it is
time to cancel the project. Once a termination decision is made, the question
then becomes how to minimize the likely disruption that such action would
cause.
As mentioned, management should repeatedly consider whether to continue
or to terminate a project throughout its life cycle. In addition, an external
group should be asked to provide input to the decision, because the project
manager and team members have a vested interest that may compromise their
candor. The external analysis should be a part of the project audit effort,
which should be designed to yield an objective evaluation of the project’s
status.
Because project success (or failure) is multidimensional, the evaluation
should at least cover the following:
Economic evaluation. Given the costs of all project efforts to date, is
project continuation justified?
Project costs and schedule evaluations. Given the current costs,
schedule, and control system’s trend predictions, should the project be
canceled?
Management objectives. Given the organization’s current objectives,
does the project serve these objectives?
Customer relations and reputation. If premature termination is justified,
then how will this affect the organization’s reputation and its customer
relationships?

Contractual and ethical considerations. Is project termination possible
given current client and supplier contracts? Is project termination
ethical?
In conjunction with these questions, the auditing process should consider a
multitude of quantitative and qualitative factors, such as the following:
Quantitative factors
Probability of commercial success
Anticipated annual growth rate
Capital requirements
Project use
Investment return
Annual costs
Probability of technical success
Amount of time actual project costs equaled budgeted project costs
Qualitative factors
Degree of consumer acceptance of the project’s outcome
Probability of government restrictions
Ability to react successfully to competition
Degree of innovation
Degree of linkage with other ongoing projects
Degree of top management support

Degree of R&D management support
Degree of the project leader’s commitment
Degree of the project personnel’s commitment as perceived by top
management, R&D management, and project leaders
Presence of people with sufficient influence to keep the project
going
One methodology that supports a project termination decision is the early
termination monitoring system (ETMS), designed to generate an overall
index of a project’s viability (Meredith 1988). By using input from the
project’s control system, ETMS reports the effects of an early termination on
the organization’s image, the project team’s performance, the marketplace
economics, and the penalty costs that will be incurred.
Finally, Table 15.1 enumerates 10 critical reasons identified by Dean (1968)
in a study of 36 companies for premature R&D project termination. In
conjunction with the lists above, we begin to see why this life-cycle phase is
so difficult to manage. The difficulty stems from the many factors involved in
the decision to begin phase-out and the complexity of termination planning
and execution.
TABLE 15.1 Major Reasons
for Canceling R&D Projects
Factors Reportingfrequency
Technical
   Low probability of achieving technical
objectives or commercial results 34
   Available R&D skills cannot solve the
technical manufacturing problems 11

   R&D personnel or funds required for higher-
priority projects
10
Economic
   Low investment profit or return 23
   Individual product development too costly 18
Market
   Low market potential 16
   Change in competitive factors or market needs 10
Other
   Too much time to achieve commercial results  6
   Negative effects on other projects or products  3
   Patent problems  1

15.3 Planning for Project
Termination
Like any other phase in the project life cycle, termination planning aims at
increasing a project’s probability of success. Once management approves
cancellation, the following action should be taken:
Set project termination milestones
Establish termination phase target costs and budget allocations
Specify major milestone deliverables
Define desired organizational structure and workforce after termination
Although each project may have a different set of goals, the following list of
activities is typically required in a project’s termination phase.
Project office (PO) and project team (PT) organization
Conduct project closeout meetings
Establish PO and PT releases and reassignments
Carry out necessary personnel actions
Prepare a personal performance evaluation for each PT member
Instructions and procedures
Terminate the PO and PT
Close out all work orders and contracts
Terminate the reporting procedures

Prepare the final report(s)
Complete and dispose of the project file
Financial
Close out the financial documents and records
Audit the final charges and costs
Prepare the final project financial report(s)
Collect the receivables
Project definition
Document the final approved project scope
Prepare the project’s final breakdown structure, and enter it into the
project file
Plans, budget, and schedules
Document the actual delivery dates of all contractual deliverable
end items
Document the actual completion dates of all other contractual
obligations
Prepare the project’s final and task status reports
Work authorization and control
Close out all work orders and contracts
Project evaluation and control
Ensure the completion of all action assignments

Prepare the final evaluation report(s)
Conduct the final review meeting
Terminate the financial, personnel, and progress reporting
procedures
Management and customer reporting
Submit the project’s final report to the customer
Submit the project’s final report to management
Marketing and contract administration
Compile the final contract documents, including revisions, waivers,
and related correspondences
Verify and document compliance with all contractual terms
Compile the required proofs of the shipment and customer
acceptance documents
Officially notify the customer of the contract’s completion
Initiate and pursue any claims against the customer
Prepare and conduct the defense against the customer’s claims
Initiate public relations announcements regarding the contract’s
completion
Prepare the final contract status report
Extensions—new business
Document the possibilities for project or contract extensions or
other related new business

Obtain an extension commitment
Project records control
Complete the project file and transmit it to the designated manager
Dispose of other project records as required by established
procedures
Purchasing and subcontracting (for each purchase order and subcontract)
Document compliance and completion
Verify the project’s final payment and proper accounting
Notify the vendor/contractor of the project’s completion
Engineering documentation
Compile and store all engineering documents
Prepare the final technical report
Site operations
Close down all site operations
Dispose of all equipment and materials
On the basis of this list and additional (project specific) activities,
management can perform a project scheduling analysis of the termination
phase. The results obtained from the analysis form the basis for budgeting
and staffing during phase-out. Spirer (1983) suggested a work breakdown
structure (WBS), as shown in Fig. 15.1, to identify the problems that are
likely to arise in the process.

Figure 15.1
WBS for problems that accompany termination.
Figure 15.1 Full Alternative Text
The project termination phase has a significant emotional impact on the
people involved. Four types of groups may be identified: end-users,
customers, team members and producers, and consultants and maintenance

personnel. The following example clarifies the differences among the groups.
A company that manufactures elevators is the producer, its customer is the
builder, the end-users are the tenants who are going to occupy the building,
and maintenance personnel are those who maintain the elevator. Each of the
four groups is involved and affected differently by project termination.
Although the contractor is the immediate customer of the elevator
manufacturer, the end-users and other interested parties, such as the
maintenance crew and the consultants, represent future customers who should
be taken into account. The immediate customer may want to terminate the
project as soon as possible, even if the unit installed has not been tested
sufficiently under normal operating conditions. However, if this unit does not
meet the expectations of the end-users, then costly rework may be required
and the reputation of the elevator company may be damaged.
Below we identify the typical problems that employees who work on a
project may face during the termination phase:
Loss of interest in the project
Insecurity regarding their prospect to get new jobs
Insecurity regarding the uncertainty involved in a new project
Problems in handing the project to the customer
From an emotional point of view, project termination has a separation effect.
Each project team member faces the following troublesome questions:
What, if any, are my plans after the project?
What is my future role in the organization?
What is my next assignment?
The project manager should consider specific answers as well as the best way
to communicate these answers to team members. Furthermore, the project
manager may also worry about his or her own future after closeout. Planning
ahead on how to resolve potential personnel problems and fears will help to

reduce anxiety among all team members.
During phase-out, as a result of the natural feelings of uncertainty, project
team members may experience low morale, lose their interest in the project,
or try to delay its termination. The frequency and intensity of conflicts tend to
increase, and even termination of successful projects may leave many
members feeling angry, upset, or both. To minimize these effects,
management should try to reduce the members’ uncertainty levels. Suddenly
canceling a project may be disastrous. Team members may find it difficult to
terminate the project effectively if they face sudden unexpected changes,
requiring them to invest their time and energy developing adaptive strategies.
Consequently, management’s sensitivity, thoughtful planning, and
consideration of members’ emotions can reduce the negative effects of
cancellation and support a project’s successful closing.

15.4 Implementing Project
Termination
Once management decides to cancel a project and develops a closeout plan, a
termination phase leader must be chosen. Project managers are natural
candidates, but, if they are uncertain about their own futures, then they might
not be able to do a reliable job. A second candidate is a professional project
termination manager who may be unfamiliar with the project’s substance but
experienced and well trained in closing down projects efficiently and
effectively. The choice depends on the answers to the following questions:
Did the project achieve its goals?
Is the project manager assigned to a new project? If yes, then when will
the new assignment begin?
Is the client satisfied?
Is an experienced project termination manager available?
If the project is completed successfully, the client is satisfied, and the project
manager knows his or her next assignment, then the project manager is the
best candidate to head up the termination effort. Otherwise, appointing an
experienced alternative is a wiser choice because the current project manager
may not be motivated to do the job conscientiously.
The termination leader should implement the closeout plan by notifying all
project team members of the decision to terminate the project.
Communicating with team members and laying out a road map for their
futures reduce their uncertainty levels. Once this is accomplished, the next
step is to reduce and eventually eliminate the use of all resources while
implementing procedures that will facilitate a smooth transition of all
personnel to their next assignments.

Throughout project termination planning, implementation, and execution,
management should be extremely sensitive to human relations. The need for
cooperation in future projects should guide all interactions with current team
members, the client, suppliers, and subcontractors. The termination phase is a
bridge to future projects. One cornerstone of this bridge is the final report.

15.5 Final Report
A company that wishes to survive in today’s competitive environment should
strive for continuous improvement. Because each project has a limited
lifetime, improvement should be the goal from one project to the next. To
facilitate this goal, one important outcome of the termination phase is the
final report, which documents activities at each stage of the project’s life
cycle. Such a report emphasizes weak points in the planning and
implementation phases to improve organizational procedures and practices.
The report also explains working procedures that were developed during the
project’s life cycle and contributed to its success, and proposes adopting
these procedures in future projects. The report helps management to plan
future projects and to train future managers and team members. Thus, the
report forms the basis for improving organizational project management
practices and developing new and improved working procedures.
To accomplish these objectives, the final report begins by stating the
project’s mission. Next, it discusses in detail the plans developed to achieve
that mission, the tradeoff analyses conducted, and the planning tools used.
Finally, the report compares the project’s original mission and plans with the
actual results and deviations, and explains why such deviations occurred.
On the basis of this analysis, the report evaluates the project’s specific
procedures and tools for planning, monitoring, and control. Details should be
furnished on any new procedures and analytical methods developed during
the project, and recommendations should be made regarding their adoption if
it is believed that they can be implemented successfully by the entire
organization. Recommendations on the future uses of or modifications to
existing procedures should also be cited. Next, the report evaluates resource
use and the performance of vendors and subcontractors, judging specifically
whether they should be included in future projects. Finally, the report
evaluates and documents the performance of project team members, auxiliary
personnel, and functional unit managers.
Developing a standard format for final reports allows an organization to store

the information collected in a database, making it accessible for future
projects. Many standard formats are designed around one of the following:
Standard WBS, such as the one suggested by MIL-STD-881A. Using a
standard WBS allows management to retrieve information on relevant
WBS elements in past projects.
Standard cost breakdown structure (CBS). Storing cost information in a
standard CBS allows cost estimators and life-cycle cost analysts easy
access to this type of data for future project use.
Standard statement of work (SOW). Storing work statements in a
standard format makes responding to future requests for proposals
easier, because similar SOWs from past projects can serve as a basis for
new proposals.
A well-structured final report can help an organization improve and learn
from its experience. Submitting the report to management is the last step in
any well-managed project.
TEAM PROJECT Thermal
Transfer Plant
The rotary combustor was assembled, tested, and successfully delivered to
the client organization. Total Manufacturing Solutions (TMS) management
wants to learn from your experience with the project and has requested a final
report. This report should be a prototype for future project teams at TMS to
use.
Explain in your report the plan for phasing out the rotary combustor project.
Present a schedule to execute this task and list resources required. Comment
on the experience that you have gained, the lessons that you have learned,
and the mistakes that you have made and how this information can be used to
guide others in future projects. Include in your report a chronological review
of recommendations regarding project management tools and techniques used

throughout the project’s life cycle and all of the data that might be helpful in
TMS’s future development activities.

Discussion Questions
1. Develop a flow diagram that shows how project termination decisions
should be made.
2. Explain the difference between termination by integration and
termination by inclusion, using an example for each process.
3. In what ways does the termination phase of a project differ from the
closedown of a failed company? What are the similarities?
4. How might the input requirements differ for a project control system
versus an audit team evaluating the process that accompanied a project
termination decision?
5. In what way should the planning of the project termination phase be
influenced by personnel considerations?
6. Why do some projects that are clearly “losers” seem to go on forever?
Can you identify a few at the national level? State level? Local level?
7. What is the most important information that a final report should
contain?
8. Several years ago, the U.S. Congress canceled funding for the
development of a battery-powered electric vehicle. Do you think that
was a good decision? Can you imagine what the pros and cons were?
9. Assume that you are working for a computer manufacturer as a software
engineer and that you are told abruptly that your project will be canceled
within 4 weeks. List the questions that you would have for management.
After absorbing the shock, what would you do?
10. Identify the closeout costs for a big project, such as the International
Space Station after it becomes operational but before it is occupied or a
nuclear power plant that is, say, 90% complete.

11. Many people in and out of government have proposed sunset laws for all
projects and agencies. That is, after a fixed amount of time, a project or
an agency would be closed down unless sufficient justification to
continue its activities were offered. Why is such a law needed? What
might constitute “sufficient justification”?
12. List the political and sociological reasons that a project might continue
to be supported even though it cannot be justified economically. Can
you identify such a project in your private life?

Exercises
1. 15.1 Develop guidelines for writing a project final report.
2. 15.2 Write a job description for a project termination manager.
3. 15.3 Develop a “generic” project termination plan that is based on the
list of activities presented in the chapter. What are the precedence
relations among these activities? Develop a linear responsibility chart
for the termination phase.
4. 15.4 Develop a CBS for the termination phase, assuming that only
activities that are not related to the substance of the project are
performed in that phase.
5. 15.5 In the United States, a flagrant example of a program that has
outlived its mission is the Rural Electrification Program that was started
in the 1930s by the Roosevelt administration. Its original goal was to
bring electricity to all rural communities. Today, with its mission long
since accomplished, the program, budgeted at $5 billion annually,
provides subsidies to such unneedy giants as MCI, Houston Lighting
and Power, and Worldcom. The Office of Management and Budget has
tried periodically to shut down this program, but has never been able to
prevail over its powerful beneficiaries. Nevertheless, anticipating the
emergence of more rational heads, you have been asked to write a final
termination report for this program. The report should document its
beginnings, its successes, and the reasons that it has flourished for so
long, as well as the more traditional information associated with
termination.
6. 15.6 Identify two projects in which you have been involved recently.
1. Describe each project briefly.
2. Suggest criteria that may have been used to identify the start of the
termination phase of each project.

3. Give two examples of activities that were performed poorly during
the termination phase of either project, and suggest measures that
might have been taken to improve the situation.
7. 15.7 Develop a questionnaire to capture the importance of various
activities that should be performed during the termination stage.
1. Administer the questionnaire to a sample of project managers.
2. Summarize and analyze the results.
8. 15.8 Identify two projects (local or national) that were terminated
prematurely.
1. Analyze the reasons that each was canceled.
2. Compare the results of the two cases.
9. 15.9 Discuss the following statement made by a project manager: “We
have already spent 70% of the budget required to complete the project
so it would be a waste of money to abandon it at this stage.”

Bibliography
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John Wiley & Sons, New York, 1976.
Balachandra, R. and K. Brockhoff, “Are R&D Project Termination
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Balachandra, R., K. Brockhoff, and A. Pearson, “R&D Project
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Balachandra, R. and J. A. Raelin, “How to Decide When to Abandon a
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Brockhoff, K., “R&D Project Termination Decisions by Discriminant
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Sons, New York, 2001.
Dean, B. V., Evaluating, Selecting, and Controlling R&D Projects,
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Deutch, M. S., “An Exploratory Analysis Relating the Software
Management Process to Project Success,” IEEE Transactions on
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Kumar, V., A. Persuad, and U. Kumar, “To Terminate or Not Ongoing
R&D Project: A Managerial Dilemma,” IEEE Transaction on
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Meredith, J. R., “Project Monitoring and Early Termination,” Project
Management Journal, Vol. XIX, No. 5, pp. 31–38, 1988.
Meredith, J. R. and S. J. Mantel, Jr., Project Management: A
Managerial Approach, Fifth Edition, John Wiley & Sons, New York,
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Items, U.S. Department of Defense, Washington, DC, 1975.
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Transactions on Engineering Management, Vol. 37, No. 4, pp. 269–276,
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Knowledge (PMBOK), Project Management Institute, Newtown Square,
PA, 2012 (http://www.PMI.org).
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I. Cleland, (Editor), Field Guide to Project Management, pp. 377–394,
Van Nostrand Reinhold, New York, 1997.
Shafer, Scott M., and S. J. Mantel Jr. “A decision support system for the
project termination decision: A spreadsheet approach.” Project
Management Journal, Vol. 20.2, pp. 23–28, 1989.
Spirer, H. F., ”Phasing Out the Project,” in D. I. Cleland and W. R. King
(Editors), Project Management Handbook, pp. 245–262, Van Nostrand
Reinhold, New York, 1983.
Staw, B. M. and J. Ross, “Knowing When to Pull the Plug,” Harvard
Business Review, Vol. 65, No. 2, pp. 68–74, 1987.
Toffel, M. W., “The Growing Strategic Importance of End-of-Life
Project Management,” California Management Review, Vol. 45, No. 3,
pp. 102–129, 2003.

http://www.PMI.org

Chapter 16 New Frontiers in
Teaching Project Management in
MBA and Engineering Programs
16.1 Introduction
The number of undergraduate and graduate programs that offer courses in
project management is a good indication for the growing need for
experienced, well-trained project managers. In addition, the number of books
on project management and the number of case studies and other teaching
material developed around the globe increased. Like many other fields,
lectures, books, and case studies are not sufficient, and on-the-job training is
an important part of the development of project teams and project managers.
In some fields, sophisticated simulators replace on-the-job training or reduce
it to a minimum while ensuring that the quality of training is the highest
possible. This is common, for example, in training pilots who spend many
hours on advanced simulators to save on the high cost of actual flights. The
cost of on-the-job training in this case should also include the cost of risks
associated with mistakes frequently made by inexperienced pilots. In a
similar way, training project managers and team members on the job is
expensive due to the high cost of mistakes done by inexperienced managers,
and the use of simulation-based training is the logical solution.

