Read the scenario carefully and make sure you know what the deliverables  are.  In all the discussions you will be creating professional  correspondence (memos, reports, plans, studies, etc.) that you are  likely to prepare in industry.  Please do not submit APA formatted academic research papers since the discussions call for professional correspondence.   

Scenario

You are creating a vendor comparison matrix  for use in the receiving department, and a memo to the COO detailing  how the matrix will be implemented.  

Your target audience for the vendor comparison matrix is the  receiving department staff, not the COO, so you want to prepare the  matrix so the staff can use it.  In the memo, explain to the COO how the  matrix will be used and how it will help the organization.  The thing  to keep in mind is the matrix is a directive and the memo is to inform,  so you want to write them from that perspective.

Chapter 3

Forecast

ing

© McGraw-Hill Education.

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1

Learning Objectives (1 of 2)

You should be able to:

3.1 List features common to all forecasts

3.2 Explain why forecasts are generally wrong

3.3 List elements of a good forecast

3.4 Outline the steps in the forecasting process

3.5 Summarize forecast errors and use summaries to make decisions

3.6 Describe four qualitative forecasting techniques

3.7 Use a naïve method to make a forecast

3.8 Prepare a moving average forecast

3.9 Prepare a weighted-average forecast

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© McGraw-Hill Education.

Learning Objectives (2 of 2)

3.10 Prepare an exponential smoothing forecast

3.11 Prepare a linear trend forecast

3.12 Prepare a trend-adjusted exponential smoothing forecast

3.13 Compute and use seasonal relatives

3.14 Compute and use regression and correlation coefficients

3.15 Construct control charts and use them to monitor forecast errors

3.16 Describe the key factors and trade-offs to consider when choosing a forecasting technique

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Forecast

Forecast – a statement about the future value of a variable of interest

We make forecasts about such things as weather, demand, and resource availability

Forecasts are important to making informed decisions

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Two Important Aspects of Forecasts

Expected level of demand

The level of demand may be a function of some structural variation such as trend or seasonal variation

Accuracy

Related to the potential size of forecast error

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Forecast Uses (1 of 2)

Plan the system

Generally involves long-range plans related to:

Types of products and services to offer

Facility and equipment levels

Facility location

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Forecast Uses (2 of 2)

Plan the use of the system

Generally involves short- and medium-range plans related to:

Inventory management

Workforce levels

Purchasing

Production

Budgeting

Scheduling

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Learning Objective 3.1

Features Common to All Forecasts

Techniques assume some underlying causal system that existed in the past will persist into the future

Forecasts are not perfect

Forecasts for groups of items are more accurate than those for individual items

Forecast accuracy decreases as the forecasting horizon increases

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Learning Objective 3.2

Forecasts Are Not Perfect

Forecasts are not perfect:

Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for.

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Learning Objective 3.3

Elements of a Good Forecast

The forecast

Should be timely

Should be accurate

Should be reliable

Should be expressed in meaningful units

Should be in writing

Technique should be simple to understand and use

Should be cost-effective

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Learning Objective 3.4

Steps in the Forecasting Process

Determine the purpose of the forecast

Establish a time horizon

Obtain, clean, and analyze appropriate data

Select a forecasting technique

Make the forecast

Monitor the forecast errors

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Learning Objective 3.5

Forecast Accuracy and Control

Allowances should be made for forecast errors

It is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs

Forecast errors should be monitored

Error = Actual – Forecast

If errors fall beyond acceptable bounds, corrective action may be necessary

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© McGraw-Hill Education.

Learning Objective 3.5

Forecast Accuracy Metrics

MAD weights all errors evenly

MSE weights errors according to their squared values

MAPE weights errors according to relative error

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Learning Objective 3.5
Forecast Error Calculation
Period Actual
(A) Forecast
(F) (A-F) Error |Error| Error2 [|Error|/Actual]x100
1 107 110 -3 3 9 2.80%
2 125 121 4 4 16 3.20%
3 115 112 3 3 9 2.61%
4 118 120 -2 2 4 1.69%
5 108 109 1 1 1 0.93%
Sum 13 39 11.23%
n = 5 n-1 = 4 n = 5
MAD MSE MAPE
= 2.6 = 9.75 = 2.25%

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Learning Objective 3.6
Forecasting Approaches (1 of 2)
Qualitative forecasting
Qualitative techniques permit the inclusion of soft information such as:
Human factors
Personal opinions
Hunches
These factors are difficult, or impossible, to quantify

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Learning Objective 3.6
Forecasting Approaches (2 of 2)
Quantitative forecasting
These techniques rely on hard data
Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast

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Learning Objective 3.6
Qualitative Forecasts (1 of 2)
Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts
Executive opinions
A small group of upper-level managers may meet and collectively develop a forecast
Sales force opinions
Members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future

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Learning Objective 3.6
Qualitative Forecasts (2 of 2)
Consumer surveys
Since consumers ultimately determine demand, it makes sense to solicit input from them
Consumer surveys typically represent a sample of consumer opinions
Other approaches
Managers may solicit 0pinions from other managers or staff people or outside experts to help with developing a forecast.
The Delphi method is an iterative process intended to achieve a consensus

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Time-Series Forecasts
Forecasts that project patterns identified in recent time-series observations
Time-series – a time-ordered sequence of observations taken at regular time intervals
Assume that future values of the time-series can be estimated from past values of the time-series

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Time-Series Behaviors
Trend
Seasonality
Cycles
Irregular variations
Random variation

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Trends and Seasonality
Trend
A long-term upward or downward movement in data
Population shifts
Changing income
Seasonality
Short-term, fairly regular variations related to the calendar or time of day
Restaurants, service call centers, and theaters all experience seasonal demand

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Cycles and Variations (1 of 2)
Cycle
Wavelike variations lasting more than one year
These are often related to a variety of economic, political, or even agricultural conditions
Irregular variation
Due to unusual circumstances that do not reflect typical behavior
Labor strike
Weather event

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© McGraw-Hill Education.

Cycles and Variations (2 of 2)
Random Variation
Residual variation that remains after all other behaviors have been accounted for

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Learning Objective 3.7
Time-Series Forecasting – Naïve Forecast
Naïve forecast
Uses a single previous value of a time series as the basis for a forecast
The forecast for a time period is equal to the previous time period’s value
Can be used with
A stable time series
Seasonal variations
Trend

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Learning Objective 3.8
Time-Series Forecasting – Averaging
These techniques work best when a series tends to vary about an average
Averaging techniques smooth variations in the data
They can handle step changes or gradual changes in the level of a series
Techniques
Moving average
Weighted moving average
Exponential smoothing

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© McGraw-Hill Education.

Learning Objective 3.8
Moving Average (1 of 2)
Technique that averages a number of the most recent actual values in generating a forecast

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© McGraw-Hill Education.

Learning Objective 3.8
Moving Average (2 of 2)
As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then re-computing the average
The number of data points included in the average determines the model’s sensitivity
Fewer data points used—more responsive
More data points used—less responsive

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Learning Objective 3.9
Weighted Moving Average
The most recent values in a time series are given more weight in computing a forecast
The choice of weights, w, is somewhat arbitrary and involves some trial and error

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© McGraw-Hill Education.

Learning Objective 3.10
Exponential Smoothing
A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error

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© McGraw-Hill Education.

Learning Objective 3.11
Linear Trend
A simple data plot can reveal the existence and nature of a trend
Linear trend equation

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Learning Objective 3.11
Estimating Slope and Intercept
Slope and intercept can be estimated from historical data

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© McGraw-Hill Education.

Learning Objective 3.12
Trend-Adjusted Exponential Smoothing (1 of 2)
The trend adjusted forecast consists of two components
Smoothed error
Trend factor

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Learning Objective 3.12
Trend-Adjusted Exponential Smoothing (2 of 2)
Alpha and beta are smoothing constants
Trend-adjusted exponential smoothing has the ability to respond to changes in trend

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© McGraw-Hill Education.

Learning Objective 3.13
Techniques for Seasonality (1 of 2)
Seasonality – regularly repeating movements in series values that can be tied to recurring events
Expressed in terms of the amount that actual values deviate from the average value of a series
Models of seasonality
Additive
Seasonality is expressed as a quantity that gets added to or subtracted from the time-series average in order to incorporate seasonality

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© McGraw-Hill Education.

Learning Objective 3.13
Techniques for Seasonality (2 of 2)
Multiplicative
Seasonality is expressed as a percentage of the average (or trend) amount which is then used to multiply the value of a series in order to incorporate seasonality

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© McGraw-Hill Education.

Learning Objective 3.13
Seasonal Relatives (1 of 2)
Seasonal relatives
The seasonal percentage used in the multiplicative seasonally adjusted forecasting model
Using seasonal relatives
To deseasonalize data
Done in order to get a clearer picture of the nonseasonal (e.g., trend) components of the data series
Divide each data point by its seasonal relative

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© McGraw-Hill Education.

Learning Objective 3.13
Seasonal Relatives (2 of 2)
To incorporate seasonality in a forecast
Obtain trend estimates for desired periods using a trend equation
Add seasonality by multiplying these trend estimates by the corresponding seasonal relative

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© McGraw-Hill Education.

