NEED back tomorrow evening must follow all directions and no plagiarism.

Required Resources

Text

Vonderembse, M. A., & White, G. P. (2013). 

Operations management

 [Electronic version]. Retrieved from https://content.ashford.edu/

· Chapter 5: Supply Chain Management: A Strategic Perspective

· Chapter 6: Models and Forecasting

Recommended Resources

Article

Terreri, A. (2007). Who’s minding the yard? Food Logistics, (97), 24-30. Retrieved from the ProQuest database.

Multimedia

July, E. (Producer) & Rodrigo, J. M. (Director). (2003). 

Business is blooming: The international floral industry (Links to an external site.)

 [Video file]. Retrieved from the Films On Demand database.

Discussion 1

Suppliers

Should a firm attempt to have fewer or more suppliers? What are the advantages and disadvantages of each approach? Your initial post should be 200-250 words.

Discussion 2

Forecasting Methods

Read Problem 6 in Chapter 6 of your textbook. Calculate and answer parts a through d. Include all calculations and spreadsheets in your post. Explain why the moving average method was used instead of another forecasting method. What might be another forecasting method that could prove to be just as useful? Your initial post should be 200-250 words.

5

©JohnW Banagan/Photographer’s Choice/Ge�y Images

Supply Chain Management: A Strategic
Perspective

Learning Objec�ves
A�er comple�ng this chapter, you should be able to:

Define supply chain management.
Explain the consequences that occur when informa�on is not shared, and describe some of the informa�on
that can be shared in a supply chain.
Discuss various op�ons in supply chain structure.
Compare insourcing, outsourcing, and ver�cal integra�on.
Compare agile supply chains to lean supply chains.
Discuss the impact of e-commerce on supply chain management.
Explain how ERP facilitates e-commerce.
Describe some supply chain performance measures.
Discuss global issues in supply chain management.

Apple is the focal firm in its supply chain. A focal firm is the most
important organiza�on in the supply chain and the firm that o�en
interfaces with the final consumer.

Pablo Mar�nez Monsivais/ASSOCIATED PRESS/AP Images

5.1 Foundations of Supply Chains

The term supply chain is commonly used to refer to the network of organiza�ons that par�cipate in producing
goods or providing services. A supply chain encompasses all ac�vi�es associated with the flow and transfer of
goods and services, from raw material extrac�on through use by the final consumer. The ac�ons of the par�cipants
in the supply chain are coordinated by the focal firm, which directs the flow of informa�on much like a conductor
coordinates the ac�vi�es of an orchestra. The be�er the focal firm is at moving informa�on among par�cipants,
the be�er the supply chain will perform. The focal firm is o�en, but not always, the firm that interfaces with the
final consumer. The focal firm designs and manages the supply chain by selec�ng suppliers. For example, Apple is
the focal firm in its supply chain. Apple is primarily a product design and marke�ng focused firm with no
manufacturing ac�vi�es, yet it is the focal firm because its brand dominates the market. There are also cases
where the most important firm does not sell directly to the consumer. For example, the oil industry is shi�ing from
a model in which one firm owns the oil fields, pipelines, refineries, and retail gasoline sta�ons to a model in which
independent companies operate the retail opera�ons. Bri�sh Petroleum, commonly known as BP, has been
reducing the number of retail outlets in the United States for several years. In this example, the company that
owns the refinery and the oil fields is the focal firm because it controls the key resource in the supply chain.

A supply chain may be contained within a single organiza�on as shown in Figure 5.1. Exxon Mobil owns oil fields,
refineries, distribu�on networks, and retail gasoline sta�ons that deliver fuel to the consumer. Owning mul�ple
assets in a supply chain is called ver�cal integra�on. The more assets a company owns, the greater the degree of
ver�cal integra�on.

Figure 5.1: Example of a ver�cally integrated internal supply chain

Traditional Supply Chains

In most cases, different companies own the assets in a supply chain as shown in Figure 5.2. For example, suppose a consumer purchases a DVD player from a retailer. The retailer
obtained that DVD player through a distributor, which originally purchased the player from the manufacturer. All of those different companies, as well as the consumer, are part of
the supply chain. However, the supply chain does not end there. The manufacturer purchased component parts from various �er 1 suppliers, who have purchased materials from
�er 2 suppliers, such as companies that produce the chemicals for making plas�c. Finally, those �er 2 suppliers could have also purchased the raw materials to make those
chemicals from �er 3 suppliers who extract petroleum from the earth. The supply chain also includes companies that move these items, such as trucking companies, railroads, and
shipping companies, as well as warehouses or distribu�on centers where items may be temporarily stored during movements within the supply chain. Logis�cs involves managing
the movement of materials and components from point to point in the supply chain.