16.2 Motivation for Simulation-
Based Training
Confucius said: “I hear and I forget. I see and I remember. I do and I
understand.”
This is the essence of simulation-based training. We must do things ourselves
in order to really understand them.
Grieshop (1987) listed some of the benefits of games and simulations:
1. Emphasizes questioning over answering on the part of players.
2. Provides opportunities to examine critically the assumptions and
implications that underlie various decisions.
3. Exposes the nature of problems and possible solution paths.
4. Creates an environment for learning that generates discovery learning.
5. Promotes skills in communicating, role-taking, problem solving,
leading, and decision-making.
6. Increases the motivation and interest in a subject matter.
Grieshop (1987) states that evidence is offered for:
1. Increased retention,
2. Energizing the learning process.
3. Facilitation of understanding the relationships between areas within a
subject matter.
Since the publication of Grieshop’s work in 1987, simulation has been used

for training in a wide range of fields: In engineering (International Journal of
Engineering Education, Special Issue on Simulators for Engineering
Education and for Professional Development, 2009), in management of
quality (Wang, 2004), in supply chain management (Knoppen & Sáenz,
2007), and in process re-engineering (Smeds & Riis, 1998; Thoben, Hauge,
Smeds, & Riis, 2007). Empirical research (Millians, 1999; Ruben, 1999;
Randel, Morris, Wetzel, & Whitehill, 1992; Wolfe & Keys, 1997; Meijer,
Hofstede, Beers, & Omta, 2006) expanded our knowledge of this training
approach presenting new ways of understanding and implementing
simulation for training. Today it is widely accepted that learning through
simulation is based on three pillars (Keys, 1976; Kolb, 1984; Kirby, 1992):
1. Learning from content—the dissemination of new ideas, principles, or
concepts.
2. Learning from experience—an opportunity to apply content.
3. Learning from feedback—the results of actions taken and the
relationship between the actions and performance.
A well-designed simulator supports a process of action-based learning.
Simulators offer an opportunity to experiment with various methods without
risking the consequences of doing so in the real world.
Simulators create an environment that requires the participant to be involved
in a meaningful task. The source of learning is what the participants do rather
than what they are told by the trainer.
Thompson, Purdy, & Fandt (1997) listed the advantages of using simulations
as a learning tool:
1. Simulators are characterized as tools enabling the acquisition of
practical experience and acceptance of an immediate response of the
learned system to the user’s decisions and actions.
2. Simulators offer a realistic model of the interdependence of decisions
that the trainee makes.

3. Simulation-based training reduces the gaps between the learning
environment and the “real” environment.
4. Simulators facilitate training in situations that are difficult to obtain in
the “real world.”
5. Simulations promote active learning, especially at the stage of debates
that arise because of the complexity, interconnectedness, and novelty of
decision-making.
Wolfe (1993) notes that simulations develop critical and strategic thinking
skills. He claims that the skills of strategic planning and thinking are not easy
to develop and that the advantage of simulators is that they provide a strong
tool for dealing with this problem.
An important development in the design of training simulators is to provide
users with automatic or semi-automatic feedback on their progress. A
learning history mechanism was used in several simulation-based teaching
tools. A user of these systems obtains access to past states and decisions and
insights into the consequences of these decisions. Learning histories
encourage users to monitor their behavior and reflect on their progress
(Beyerlein, Ford, & Apple, 1996; Guzdial, Kolodner, Hmelo, Narayanan,
Carlso, & Rappin, et al., 1996). Learning histories enable analysis of the
decision-making process as opposed to analysis of results only. The direct
influence on a user’s actions can be revealed. For example, learning history is
used as a quality improvement tool for programmers (Prechelt, 2001).
The most basic view of history recording and inquiry is the temporal
sequence of actions and events. In its simplest form, user actions are logged
and recorded, and are then accessible in various ways for recovery and
backtracking purposes (Vargo, Brown, & Swierenga, 1992). Such a
mechanism is used as “undo.” Several recovery mechanisms have been
developed using the simple undo/undo or undo/redo (e.g., see Archer,
Conway, & Schneider, 1984).
Parush, Hamm, & Shtub (2002) described simulation-based teaching of the
order fulfillment process in a manufacturing context, using the Operations
Trainer (Shtub, 1999; 2001) with a built-in learning history recording and

inquiry mechanism. The study addressed two basic questions:
1. Can history recording and inquiry affect the learning curve during the
training phase with the simulator?
2. Can history recording and inquiry affect the transfer of what was learned
with the simulator?
With learning history recording and inquiry available to users, better
performance was obtained during the learning process itself. In addition, the
performance of learners with the history mechanism was better transferred to
a different context, compared to learners without the history mechanism. The
studies reviewed above demonstrated that having an opportunity to review
learning history had a positive impact on learning. However, these studies did
not examine whether the mode of history recording could have an impact on
learning. History recording can be done either by automatic mechanism or by
learner control. In automatic history recording, the training system such as
the simulator determines when to record a given state in the learning process.
These recording points are predetermined by the simulator designer or the
instructor that prepares the training program. In such a situation, the learner is
not involved in the decision when to keep a specific state in the learning
process. In contrast, in a learner-controlled mode, the learner determines if
and when to keep a specific state in the learning process. It was shown,
however, that giving the learners some control over the learning environment
by letting them actively construct the acquired knowledge produces better
learning (Cuevas, Fiore, Bowers, & Salas, 2004).
The successful use of a simulator for teaching project management was
reported in several studies (Davidovitch, Parush, & Shtub, 2006, 2008, 2009).
The simulator called the Project Management Trainer (PMT) was used in
those studies as a teaching aid designed to facilitate the learning of project
management in a dynamic, stochastic environment. The research focused on
the effect of the history recording mechanism on the learning process. Two
types of history mechanisms were tested: the automatic history mechanism,
in which a predefined scenario’s states are always saved, and the manual
history mechanism, in which the trainee had to show an active involvement
and to save selected states manually. In Davidovitch, Parush, & Shtub (2006),
the study focused on how project managers’ decisions to record the history

affected the learning process and on the effects of history inquiry when the
ability to restart the simulation from a past state is not enabled. In
Davidovitch, Parush, & Shtub (2008), the study focused on the forgetting
phenomenon and on how the length of a break period and history mode
affected the learning, forgetting, and relearning (LFR) process. Both studies
revealed that history recording improved learning; furthermore, with the
manual history mechanism, learners achieved the best results.
The issue of a simulator’s functional fidelity is also of great interest. The
fidelity of a simulator is a measure of its deviation from the real situation; it
has three dimensions: perceptual, functional, and model fidelity. Perceptual
fidelity refers to the level of realism it evokes in terms of its look and feel
relative to the real system. Functional fidelity refers to the way users or
trainees use and control the simulation, its behavior, and responses to user
actions. Finally, model fidelity refers to the extent to which the mathematical
or logical model underlying the simulation is close to the real processes and
phenomena.
The fidelity of the simulator has been recognized as a critical factor
influencing the transfer of learning (Alessi, 1998). In order to provide a
higher level of functional fidelity, the Project Team Builder (PTB) simulator
includes two functionalities: the ability to control the level of human
resources and the ability to control the execution of the tasks. These
functionalities are made available to trainees as part of the scenario
development. The ability to control the level of human resources refers to the
decision to hire or fire employees in accordance with the changing demand
for resources during the project execution; the project manager can control
the number of employees in the project in order to match availability to
needs. The ability to control the execution of the tasks refers to the decision
to split tasks during execution—a task can begin, stop for a while, and
continue later.
Davidovitch, Parush, & Shtub (2009) found that higher fidelity improved
performances in the learning phase and in the transfer phase to a different
scenario.

16.3 Specific Example—The Project
Team Builder (PTB)
The Project Team Builder (PTB) is a training aid designed to facilitate the
training of project management in a dynamic, stochastic environment. The
design of PTB is based on the research findings described in the previous
sections. PTB provides high fidelity by supporting the simulation of any (real
or imaginary) project. A history mechanism is built into the PTB that allows
a user to go back in simulation time to review past decisions and to restart the
simulation from any past simulation time.
PTB is available from Sandboxmodel, a company partially owned by the
Technion Israel Institute of Technology: http://www.sandboxmodel.com/
The PTB is based on the following principles:
A simulation approach—the PTB simulates one or more projects or
several work packages of the same project. The simulation is controlled
by a simple user interface and no knowledge of simulation or simulation
languages is required.
A case study approach—the PTB is based on a simulation of case
studies called scenarios. Each case study is a project or a collection of
projects performed in a dynamic, stochastic environment. In some
scenarios, the projects are performed under schedule, budget, and
resource constraints. The details of these case studies are built into the
simulation while all the data required for analysis and decision making
is easily accessed by the user interface.
A dynamic approach—the case studies built into the PTB are dynamic in
the sense that the situation changes over time. A random effect is
introduced to simulate uncertainty in the environment, and decisions
made by the user cause changes in the state of the system simulated.

http://www.sandboxmodel.com/

A model-based approach—a decision support system is built into the
PTB. This system is based on project management concepts. The model
base contains well-known models for scheduling, budgeting, resource
management, and monitoring and control. These models can be
consulted at any time.
To support decision-making further, a database is built into the PTB.
Data on the current state of the simulated system is readily available to
the users; it is possible to use the data as input to the models in the
model base to support decision making. Furthermore, by using special
history mechanisms a user can access data on past decisions and their
consequences.
User friendliness and graphical user interface (GUI)—the PTB is
designed as a teaching and training tool. As such, its Graphic User
Interface (GUI) is friendly and easy to learn. Although quite
complicated scenarios can be simulated, and the decision support tools
are sophisticated, a typical user can learn how to use the PTB within an
hour.
An integrated approach—several projects can be managed
simultaneously on the PTB. These projects can share the same resources
and a common cash flow.
Integration of processes—planning processes, executing processes, and
monitoring and controlling processes. All these processes are performed
simultaneously in a dynamic stochastic environment.
PTB is integrated with Microsoft Project so that the users can export the
data to Microsoft Project in order to analyze the scenario and to support
its decisions with tools that are commercially available.

16.4 The Global Network for
Advanced Management (GNAM)
MBA New Product Development
(NPD) Course
Innovators developing new products need a keen awareness of both the
global and local environments in which they’ll be sold. The percentage of
sales of successful business organizations tied to the successful introduction
of new products and services is high. Given the fact that the failure rate of
these introductions is also high, there is a need for tools and techniques to
manage the New Product Development (NPD) projects.
A course titled “New Product Development Projects” was developed at the
Technion—Israel Institute of Technology and taught to students at member
schools of the Global Network for Advanced Management (GNAM) around
the world. The GNAM is a group of business schools from both economically
strong countries and those on the horizon of economic development.
In the NPD course, students attend lectures and discussions based on the
previous chapters of this book delivered through an online video conference
platform. Following the lectures the students develop NPD projects in virtual
distributed teams using the PTB, a simulator that models the new product
development process.
Using the PTB software, student teams follow the development life cycle of a
project from its inception to its practical implementation, facing questions
regarding available resources, time management, and production goals.
Students learn how to develop and test an efficient NPD project plan and how
to execute it.
The focus of the course is on “g-local” products: goods that are global in their
conception, but locally targeted. Multinational businesses adapt a popular

product from one country or region to another. To be successful, managers
must understand how the local culture and environment will impact sales.
In the lectures, the material covered in the previous chapters is discussed
along with specific case studies—for example how products like the Big Mac
sandwich and Chicken McNuggets were adapted for India, where many
consumers don’t eat beef and some are entirely vegetarian. This was done by
creating the Chicken Maharaja Mac sandwich and Veggie McNuggets. The
end result for the company is a product that is more profitable than one that
attempts to be universal.
The students learn how to analyze the difference between the needs and
expectations of customers within different countries and develop a product to
satisfy those needs.
Through the PTB software, students can take risks without suffering the
consequences they could face in the real world. Students can rewind or fast-
forward the development process within the software, in order to see what
challenges they may face and how one decision can impact choices in the
future.
Uncertainty is typical to NPD projects. This uncertainty leads to risks (and
opportunities) and to the need for proper risk management. Simulation-based
Training (SBT) presents a unique approach to the teaching and training of the
management of NPD projects.
The GNAM course focused on the tools, techniques, and best practices
developed to support projects aimed at development and marketing of new
products and systems. The course is aimed at teaching the tools and
techniques developed to support the NPD process, to gain insight from real
NPD success and failure case studies, and to implement the tools, techniques,
and insights in a simulated environment.
Each student is assigned to a team. Each team “develops” a new product
using the PTB simulator. The team prepares an NPD plan and executes it on
the simulator. A final report is submitted along with the information on the
NPD project plan and the results of executing the plan on the PTB.

16.5 Project Management for
Engineers at Columbia University
The PTB software was used to teach a project management course in the
Industrial Engineering and Operations Research Department at the Columbia
School of Engineering. The course focused on teaching various project
management methodologies, for example, CPM and PERT. The engineering
students were highly quantitative and had strong backgrounds in
optimization, probability and statistics, and simulation. The software was
used to illustrate the trade-offs that inevitably arise in managing and
executing a project. For example, a project manager may choose to hire less-
expensive labor resources in order to manage project costs. However, the on-
time delivery of project milestones as well as the quality of the delivered
milestones may suffer. The software enabled the students to visually grasp
the trade-offs and to rapidly evaluate alternative scenarios.
As part of the course, students formed small groups and “created” a project of
their own. The range of project applications was quite broad including:
Manufacturing of consumer electronics products such as an urban
transportation planner.
Event planning such as hosting a soccer tournament or a charity
fundraiser.
Construction such as renovation of a building on campus.
Software development such as design and development of a new social
networking site.
New product introduction such as opening a brewery to produce beer
using new brewing methods.
Each student team used the PTB software to model their project. The

software enabled the teams to easily evaluate different scenarios on the basis
of makespan, project cost, and resource usage.

16.6 Experiments and Results
Iluz and Shtub (2013) conducted controlled experiments to test the PTB as a
teaching tool:
Experiment #1: Individual
Participants
Three groups participated in this experiment:
A group of 16 very experienced project managers with experience of
over 5 years.
A group of 17 experienced project managers with experience under 5
years.
18 graduate students.
The essence of the experiment is to let the trainees “manage” a project of new
product development themselves. Their goal is to optimize the ratio between
system performance and costs (cost benefit analysis). Upon completion of the
simulation, each participant was handed a questionnaire, focused on tradeoff
analysis and decision making.
Experiment #2: Project Teams
Nineteen project teams participated in this experiment with a sample size
(i.e., the number of participants) of N=57. Both PTB and Microsoft Project
(MSP) were used as teaching tools and a crossover (PTB/MSP) experiment
was designed to test whether SBT improves tradeoff analysis and decision
making.

Participants were randomly divided into teams and roles, each including a
Project Manager, a Systems Engineer, and a Quality Assurance Engineer.
The teams’ target was to optimize the ratio between system performance and
costs.
Upon completion of the PTB/MSP project plans and runs, participants were
requested to record the plan results: duration, cost, performance, as well as to
fill out a questionnaire focused on tradeoff analysis and decision making.
Data Analysis
The data were analyzed using two statistical procedures: the Chi-square test
and the Analysis of Variance (ANOVA). ANOVA is aimed at testing the
differences between the means of more than two samples, and is based on the
partitioning of the variance in the data into different sources.
The results of the analysis are reported below. The value of the statistic in the
test was performed, Chi-square, is presented first, followed by the number of
Degrees of Freedom (df) that were used in the test. Finally, the significance is
indicated by p, which is the probability of making an error in claiming that
the difference is significant. Any probability less than 5% is interpreted in
Behavioral Science as a significant difference.
Results
There are three clusters of compliance with performance: low (benefit under
20,000), moderate (benefit between 20,000 and 80,000) and high (benefit
over 80,000).
The effect on tradeoff analysis is shown by a significant correlation between
performance and cost, as illustrated in Figure 16.1. (Chi
square=5.99, df=2, p<0.05)—the better the performance, the higher the cost. Figure 16.1 One way analysis of cash by benefit group. Figure 16.1 Full Alternative Text 1. The relationship between the notion that the tool supports decision making and the will to integrate it before or during project life: There is a significant correlation (F=3.5, df=4, p<0.05) between perceiving the simulator as a supporting tool for making decisions and the will to integrate it as a tool for making decisions before or during project performance. 2. Analyzing the questionnaire answers. A Signed-Rank test was performed on two independent samples. This test resembles a single t- test. The differences (PTB-MSP) between the answer given following use of the PTB and the answer given following use of MSP were analyzed. In case the mean value is positive and the p-value <0.05 (indicating significant statistic), the result is in favor of PTB. In other cases, the average result was negative, but the p-value was not significant (>0.05). In these cases no conclusion could be drawn in favor

of the MSP. An example of the analysis results is depicted in Table 16.1.
TABLE 16.1 Difference
Analysis Results Summary on
the Question Level. Significant
Results are Indicated by Gray
Highlight
Question
number
Question
description
Average
difference
(PTB-
MSP)
Std Error
difference
(PTB-
MSP)
p-
Value
(2
sides)
p-
Value
(1 side)
2 (2A)
How well do you
understand the
project work
process?
0.3508772 0.1213211 0.0045 0.0023
3
How well do you
understand the
possible trade-
offs within the
project?
0.2280702 0.1301087 0.0718 0.0359
4
How clear are
the decisions you
are required to
make?
0.2807018 0.1270308 0.0330 0.0165
6
How well do you
believe the other
team members
understand the
relationship 0.2982456 0.1250687 0.0204 0.0102

between time
and performance
within the
project?
Ten out of sixteen questions (over 60%) yielded statistical significance with
regard to using SBT. However, even when there is no significance, no
advantage is seen in favor of the MSP. Even when the observed difference is
negative, no significance is detected.
The conclusion is, therefore, that SBT improves tradeoff analysis and
decision making.