Learning Objective 3.14
Associative Forecasting Techniques
Associative techniques are based on the development of an equation that summarizes the effects of predictor variables
Predictor variables – variables that can be used to predict values of the variable of interest
Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms

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Learning Objective 3.14
Simple Linear Regression
Regression – a technique for fitting a line to a set of data points
Simple linear regression – the simplest form of regression that involves a linear relationship between two variables
The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion)

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Learning Objective 3.14
Least Squares Line

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Learning Objective 3.14
Correlation Coefficient (1 of 2)
Correlation, r
A measure of the strength and direction of relationship between two variables
Ranges between -1.00 and +1.00

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Learning Objective 3.14
Correlation Coefficient (2 of 2)
r2, square of the correlation coefficient
A measure of the percentage of variability in the values of y that is “explained” by the independent variable
Ranges between 0 and 1.00

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Learning Objective 3.14
Simple Linear Regression Assumptions
Variations around the line are random
Deviations around the average value (the line) should be normally distributed
Predictions are made only within the range of observed values

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Learning Objective 3.14
Issues to Consider:
Always plot the line to verify that a linear relationship is appropriate
The data may be time-dependent
If they are
use analysis of time series
use time as an independent variable in a multiple regression analysis
A small correlation may indicate that other variables are important

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© McGraw-Hill Education.

Learning Objective 3.15
Monitoring the Forecast (1 of 2)
Tracking forecast errors and analyzing them can provide useful insight into whether forecasts are performing satisfactorily
Sources of forecast errors:
The model may be inadequate due to
omission of an important variable
a change or shift in the variable the model cannot handle
the appearance of a new variable

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© McGraw-Hill Education.

Learning Objective 3.15
Monitoring the Forecast (2 of 2)
Irregular variations may have occurred
Random variation
Control charts are useful for identifying the presence of non-random error in forecasts
Tracking signals can be used to detect forecast bias

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© McGraw-Hill Education.

Learning Objective 3.15
Control Chart Construction

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© McGraw-Hill Education.

Learning Objective 3.16
Choosing a Forecasting Technique
Factors to consider
Cost
Accuracy
Availability of historical data
Availability of forecasting software
Time needed to gather and analyze data and prepare a forecast
Forecast horizon

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© McGraw-Hill Education.

Operations Strategy (1 of 2)
The better forecasts are, the more able organizations will be to take advantage of future opportunities and reduce potential risks
A worthwhile strategy is to work to improve short-term forecasts
Accurate up-to-date information can have a significant effect on forecast accuracy:
Prices
Demand
Other important variables

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© McGraw-Hill Education.

Operations Strategy (2 of 2)
Reduce the time horizon forecasts have to cover
Sharing forecasts or demand data through the supply chain can improve forecast quality

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© McGraw-Hill Education.

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End of Presentation

© McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw-Hill Education.
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Vendor Comparison Matrix:

Vendor

AoC

QoO

PoO/DPU

AoQ

NoD

DA

MOPW

EI

Owens Textiles

Freeway Fabrics

Alpine

Preston Premium

Red Maple Fabrics

Reliable Clothing

Tigerlily Textiles

United Fabrics

Eval Standards

99%+

95%+

<$ 99%+ 3%- Yes > Vol

> Env Impact

Weekly MAD/MSE/MAPE

Column Definitions/Measurements:

AoC: Accuracy of Order

1. Calculation: Number of items received / Number of items ordered * 100

1. Evaluation: 99% or higher

QoC: Quality of Order

1. Calculation: Post inspection review of quality total / Ordered quality total * 100

1. Evaluation: Evaluation: 95% or higher

PoO/DPU: Price of Order

1. Calculation: Dollar value of order & Dollar per unit (e.g.: $1000/$1.25)

1. Evaluation: Lowest dollar per unit

AoQ: Accuracy of Quote

1. Calculation: Invoice amount / Quoted Amount * 100

1. Evaluation: 99% or higher

NoD: Number of Defects

1. Calculation: DPU- Number of defects observed / number of units inspected

1. Evaluation: 3% or less

DA: Discounts Applied

1. Calculation: Binary (Yes/No)

1. Evaluation: Discount “Yes”

MOPW: Max Order Per Week

1. Calculation: Max number of units that the supplier can deliver

1. Evaluation: Vendor with the largest order volume

EI: Environmental Impact

1. Calculation: Carbon footprint (in lbs) used in manufacturing and delivery of units to Kibby and Strand.

1. Evaluation: Vendor with the lowest carbon footprint