Figure 5.2: Example of an external supply chain

In addi�on to materials, informa�on flows through a supply chain. If a DVD player model is selling extremely well and the retailer wants to stock more of them, then that retailer
provides informa�on to the distributor to ship more of that model. The distributor informs the manufacturer to make more, and the manufacturer no�fies its suppliers to provide
more of the component parts. Ideally, the informa�on would be shared with the en�re supply chain simultaneously, not only those companies with which each member deals
directly. Taking ac�ons to have all members of the supply chain work together, coordinate their ac�vi�es, and share informa�on is known as supply chain management.

When it is necessary to return defec�ve products to the manufacturer for repair or replacement, the process is known as reverse logis�cs. Reverse logis�cs includes efforts to reuse
and recycle materials. In Europe, the role of reverse logis�cs is being expanded beyond tradi�onal recycling. The no�on is that manufacturers who create a good are responsible for

it at the end of the product’s useful life. This requires that producers of goods have a vested interest in crea�ng designs, selec�ng materials, and using manufacturing processes that
facilitate recycling. Because firms are responsible for the end-of-life recycling cost, they will make decisions that lower the cost of recycling. There is no legal requirement for
companies to do this in the United States, but the idea of designing to facilitate recycling is sound.

Walmart, Dell, Toyota, and The Home Depot have fine-tuned their supply chains to provide a strong compe��ve advantage in terms of service and price. This chapter discusses how
these companies and others have used supply chain management to their advantage.

Globaliza�on allows products and services to reach all corners of the
world and results in increased compe��on. Few companies have been
as successful at globalizing their brands as Coca-Cola.

©Liu xianglong–Imaginechina/AP Images

Wal-Mart Manages Logis�cs; The Age of Wal-Mart:
Inside America’s Most Powerful Company

5.2 Overview of Supply Chain Management

Tradi�onally, each company in a supply chain acted in its own best interests, not those of the en�re supply chain.
Informa�on was not adequately shared among members of the supply chain. Only limited informa�on was shared
between a company and its immediate suppliers and between that company and its customers. As a result,
important decisions including how much to produce, store, and move along the supply chain were based on local
condi�ons rather than what was best for the supply chain. Several factors have emerged that encourage companies
to adopt supply chain management as part of their compe��ve strategy. Those factors are:

Increasing globaliza�on
More intense compe��on
Shorter product life cycles
Developments in informa�on technology and data communica�on

Globaliza�on has led to new markets, but
also to more companies producing and
selling compe�ng products—Toyota sells cars
in the United States, Intel sells computer
chips worldwide, Goldman Sachs provides
financial services in the United Kingdom, and
Caterpillar sells construc�on equipment in
China. These are but a few examples of the
increase in global compe��on and global
trade since the 1960s. Established markets
have become more compe��ve as
companies iden�fy new ways of winning
market share through process improvements
that lower cost, improve product
performance, and increase product quality.
Some firms have opted to increase market
share by introducing new products. As firms
introduce new products and their
compe�tors respond, market change accelerates and product life cycles become shorter. This means that new
products must be profitable quickly and pay the needed return on investment in less �me than prior products.
Be�er supply chain management is one way to do this. Informa�on and communica�on technologies have
opened up new ways of buying and selling through the Internet and mobile devices. This has also allowed

companies to obtain and disseminate informa�on much more rapidly than before, thereby providing the consumer with more informa�on—not just price and features, but
availability, delivery op�ons and �ming, service a�er the sale, repair services, and more.

Because of these changes, companies have been forced to be more compe��ve. Supply chain management can make a company more compe��ve by coordina�ng all supply chain
ac�vi�es to ensure that the customer obtains the desired product at the desired �me for a compe��ve price. Companies should work together to minimize costs over the en�re
supply chain, thus benefi�ng all the members. Supply chain management is the integrated coordina�on of all components of the supply chain—from raw materials to the final
customer—so that informa�on and materials flow smoothly.

Wal-Mart
Manages
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5.3 The Role of Information Sharing

Tradi�onally, limited informa�on has been shared between adjacent supply chain pairs. For example, a retailer may order a certain number of units from a distributor, informing the
distributor only of the number of units wanted at that �me and when those units should be delivered. Very li�le informa�on, such as expected future changes in demand, would be
shared between the retailer and the distributor. The small amount of informa�on that was shared would be shared only by those two members of the supply chain. This limited
approach to informa�on sharing does not op�mize the performance of the supply chain, and can even lead to detrimental results such as the “bullwhip effect.”