16.7 The Use of Simulation-Based
Training for Teaching Project
Management in Europe
Prof. Dr. Rainer Kolisch from Technische Universität München TUM School
of Management used PTB for teaching undergraduates and MBA students:
“There is a big gap between project management in practice and the
issues addressed in project management textbooks. The reason is that
projects, by their nature, are complex (e.g., difference criteria and
constraints), stochastic and conflict driven (e.g., between the members
of the project team, between the stakeholders of the project). All these
issues have to be considered in practice whereas project management
textbooks typically relax many of the real aspects in order to deliver
simplified views on the essentials such as scheduling, costs, resources
and project control. Consequently, students primailry learn single
stylized aspects but are not exposed to the complex situation that awaits
them in practice.
Here, the PTB makes an important step in the right direction. It employs
simulation in order to put the student in the real situation where he has
to plan and execute projects by handling all issues at once. In particular,
it puts the student in a situation where his project is exposed to risk. By
this the student has to combine the isolated and simplified views on
projects and he learns that risk can materialize and that he has to plan
and execute the project accordingly. This is a very important aspect of
project management which is learnt by doing (and failing) and which
has not been delivered this way before.”
Willy Herroelen, Emeritus Professor of Operations Management, Katholieke
Universiteit Leuven note:
“The Project Team Builder (PTB) meets the need for an effective

teaching and training tool of project management. The software
introduces the user to the full dynamics of project planning, monitoring
and control, moving scenario-wise from the easy, fundamental issues to
the more involved, complex ones. Based on a sound conceptual
foundation, it provides the ideal individual and team training support for
bringing projects to completion effectively and efficiently in a dynamic
stochastic environment. Highly recommended.”

16.8 Summary
Project Management is a combination of art and science. It is the art of
dealing with people in a dynamic, uncertain environment and the art of riding
the learning curve in a non-repetitive environment. It is the science of solving
hard, combinatorial, stochastic problems of project planning, monitoring, and
control under resource and budget constraints. SBT supports training in both
aspects of project management. By using the PTB in team settings, the art of
project management can be practiced; by using SBT to plan, monitor, and
control projects, the science of project management is mastered.

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Index

A
ABC analysis, 175–76
Activity
on arrow, on node (AOA, AON) (See Network techniques)
critical, 398
criticality index, 447
hammock, 443–44
Activity length, 401–12
benchmark job technique, 407
beta distribution, 403
deterministic approach, 406
estimating, 62
learning, 393–95
modular technique, 406–7
parametric technique, 407–8
stochastic approach, 402
Activity splitting, 487–88
Actual cost (AC), 557
Actual cost of work performed (ACWP), 557

Advanced development phase, 27
Akao, 356
Analytic hierarchy process (AHP), 254–62
axioms, 257
case study, 279–86
comparison with MAUT, 275–91
consistency, 260–61
eigenvalue equation, 258
geometric mean, 259
global priorities, 261–62
local priorities, 255–60
pairwise comparisons, 254
Annual worth (AW), 98–99
Authorization management system, 58

B
Baldrige award, 354, 386
Bayes’ theorem, 217, 220, 239–40
Benefit-cost analysis, 187–95
Bernoulli’s principle, 128–29
Beta distribution, 403, 405, 445, 449, 454
Bottleneck, 488
Brainstorming, 330
Breakeven analysis, 111–14
Breakthrough projects, 599
Budget at completion, 566
Budget overruns. See Cost overruns
Budgeted cost of work performed (BCWP), 557–58
Budgeted cost of work scheduled (BCWS), 557
Budgeting, 22, 509
bottom-up, 514–15
budget preparation, 637–38
crashing, 520–27
dimensions of, 511

iterative, 515–16
long-range/strategic, 510
management techniques, 516–27
midrange/tactical, 510, 529
and organizational goals, 511–13
preparation of, 513–16
presentation of, 527–29
project execution, 529–30
purposes of budget, 517
risk, 512
short-range/operational, 510
slack management, 516–20
top-down, 514
types of budgets, 510
Buffer management, 500

C
Capital expansion decision, 116–18
Capital recovery cost, 98
Case studies
comparison of MAUT with AHP, 275–91
R&D portfolio management, 622–26
Cash flow, 512
Central limit theorem, 447, 448, 450, 454
Change control board (CCB), 59, 364
Chebyshev’s inequality, 405, 453, 454
Checklists, 184–87
Communication skills, 306
Communications management, 67–69
Compound interest formulas, 84–86
Computer support for project management, 627–43
Conceptual design phase, 26–27
Concurrent engineering, 346–49
Configuration control board (CCB), 569
Configuration identification, 59, 362

Configuration management, 48, 59, 60, 361–65
software support, 638–39
Configuration selection, 358–61
Configuration test and audit, 569
Conflict resolution, 306
Contract management, 73
Contracts
types of, 334
Control of projects
activities, duration for, 549
common forms of, 548–51
cost and schedule estimation, 566–68
cost control, 548
cost deviations, 559
cost index (CI), 559
design and implementation, 545
earned value approach, 556–65
forecasts, 546
hierarchical structures, 552–56
line of balance, 569–73

management information system (MIS), 547
measurements for, 547
overhead control, 574–76
progress reporting, 565–66
real-time control, 546
relationship to OBS-WBS, 551–65
schedule control, 548
schedule deviations, 558
schedule index (SI), 559
schedule variance (SV), 558
software support, 641
stand-alone independent control, 550
technological control, 569
triggers, 546
Cost account, 554
Cost breakdown structure (CBS), 161
example, 174
final report, project termination, 682
software support, 638
Cost deviations, 559

Cost estimating relationship (CER), 157, 171, 173
sensitivity analysis, 175
Cost estimation, 63, 169, 637
Cost index (CI), 559
Cost management, 63–64
Cost overruns, 548, 574, 576
Cost variance (CV), 559
Cost-based competition, 512
Cost-benefit analysis. See Benefit-cost analysis
Cost-effectiveness analysis, 195–98
Costs
capital recovery, 98
opportunity, 121, 146
overruns, 595
Cost/schedule control systems criteria (C/SCSC), 566
Crashing, 520–27
Critical chain, 488, 496
buffer management, 455–56
project management, 25–26, 455
Critical design review (CDR), 362, 550

Critical path, 427, 438
simulation approach, 445–47
Critical path method (CPM), 47, 399–400, 420–36
activity-on-arrow (AOA) network, 420–33
activity-on-node (AON) network, 433–36
assumptions, validity, 454–55
backward pass, 429, 430, 433, 434, 439, 440
calculating activity times, 431
calculating event times, 428–31
diagramming rules, 420, 421
forward pass, 428, 434, 435, 439
lack calculation for AON, 436
linear programming model, 442–43
slack calculation for AOA, 432–33
Critical resource, 488
Criticality index, 447
Crosby, 375
Customer organization. See Organizational structures

D
Decision making
analytic techniques
benefit-cost analysis, 187–95
checklists, 184–87
cost-effectiveness analysis, 195–98
decision trees, 210–23
real options, 223–25
risk-benefit analysis, 207–10
scoring (screening) models, 184–87
project selection and evaluation, 181–225
risk issues, 198–210
Decision trees, 210–23
assessment, 222–23
Bayes’ theorem, 217
diagramming, 218
expected monetary value (EMV), 212
Decision variables, 498
Decision-making skills, 306

Deliverables, 320
Delphi method, 263, 330
Deming, 371–74
Department of Defense (DOD), 51
Depreciation, 114–15
effect on taxes, 114
modified accelerated cost recovery system (MACRS), 116
salvage value, 114
straight-line method, 116
sum-of-the-years digits (SOYD), 116
useful life, 120
Derivative projects, 598
Design-to-cost, 358
Detailed design phase, 27–28
Direct overhead costs (DOH), 574
Discount rate, 83–84
definition, 91
Discounted cash flow, 93–95
Due-date constraints, 481–86

E
Early termination monitoring system (ETMS), 676
Early-start schedule, 416, 417
Earned value approach, 26, 556–65
Economic analysis
breakeven analysis, 111–14
capital expansion decision, 116
capital recovery cost, 98
comparison of alternatives, 92–96
compound interest formulas, 84–86
depreciation, 114
discount rate, 83–84
discounted cash flow, 92, 93
equivalent uniform annual cost (EUAC), 86–88, 98
equivalent worth methods, 97
interest rate, 83–84
lease-or-buy decision, 124
life of project
economic, physical, tax, useful, 120

make-or-buy decision, 123
minimum acceptable rate of return (MARR), 84, 97
net present value (NPV), 82, 93
payback method, 109
present worth (PW), annual worth (AW), future worth (FW),
86–89, 97–102
repeatability assumption, 96
replacement decision, 118
risk, 92
sensitivity analysis, 111–14
taxes, 114
time value of money, 82–83
useful life, 96, 114, 120, 122
Energy cost/schedule control systems criteria, 583–86
Engineering change order (ECO), 59, 365
Engineering change request (ECR), 59, 175
Equivalent uniform annual cost (EUAC), 87, 98, 120
Expected monetary value (EMV), 126–27, 212, 223

F
Functional organization, 11–12. See also Organizational structures
Funds rate, 90
Future worth (FW), 86–89, 99–101

G
Gantt chart, 47, 416–19, 481–84
control of project, 555
early start, late start, 416, 417
Geometric mean, 259
Global network for advanced management (GNAM), 692–93
Goldratt critical chain, 488
Group decision making, 262–66
decision support systems, 265–66
group composition, 263–64
implementation, 265
running the session, 264

H
Hammock activities, 443–44
House of quality (HOQ), 330, 357, 358
Human resources management, 324–35

I
Imai approach, 371, 376
Inflation, 90–92
interest rate, 91
Integrated product team (IPT), 348, 350
Interest rate, 83–84
discount rate, 91
effective, 89–90
funds rate, 90
inflation, 91
nominal, 89–90
Internal rate of return (IRR), 102–9
no single solution, 106
Interpersonal skills, 306, 332
ISO 9000, 384

J
Juran approach, 374–75

K
Kaizen approach, 375
Kolmogorov-Smirnov test, 403

L
Late-start schedule, 416–18
Laws of project management, 8–9
Leadership, 306, 331–34
Lead-lag relationships, 436–42
Learning, 412–14
Learning curve, 413
tables, 473–75
Lease-or-buy decision, 124–25
Life-cycle cost (LCC)
classification of, 161–68
coding, 169
cost breakdown structure (CBS), 169
cost estimating relationship (CER), 157, 171, 173
models, 157
developing, 168–75
example, 171, 173
uncertainty in, 158
need for analysis, 155–58

phases
product, 155
software support, 638
Life-cycle phases, 26–29
costs, 161–63
product, 155
Line of balance (LOB), 569–73
Linear programming
critical path method (CPM), 442–43
Linear responsibility chart (LRC), 323–24

M
Make-or-buy decision, 123–24
Management
functions of, 44
R&D projects, 595–96
Management of technology, 344–45, 595–96
Manufacturing process, 598
Master production schedule (MPS), 570
Matrix organization, 12. See also Organizational structures
Mean time between failures (MTBF), 342
Mean time to repair (MTTR), 342
Milestones, 398–399, 444–45
Minimum acceptable rate of return (MARR), 83–84
inflation, 91
risk-adjusted, 204, 205
Monte Carlo simulation, 445
Multiattribute utility theory (MAUT), 244–49
additive model, 245
case study, 286–90

comparison with AHP, 275–91
multiplicative model, 245
Multiple attributes, criteria, goals
definition, 242
group decision making, 262–66
objectives, 242–44
value model, 244
Multiple project management
software support, 642

N
Net present value (NPV), 82, 93
real options versus, 223
Network techniques
activity-on-arrow (AOA), 420–33
diagramming rules, 420
node numbering algorithm, 426
activity-on-node (AON), 433–36
precedence relations, 436–41
CPM, 420–36
critical path, 417, 427
longest path, 417
parallel funding, 603–7
PERT, 447–54
Q-GERT, 606–7
New product development (NPD) course, 692–93
Normal distribution, 403–5, 448, 476
table, 476

O
Operational phase, 29
Opportunity cost, 121, 146
Organizational breakdown structure (OBS), 20, 293, 303–5
combining with WBS, 322–24
control of project, 553
relationship to WBS, 551–65
Organizational structures, 14, 293–303
advantages and disadvantages, 296
criteria for choosing, 302–3
customer organization, 298–99
functional organization, 295–96
linear responsibility chart (LRC), 323–24
matrix organization, 299–302
advantages and disadvantages, 302
product organization, 298
project organization, 297–98
advantages and disadvantages, 298
territorial organization, 299

P
Pacing technologies, 597
Parallel funding, 603–7
Pareto analysis, 175–76
Payback period, 109–11
Perceived needs, 513
Performance measures, 342–44
Planned value (PV), 557
Platform projects, 599
PMI software evaluation checklist, 660–69
Portfolio management, 607
case study, 622–26
critical factors, 609–10
monitoring scheme, 612
variables, 610–12
Portfolio models, 183
Precedence relations, 414–16
definitions, 414
lead-lag relationships, 436–42

Preliminary design review (PDR), 362, 550
Present worth (PW), 86–89, 97–98
Product data management systems (PDMSs), 639
Production phase, 28
Production systems, 2–4
Profit, 512
Program evaluation and review technique (PERT), 399–400, 447–54
assumptions, validity, 454–55
simulation approach, 445–47
Project
archives, 672
audits, 674
budget dependency, 499
budgeting (See Budgeting)
charter, 313
closure, 672
control (See Control of projects)
design issues, 341–42
evaluation process, 181–83
lessons learned, 672

milestones, 398–399, 444–45
monitoring, 639–41
performance measures, 342–44
request for proposal (RFP), 181
Resource dependency, 499
team
developing and managing, 325–29
encouraging creativity, 329–31
performance model, 328
technological dependency, 499
termination (See Termination of projects)
Project management
by constraints, 496
critical chain, 488, 496
definition, 627
deliverables, 312, 313
for engineers at Columbia University, 693
ethical and legal aspects of, 334–35
simulation-based training
in Europe, 695–96

motivation for, 687–91
software support, 627
software vendors, 656–57
teaching tools
experiments, 694
results on, 694–95
Project Management Body of Knowledge (PMBOK)
termination of project, 671–72
Project Management Institute (PMI)
code of ethics, 334
standards of conduct, 332
Project management software. See Software
Project manager, 305
authority of, 303, 308, 331
versus functional manager, 309
leadership, 331–34
responsibilities, roles, 294, 303, 345–46
skills, 305–9
Project office, 309–12
Project scheduling. See Scheduling

Project scope, 312–13
Project selection, 183–225
analytic techniques
analytic hierarchy process (AHP), 254–62
benefit-cost analysis, 187–95
checklists, 184–87
cost-effectiveness analysis, 195–98
decision trees, 210–23
multiattribute utility theory (MAUT), 244–49
multiple criteria methods, 242–44
portfolio models, 183
real options, 223–25
scoring (screening) models, 184–87
group decision making, 262–66
risk issues, 198–210
sensitivity analysis, 202–3
uncertainty, 201–2
Project Team Builder (PTB), 691–92
Project termination. See Termination of projects

Q
Q-GERT, 606–7
Quality, 360
Baldrige award, 354, 386
cost of, 385–87
definition, 383
house of, 357, 358
leaders in quality movement, 371
Quality assurance, 383
Quality control, 369
Quality function deployment (QFD), 330, 344, 355–58
Quality management, 370–82
components of, 371
Crosby’s 14 steps, 375
Deming’s 14 principles, 371–74
Imai approach, 376
Juran approach, 374–75
Kaizen approach, 376
Lean Principle, 376–82

Quality planning, 371, 374, 383
Quality-based competition, 512

R
R&D projects, 587
parallel funding, 603–7
portfolio management, 607–18
real options, 223–25
reasons for termination, 677
relationship to projects, 598–600
risk factors, 589–93
strategic planning, 600–603
technology management, 595–600
Real options, 223–25
Regression analysis, 471–72
activity length estimation, 427–32
stepwise, 410
Reliability
definition, 342
MTBF, MTTR, 342
Replacement decision, 118–22
defender and challenger, 118

Report generation
software support, 642–43
Request for proposal (RFP), 181
Resource allocation
activity resource (ACTRES), 494
activity time (ACTIM) algorithm, 493
mathematical models for, 496–99
parallel projects, 499–500
priority rules, 491–96
Resource management, 22
classification of, 478–81
priority rules, 491–96
project planning, 477–78
resource availability constraints, 487–91
software support, 627
Resources, 513
availability, implications of, 491
capacity, 480
depletable, 478
doubly constrained, 478

leveling, 484
planning, 480
profile, 480
renewable, 478
unconstrained, 478
use of alternative, 488
utilization, 488
Risk, 92
analysis, 369
attitudes toward, 135–37
aversion toward, 130
budgeting, 512
factors related to, 589–93
identification, 367–69
issues in project selection, 198–210
limits of analysis, 210
management of, 200–1
monitoring and control, 370
R&D projects, 589–93
scenario analysis, 203

sensitivity analysis, 202
sources of, 368
technical versus commercial success, 589
Risk management, 365–70
planning, 367
Risk-benefit analysis, 207–10
Roll-up mechanism
project control, 565, 566
related to WBS and OBS, 322

S
Salvage value, 87, 96
Scenario analysis, 202, 204
Schedule deviations, 558
Schedule index (SI), 559
Schedule variance (SV), 558
Scheduling, 21, 395–401
activity duration, 420–32
activity-on-arrow (AOA), 420–33
activity-on-node (AON), 433–36
aggregating activities, 443–45
conflicts, 457–58
critical chain project management, 457
due-date constraints, 457
Gantt chart, 416–20
linear programming approach, 442–43
milestones, 398–399, 444–45
network techniques, 399–400
CPM, 399, 420–36

PERT, 399, 447–54
Q-GERT, 606–7
precedence relations, 395, 400, 402, 414–16
simulation approach, 445–47
software support, 630–33
theory of constraints, 455
uncertainty, 445–54
Scoring (screening) models, 184–87
Sensitivity analysis, 111–14, 202
Simulation, 445–47
Skunkworks, 587
Slack (float)
calculation for AOA, 432–33
calculation for AON, 436
free, total, 428
Slack management, 516–20
Software
budget preparation, 637–38
configuration management, 638–39
cost breakdown structure (CBS), 638

crashing, 638
hammocks and subnets, 633–34
hardware requirements, 643
implementation, 650–56
internet access, 642
life-cycle support, 642
mobile applications, 642
multiple project management, 642
OBSs, 629–630
portfolio management, 642
product data management systems (PDMSs), 639
project control, 641
project monitoring, 639–41
report generation, 642–43
resource management, 636–37
resource planning, 635–36
scheduling, 630–33
selection
checklist, 651–55
criteria, 643–48

process, 648–50
sensitivity analysis, 639–41
vendor support, 643
WBSs, 629, 644
St. Petersburg paradox, 127
Standard normal deviate, 448
Statement of work (SOW), 20, 682
Stochastic approach
activity length estimation, 402–6
Strategic R&D planning, 600–603
Suboptimization, 95

T
Taxes, 114–116
Teamwork, 348. See also Concurrent engineering
guideposts for success, 352–54
integrated product team (IPT), 348, 350
Technological ability, 512–13
Technological management, 22
Technology
classification, 596–97
management, 595–96
mature, 597–98
relationship to projects, 598–600
Termination of projects, 23–24
approaches, 673–74
audits, 674
decision factors, 675
early termination monitoring system (ETMS), 676
by extinction, 674
final report, 682–83

guidelines, 671–72
implementation, 681–82
by inclusion, 674
by integration, 674
lessons learned, 672
management, 671
personnel problems, 679–81
planning for, 677–81
PMBOK lists, 671–72
questions to ask, 675
R&D projects, 677
work breakdown structure (WBS), 679
Termination phase, 28, 674, 677
Territorial organization. See Organizational structures
Theory of constraints, 455
Time management, 349–52
Time overruns, 595
Time value of money, 82–83
Time-based competition, 347–49, 512
Time-cost tradeoff with excel, 539–43

Total manufacturing solutions (TMS), 657
Total Manufacturing Solutions, Inc. (TMS), 531, 683
Total quality management (TQM). See Quality management

U
Uncertainty, 7
in project scheduling, 445–54
project selection, 202
R&D projects, 603–7
Utility theory, 125–37
attitudes toward risk, 135–37
axioms, 128
Bernoulli’s principle, 128
certainty equivalent, 128, 130
constructing the utility function, 129–33
expected monetary value (EMV), 126
expected utility maximization, 126–27
multiattribute (MAUT), 244–49

V
Value model, 244

W
Waterfall model, 51, 52
Work breakdown structure (WBS), 313–20, 581–82
combining with OBS, 322–24
control of project, 554
dictionary, 315
final report, project termination, 682
relationship to OBS, 551–65
termination of project, 679–81
Work packages (WPs), 510, 553
design, 320–22
Work remaining, 566

Contents
1. Project Management Processes, Methodologies, and Economics
2. Contents
3. Nomenclature
4. Preface
5. What’s New in this Edition
6. Chapter 1 Introduction
1. 1.1 Nature of Project Management
2. 1.2 Relationship Between Projects and Other Production Systems
3. 1.3 Characteristics of Projects
1. 1.3.1 Definitions and Issues
2. 1.3.2 Risk and Uncertainty
3. 1.3.3 Phases of a Project
4. 1.3.4 Organizing for a Project
4. 1.4 Project Manager
1. 1.4.1 Basic Functions
2. 1.4.2 Characteristics of Effective Project Managers
5. 1.5 Components, Concepts, and Terminology
6. 1.6 Movement to Project-Based Work
7. 1.7 Life Cycle of a Project: Strategic and Tactical Issues
8. 1.8 Factors that Affect the Success of a Project
9. 1.9 About the Book: Purpose and Structure
1. Introduction
2. Total Manufacturing Solutions, Inc.
10. Discussion Questions
11. Exercises
12. Bibliography
13. Appendix 1A Engineering Versus Management
1. 1A.1 Nature of Management
2. 1A.2 Differences between Engineering and Management
3. 1A.3 Transition from Engineer to Manager
4. Additional References
7. Chapter 2 Process Approach to Project Management
1. 2.1 Introduction

1. 2.1.1 Life-Cycle Models
2. 2.1.2 Example of a Project Life Cycle
3. 2.1.3 Application of the Waterfall Model for Software
Development
2. 2.2 Project Management Processes
1. 2.2.1 Process Design
2. 2.2.2 PMBOK and Processes in the Project Life Cycle
3. 2.3 Project Integration Management
1. 2.3.1 Accompanying Processes
2. 2.3.2 Description
1. Project charter development
2. The project plan
3. Execution of the plan
4. Integrated change control
4. 2.4 Project Scope Management
1. 2.4.1 Accompanying Processes
2. 2.4.2 Description
5. 2.5 Project Time Management
1. 2.5.1 Accompanying Processes
2. 2.5.2 Description
6. 2.6 Project Cost Management
1. 2.6.1 Accompanying Processes
2. 2.6.2 Description
7. 2.7 Project Quality Management
1. 2.7.1 Accompanying Processes
2. 2.7.2 Description
8. 2.8 Project Human Resource Management
1. 2.8.1 Accompanying Processes
2. 2.8.2 Description
9. 2.9 Project Communications Management
1. 2.9.1 Accompanying Processes
2. 2.9.2 Description
10. 2.10 Project Risk Management
1. 2.10.1 Accompanying Processes
2. 2.10.2 Description
11. 2.11 Project Procurement Management
1. 2.11.1 Accompanying Processes

2. 2.11.2 Description
12. 2.12 Project Stakeholders Management
1. 2.12.1 Accompanying Processes
2. 2.12.2 Description
13. 2.13 The Learning Organization and Continuous Improvement
1. 2.13.1 Individual and Organizational Learning
2. 2.13.2 Workflow and Process Design as the Basis of Learning
14. Discussion Questions
15. Exercises
16. Bibliography
8. Chapter 3 Engineering Economic Analysis
1. 3.1 Introduction
1. 3.1.1 Need for Economic Analysis
2. 3.1.2 Time Value of Money
3. 3.1.3 Discount Rate, Interest Rate, and Minimum Acceptable
Rate of Return
2. 3.2 Compound Interest Formulas
1. 3.2.1 Present Worth, Future Worth, Uniform Series, and
Gradient Series
1. Solution
2. Solution
3. Solution
2. 3.2.2 Nominal and Effective Interest Rates
1. Solution
2. Solution
3. 3.2.3 Inflation
1. Solution
4. 3.2.4 Treatment of Risk
3. 3.3 Comparison of Alternatives
1. 3.3.1 Defining Investment Alternatives
1. Explicit set of alternatives
2. Implicit set of alternatives
2. 3.3.2 Steps in the Analysis
4. 3.4 Equivalent Worth Methods
1. 3.4.1 Present Worth Method
1. Solution
2. 3.4.2 Annual Worth Method

1. Calculation of capital recovery cost
1. Solution
3. 3.4.3 Future Worth Method
1. Solution
2. Solution
4. 3.4.4 Discussion of Present Worth, Annual Worth, and Future
Worth Methods
5. 3.4.5 Internal Rate of Return Method
1. IRR method for single project
1. Solution
2. IRR Method for Comparing Mutually Exclusive
Alternatives
1. Solution
2. Solution
3. 3.4.6 Payback Period Method
5. 3.5 Sensitivity and Breakeven Analysis
1. Solution
6. 3.6 Effect of Tax and Depreciation on Investment Decisions
1. 3.6.1 Capital Expansion Decision
1. Solution
2. 3.6.2 Replacement Decision
1. Solution
2. Decision Emetic
3. Note
4. Solution
5. Solution
3. 3.6.3 Make-or-Buy Decision
1. Solution
2. Decision
3. Perspective
4. 3.6.4 Lease-or-Buy Decision
1. Solution
1. Decision
2. Note
7. 3.7 Utility Theory
1. 3.7.1 Expected Utility Maximization
2. 3.7.2 Bernoulli’s Principle

3. Expected Utility Theorem
4. 3.7.3 Constructing the Utility Function
5. 3.7.4 Evaluating Alternatives
1. Solution
6. 3.7.5 Characteristics of the Utility Function
8. Discussion Questions
9. Exercises
10. Bibliography
9. Chapter 4 Life-Cycle Costing
1. 4.1 Need for Life-Cycle Cost Analysis
2. 4.2 Uncertainties in Life-Cycle Cost Models
3. 4.3 Classification of Cost Components
4. 4.4 Developing the LCC Model
5. 4.5 Using the Life-Cycle Cost Model
6. Discussion Questions
7. Exercises
8. Bibliography
10. Chapter 5 Portfolio Management—Project Screening and Selection
1. 5.1 Components of the Evaluation Process
2. 5.2 Dynamics of Project Selection
3. 5.3 Checklists and Scoring Models
4. 5.4 Benefit-Cost Analysis
1. Solution
2. Solution
1. Outcome
2. Conclusion
3. 5.4.1 Step-by-Step Approach
4. 5.4.2 Using the Methodology
5. 5.4.3 Classes of Benefits and Costs
6. 5.4.4 Shortcomings of the Benefit-Cost Methodology
5. 5.5 Cost-Effectiveness Analysis
6. 5.6 Issues Related to Risk
1. 5.6.1 Accepting and Managing Risk
2. 5.6.2 Coping with Uncertainty
3. 5.6.3 Non-probabilistic Evaluation Methods when Uncertainty
Is Present
1. Solution

2. Solution
3. Solution
4. Solution
4. 5.6.4 Risk-Benefit Analysis
5. 5.6.5 Limits of Risk Analysis
7. 5.7 Decision Trees
1. 5.7.1 Decision Tree Steps
2. 5.7.2 Basic Principles of Diagramming
3. 5.7.3 Use of Statistics to Determine the Value of More
Information
4. 5.7.4 Discussion and Assessment
8. 5.8 Real Options
1. 5.8.1 Drivers of Value
2. 5.8.2 Relationship to Portfolio Management
9. Discussion Questions
10. Exercises
11. Bibliography
12. Appendix 5A Bayes’ Theorem for Discrete Outcomes
11. Chapter 6 Multiple-Criteria Methods for Evaluation and Group Decision
Making
1. 6.1 Introduction
2. 6.2 Framework for Evaluation and Selection
1. 6.2.1 Objectives and Attributes1
2. 6.2.2 Aggregating Objectives into a Value Model
3. 6.3 Multiattribute Utility Theory
1. 6.3.1 Violations of Multiattribute Utility Theory
4. 6.4 Analytic Hierarchy Process
1. 6.4.1 Determining Local Priorities
2. 6.4.2 Checking for Consistency
3. 6.4.3 Determining Global Priorities
5. 6.5 Group Decision Making
1. 6.5.1 Group Composition
2. 6.5.2 Running the Decision-Making Session
3. 6.5.3 Implementing the Results
4. 6.5.4 Group Decision Support Systems
6. Discussion Questions
7. Exercises

8. Bibliography
9. Appendix 6A: Comparison of Multiattribute Utility Theory with
the Analytic Hierarchy Process: Case Study2
1. 6A.1 Introduction and Background
2. 6A.2 The Cargo Handling Problem
1. 6A.2.1 System Objectives
2. 6A.2.2 Possibility of Commercial Procurement
3. 6A.2.3 Alternative Approaches
3. 6A.3 Analytic Hierarchy Process
1. 6A.3.1 Definition of Attributes
1. Performance
2. Risk
3. Cost
4. Program Objectives
2. 6A.3.2 Analytic Hierarchy Process Computations
3. 6A.3.3 Data Collection and Results for AHP
4. 6A.3.4 Discussion of Analytic Hierarchy Process and
Results
4. 6A.4 Multiattribute Utility Theory
1. 6A.4.1 Data Collection and Results for Multiattribute
Utility Theory
2. 6A.4.2 Discussion of Multiattribute Utility Theory and
Results
5. 6A.5 Additional Observations
6. 6A.6 Conclusions for the Case Study
7. References
12. Chapter 7 Scope and Organizational Structure of a Project
1. 7.1 Introduction
2. 7.2 Organizational Structures
1. 7.2.1 Functional Organization
2. 7.2.2 Project Organization
3. 7.2.3 Product Organization
4. 7.2.4 Customer Organization
5. 7.2.5 Territorial Organization
6. 7.2.6 The Matrix Organization
7. 7.2.7 Criteria for Selecting an Organizational Structure
3. 7.3 Organizational Breakdown Structure of Projects

1. 7.3.1 Factors in Selecting a Structure
2. 7.3.2 The Project Manager
1. Leadership
2. Interpersonal skills
3. Communication skills
4. Decision-making skills
5. Negotiation and conflict resolution
6. Tradeoff analysis skills
7. Responsibility
8. Authority
9. Time horizon
10. Communication
3. 7.3.3 Project Office
4. 7.4 Project Scope
1. 7.4.1 Work Breakdown Structure
2. 7.4.2 Work Package Design
5. 7.5 Combining the Organizational and Work Breakdown Structures
1. 7.5.1 Linear Responsibility Chart
6. 7.6 Management of Human Resources
1. 7.6.1 Developing and Managing the Team
2. 7.6.2 Encouraging Creativity and Innovation
3. 7.6.3 Leadership, Authority, and Responsibility
4. 7.6.4 Ethical and Legal Aspects of Project Management
7. Discussion Questions
8. Exercises
9. Bibliography
13. Chapter 8 Management of Product, Process, and Support Design
1. 8.1 Design of Products, Services, and Systems
1. 8.1.1 Principles of Good Design
2. 8.1.2 Management of Technology and Design in Projects
2. 8.2 Project Manager’s Role
3. 8.3 Importance of Time and the Use of Teams
1. 8.3.1 Concurrent Engineering and Time-Based Competition
2. 8.3.2 Time Management
1. Toyota example
3. 8.3.3 Guideposts for Success
4. 8.3.4 Industrial Experience

5. 8.3.5 Unresolved Issues
4. 8.4 Supporting Tools
1. 8.4.1 Quality Function Deployment
2. 8.4.2 Configuration Selection
3. 8.4.3 Configuration Management
1. Configuration identification
2. Configuration change control
3. Configuration status accounting
4. Review and audits
4. 8.4.4 Risk Management
1. Risk management planning
2. Risk identification
3. Risk analysis
4. Response planning
5. Risk monitoring and control
5. 8.5 Quality Management
1. 8.5.1 Philosophy and Methods
1. Deming approach
2. Juran approach
3. Crosby approach
4. Imai approach
5. Lean Approach:
2. 8.5.2 Importance of Quality in Design
3. 8.5.3 Quality Planning
4. 8.5.4 Quality Assurance
5. 8.5.5 Quality Control
6. 8.5.6 Cost of Quality
6. Discussion Questions
7. Exercises
8. Bibliography
14. Chapter 9 Project Scheduling
1. 9.1 Introduction
1. 9.1.1 Key Milestones
2. 9.1.2 Network Techniques
2. 9.2 Estimating the Duration of Project Activities
1. 9.2.1 Stochastic Approach
2. 9.2.2 Deterministic Approach

3. 9.2.3 Modular Technique
4. 9.2.4 Benchmark Job Technique
5. 9.2.5 Parametric Technique
3. 9.3 Effect of Learning
4. 9.4 Precedence Relations Among Activities
5. 9.5 Gantt Chart
6. 9.6 Activity-on-Arrow Network Approach for CPM Analysis
1. 9.6.1 Calculating Event Times and Critical Path
2. 9.6.2 Calculating Activity Start and Finish Times
3. 9.6.3 Calculating Slacks
7. 9.7 Activity-on-Node Network Approach for CPM Analysis
1. 9.7.1 Calculating Early Start and Early Finish Times of
Activities
2. 9.7.2 Calculating Late Start and Late Finish Times of
Activities
8. 9.8 Precedence Diagramming with Lead–Lag Relationships
9. 9.9 Linear Programming Approach for CPM Analysis
10. 9.10 Aggregating Activities in the Network
1. 9.10.1 Hammock Activities
2. 9.10.2 Milestones
11. 9.11 Dealing with Uncertainty
1. 9.11.1 Simulation Approach
2. 9.11.2 PERT and Extensions
12. 9.12 Critique of Pert and CPM Assumptions
13. 9.13 Critical Chain Process
14. 9.14 Scheduling Conflicts
15. Discussion Questions
16. Exercises
17. Bibliography
18. Appendix 9A Least-Squares Regression Analysis
19. Appendix 9B Learning Curve Tables
20. Appendix 9C Normal Distribution Function
15. Chapter 10 Resource Management
1. 10.1 Effect of Resources on Project Planning
2. 10.2 Classification of Resources Used in Projects
3. 10.3 Resource Leveling Subject to Project Due-Date Constraints
4. 10.4 Resource Allocation Subject to Resource Availability