The Bullwhip Effect

The bullwhip effect is an example of what can happen when informa�on is not fully shared in a supply chain or when forecasts are updated, causing an unan�cipated shi� in
expected demand. This effect is further complicated by batching orders that concentrate demand at one point in �me, price fluctua�ons that change demand, and a�empts to ra�on
product or otherwise game the system. The bullwhip effect is caused when a retailer experiences a slight increase in demand and increases its order quan�ty to avoid running out
of a product. The distributor also no�ces the increased order from its customer (the retailer) and, also to avoid running out, increases its order to the factory by a larger amount.
The factory, in turn, will further increase its orders to suppliers of raw materials. The end result is that a slight increase in demand at the retail level increases nearly exponen�ally,
crea�ng a huge demand increase at the supplier level, as shown in Figure 5.3. This increase in demand may cause the supplier to work over�me, thereby increasing costs. When the
retailer places the next order, which is the same size as the prior order (more or less), each par�cipant in the supply chain will have too much inventory, so a cut back is required.
The supplier, who overes�mated the most (see Figure 5.3), will dras�cally reduce produc�on. As a result, the supplier may lay off staff because much of the demand can be met
from inventory. In this system that uses sequen�al communica�on, the supplier at the end of the chain is “whipped” from one extreme to the other, from high demand requiring
over�me costs to low demand leading to layoffs or excess inventory. Both of these op�ons increase the supplier’s costs.

Figure 5.3: The bullwhip effect

To avoid problems such as the bullwhip effect, informa�on must be shared via real-�me communica�on methods rather than �me delayed, sequen�al communica�on. The hub and
spoke approach is one way to do this. Each spoke represents a connec�on to a member of the supply chain. All members of the supply chain transmit informa�on to a central hub,
and each member has access to the informa�on. The focal firm o�en determines the informa�on that must be shared in this manner. For example, if a company wants to supply
components to a Chrysler assembly plant, it must provide the informa�on determined by Chrysler, or the supplier will not be accepted. By sharing this informa�on, all supply chain
partners see changes occurring anywhere in the supply chain, and respond to those changes accordingly. The following sec�ons indicate some ways for data to be shared. Electronic
data interchange (EDI) is a method of exchanging relevant informa�on between suppliers and customers in real �me. Collabora�ve planning, forecas�ng, and replenishment (CPFR)
goes beyond the exchange of data to include joint planning efforts.

Electronic Data Interchange

Electronic data interchange (EDI) connects the databases of different companies. In one early use, EDI allowed companies u�lizing material requirements planning (MRP) to inform
suppliers of upcoming orders by providing them with access to the database of planned orders. Although this approach was innova�ve at the �me, it s�ll represented only limited
sharing of informa�on between adjacent links in the supply chain. In supply chain management, EDI is a way to share informa�on among all members of a supply chain. Shared
databases can ensure that all supply chain members have access to the same informa�on, providing visibility to everyone and avoiding problems such as the bullwhip effect.

Collabora�ve Planning, Forecas�ng, and Replenishment (CPFR)

Theore�cally, informa�on is shared easily among all partners in a supply chain. In prac�ce, however, the process o�en does not work very smoothly. As a result, members of the
supply chain may make assump�ons about future ac�ons of other supply chain members. For example, each supplier must forecast the demand of its customers. Collabora�ve
planning, forecas�ng, and replenishment (CPFR) is a process that accomplishes more than data exchange. It seeks to minimize the lack of informa�on through enabling
collabora�on among supply chain partners that jointly develop a plan specifying what is to be sold and how, where, and during what �me period it will be marketed and promoted.
Sharing of informa�on is facilitated by using a common set of communica�on standards. All partners are involved in the development of plans and forecasts for the en�re group.
Because these plans and forecasts have been jointly agreed upon, considerable uncertainty is removed from the process.