Constraints
5. 10.5 Priority Rules for Resource Allocation
6. 10.6 Critical Chain: Project Management by Constraints
7. 10.7 Mathematical Models for Resource Allocation
8. 10.8 Projects Performed in Parallel
9. Discussion Questions
10. Exercises
11. Bibliography
16. Chapter 11 Project Budget
1. 11.1 Introduction
2. 11.2 Project Budget and Organizational Goals
3. 11.3 Preparing the Budget
1. 11.3.1 Top-Down Budgeting
2. 11.3.2 Bottom-Up Budgeting
3. 11.3.3 Iterative Budgeting
4. 11.4 Techniques for Managing the Project Budget
1. 11.4.1 Slack Management
2. 11.4.2 Crashing
5. 11.5 Presenting the Budget
6. 11.6 Project Execution: Consuming the Budget
7. 11.7 The Budgeting Process: Concluding Remarks
8. Discussion Questions
9. Exercises
10. Bibliography
11. Appendix 11A Time–Cost Tradeoff With Excel
17. Chapter 12 Project Control
1. 12.1 Introduction
2. 12.2 Common Forms of Project Control
3. 12.3 Integrating the OBS and WBS with Cost and Schedule Control
1. 12.3.1 Hierarchical Structures
2. 12.3.2 Earned Value Approach
4. 12.4 Reporting Progress
5. 12.5 Updating Cost and Schedule Estimates
6. 12.6 Technological Control: Quality and Configuration
7. 12.7 Line of Balance
8. 12.8 Overhead Control
9. Discussion Questions

10. Exercises
11. Bibliography
12. Appendix 12A Example of a Work Breakdown Structure
13. Appendix 12B Department of Energy Cost/Schedule Control
Systems Criteria
18. Chapter 13 Research and Development Projects
1. 13.1 Introduction
2. 13.2 New Product Development
1. 13.2.1 Evaluation and Assessment of Innovations
2. 13.2.2 Changing Expectations
3. 13.2.3 Technology Leapfrogging
4. 13.2.4 Standards
5. 13.2.5 Cost and Time Overruns
3. 13.3 Managing Technology
1. 13.3.1 Classification of Technologies
2. 13.3.2 Exploiting Mature Technologies
3. 13.3.3 Relationship between Technology and Projects
4. 13.4 Strategic R&D Planning
1. 13.4.1 Role of R&D Manager
2. 13.4.2 Planning Team
1. Research Managers Form The Planning Team
2. Good Managers Do Not Delegate The Planning Process
3. Planning Is A Multistage Process
5. 13.5 Parallel Funding: Dealing with Uncertainty
1. 13.5.1 Categorizing Strategies
2. 13.5.2 Analytic Framework
3. 13.5.3 Q-GERT
6. 13.6 Managing the R&D Portfolio
1. 13.6.1 Evaluating an Ongoing Project
1. Critical factors
2. Key Variables
3. Monitoring Scheme
2. 13.6.2 Analytic Methodology
1. Model formulation
2. Implementation
7. Discussion Questions
8. Exercises

9. Bibliography
10. Appendix 13A Portfolio Management Case Study
19. Chapter 14 Computer Support for Project Management
1. 14.1 Introduction
2. 14.2 Use of Computers in Project Management
1. 14.2.1 Supporting the Project Management Process Approach
2. 14.2.2 Tools and Techniques for Project Management
3. 14.3 Criteria for Software Selection
4. 14.4 Software Selection Process
5. 14.5 Software Implementation
6. 14.6 Project Management Software Vendors
7. Discussion Questions
8. Exercises
9. Bibliography
10. Appendix 14A PMI Software Evaluation Checklist
20. Chapter 15 Project Termination
1. 15.1 Introduction
2. 15.2 When to Terminate a Project
3. 15.3 Planning for Project Termination
4. 15.4 Implementing Project Termination
5. 15.5 Final Report
6. Discussion Questions
7. Exercises
8. Bibliography
21. Chapter 16 New Frontiers in Teaching Project Management in MBA
and Engineering Programs
1. 16.1 Introduction
2. 16.2 Motivation for Simulation-Based Training
3. 16.3 Specific Example—The Project Team Builder (PTB)
4. 16.4 The Global Network for Advanced Management (GNAM)
MBA New Product Development (NPD) Course
5. 16.5 Project Management for Engineers at Columbia University
6. 16.6 Experiments and Results
7. 16.7 The Use of Simulation-Based Training for Teaching Project
Management in Europe
8. 16.8 Summary
9. Bibliography

22. Index
1. A
2. B
3. C
4. D
5. E
6. F
7. G
8. H
9. I
10. J
11. K
12. L
13. M
14. N
15. O
16. P
17. Q
18. R
19. S
20. T
21. U
22. V
23. W
List of Illustrations
1. Figure 1.1
2. Figure 1.2
3. Figure 1.3
4. Figure 1.4
5. Figure 1.5
6. Figure 1.6
7. Figure 1.7
8. Figure 1.8
9. Figure 1.9

10. Figure 1.10
11. Figure 1.11
12. Figure 1.12
13. Figure 2.1
14. Figure 2.2
15. Figure 2.3
16. Figure 3.1
17. Figure 3.2
18. Figure 3.3
19. Figure 3.4
20. Figure 3.5
21. Figure 3.6
22. Figure 3.7
23. Figure 3.8
24. Figure 3.9
25. Figure 3.10
26. Figure 3.11
27. Figure 3.12
28. Figure 3.13
29. Figure 3.14
30. Figure 3.15
31. Figure 4.1
32. Figure 4.2
33. Figure 4.3
34. Figure 4.4
35. Figure 4.5
36. Figure 4.6
37. Figure 4.7
38. Figure 4.8
39. Figure 4.9
40. Figure 5.1
41. Figure 5.2
42. Figure 5.3
43. Figure 5.4
44. Figure 5.5
45. Figure 5.6
46. Figure 5.7

47. Figure 5.8
48. Figure 5.9
49. Figure 5.10
50. Figure 5.11
51. Figure 5.12
52. Figure 5.13
53. Figure 5.14
54. Figure 5.15
55. Figure 5.16
56. Figure 5.17
57. Figure 6.1
58. Figure 6.2
59. Figure 6.3
60. Figure 6.4
61. Figure 6.5
62. Figure 6.6
63. Figure 6.8
64. Figure 6.9
65. Figure 6A.1
66. Figure 6A.2
67. Figure 6A.3
68. Figure 7.1
69. Figure 7.2
70. Figure 7.3
71. Figure 7.4
72. Figure 7.6
73. Figure 7.7
74. Figure 7.8
75. Figure 7.9
76. Figure 7.10
77. Figure 7.11
78. Figure 8.1
79. Figure 8.2
80. Figure 8.3
81. Figure 9.1
82. Figure 9.2
83. Figure 9.3

84. Figure 9.4
85. Figure 9.5
86. Figure 9.6
87. Figure 9.7
88. Figure 9.8
89. Figure 9.9
90. Figure 9.10
91. Figure 9.11
92. Figure 9.12
93. Figure 9.13
94. Figure 9.14
95. Figure 9.15
96. Figure 9.16
97. Figure 9.17
98. Figure 9.18
99. Figure 9.19
100. Figure 9.20
101. Figure 9.21
102. Figure 9.22
103. Figure 9.23
104. Figure 9.24
105. Figure 9.25
106. Figure 9.26
107. Figure 9.27
108. Figure 9.28
109. Figure 9.29
110. Figure 9.30
111. Figure 9.31
112. Figure 9.32
113. Figure 9.33
114. Figure 9.34
115. Figure 9.35
116. Figure 9.36
117. Figure 9.37
118. Figure 9.38
119. Figure 9.39
120. Figure 9.40

121. Figure 9.41
122. Figure 9.42
123. Figure 9.43
124. Figure 10.1
125. Figure 10.2
126. Figure 10.3
127. Figure 10.4
128. Figure 10.5
129. Figure 10.6
130. Figure 11.1
131. Figure 11.2
132. Figure 11.3
133. Figure 11.4
134. Figure 11.5
135. Figure 11A.1
136. Figure 11A.2
137. Figure 11A.3
138. Figure 11A.4
139. Figure 11A.5
140. Figure 12.1
141. Figure 12.2
142. Figure 12.3
143. Figure 12.4
144. Figure 12.5
145. Figure 12.6
146. Figure 12.7
147. Figure 12.8
148. Figure 12.9
149. Figure 12.10
150. Figure 12.11
151. Figure 12.12
152. Figure 12.13
153. Figure 13.1
154. Figure 13.2
155. Figure 13.3
156. Figure 13.4
157. Figure 14.1

158. Figure 14.2
159. Figure 14.3
160. Figure 14.4
161. Figure 14.5
162. Figure 14.6
163. Figure 14.7
164. Figure 14.8a
165. Figure 14.8b
166. Figure 14.9
167. Figure 14.10
168. Figure 14.11
169. Figure 14.12
170. Figure 14.13
171. Figure 15.1
172. Figure 16.1
List of Tables
1. TABLE 1.1 Partial WBS for Space Laboratory
2. TABLE 1.2 Advantages and Disadvantages of Two Organizational
Structures
3. TABLE 1.3 TMS Financial Data: Income Statement
4. TABLE 1.4 TMS Financial Data: Balance Sheet
5. TABLE 1A.1 Functions of Management
6. TABLE 1A.2 Engineering Versus Management
7. TABLE 3.1 Assessed Utilities for Project Manager
8. TABLE 3.2  Payoff Matrix for New Product Development Example
9. TABLE 3.3  Utility Matrix for New Product Development Example
10. TABLE 4.1 LCC Estimates for Appliances
11. TABLE 4.2 Example of an LCC Model ($1,000)
12. TABLE 4.3 Coding and Classification Scheme for LCC
13. TABLE 4.4 Partial CBS for Residential Building Example
14. TABLE 5.1 An Example of a Checklist for Screening Projects
15. TABLE 5.2  An Example of a Scoring Model for Screening Projects
16. TABLE 5.3  Environmental Scoring Form Used by Niagara Mohawk
17. TABLE 5.4  Input Data and Results for Incremental Analysis

18. TABLE 5.5  Data for C-E Analysis
19. TABLE 5.6 Some Definitions Related to Risk
20. TABLE 5.7  Data and Results for Reduction of Useful Life Example
21. TABLE 5.8  Computational Results for Replacement Problem in
Figure 5.12
22. TABLE 5.9  Computations for Replacement Problem with 12%
Interest Rate
23. TABLE 5.10  Expected NPV Calculations for the Automation
Problem
24. TABLE 5.11  Computation of Posterior Probabilities Given That
Investigation-Predicted Demand is High (h)
25. TABLE 5.12  Computation of Posterior Probabilities Given That
Investigation-Predicted Demand is Low ( )
26. TABLE 5.13  Expected NPV Calculations for Replacement Problem
in Figure 5.13
27. TABLE 5.14 
28. TABLE 5.15 
29. TABLE 5.16 
30. TABLE 5.17 
31. TABLE 5.18 
32. TABLE 5A.1  Format for Applying Bayes’ Theorem
33. TABLE 6.1 Scale used for Pairwise Comparisons
34. TABLE 6.2 Priority Vector for Major Criteria
35. TABLE 6.3 Local and Global Priorities for the Problem of Selecting
an In-Orbit Assembly System
36. TABLE 6.4 Example GDSS Features to Support Six Task Types
37. TABLE 6.5 
38. TABLE 6.6 
39. TABLE 6A.1 Priority Vector for Major Criteria
40. TABLE 6A.2 Local and Global Priorities
41. TABLE 6A.3: Comparison of Responses Using the AHP
42. TABLE 6A.4: Summary of Results for the AHP Analysis
43. TABLE 6A.5 Attribute Data for Decision Maker 1
44. TABLE 6A.6 Scale used for “Mission Objectives” Attribute
45. TABLE 6A.7  Comparison of AHP Weights and MAUT Scaling
Constants for the Five Decision Makers
46. TABLE 6A.8 Summary of Results for MAUT Analysis

47. TABLE 7.1 Concerns of Project and Functional Managers
48. TABLE 7.2 Similar Organizational Units that Perform Project
Management Related Tasks
49. TABLE 7.3 Example of an LRC
50. TABLE 8.1 Factors that Affect the Tempo of Manufacturing Firms
51. TABLE 8.2 Quality Chart for New Bicycle Design
52. TABLE 9.1 Data for Regression Analysis
53. TABLE 9.2 Data for Example Project
54. TABLE 9.3 Sequences in the Network
55. TABLE 9.4 Summary of Event Time Calculations
56. TABLE 9.5 Summary of Start and Finish Time Analysis
57. TABLE 9.6 Early Start and Early Finish of Project Activities
58. TABLE 9.7 Late Finish and Late Start of Project Activities
59. TABLE 9.8 Statistics for Example Activities
60. TABLE 9.9 Summary of Simulation Runs for Example Project
61. TABLE 9.10 Mean Length and Standard Deviation for Sequences in
Example Project
62. TABLE 9.11 Probability of Completing Each Sequence in 22 Weeks
63. TABLE 9.12 Principal Assumptions and Criticisms of PERT/CPM
64. TABLE 9.13
65. TABLE 9.14
66. TABLE 9.15
67. TABLE 9.16
68. TABLE 9.17
69. TABLE 9.18
70. TABLE 9.19
71. TABLE 9.20
72. TABLE 9.21
73. TABLE 9B.1 Learning Curve Values for n β
74. TABLE 9B.2 Cumulative Learning Curve Values for n β
75. TABLE 9C.1 Cumulative Probabilities of the Normal Distribution
(areas under the standardized normalized curve from −∞ to z)
76. TABLE 10.1 Resource Requirements for the Example Project
77. TABLE 10.2 Implications of Resource Availability
78. TABLE 10.3 Longest Duration First Heuristic
79. TABLE 10.4 ACTIM Example Data
80. TABLE 10.5 Actres Heuristic

81. TABLE 10.6 Data for Minimum Total Slack Heuristic
82. TABLE 10.7 Minimum Total Slack Heuristic
83. TABLE 10.8 Minimum Total Slack Heuristic
84. TABLE 10.9
85. TABLE 10.10
86. TABLE 10.11
87. TABLE 10.12
88. TABLE 10.13
89. TABLE 10.14
90. TABLE 10.15
91. TABLE 10.16
92. TABLE 11.1 The Top-Down Approach to Budget Preparation
93. TABLE 11.2 Bottom-Up Approach to Budget Preparation
94. TABLE 11.3 Project Activity Durations and Costs
95. TABLE 11.4 Cash Flow of an Early-Start Schedule
96. TABLE 11.5 Cash Flow of the Late-Start Schedule
97. TABLE 11.6 Duration and Cost for Normal and Crashed Activities
98. TABLE 11.7 Crashing the Project (Cost in $1,000, Duration in Weeks)
99. TABLE 11.8 Project Costs as a Function of its Duration
100. TABLE 11.9 Parametric Solution to Time–Cost Tradeoff Example (Cost
in $, Duration in Weeks)
101. TABLE 11.10 Breakdown of the Budget by Organizational Units
102. TABLE 11.11
103. TABLE 11.12
104. TABLE 11.13
105. TABLE 11.14
106. TABLE 11.15
107. TABLE 11.16
108. TABLE 11.17
109. TABLE 12.1 Measurements for Project Control
110. TABLE 12.2 Duration and Cost for Activities Performed in Month 1
111. TABLE 12.3 Actual Performances in Month 1
112. TABLE 12.4 Summary Report for Weeks 1-4
113. TABLE 12.5 The Values of BCWS, BCWP, and ACWP for Weeks 1–
4
114. TABLE 12.6 Values of SI and CI for Weeks 1–4
115. TABLE 12.7 Cumulative Cost and Schedule Control Report by OBS

Element (Weeks 1-4)
116. TABLE 12.8 Cost and Schedule Control Report by WBS Element
117. TABLE 12.9 Schedule of Milestones or Control Points
118. TABLE 12.10 Delivery Schedule for the 110 Systems
119. TABLE 12.11 Scheduled Milestones at the End of Week 5
120. TABLE 12.12
121. TABLE 12.13
122. TABLE 12.14
123. TABLE 12.15
124. TABLE 13.1 Stages of the Strategic Technical Planning Process
125. TABLE 13.2 Characteristics of Database for Determining Critical
Factors
126. TABLE 13A.1 Input Data For R&D Case Study
127. TABLE 13A.2 Relationship Between Probability of Technical Success
and Funding Level
128. TABLE 13A.3 Funding for Basic Portfolio
129. TABLE 13A.4 Results for Updated Portfolio
130. TABLE 14.1 Relative Weights Used in the Scoring Model
131. TABLE 14.2 Calculations for the Operational Criteria
132. TABLE 14.3 Cost Data for Selection Problem
133. TABLE 14.4 Weighted Scores for Criteria Sets and Results
134. TABLE 15.1 Major Reasons for Canceling R&D Projects
135. TABLE 16.1 Difference Analysis Results Summary on the Question
Level. Significant Results are Indicated by Gray Highlight
Landmarks
1. Frontmatter
2. Start of Content
3. backmatter
4. List of Illustrations
5. List of Tables
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Long description
Projects, batch systems, and mass production systems overlap in terms of
batch size versus volume. However, projects tend to be low volume and low
batch size. Batch systems tend to be medium volume and medium batch size,
and mass production systems tend to be high volume and high batch size.

Long description
The steps in the project management process are as follows: 1, identify a
need for a product or service; 2, define the goals of the project and their
relative importance; 3, select appropriate performance measures; 4, develop a
schedule, a budget, and the technological concept or process; 5, integrate the
schedule, budget, and process into a project plan; 6, implement the plan; 7,
monitor and control the project with regard to the schedule, budget, and
process; 8, evaluate the project success based on the goals established in step
2.

Long description
Two pie chart shows data related to project 1 and project 2. The data inferred
from the Project 1 chart is as follows, in a clockwise direction. Cost 33.3
percent, Schedule 33.3 percent, and performance 33.3 percent. The data
inferred from the project 2 chart is as follows, in a clockwise direction. Cost
20 percent, Schedule 10 percent, and performance 70 percent.