Real World Scenarios: Eroski Supermarkets

Eroski operates supermarkets and hypermarkets in Spain and France. Henkel, a German company, is one of the suppliers for Eroski stores. Although Henkel had u�lized EDI with
its customers to improve inventory reordering, Eroski stores con�nued to run out of Henkel products on a regular basis. The two companies decided to pursue CPFR, beginning
with joint demand forecas�ng, which requires them to work together to es�mate demand. Before implemen�ng CPFR, about one-half of Henkel’s forecasts of demand had
been miscalculated by 50% or more. As a result, Eroski’s supermarkets ran out of Henkel’s products. A�er implementa�on of CPFR, 75% of forecasts were within 20% of actual
demand, and Henkel products were in stock at Eroski stores 98% of the �me.

Like long-range forecasts offered in the annual Old Farmer’s Almanac, companies also predict
forecasts for the future. The condi�ons predicted can be drama�cally different from what actually
happens.

©Brian Snyder/Reuters/Corbis

CPFR (pronounced C-P-Far) requires that all supply chain par�es be commi�ed to
the plans developed jointly and that they be commi�ed to upda�ng the plan on a
regular basis. A retailer will share informa�on about demand forecasts and planned
product promo�ons with its suppliers. Likewise, the suppliers share informa�on
about possible limita�ons on supply or periods during which produc�on facili�es
may be shut down. Once a plan is developed, suppliers can begin produc�on
knowing that their customers in the supply chain have commi�ed to those orders.
Plans must be revisited regularly to ensure that adjustments are made when
appropriate.

Forecast Accuracy

One problem with sharing informa�on is that some of that informa�on may not be
accurate, especially forecasts. For example, a retailer may forecast future sales of a
par�cular clothing line. When demand actually occurs, it may differ significantly. If
the forecast was too high, then the retailer may be le� with excess inventory that
must eventually be sold at a loss. On the other hand, a forecast that is too low can
mean unmet demand and lost sales.

Simply realizing that forecasts are likely to be inaccurate can lead to improving
supply chain management. For instance, quick response is one technique that the
fashion industry has developed to address uncertainty in demand. In general, any
�me a firm can reduce the lead �me between a customer order and its delivery,
responsiveness is improved and forecas�ng errors become less relevant.

Historical informa�on about forecast accuracy can be used to develop a confidence
interval for demand. The supplier may be able to predict that there is a certain

probability that demand will not vary from the forecast by more than a specific amount. This informa�on can help the supplier to plan for a certain range of possible demand
values.

Real World Scenarios: Walmart

Walmart is one company that has used EDI to improve forecast accuracy. Vendors who provide products to Walmart can use Walmart’s satellite network system to directly
access real-�me, point-of-sale (POS) data coming in from the cash registers at Walmart stores. Vendors can use this up-to-the-minute informa�on to improve forecasts by
spo�ng trends the moment they occur.

Also, most forecasters know that it is easier to forecast demand as the �me horizon is shorter. If the supplier has Walmart’s up-to-the-minute demand for Sauder television
stands, it can respond quickly to any demand change. There is no �me delay in ge�ng an order because Sauder has the most recent sales data. If Sauder can combine this
with a shorter lead �me—that is, they can be more responsive—errors in forecas�ng will be less important. If Sauder takes two weeks from the �me it receives an order un�l
it delivers the product, a forecas�ng error is more likely to cause a supply disrup�on than if Sauder can respond in three days. With a two-week response �me, Sauder’s
customer may be out of stock for several days to as much as two weeks. With a three-day response �me, Walmart is far less likely to be out of stock, and if an inventory
shortage occurs, it is likely to be only a day or two before more inventory arrives at the retail outlet.

Due to low labor costs in developing na�ons, global outsourcing has dras�cally increased in the past
decade as firms seek to find low-cost suppliers.

iStockphoto/Thinkstock

Striking a Regulatory Balance

5.4 Structure of Supply Chains

As shown in Figure 5.2, the upstream supply chain includes suppliers, which may be �er 1, �er 2, or �er 3. Each �er of the upstream supply chain may include mul�ple suppliers for
the same good or service. The upstream side of the supply chain also includes produc�on planning and purchasing as well as logis�cs, which is responsible for moving materials
between supply chain members. On the downstream side, supply chain partners are divided into echelons. For example, echelon 1 includes organiza�ons, such as distributors,
importers, or exporters that receive the product directly from the organiza�on that produces it. Echelon 2 organiza�ons would receive the product from those at echelon 1. Echelon
2 may include retailers, dealers, or final consumers.

It is important to realize that Figure 5.2 is a greatly simplified diagram of a supply chain. There are many more organiza�ons that provide required goods and services and move
materials and informa�on than can be shown in Figure 5.2. How these numerous organiza�ons are arranged and relate to one another is what determines supply chain structure.
The next sec�on will briefly discuss how supply chains can be structured.

Number of Suppliers

At each �er of the upstream supply chain, companies can decide whether to use many suppliers for a par�cular good or service or few suppliers. Using many suppliers o�en allows
a company to take advantage of compe��on among those suppliers to meet the company’s demands for cost, quality, and delivery. If one supplier goes out of business or is unable
to provide the good or service as requested, it is a simple ma�er to use another supplier.

On the other hand, there are some advantages to having only a few suppliers, or even one supplier for a good or service. Chief among these is the long-term partnership
arrangements that can be developed. Such rela�onships enable both par�es to work together for greater integra�on of the supply chain and for development of methods that can
improve quality and lower costs. These close partnerships o�en lead to high levels of dependency between the customer and the supplier.

Highlight: TMD and Chrysler Toledo Assembly Complex

TMD’s Toledo facility is the sole source of instrument panels for the Jeep Wrangler, which is produced at Chrysler’s Toledo Assembly Complex (CTAC). All of the output from
TMD’s Toledo opera�ons is delivered to CTAC, which is located less than three miles from the assembly facility, thereby keeping shipping costs low. Because of close interac�on
and very short travel �me, the inventory of instrument panels is enough to sa�sfy demand at CTAC for only a couple of hours. These two organiza�ons have developed such a
close rela�onship that there have been very few supply disrup�ons, administra�ve and accoun�ng costs are low, and quality is high. The rela�onship has worked well for both.
Because these companies are highly dependent, they have worked hard to develop con�ngency plans to deal with unexpected problems.

Insourcing Versus Outsourcing

Organiza�ons use a wide range of goods and services when making and delivering
products. If those goods and services are provided by the organiza�on itself, they
are insourced. Goods and services obtained from outside suppliers are outsourced.
One reason companies decide to outsource is that the goods or services can o�en
be obtained less expensively from outside suppliers. Outside suppliers may
specialize in producing that good or service, enabling them to maintain high quality
while keeping costs low. Suppliers may have proprietary technology that provides
them a compe��ve advantage.

In the past decade or
more, global outsourcing
has grown drama�cally
as firms seek to find low-
cost suppliers. This push,
driven primarily by low
labor costs in developing
economies such as
Mexico, China, India, and

Vietnam, has lengthened the supply chain, which increases transporta�on and inventory costs. With longer supply chains
as well as poli�cal uncertainty and cultural differences, there is also an increased risk of supply chain disrup�on. Yet, the
allure of lower costs is a powerful force. As some of the disadvantages of global sourcing are being examined, including
concerns about quality and rising labor costs in some developing countries, there are signs of produc�on returning to the
United States. It is too early to tell if these instances are the beginning of a growing trend. It should be clear that global
outsourcing is not just a manufacturing phenomenon. Engineering work, informa�on systems development, and
examina�on of medical images are being outsourced to developing countries.

Vertical Integration

Supply chain management requires close coordina�on with suppliers, but if those suppliers are separate organiza�ons, there may be difficulty coordina�ng among one another. One
way to promote coordina�on is for a company to own its suppliers. This is called backward ver�cal integra�on.

Highlight: Henry Ford and Backward Ver�cal Integra�on

The early Ford Motor Company provides a classic example of backward ver�cal integra�on. Henry Ford believed that owning his sources of supply was the best way to
guarantee an uninterrupted supply of compe��vely priced component parts and raw materials to build his Model T. He purchased iron mines, rubber planta�ons, and shipping
companies. In order for this complex system to be efficient without the informa�on and communica�on technology that is present today, it required centralized planning with
long lead �mes to move product from raw materials to create the finished automobile. As a result, Ford’s massive system eventually became unwieldy and inflexible, resul�ng
in severe problems when compe�tors began offering product variety that Ford was unable to provide. Could Ford’s approach work today given that informa�on and
communica�on technologies give real-�me access to data that supports decision making? There are s�ll significant issues to overcome including the level of exper�se required
to manage diverse holdings such as iron mines and rubber planta�ons. Demand must be large enough to generate economies of scale when producing each component or raw
material. It is challenging to build an efficient and responsive organiza�onal structure that can manage such a large organiza�on, so in some cases it is be�er to let market
forces drive compe��on between suppliers.

At the other end of the supply chain, a company can own the distribu�on systems and retail outlets that sell their products; this is forward ver�cal integra�on. Many large grocery
chains, such as Kroger, Publix, and Safeway, own their distribu�on networks as well as the retail stores. These companies may own all aspects of the distribu�on system, including
transporta�on.