Long description
The x-axis is divided into the following project phases from left to right:
conceptual design, advanced development, detailed design, production, and
termination. The graphs for money committed and money spent both start at
the origin, before rising to the same point. The higher money committed
curve rises with increasing steepness to a point in the latter half of advanced
development, before rising with decreasing steepness. The money spent curve
rises with increasing steepness.

Long description
Assisted by staff, the general manager oversees the financial manager, chief
engineers, manufacturing manager, and marketing manager. The chief
engineer also has staff, and together they oversee supervisors of different
engineering specialties, with their own staffs. Many project managers belong
to the staff of the managers in the top two tiers, or they are supervisors.

Long description
The president oversees the highest tier of management: chief program
manager, vice president of engineering and testing, vice president of quality
assurance, vice president of administration, vice president of manufacturing.
Each vice president oversees three tiers of employees with vertical and
horizontal relationships. The vertical relationships represent functional
authority exerted from top to bottom. The horizontal relationships represent
project authority expressed along tiers.

Long description
The eight important skills for a project manager are as follows: budgeting and
cost skills; scheduling and time management skills; technical skills or scope
of project; leadership skills related to goals and performance measures;
resource management and human relationship skills; communication skills;
negotiating skills; marketing, contracting, and customer relationship skills.

Long description
The client R F P, personnel, and management work together to identify a
need. Marketing management asks if the need is important. If the answer is
yes, then the engineering management conducts a technical evaluation. Based
on the technical evaluation, engineering and finance management ask if the
project is feasible. If the answer is yes, then engineering and R and D develop
alternatives. Engineering, marketing, and finance perform cost-benefit
analyses on the alternatives. Management selects the best alternative, based
on the analyses, and then they define the project.

Long description
The team asks, is the conceptual design approved? If the answer is no, then
they return to the conceptual design phase. If the answer is yes, they ask, is
the detailed design acceptable? If the answer is yes, then they proceed to the
production phase. If the answer is no, then they prepare a technological
baseline and a detailed schedule. They then conduct resource requirements
analysis and prepare a budget, before returning to the beginning of the
process.

Long description
The graph plots resources and effort versus phase. The graph shows that
resources and effort rise to a peak in phase 4, production, before falling. The
phases are as follows. Phase 1, conceptual design: goals, scope, baseline,
requirements, feasibility, desirability. Phase 2, advanced development: plan,
budget, schedule, bid proposal, management commitment. Phase 3, detailed
design: responsibility definition, team, organizational structure, detailed plan,
kickoff. Phase 4, production: manage, measure, control, update and re-plan,
problem solving. Phase 5, termination: closeout, document, suggest
improvements, transition, reassign, dissolve team.

Long description
The president directly oversees the New York office vice president, the
Nashville office, and the Los Angeles office vice president. The Nashville
office has a marketing vice president, engineering vice president, and
controller. The engineering vice president is in charge of quality assurance,
mechanical engineering, electrical engineering, industrial engineering, and
controller operations, which consist of purchasing, personnel, and
accounting.

Long description
The graph plots cumulative cost versus review. Counterclockwise from top-
right, the quadrants represent the following: quadrant 1, evaluate alternatives
and identify and resolve risks; quadrant 2, determine objectives, alternatives,
and constraints; quadrant 3, plan next phases; quadrant 4, develop and verify
the next level product. The graph spirals outward from near the origin in the
clockwise direction. Commitment partitions separate the phases, and the
phases are as follows, from beginning to end: requirements plan, risk
analysis, prototype 1, concept of operation, life cycle plan, risk analysis,
prototype 2, emulations, software requirements, requirements validation,
development plan, risk analysis, prototype 3, models, software product
design, design validation and verification, integration and test plan, risk
analysis, operational arototype, benchmarks, detailed design, code, unit test,
integration and test, acceptance test, implementation. In this progression, risk
analysis and prototype phases occur in quadrants 2 and 1.

Long description
The timeline is as follows: determination of mission needs; milestone 0,
concept studies and approval; phase 0, concept exploration and definition;
milestone 1, concept demonstration approval; phase 1, demonstration and
validation; milestone 2, development approval; phase 2, engineering and
manufacturing development; milestone 3, production approval; phase 3,
production and development; milestone 4, major modification approval as
required; phase 4, operations and support.

Long description
In the waterfall model, each stage cascades to the next. 1, The team uses the
system segment specification to define the system requirements. 2, The tem
uses software requirements documents to analyze the requirements. 3, The
team uses the top level and preliminary design documents to create a
preliminary design, with an accompanying system design review. 4, The team
uses the final software design document to create a detailed design, with an
accompanying software specification review. 5, The team uses computer
software units to perform coding and unit testing, with an accompanying
preliminary design review. 6, The team uses computer software components
to conduct component integration and testing, with an accompanying critical
design review. 7, The team uses a computer S O W configuration item, C S C
I, to perform integration testing. 8, The team uses tested software to perform
system testing, with an accompanying test readiness review. 9, The team then
maintains the software, with accompanying F G A, F D R, and F C A.

Long description
A flow diagram has upward arrow represents payments and downward arrow
represents savings. The horizontal axis represents the Time period from 1 to
n. Initially, a payment labeled P, a slight decrease in payment labeled A
maintains the same from end of time period 1 to end of time period 3. There
is a slight increase of savings at end of time period 2 labeled G and gradual
increase at end of time period 3 labeled 2G. The process is a s follows till the
time period (n minus 1) with interest i percentage. At the end of (n minus 1)
time period a payment labeled A remains the same and savings has a slight
increase labeled (n minus 2) G. The the end of n the time period, savings
labeled (n minus 1) G and payment labeled A which represents the future
worth.

Long description
The flow diagram has an upward arrow that represents payments and a
downward arrow that represents savings. The horizontal axis represents the
Time period ranging from 1 to 5 with increments of 1. Initially, the savings is
20,000 dollars. After the time period 1, there is a decrease in savings which
remains the same till the end of time period 5. At the end of the time period 5,
the payment becomes 4000 dollars at an interest of 15 percent. A text given
in a box below reads, (a) A equals question mark; (b) B equals question
mark.

Long description
At point a, at the beginning of period 1, P sub o is unknown. At the end of
period 2, P sub 2=F sub 2. At the end of 3, savings=10 million, with i=20%.
Savings increase by G=1 million each year. At point b, at the end of period 7,
F sub 7 is unknown.

Long description
The plot of P W of cost in dollars versus year second stage constructed. The
plot for two-stage construction falls with decreasing steepness from (0,
220,000) falls through (15, 140,000). Full capacity occurs at the breakeven
point at x=15 years. All values estimated.

Long description
The plot of N P V of C is horizontal at NPV=3100. The plot of N P V of A is
horizontal at y=2700. The plot of N P V of B falls diagonally through N P V
of C at (4200, 3100) and N P V of A at (4600, 2700). To the left of x=4200,
alternative B is preferred, and to the right of x=4200, alternative C is
preferred. All values estimated.

Long description
The plot of U of A versus monetary outcome A in thousands of dollars rises
with decreasing steepness from (negative 500, 0) through (C E=negative 250,
0.5) and (100, 0.75). All values estimated. C E=negative 250 for the lottery in
Figure 3.10. All values estimated.

Long description
The risk neutral plot rises diagonally from (negative 500, 0) to (1000, 1). The
risk verse curve rises with decreasing steepness from (negative 500, 0) to
(1000, 1). The risk prone plot rises from (negative 500, 0) to (1000, 1). All
values estimated.

Long description
A boom-mounted bucket drops material into a diagonal chute. Rams at the
base of the shoot push the material through the resistance door into the rotary
combustor. Off gases rise from the top of the combustor, and ash is removed
from the base of the combustor opposite the resistance door.

Long description
Feed rams push solid waste from the base of the chute into an angled
cylindrical drum rotating about its axis. The feed rams operate one at a time:
they extend 1 to 8 minutes, and retract for 30 seconds. A hydraulic motor and
speed reducer turn the drum at 1 to 5 revolutions per hour.

Long description
The time axis is divided into the following phases from left to right:
conceptual design phase, advanced development and detailed design phase,
production phase, operations and maintenance phase, divestment or disposal
phase. The plot falls with decreasing steepness from (0, 100) through
(advanced development, 30%) and (operations and maintenance, 5%). All
values estimated.

Long description
The time axis is divided into the following phases from left to right:
conceptual design phase, advanced development and detailed design phase,
production phase, operations and maintenance phase, divestment or disposal
phase. The plot falls with decreasing steepness through (conceptual phase,
28%) and (advanced development, 15%). All values estimated.

Long description
The bar graph represents percent of L O C for projects A and B for different
project phases. The following list provides the percentages for projects A and
B for different phases: conceptual design, 20, 10; advanced development and
detailed design, 30, 20; production, 28, 50; operations and maintenance, 15,
15; divestment or disposal, 5, 5. All values estimated.

Long description
The following costs contribute to direct cost: direct labor cost, cost of labor
used to manufacture the system; direct material cost, cost of material used in
the system; direct expense, cost of subcontracting used to make the system.
The following costs contribute to indirect cost: indirect material cost, cost of
coolant for machine tools and so on; indirect labor cost, cost of quality
control, supervision, and so on; indirect expense, cost of rent, depreciation,
and so on. Direct and indirect cost contribute to the total cost of
manufacturing.

Long description
A trend graph shows classification of cost component with two variables
Cumulative cost and monthly cost. The horizontal axis represents month
ranging from 0 to 12 with increments of 1 and vertical axis represents cost
(1000 dollars) ranging from 0 to 25 with increments of 5. The curve of
cumulative cost rises sharply from (0, 0) to (12, 25). The curve of monthly
cost rises gradually from (0, 0) to (8, 3), and falls to (12, 1). The data plotted
are approximate.

Long description
The plots represent the cost during each phase of a project. The following list
provides the peak coordinates for the different phase pots: conceptual design,
(3, 5); advanced development phase, (6, 8); production, (8, 18); O and M,
(12, 8); divestment, (14.5, 4). The cumulative L C C plot rises from (0, 0)
through (5, 16) and (10, 120) to (18, 170). All values estimated.

Long description
At quarter 8, the plots for material cost, labor cost, and total cost peak at the
following heights: 6, 14, 20. The cumulative L C C plot rises from (0, 0)
through (5, 16) and (10, 120) to (18, 170). All values estimated

Long description
Once the team has an R F P and ideas, they begin the screening process.
During screening, data collection leads to the following decision tree. Should
we pursue the idea? If no, abandon the idea, or backlog the idea, leading to
time delay and additional data collection. If yes, proceed to the evaluation
phase, during which the team develops a project proposal. developing the
project proposal leads to the following decision tree. Should we pursue the
idea? If no, abandon the idea, or amplify the proposal, leading to further
development of the proposal. If yes, proceed to the prioritizing phase, during
which all proposals are reviewed, and priorities and resources are assigned.
During this phase, current ideas might be abandoned, and backlogged ideas
might be reconsidered. After the prioritizing phase, the team conducts a
portfolio analysis, during which they develop and review their portfolio. In
the process, they might consider how to reassign priorities and resources.
Then they ask, is the proposal approved? If no, then they return to prioritizing
and portfolio review, or they update and recycle, returning to the early
phases. If yes, the team initiates the effort. If an item is urgent, it can be fast-
tracked through screening, evaluation, and prioritizing.

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Long description
The plot for the efficient frontier is a rising series of end-to-end line segments
with the following end points: (200, 54), (275, 75), (350, 79), (450, 80). The
region below y=72 is the unacceptable region. All values estimated.

Long description
The line for initial investment falls through (0, 2000) and (18, 0). The line for
salvage value is roughly horizontal through (0, 2000). The line for useful life
rises through (0, 2000) and (40, 5200). The line for revenues rises through (0,
2000) and (40, 7500). All values estimated.

Long description
Each plot falls with decreasing steepness. The plot for N P V of P falls
through (0, 80,000), (9, 40,000), and (25, 0). The plot for N P V of Q falls
through (0, 100,000), (9, 40,000), and (19, 0). All values estimated.

Long description
The systems engineering approach to risk assessment involves formulation,
analysis, and interpretation. Formulation consists of problem or risk
definition, value system design, and system synthesis. Analysis consists of
systems analysis and modeling, optimization and refinement of alternatives.
Interpretation consists of decision making, planning for action,
implementation.

Long description
The plot for project 1 rises through (80, 0.2) to (100, 83), before falling
through (125, 0.25). The plot for project 2 rises through (80, 0.2) and (125,
0.25) to (200, 0.42), before falling. Left of (80, 0.2), the region between the
project 1 and 2 plots represents the downside risk. All values estimated.

Long description
The tree consists of branches connecting nodes, with each node associated
with an expected monetary value, E M V. Decision node 1 branches through
alternatives A sub 1 and A sub 2 to chance nodes 2 a and 2 b, respectively.
Each chance node branches to payoffs. Each payoff branch has an associated
S sub i value for state of nature t, as well as probability p sub i that S sub i
will occur.

Long description
Segment a: The decision node branches to alternatives A sub 1 to A sub 3 of
f. Segment b: The chance node branches to states S sub 1 to S sub 3 of t.
Each branch is associated with probability p sub i of S sub i.

Long description
Decision node 1 has E M V 33,200 dollars, and it has two branches. First
branch: new, 5 000 dollars per year for 9 years, minus 15,000 dollars. Second
branch to decision node 2: old, 4000 dollars per year for 3 years, minus 800
dollars. Decision node 2 has E M V 22,000 dollars, and it has two branches.
First branch: new, 6,500 dollars per year, for 6 years, minus 17,000 dollars.
Second branch to decision node 3: old, 3,500 dollars per year, for 3 years,
minus 1000 dollars. Decision node 3 has E M V 7000 dollars, and it has two
branches. First branch: new, 6500 dollars per year for 3 years, minus 18,000
dollars. Second branch: old, 3000 per year for 3 years.

Long description
Decision node 1 has E M V 27,000 dollars. The don’t automate branch from
node 1 is associated with 0 dollars. The automate branch leads to chance
node 1 a with E M V 27,000 dollars. Three branches extend from 1 a. The
branches have the following values: poor, 0.5, minus 90,000 dollars; fair, 0.3,
40,000 dollars; excellent, 0.2, 300,000 dollars.

Long description
Decision node 1 has three branches. The don’t automate branch has value 0
dollars. The automate branch leads to chance node 1 a, and three branches
lead from 1 a. Each branch has a rating, a probability, and a monetary value,
as follows: poor, 0.5, minus 90,000 dollars; fair, 0.3, 40,000 dollars;
excellent, 0.2, 300,000. Node 1 also has a technology study branch with
minus 10,000 dollars leading to chance node 1 b. Node 1 b has three branches
with associated probabilities: shaky, 0.41, to decision node 2 A; promising,
0.35, to decision node 2 B; solid, 0.24, to decision node 2 C. Each second-tier
decision node has a don’t automate branch with 0 dollars, as well as an
automate branch leading to a second-tier chance node. Decision node 2 A
leads to chance node 2 a, and the branches from 2 a have the following
values: poor, 0.73, minus 90,000 dollars; fair, 0.22, 40,000 dollars; excellent,
0.05, 300,000 dollars. Decision node 2 B leads to chance node 2 b, and the
branches from 2 b have the following values: poor, 0.43, minus 90,000
dollars; fair, 0.34, 40,000 dollars; excellent, 0.23, 300,000 dollars. Decision
node 2 C leads to chance node 2 c, and the branches from 2 c have the
following values: poor, 0.21, minus 90,000 dollars; fair, 0.37, 40,000 dollars;
excellent, 0.42, 300,000 dollars.

Long description
Decision node 1, with E M V 29 million dollars, has branch, old system
minus 10 million, leading to chance node 1 a, with E M V 36.25 million
dollars. 1 a has two branches: H, 0.5, 45 million; L, 0.5, 27.5 million. Node 1
has branch new F M S minus 35 million, leading to chance node 1 b, with E
M V 64 million. Node 1 b has two branches: H, 0.5, 80 million; L, 0.5, 48
million.

Long description
Decision node 1 has branch old system minus 10 million to chance node 1 a.
Node 1 a has two branches: H, 0.5, 45 million; L, 0.5, 275 million. Node 1
has branch technology minus 2 million, leading to chance node 1 b for
investigation. Node 1 b has two branches: predict high, h, 0.45, ad predict
low, l, 0.55. Branch h leads to decision node 2 A, which has two branches.
Branch old system minus 10 million leads to chance node 2 a, which has two
branches: H, 0.78, 45 million; L, 0.22, 275 million. Branch new F M S minus
35 million leads to chance node 2 b, which has two branches: H, 0.78, 80
million; L, 0.22, 48 million. Branch l leads to decision node 2 B, which has
the same branches as 2 A. Node 1 also has branch new F M S minus 35
million, leading to chance node 1 c, which has two branches: H, 0.5, 80
million; L, 0.5, 48 million.

Long description
The following list provides different decibel levels: 40, home; 50, business
office; 60, conversational speech at 3 feet; 70, dishwasher; 75, vacuum
cleaner; 85, heavy traffic at 25 to 50 feet; 95, subway train at 20 feet; 100,
rock and roll band; 115, un-muffled motorcycle; 120, four-engine jet aircraft
at 500 feet; 140, threshold of pain.

Long description
An assessment of the potential of non-petroleum passenger vehicles considers
minimizing cost, maximizing performance, minimizing technical difficulty,
and maximizing safety. Minimizing cost involves initial cost and life-cycle
cost. maximizing performance involves fuel economy, in terms of miles per
gallon; response time, in terms of refuel time and startup time; and range, in
terms of unrefueled range. Minimizing technical difficulty involves
maintainability or reliability, which are measured on a subjective scale.
Maximizing safety involves leakage prevention, measured on a subjective
scale.

Long description
The plot is a series of rising end-to-end line segments with estimated
endpoints (20, 0), (55, 0.25), (65, 0.5), (70, 0.75), and (80, 1). All values
estimated.