Highlight: La-Z-Boy Furniture and Forward Ver�cal Integra�on

La-Z-Boy Furniture has used its worldwide brand appeal to build a chain of retail outlets in approximately 50 countries including Jakarta, Indonesia and Bogotá, Colombia. Its
strong brand recogni�on draws customers into these retail outlets to buy La-Z-Boy products as well as products from other companies. Its high level of demand is the
founda�on for genera�ng a high level of sales in each store. These two factors, high brand recogni�on and sales volume, provide La-Z-Boy with the opportunity for forward
ver�cal integra�on.

Virtual Organizations

Today, outsourcing is gaining popularity because of cost advantages and the opportuni�es for greater coordina�on that have been provided by the improved communica�on
technologies of e-commerce. The applica�on of this technology has led to virtual corpora�ons, that is, companies that exist only as an administra�ve shell, with all other func�ons
outsourced. Outsourcing provides a great deal of flexibility because the company can change sources as the requirements of its products or markets change. Apple is not a virtual
corpora�on because it has product design capabili�es, marke�ng, accoun�ng, and other func�ons. Apple does have virtual manufacturing opera�ons through suppliers from around
the globe.

Real World Scenarios: Amazon.com as a Virtual Retailer

Amazon.com is a large online retailer that buys and sells books and hundreds of other items. Amazon.com acts like a virtual organiza�on when it opens its website to other
companies who want to list their products. Amazon.com holds the informa�on about the loca�on and the cost of books or other items, but it never takes possession or owns
the items. When a customer locates an item on Amazon.com’s website, the order is placed with Amazon.com and payment is collected by Amazon.com. The informa�on about
the order, including the item iden�fier, the ship to address, and the payment (less Amazon.com’s commission), is sent to the firm that owns and possesses the merchandise.
The firm then sends the item to the customer. In this example, Amazon.com took no risk, owned no property, and incurred no cost except those related to lis�ng the item on
its site. It has used the power of its brand to drive both buyers and sellers to its site, thereby ac�ng as an intermediary.

Disintermediation

An intermediary is a business en�ty that exists between a customer and a supplier. For example, travel agents are an intermediary between the travelers who buy airline �ckets and
the airlines that sell those �ckets. A growing trend today is to achieve efficiencies in the supply chain by elimina�ng some intermediaries. This process is known as
disintermedia�on. Airlines now have their own websites through which travelers can purchase �ckets directly from the airline, without using a travel agent’s services. For the
traveler, this process may be advantageous because the traveler can readily compare all different flight �mes and rou�ng op�ons, browse special promo�ons that are currently being
offered by the airline, and even compare prices among different airlines by visi�ng other websites. Ge�ng flight informa�on and pricing from a travel agent may be more difficult.
Some companies have made the search process more efficient by pu�ng nearly all airline prices on one website.

Real World Scenarios: Travelocity

Travelocity has created a successful business by using the Internet to provide travelers with easily accessible informa�on from airlines, hotels, and car rental companies. The
advantage of Travelocity is that it enables a customer to compare prices offered by many different travel service suppliers on one single website. In response, a group of major
airlines began compe�ng directly with Travelocity through its own website, Orbitz. Orbitz was started by American Airlines, United Airlines, and Delta Air Lines and promised to
provide airfares that are lower than those through Travelocity, thus seeking to eliminate Travelocity as an intermediary. Kayak, Expedia, and Cheapflights have also begun to
compete in this market. With limited barriers to entry, this space has become crowded.

The fashion industry is con�nuously evolving; therefore, an agile supply
chain is u�lized because it is able to rapidly respond to new products
and changes in demand.

iStockphoto/Thinkstock

5.5 Supply Chain Strategies

As top managers have begun to understand the value of effec�ve supply chain management, it has gained
recogni�on as a strategically important issue. Becoming successful when managing supply chains requires the
support and involvement of top management. Various approaches to managing supply chains have been
developed. Many of these strategies can be used together, although some may be more relevant to certain types
of supply chains or certain structures.

Agile Supply Chains

Markets such as fashion and technology are characterized by frequent innova�on, making product demand
unpredictable and, therefore, requiring the en�re supply chain to respond quickly as new products are introduced
and demand changes. The supply chain must be able to transmit customer responses to new products and
informa�on about what customers would like to see in future products.

A par�cular type of supply chain, known as an agile supply chain, is needed to meet these requirements.
Members of such a supply chain are selected based upon their speed and flexibility, and their capacity to transit
informa�on reliably, accurately, and quickly from the marketplace to supply chain members. An agile supply chain
a�empts to assess in great detail the needs of its customers so it can provide customized products that be�er
meet the customers’ expecta�ons. An agile supply chain is more than transferring data between companies (EDI)
or replenishing inventory more effec�vely and efficiently (CPFR). An agile supply chain is a coopera�ve rela�onship;
supplies help to design and develop new products that can meet individual customer needs be�er. It creates a
flexible and responsive produc�on process that allows the supply chain to deliver differen�ated products. It also
allows companies to work on quality improvement projects that affect the company and its suppliers, and allows
all members of the supply chain to work together to keep costs aligned with customer expecta�ons. Agile supply
chains are frequently used in the fashion industry.

Real World Scenarios: Sport Obermeyer

Sport Obermeyer is a maker of fashion skiwear. When designs are created, suppliers help Sport Obermeyer to iden�fy new material that can be used in its innova�ve and
fashionable designs, and to create produc�on systems that can respond to changing customer demands. Product mix flexibility is the agility to shi� produc�on from one
product to another with very limited lost �me or very small cost increases. Product volume flexibility is the agility to increase produc�on levels if demand is greater than
expected or to reduce produc�on volume if demand is less than expected. This agility in the supply chain allows Sport Obermeyer to respond quickly and efficiently once
customers vote with their money and decide that they like one style and color of ski equipment over another. Agility allows Sport Obermeyer to keep inventory at op�mum
levels. When produc�on begins, the company can only es�mate how many units of each product will actually be sold. If the supply chain cannot adjust, Sport Obermeyer will
have too many units of products that are not selling well and not enough of high-demand items, resul�ng in excessive inventory, high inventory carrying costs, and lost sales.

Vendor Managed Inventory (VMI)

Instead of a retailer following the tradi�onal approach of placing inventory replenishment orders with its suppliers, the suppliers can use informa�on from the retailer regarding
product sales to determine when they should replenish the supplier’s inventory. Walmart and other larger retailers, in conjunc�on with suppliers such as Procter & Gamble, have
implemented vendor managed inventory (VMI). Under VMI, the vendor, or supplier, can be�er coordinate its own produc�on with the replenishment of supplier inventory, thus
reducing costs and improving delivery performance between the supplier and the retailer. To make this work, the suppliers receive daily point-of-sale (POS) data from the retail
stores, and they also have access to retailer’s inventory files. In this way, the supplier (Procter & Gamble) has sales data and on-hand inventory at the retailer (Walmart). The
supplier can plan produc�on to keep the retailer stocked with its product and effec�vely manage its produc�on to keep costs low. Customers benefit because the product they want
is in stock, the retailer benefits because it has product to sell and inventory cost is low, and the supplier benefits because it sells more product while keeping produc�on costs low.

Some companies such as Bose, which manufactures audio components, have further u�lized vendor managed inventory by having personnel from their suppliers work within Bose’s
purchasing department. Bose has called this approach Just-in-Time II (JIT II). In the past, Bose’s purchasing personnel handled all purchasing from outside suppliers. But, because the
Bose personnel worked with many different suppliers, they were not fully knowledgeable about the full range of products offered by each supplier, nor were they aware of the
inventory levels and produc�on plans of those suppliers. Under JIT II, employees of major suppliers work in the Bose purchasing department and handle all purchases from their
companies. These employees are aware of all products offered by their companies. Thus, they are o�en able to suggest be�er alterna�ves. Furthermore, because these personnel
are employees of the suppliers, they are aware of all supplier informa�on, such as current inventory levels of products and plans for future produc�on. This knowledge enables
personnel to foresee possible shortages and avoid problems before they occur.

Lean Supply Chains

Products such as can openers that have a long product life cycle, stable
demand, and a low profit margin use lean supply chains because they
keep costs down.

©BananaStock/Thinkstock

A very different approach is needed for products that are standard func�onal items, such as power drills or can
openers. These products have long product life cycles, stable and predictable demand, and minimal innova�on.
They are also o�en characterized by low profit margins. For these products, the supply chain must focus on
opera�ng efficiently to minimize costs. Such supply chains are known as lean supply chains, and the members
are chosen based upon their ability to keep costs down and minimize inventory in the system.