Long description
For the top four levels of the hierarchy, each item is connected all items on
the next level down. The top four levels are numbered 1 to 4 from top to
bottom. Level 1: human productivity. Level 2: workload, support
requirements, acceptability, human-machine interfaces. Level 3: onboard,
ground. Level 4: training, logistics, performance, organizational structure,
health, decision making. Each item in level 4 is connected to every
alternative, from 1 to n. Training consists of regimen, time, tools, and
support. Logistics consists of planning and scheduling, maintenance, and
rescue. Performance consists of stability in zero gravity and working
environment. Organizational structure consists of conflict resolution and
human reliability. Health consists of physical and psychological factors.
Decision making consists of human intelligence, information processing, and
sensory load.

Long description
The cargo handler has operational vision and sensors on the end of the boom,
as well as the front end of the vehicle system, and the vehicle’s vision and
communication systems are mounted at the top of a post rising from the
control station. The handler also includes vehicle control and electronics.

Long description
The tradeoff determination to select a next-generation cargo handler involves
assessing performance, risk, cost, and program objectives. Performance
involves mission objectives, reliability, availability, maintainability, and
safety. Risk involves system integration, technical performance, cost overrun,
schedule overrun. Cost involves research, development, testing, evaluation,
and life-cycle cost. Program objectives involve implementation time table,
technological opportunities, customer acceptability. All factors are connected
to the baseline, upgraded system, U S D C H.Abbreviated version of the
objective hierarchy.

Long description
The company has elements of a traditional functional organization and a
project-oriented organization. Some vice presidents are in charge of
difference departments, such as engineering, manufacturing, and marketing.
These departments are represented by vertical arrangements of workers under
the departmental vice presidents. Other vice presidents are in charge of
projects. The projects are represented by horizontal arrangements of workers
from different departments.

Long description
The organizations types are listed as follows, from left to right on the x-axis:
functional organization, weak matrix, strong matrix, project organization. The
plot rises with increasing steepness through (functional, 0), (strong matrix,
30), and (project, 100).

Long description
Level 1: 1, develop M B A curriculum. Level 2: course subjects from 1.1
courses in finance to 1.6 courses in education. Level 2: individual courses
under each course subject. For example, under 1.1 courses in finance, the
courses are 1.1.1 introduction in finance to 1.1.4 corporate finance.

Long description
Level 1, develop a curriculum; level 2, first and second year courses; and
level 3, course subjects are arranged in tiers from top to bottom. The
individual courses in level 4 are arranged vertically below each level 3 course
subject.

Long description
The form is divided into three sections from top to bottom. The top section
lists the W P identification information, such as name, code, and deliverables,
as well as resources required. The middle section lists the labor and other
resources, in terms of type, labor days, type, quantity, and cost, as well as the
required prerequisites, acceptance tests, work period, and possible risk
events. The bottom section lists the timeline milestones, deliverables, meeting
date, participants, and approvals.

Long description
The portion of the iceberg above the water represents management processes,
including scope definitions, W B S, schedule, development, budgeting risk
analysis, and control. The portion of the iceberg below the water represents
human processes, including emotions, moods desires, conflicts, atmosphere,
power struggles, hidden agendas, commitment, and so on.

Long description
Level 1 is the new restaurant. Level 2 consists of the following: purchases
(kitchen equipment, fixtures, furniture, perishables, staples), process design
(cold dishes, warm dishes, outside food), product design, specification need
(conduct survey, study competition, possible food, possible service),
management (all functions, coordination with university), advertising (on
campus, off campus), local preparation (construction, electricity, plumbing,
equipment installation, furniture), start-up (preparation, pilot run, analysis),
workforce. Product design has the following level 4 elements: menu design
(cold dishes, warm dishes). Workforce has the following level 4 elements:
service (request, training), kitchen (requests, training), cleaning (requests,
training).

Long description
Each plot rises diagonally through a period of growth, before extending
rightward through a period of stagnation, and then falling diagonally through
a period of decline. One plot initially rises from the origin. The other plot
rises from a point right of the origin. The distance between the starting points
represents the delay in reach market. Due to the delay, the second plot levels
off at a lower revenue than the first plot does. The region between the plots
represents lost revenue as a result of delay.

Long description
The foundation of the house of quality is formed by engineering measures or
design characteristics. The walls are formed by customer needs, raking of
needs, relationships between customer needs and design attributes, and
market evaluations and customer perceptions, and the roof is formed by
design attributes and system interrelationships.

Long description
The plot of process cost rises with increasing steepness. The plot of loss as a
result of bad quality falls with decreasing steepness. The plot of total cost is
upward-opening, falling above the loss curve to a minimum above the
intersection between the process cost and loss plots.

Long description
Level 1: microcomputer system. Level 2: 1.0, equipment design, includes the
following level 3 elements: 1.1, main unit; 1.2, printer; 1.3, backup unit; 1.4,
graphic display. 2.0, prototype fabrication, includes the following: 2.1,
fabricate; 2.2, testing; 2.3, quality assurance. 3.0, operations and
maintenance, includes the following: 3.1, user manuals; 3.2, quality
assurance. 4.0, marketing, includes the following: 4.1, demo program; 4.2,
advertising. 5.0, transition to manufacturing, includes, 5.1, hardware, and 5.2,
support.

Long description
The following list provides each activity duration, followed by the
corresponding number of repetitions: 10, 1; 15, 2; 20, 4; 25, 4; 30, 6; 35, 8;
40, 5; 45, 3; 50, 3; 55, 2; 60, 0; 65, 1; 70, 1. All values estimated.

Long description
Plot a: symmetric. The plot is symmetric about a vertical line at x=m. Plot b:
skewed to the right. The plot rises more quickly and it falls. Plot c: skewed to
the left. The plot rises more slowly than it falls.

Long description
The scatter plot is approximated by the linear graph of Y=b sub 0+b sub 1 X.
Data point (X sub 1, Y sub 1) is distance u sub 1 above the line. Data point
(X sub 2, Y sub 2) is distance u sub 2 below the line.

Long description
Start to start: The schedules for activities A and B overlap. The S S interval is
from the start of A to the start of B. Start to finish: the schedules of activities
A and B overlap. The S F interval is from the start of A to the finish of B.
Finish to finish: The schedules for activities A and B overlap. The F F
interval is from the finish of A to the finish of B. Finish to start: The
schedules for activities A and B are separated by a time gap. The F S interval
is from the finish of A to the start of B.

Long description
For each activity, the following list provides the start and finish weeks, as
shown in the Gantt chart: A, 0, 5; B, 0, 3; C, 5, 13; D, 5, 12; E, 0, 7; F, 13,
17; G, 17, 22. All values estimated.

Long description
For each activity, the following list provides the start and finish weeks, as
shown in the Gantt chart: A, 0, 5; B, 3, 3; C, 5, 13; D, 6, 13; E, 6, 13; F, 13,
17; G, 17, 22. All values estimated.

Long description
The schedule represents each activity with a horizontal bar. Segments of the
bar can be shaded or unshaded, and the bar can be bold-outlined. The
following list provides the bar characteristics for each activity. Activity 1,
project kickoff, W B S none: bar shaded from end of December to the second
week of January. Activity 2, equipment design, W B S 1.0: bar bold-outlined
and shaded from beginning of January to beginning of July. Activity 3,
critical design review, W B S none: bar shaded from third week of June to
beginning of July. Activity 4, prototype fabrication, W B S 2.0: bar shaded
from second week of May to second week of August, and unshaded from
second week of August to second week of September. Activity 5, test and
integration, W B S 2.2: bar bold-outlined and unshaded from beginning of
July to the beginning of October. Activity 6, operations and maintenance, W
B S 3.0: bar unshaded from second week of May to third week of November.
Activity 7, marketing, W B S 4.0: bar shaded from beginning of May to
beginning of October, and unshaded from beginning of October to beginning
of February. Activity 8, transition to manufacturing, W B S 5.0: bar bold-
outlined from beginning of October to beginning of February.

Long description
The master schedule uses the following symbols: shaded or unshaded line
segment, activity schedule; inverted, unshaded triangle: originally scheduled
milestone; unshaded triangle, rescheduled milestone; shaded triangle,
completed milestone; line segment from inverted unshaded triangle to
unshaded triangle, slippage.

Long description
Part b shows four networks. First network: solid arc A from 1 to 2, solid arc
B from 1 to 3, dashed arc D sub 1 from 2 to 3. Second network: solid arc A
from 2 to 3, solid arc B from 1 to 3, dashed arc D sub 1 from 1 to 2. Third
network: solid arc A from 1 to 3, solid arc B from 2 to 3, dashed arc D sub 1
from 1 to 2. Fourth network: solid arc A from 1 to 3, solid arc B from 1 to 2,
dashed arc D sub 1 from 2 to 3.

Long description
Network a: solid arcs A and B go to the node, and solid arcs C and E go away
from the node. Network b: The network has two nodes. Solid arcs A and C go
toward and away from the first node. Solid arcs B and E go toward and away
from the second node. Dashed arc D sub 1 go from the second node to the
first node.

Long description
In each network, arcs A, B, C, and E are solid, arc D sub 1 is dashed, and
nodes are numbered. Network a, incorrect: A to 3, B (2, 3), C (2, 5), D sub 1
(3, 5), E from 5, F from 3. Network b, correct: A to 3, B (2, 4), C (2, 5), D
sub 1 (4, 5), E from 5; F from 3.

Long description
A goes from 1 to the first node. B goes from 1 to the second node. D sub 1
goes from the node receiving A to the third node. D sub 2 goes from the node
receiving B to the third node. Three arcs go to the fourth node: C from the
node receiving A, D from the node receiving D sub 1 and D sub 2, and E
from 1. F goes from the fourth node to the fifth node. G goes from the fifth
node to the sixth node.

Long description
The six-by-six matrix has columns for finishing events 1 to 6 and rows for
starting events 1 to 6. The row entries are as follows: blank, X, X, X, blank,
blank; blank, blank, X, X, blank, blank; blank, blank, blank, X, blank, blank;
blank, blank, blank, blank, X, blank; blank, blank, blank, blank, blank, X;
blank, blank, blank, blank, blank, blank.

Long description
The network contains the following arcs: (start, A), (start, B), (start, E), (A,
C), (A, D), (B, D), (C, F), (E, F), (F G), (G, end). The following list provides
the E S and L S values for each node: start, 0, 0; A, 0, 0; B, 0, 3; C, 5, 5; D, 5,
6; E, 0, 6; F, 13, 13; G, 17, 17; end, 22, 22.

Long description
The node contains the node name above the duration. The forward pass is
detailed above the arc, and the backward pass is detailed below the arc. The
values around the node are as follows: top left, early start; top right, early
finish; bottom left, late start; bottom right, late finish. The network has arcs
(A, B) and (B, C). For each node, the following list provides the duration,
early start, early finish, late start, late finish: A, 10; 0, 10; 0, 10. B, 10; 10, 20;
10, 20. C, 10; 20, 30; 20, 30.

Long description
Arc (A, B) has S S=2 and F F=2. Arc (B, C) has S S=2 and F F=2. For each
node, the following list provides the duration, early start, early finish, late
start, late finish: A, 10; 0, 10; 0, 10. B, 10; 2, 12; 2, 12. C, 10; 4, 14; 4, 14.

Long description
In the chart, activities A, B, and C are each partitioned into segments. The
following list provides the start and finish date in days for each segment, as
shown in the chart: A sub 1, 0, 2; A sub 2, 2, 10; B sub 1, 2, 4; B sub 2, 4, 10;
B sub 3, 10, 12; C sub 1, 4, 12; C sub 2, 12, 14.

Long description
The network has the following arcs: (A sub1, A sub 2), (A sub 1, B sub 1), (A
sub 2, B sub 3), (B sub 1, B sub 2), (B sub 2, B sub 3), (B sub 1, C sub 1), (C
sub 1, C sub 2), (B sub 3, C sub 2). The values for the nodes are as follows: A
sub 1, 2; 0, 2; 0, 2. A sub 2, 8; 2, 10; 2, 10. B sub 1, 2; 2, 4; 2, 4. B sub 2, 6;
4, 10; 4, 10. B sub 3, 2; 10, 12; 10, 12. C sub 1, 8; 4, 12; 4, 12. C sub 2, 2; 12,
14; 12, 14.

Long description
The network has arcs (A, B) and (B, C). The node values are as follows: A,
10; 0, 10; 0, 10. B, 5; 10, 15; 10, 15. C, 15;15, 30; 15, 30.

Long description
The network has arc (A, B) with S S=2 and F F=1, and arc (B, C) with S S=1
and F F=3. The node values are as follows: A, 10; 0, 10; 0, 14. B, 5; 2, 11; 2,
15. C, 15; 3, 18; 3, 18.

Long description
The network has the following arcs: (A sub1, A sub 2), (A sub 1, B sub 1), (A
sub 2, B sub 3), (B sub 1, B sub 2), (B sub 2, B sub 3), (B sub 1, C sub 1), (C
sub 1, C sub 2), (B sub 3, C sub 2). The values for the nodes are as follows: A
sub 1, 2; 0, 2; 0, 2. A sub 2, 8; 2, 10; 6, 14. B sub 1, 2; 2, 3; 2, 3. B sub 2, 3;
3, 6; 11, 14. B sub 3, 1; 10, 11; 14, 15. C sub 1, 12; 3, 15; 3, 15. C sub 2, 3;
15, 18; 15, 18.

Long description
The following list provides each project length, followed by the
corresponding frequency, as shown in the bar graph: 17, 1; 18, 4; 19, 4; 20, 4;
21, 7; 222, 4; 23, 8; 24, 5; 25, 5; 26, 4; 27, 4; 28, 1; 29, 1. All values
estimated.

Long description
The plot is a bell-shaped curve with peak (22.5, 0.16). The region under the
curve left of x=25 represents 0.805. All values estimated.

Long description
For each network, every arc is followed by its weight. Network for project a:
(1, 5), 5; (1, 2), 10; (1, 4), 1; (2, 5), 8; (2, 6), 10; (2, 3), 9; (3, 6), 4; (3, 4), 3;
(4, 6), 5; (4, 7), 4; (5, 6), 7; (5, 7), 3; (6, 7), 8. Network for project b: (1, 3), 1;
(1, 2), 3; (1, 6), 7; (2, 3), 8; (2, 5), 10; (3, 4), 3; (3, 7), 10; (4, 5), 10; (4, 7),
22; (5, 6), 5; (5, 7), 12; (6, 7), 7.

Long description
The columns show p-values for z-values from 0.00 to 0.09, and the rows
show p-values for z=0.0 to 3.4 from top to bottom. The row 1 values increase
from 0.5 to 0.5359, from left to right, with values also increasing down
columns.

Long description
The horizontal axis is divided into the following phases from left to right:
conceptual design, advanced development, detailed design, production,
termination. Plot a for engineers peaks in advanced development. Plot b for
technicians levels off in detailed design and production. Plot c for material
levels off in detailed design and production.

Long description
Gantt chart a for activities A to G: A, 0, 5; B, 0, 3; C, 5, 13; D, 5, 12; E, 0, 7;
F, 13, 17; G, 17, 22. Plot b represents resource in person days per week
versus week. The plot is at 17 for weeks 1 to 3. It falls to 5 for weeks 7 to 12,
rises to 9 for weeks 13 to 17, and then falls. All values estimated.

Long description
Gantt chart a for activities A to G: A, 0, 5; B, 0, 3; C, 5, 13; D, 5, 12; E, 0, 7;
F, 13, 17; G, 17, 22. Plot b represents resource in person days per week
versus week. The plot rises to 13 for weeks 2 to 5, falls to 3 for weeks 5 and
6, rises to 10 for weeks 6 to 13, and then falls. All values estimated.

Long description
Gantt chart a for activities A to G: A, 0, 5; B, 0, 3; C, 5, 12; D, 6, 12; E, 5,
12; F, 13, 17; G, 17, 22. Plot b represents resource in person days per week
versus week. The plot is at 12 for weeks 1 to 3, falls to 8 for weeks 4 to 6,
rises to 10 for weeks 7 to 12, falls to 5 for week 13, rises to 9 for weeks 14 to
17, and then falls. All values estimated.

Long description
Gantt chart a for activities A to G: A, 0, 5; B, 5, 8; C, 5, 12; D, 8, 13; E, 8,
13; F, 14, 18; G, 18, 24. Plot b represents resource in person days per week
versus week. The plot is at 8 for weeks 1 to 5, 7 for weeks 5 to 8, 10 for
weeks 8 to 13, 7 for weeks 14 and 15, 9 for weeks 15 to 18, and 7 for weeks
19 to 24. All values estimated.

Long description
Gantt chart a for activities A to G: A, 0, 5; B, 5, 8; C, 5, 13; D, 8, 15; E, 13,
20; F, 20, 24; G, 24, 29. The plot is at 8 for weeks 1 to 5, 7 for weeks 5 to 8,
5 for weeks 8 to 13, 7 for weeks 13 to 15, 5 for weeks 15 to 20, 9 for weeks
20 to 24, and 7 for weeks 24 to 29. All values estimated.

Long description
The first plot for early start falls from (3, 2.1) to (4, 1.1), rises to (6, 1.9),
extends rightward to (13, 1.9), falls to (14, 1.5), and levels off. The second
plot for late start falls from (5, 1.4) to (6, 0.4), rises to (7, 1.9), falls to (13,
0.4), rises to (14, 1.5), and then approximates the first plot. All values
estimated.

Long description
The early start plot rises from (1, 2.5) through (7, 13) to (13, 18), before
rising diagonally through (22, 32). The late start plot rises from (1, 1) through
(6, 5) to (13, 18), before rising diagonally through (22, 32). All values
estimated.

Long description
The total cost plot falls from (14, 52) to (19, 46) and then rises to (22, 47.5).
The direct cost plot falls from (14, 44) to (22, 32). The overhead plot rises
roughly diagonally from (14, 7) to (22, 10). The penalty plot extends
rightward from (14, 0) to (18, 0), before rising to (22, 4). All values
estimated.

Long description
Cells A 1 to E 5 contain the following values from left to right and top to
bottom, with semicolons between rows: blank, value, duration, blank, value;
start 12, 0, 5, finish 12, 5; start 13, 0, 4, finish 13, 4; start 24, 0, 4, finish 13,
9; start 34, 4, 6, finish 34, 10. Cells A 8 and B 8 contain overhead cost per
period and 20, respectively. Cells A 11 to G 16 contain the following values
from left to right and top to bottom, with semicolons between rows: blank,
normal, crash, blank, blank; blank, duration, cost, duration, cost, binary
variable, direct cost; task (1, 2), 5, 100, 3, 150, 1, 100; task (1, 3), 4, 70, 3,
100, 1, 70; task (2, 4), 4, 200, 3, 300, 1, 200; task (3, 4), 6, 500, 3, 900, 1,
500. Cells A 18 to B 21 contain the following from left to right and top to
bottom, with semicolons between rows: total direct cost, 870; total overhead
cost, 200; total cost, 1070.

Long description
The inputs are as follows. By changing variable cells: $ F $ 13 : $ F $ 16, $ B
$ 2 : $ B $ 5. Subject to constraints: $ B $ 4>=$ E $ 2, $ B $ 2 : $ B $ 5>=0,
$ B $ 5>=$ E $ 3, $ F $ 13 : $ F $ 16=binary. Select a solving method:
simplex L P.

Long description
Plot a represents cumulative budget and cumulative cost versus week. The
plot of the lower limit rises from (0, 0) through (2, 450) to (4, 1100). The plot
of the cumulative budget rises from (0, 0) through (2, 550) to (4, 1200). The
plot of the upper control limit rises from (0, 0) through (2, 650) to (4, 1300).
The plot of the actual cost rises from (0, 0) through (2, 1000) to (4, 1500).
Plot b represents periodic budget and cost versus week. The plots of the
lower limit, the periodic budget, and the upper limit are horizontal at y=2020,
300, and 380, respectively. The plot of the actual cost rises from (0, 0) to (1,
500), extends rightward to (2, 500), and then falls to (4, 200). All values
estimated.

Long description
Departments 1 and 2 of the O B S are represented by entries in columns 1 and
2 of a table. W B S elements 1 to 3 are represented by the entries in rows 1 to
3 of the same table. The table entries are as follows, from left to right: row 1,
C and D, A; row 2, F, B; row 3,G, E.

Long description
The following list provides the critical status and start and finish times, in
weeks, for activities A to G: A, critical, 0 to 5; B, non-critical, 0 to 3; C,
critical, 5 to 13; D, non-critical, 5 to 12; E, non-critical, 0 to 6; F, critical, 13
to 17; G, critical, 17 to 22. All values estimated.

Long description
Part a: The plot of B C W S rises from (0, 0) through (2, 600) to (4, 1200).
The plots of A C W P and B C W P rise from (0, 0) through (2, 1000) to (4,
1500). Part b: The plots of B C W P, B C W S, and A C W P rise from (0, 0)
through (1, 1000) to (2, 2000). From this point the B C W S plot rises through
(3, 3000), and the B C W P and A C W P rise through (3, 2500) and (4,
3000). Part c: The plot of B C W P rise from (0, 0) through (2, 700) to (4,
1628). The plot of A C W P rises from (0, 0) through (2, 1500) to (4, 2900).
The plot of B C W S rises from (0, 0) through (2, 1628) to (4, 3256).

Long description
Dashed horizontal and vertical lines intersect at (1, 1). Clockwise from the
top left, the lines divide the plane into the following regions: schedule
problems, project on schedule and on budget, budget problems, schedule and
budget problems. The plot is a series of end-to-end line segments with the
following end points: week 1 (0.85, 0.78), week 2 (0.88, 0.82), week 3 (0.79,
0.79), week 4 (0.82, 0.83).

Long description
The plot represents number of systems versus week, with x=5 representing
the current control period. The plot rises from (6, 0) through (6, 30), (7, 50),
(8, 60), (9, 90), and (10, 110), before leveling off. The bar graph represents
number of systems versus milestones A to D. The bar heights for A to D
correspond to the y-values of the following points on the plot: (9, 90), (8, 60),
and (6, 30). All values estimated.

Long description
The relationship between corporate and R and D planning is represented by 3
nested ovals. The following list provides the organizations and activities
represented by each oval. Outer oval, external environment: competition,
economics, technology, regulation, politics. Middle oval, corporate planning:
financial position, marketing needs, organization, resources, customers. Inner
oval, R and D strategic plan: identify threats, opportunities, strengths,
weaknesses, and key concerns; forge strategies and tactics; and select project
portfolio.

Long description
The process begins with an idea. The company then conducts an informal
review, which indicates very little cost and commitment of a few thousand
dollars to the next stage. Stage 1, feasibility, follows the initial review. This is
a more formal review using quantitative evaluation techniques. The company
determines that commitment of a few thousand dollars will be required to
move the project to the next stage. Stage 2, development, follows. The stage
2 review involves top management, determining the cost increases with time
and formal periodic reviews will be required. Stage 3 includes test market,
and its review looks at fine tuning and market response evaluation. Full-scale
commercialization follows stage 3. If any review ends with the determination
that the project is not worth pursuing, then the team moves it to the project
burial ground.

Long description
Plot a is nonlinear; it rises with decreasing and then increasing steepness. Plot
b is piecewise linear; it consists of a series of end-to-end line segments. Plot c
is discrete; it consists of a series of separate points. Plot d is linear; it is a
rising line.

Long description
The first column of each row identifies a task, and the row includes a shaded
horizontal bar, with left and right ends marked with month and year. Arrows
lead from the finish times of one or more activity bars to the start time of a
lower bar.

Long description
In the chart, each activity is identified by start date, finish date, I D, duration
in weeks, and R e s=labor. Activities A to G have I D numbers 2 to 8, and the
flow is as follows: A to C; B and C to D; C, D, and E to F; F to G.

Long description
The chart shows activity schedules as horizontal black and gray bars, with
arrows from the right ends of some bars to the left ends of others. for each
activity, the following list provides the color, the start month in 2005, and the
finish month in 2005: example, black, beginning January, mid-May; element
1, black, beginning January, mid-March; A, black, beginning January, mid-
January; C, black, mid-January, mid-March; D, gray, mid-January, mid-
March; element 2, black, beginning January, beginning May; B, gray,
beginning January, mid-January; F, black, mid-March, end-March; element 3,
black, beginning January, mid-May; E, gray, beginning January, last quarter
January; G, black, beginning May, mid-May. The flows are as follows: A to
C; B and C to D; C, D, and E to F; F to G.

Long description
For tasks A to G, the spread sheet for the schedule summary report shows the
duration in weeks, the early start date, the late start date, the early finish date,
and the late finish date, as listed: A, 5, Monday 1/3/2005, Monday 1/3/2005,
Friday 2/4/2005, Friday 2/4/2005; B, 3, Monday 1/3/2005, Monday
1/24/2005, Friday 1/21/2005, Friday 2/11/2005; C, 8, Monday 2/7/2005,
Monday 2/7/2005, Friday, 4/1/2005, Friday 4/1/2005; D, 7, Monday
2/7/2005, Monday 2/14/2005, Friday 3/25/2005, Friday 4/1/2005; E, 7,
Monday 1/3/2005, 2/14/2004, Friday 2/18/2005; Friday 4/1/2005; F, 4,
Monday 4/4/2005, Monday 4/42005, Friday 4/29/2005, Friday 4/29/2005; G,
5, Monday 5/2/2005, Monday 5/2/2005, Friday 6/3/2005, Friday 6/3/2005.

Long description
For tasks A to G, the spread sheet shows duration in weeks, start date, finish
date, and cost in dollars, as follows: A, 5, Monday 3/1/2005, Friday 4/2/2005,
1500; B, 3, Monday 3/1/2005, Friday 3/19/20005, 2700; C, 8, Monday
4/5/2005, Friday 5/28/2005, 3300; D, 7, Monday 4/5/2005, Friday 5/21/2005,
4200; E, 7, Monday 3/1/2005, Friday 4/16/2005, 5700; F, 4, Monday
5/31/2005, Friday 6/25/2005, 6100; G,5, Monday 6/28/2005, Friday
7/30/2005, 7200.

Long description
For tasks A to G, the spread sheet shows duration in weeks, early start date,
baseline start date, early finish date, and baseline finish date, as follows: A, 5,
Monday 1/3/2005, Thursday, 1/1/2004, Friday 2/4/2005, Wednesday
2/4/2004; B, 3, Monday 1/3/2005, Thursday 1/1/2004, Friday 1/21/2005,
Wednesday 2/25/2004; C, 8, Monday 2/7/2005, Thursday 2/5/20004, Friday
4/1/2005, Wednesday 3/31/2004; D, 7, Monday 2/7/2005, Thursday
2/26/2004, Friday 3/25/2005, Wednesday 4/14/2004; E, 7, Monday 1/3/2005,
Thursday 2/26/2004, Friday 2/18/2005, Wednesday 4/14/2004; F, 4, Monday
4/4/2005, Thursday 4/15/2004, Friday 4/29/2005, Wednesday 5/12/2004; G,
5, Monday 5/2/2005, Thursday 5/13/2004, Friday 6/3/2005, Wednesday
6/16/2004.

Long description
For activities A to G, the display shows duration in weeks, percent complete,
actual cost in dollars, and cost in dollars: A, 5, 100%, 1500, 1500; B, 3,
100%,2700, 2700; C, 8, 0% 0, 3300; D, 7, 0%, 0, 4200; E, 7, 50%, 2860,
5700; F, 4, 0%, 0, 6100; G, 5, 0%, 0, 7200.

Long description
The rows of the spread sheet represent the following, from top to bottom:
example, element 1, A, C, D, element 2, B, F, element 3, E, G. The columns
represent the following from left to right: actual start, actual finish, 0%
complete, actual work in hours, finish date.

Long description
The rows of the spread sheet represent the following, from top to bottom:
example, element 1, A, C, D, element 2, B, F, element 3, E, G. The columns
represent the following: actual start, percent complete, actual work in hours,
B C W P, B C W S, A C W P, S V, and C V.

Long description
Project termination problems consist of emotional and intellectual problems.
Emotional problems can be staff-centered or client-centered. Staff-centered
emotional problems include fear of no future work, loss of interest in
remaining tasks, loss of project-derived motivation, loss of team identity,
selection of personnel to be reassigned, reassignment methodology, and
diversion of effort. Client-centered emotional problems include change in
attitude, loss of interest in project, change in personnel dealing with project,
unavailability of key personnel. Intellectual problems can be internal or
external. Internal problems include identification of remaining deliverables,
certification of needs, identification of outstanding commitments, control of
charges to project, screen of partially completed tasks, closure of work orders
and work packages, identification of facilities assigned to project,
accumulation and structuring of historical data, and disposal of unused
material. External problems include agreement with client on remaining
deliverables, obtaining required certifications, agreement with suppliers on
outstanding commitments, communicating closure, closing down facilities,
and determinations of requirements for audit trail data.

Long description
The scatter plot represents cash versus benefit group. Most data points belong
to the moderate benefit group. The median for these points is at 25,000, with
the middle 50% of points from 10,000 to 40,000. The data points for the low
benefit group has a median 22,000 with the middle 50% from 10,000 to
36,000. The high benefit group has a median at 15,000 with quadrants 1 and
4 at 8,000 and 12,000, respectively. All values estimated.

Project Management Processes, Methodologies, and Economics
Project Management Processes, Methodologies, and Economics
Project Management Processes, Methodologies, and Economics
Project Management Processes, Methodologies, and Economics
Project Management Processes, Methodologies, and Economics
Contents
Nomenclature
Preface
Whats New in this Edition
Whats New in this Edition
1.1 Nature of Project Management
1.2 Relationship Between Projects and Other Production Systems
1.3.4 Organizing for a Project
1.4.2 Characteristics of Effective Project Managers
1.5 Components, Concepts, and Terminology
1.6 Movement to Project-Based Work
1.7 Life Cycle of a Project: Strategic and Tactical Issues
1.8 Factors that Affect the Success of a Project
Total Manufacturing Solutions, Inc.
Discussion Questions
Exercises
Bibliography
Additional References
2.1.3 Application of the Waterfall Model for Software Development
2.2.2 PMBOK and Processes in the Project Life Cycle
Integrated change control
2.4.2 Description
2.5.2 Description
2.6.2 Description
2.7.2 Description
2.8.2 Description
2.9.2 Description
2.10.2 Description
2.11.2 Description
2.12.2 Description
2.13.2 Workflow and Process Design as the Basis of Learning
Discussion Questions
Exercises
Bibliography
3.1.3 Discount Rate, Interest Rate, and Minimum Acceptable Rate of Return
3.2.4 Treatment of Risk
3.3.2 Steps in the Analysis
3.4.6 Payback Period Method
Solution
Note
3.7.5 Characteristics of the Utility Function
Discussion Questions
Exercises
Bibliography
4.1 Need for Life-Cycle Cost Analysis
4.2 Uncertainties in Life-Cycle Cost Models
4.3 Classification of Cost Components
4.4 Developing the LCC Model
4.5 Using the Life-Cycle Cost Model
Discussion Questions
Exercises
Bibliography
5.1 Components of the Evaluation Process
5.2 Dynamics of Project Selection
5.3 Checklists and Scoring Models
5.4.4 Shortcomings of the Benefit-Cost Methodology
5.5 Cost-Effectiveness Analysis
5.6.5 Limits of Risk Analysis
5.7.4 Discussion and Assessment
5.8.2 Relationship to Portfolio Management
Discussion Questions
Exercises
Bibliography
Appendix 5A Bayes Theorem for DiscreteOutcomes
6.1 Introduction
6.2.2 Aggregating Objectives into a Value Model
6.3.1 Violations of Multiattribute Utility Theory
6.4.3 Determining Global Priorities
6.5.4 Group Decision Support Systems
Discussion Questions
Exercises
Bibliography
References
7.1 Introduction
7.2.7 Criteria for Selecting an Organizational Structure
7.3.3 Project Office
7.4.2 Work Package Design
7.5.1 Linear Responsibility Chart
7.6.4 Ethical and Legal Aspects of Project Management
Discussion Questions
Exercises
Bibliography
8.1.2 Management of Technology and Design in Projects
8.2 Project Managers Role
8.3.5 Unresolved Issues
Risk monitoring and control
8.5.6 Cost of Quality
Discussion Questions
Exercises
Bibliography
9.1.2 Network Techniques
9.2.5 Parametric Technique
9.3 Effect of Learning
9.4 Precedence Relations Among Activities
9.5 Gantt Chart
9.6.3 Calculating Slacks
9.7.2 Calculating Late Start and Late Finish Times of Activities
9.8 Precedence Diagramming with LeadLag Relationships
9.9 Linear Programming Approach for CPM Analysis
9.10.2 Milestones
9.11.2 PERT and Extensions
9.12 Critique of Pert and CPM Assumptions
9.13 Critical Chain Process
9.14 Scheduling Conflicts
Discussion Questions
Exercises
Bibliography
Appendix 9A Least-Squares Regression Analysis
Appendix 9B Learning Curve Tables
Appendix 9C Normal Distribution Function
10.1 Effect of Resources on Project Planning
10.2 Classification of Resources Used in Projects
10.3 Resource Leveling Subject to Project Due-Date Constraints
10.4 Resource Allocation Subject to Resource Availability Constraints
10.5 Priority Rules for Resource Allocation
10.6 Critical Chain: Project Management by Constraints
10.7 Mathematical Models for Resource Allocation
10.8 Projects Performed in Parallel
Discussion Questions
Exercises
Bibliography
11.1 Introduction
11.2 Project Budget and Organizational Goals
11.3.3 Iterative Budgeting
11.4.2 Crashing
11.5 Presenting the Budget
11.6 Project Execution: Consuming the Budget
11.7 The Budgeting Process: Concluding Remarks
Discussion Questions
Exercises
Bibliography
Appendix 11A TimeCost Tradeoff With Excel
12.1 Introduction
12.2 Common Forms of Project Control
12.3.2 Earned Value Approach
12.4 Reporting Progress
12.5 Updating Cost and Schedule Estimates
12.6 Technological Control: Quality and Configuration
12.7 Line of Balance
12.8 Overhead Control
Discussion Questions
Exercises
Bibliography
Appendix 12A Example of a Work Breakdown Structure
Appendix 12B Department of Energy Cost/Schedule Control Systems Criteria
13.1 Introduction
13.2.5 Cost and Time Overruns
13.3.3 Relationship between Technology and Projects
Planning Is A Multistage Process
13.5.3 Q-GERT
Implementation
Discussion Questions
Exercises
Bibliography
Appendix 13A Portfolio Management Case Study
14.1 Introduction
14.2.2 Tools and Techniques for Project Management
14.3 Criteria for Software Selection
14.4 Software Selection Process
14.5 Software Implementation
14.6 Project Management Software Vendors
Discussion Questions
Exercises
Bibliography
Appendix 14A PMI Software Evaluation Checklist
15.1 Introduction
15.2 When to Terminate a Project
15.3 Planning for Project Termination
15.4 Implementing Project Termination
15.5 Final Report
Discussion Questions
Exercises
Bibliography
16.1 Introduction
16.2 Motivation for Simulation-Based Training
16.3 Specific ExampleThe Project Team Builder (PTB)
16.4 The Global Network for Advanced Management (GNAM) MBA New Product Development (NPD) Course
16.5 Project Management for Engineers at Columbia University
16.6 Experiments and Results
16.7 The Use of Simulation-Based Training for Teaching Project Management in Europe
16.8 Summary
Bibliography
Index
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