Real World Scenarios: Black & Decker’s Lean Supply Chain

Black & Decker produces a variety of small appliances and hand tools for use in the home. Success in that market is predicated on manufacturing standard products that have
high quality and low cost and a modest amount of variety. Designs for these appliances and tools change slowly and demand for these products can be characterized as steady.
A lean supply chain focuses on opera�ng issues as it a�empts to eliminate non-value-added opera�ons. A lean supply chain supports the reduc�on of setup �mes to enable
the economic produc�on of small quan��es. This enables the supply chain to keep inventory costs low and achieve manufacturing cost reduc�ons, in part, by enabling
opera�ons to switch quickly among products.

Consider Black & Decker’s 3/8-inch variable speed reversing drill, which is one of its most popular products. This tool is sold primarily to homeowners who use it infrequently
to hang a shelf or repair a table. Each major component in the drill is a standard product. To create a successful supply chain, component suppliers must adopt lean
manufacturing and its con�nuous improvement philosophy. These suppliers must achieve an efficient combina�on of flexibility and cost reduc�on. Flexibility is needed because
there are several different models of drills as well as other hand tools and appliances that require similar components. Cost reduc�on is also essen�al because products, such
as drills, are produced by many compe�tors, and customers are price sensi�ve. Cost reduc�ons can be achieved when suppliers purchase large volumes of basic materials, such
as steel for the gear manufacturer, or copper for the electric motor producer. Streamlining the flow of materials and informa�on through the supply chain to drive out
inventory and non-value-added steps can also reduce cost. Because drills have low profit margins maintaining high sales and produc�on volumes is cri�cal for profitability for all
members in the supply chain. Black & Decker can switch from one supplier of electric motors to another with rela�ve ease, which is significant mo�va�on for suppliers to seek
con�nuous improvements in both component part cost and quality.

Postponement

In an a�empt to meet customers’ requests as closely as possible, firms and their supply chains may offer a product with many different op�ons. For example, a par�cular model of
automobile may be able to be built in two million or more combina�ons of paint color, trim package, engine, transmission, interior colors, and other op�ons. Because of this large
number of possibili�es, manufacturers find it extremely difficult to accurately forecast demand for each possible combina�on of op�ons. Inaccurate forecasts mean that the
company may end up with a large inventory of unsold products consumers do not want, and a small inventory of the products consumers do want. Building sufficient inventory in
each of the many op�ons results in excessive inventory levels and costs. Conversely, wai�ng to produce a product un�l the customer actually wants it may disrupt the efficiency of
the produc�on process and entail very long lead �mes.

To overcome these problems, companies may use either product or process postponement. Electronics manufacturers such as Hewle�-Packard (HP) use product postponement, also
known as delayed differen�a�on, by producing a generic product at the central manufacturing facility, then adding specific components needed to customize the product for the
final consumer at the latest possible point in the distribu�on system. Thus, product postponement delays the final configura�on of a product un�l the last possible step in the
supply chain.

The elements of a printer, which are common to all configura�ons of the printer, are produced and assembled at a central loca�on. These undifferen�ated units are shipped to
distribu�on centers around the world as needed. At the distribu�on centers, the electric module with the correct voltage, amperage, plug, so�ware, and instruc�ons are added to
the unit. In this example, differen�a�on takes place just prior to a product’s arrival at retail opera�ons rather than at the factory that assembles the printers. In this way, the
produc�on process is very efficient and inventories are kept very low. If demand is unexpectedly high in China and low in Europe, HP can adjust shipments at its factory because the
product is s�ll undifferen�ated.

Product postponement is also u�lized to some extent by automobile manufacturers. Certain op�ons are added to automobiles, customizing them for the U.S. market a�er the cars
are received in the United States. Carmakers in the United States offer detail packages that add special trim or increase performance. These upgrades can take place a�er the
vehicle exits the assembly line and before it reaches the dealer, or they may happen a�er purchase from the dealership and before the customer takes delivery.

In process postponement, certain steps in the produc�on process are delayed un�l the last possible moment. Instead of maintaining an inventory of finished products, a company
will maintain inventory of component parts and then process the products when orders are received. Ideally, the finished product will be produced only a�er customer orders have
been received. This is commonly used in “sit-down” restaurants rather than in fast-food restaurants. For example, if the menu lists a perch dinner, the customer may be able to
choose whether the fish is fried, baked, broiled, or blackened, and whether the accompanying potatoes are deep fried, baked, or home fried. Restaurants can offer these op�ons
because the �me that a customer expects to be in the restaurant is long enough to fix the food using a different process.

When process postponement is implemented there are many op�ons for finished products with few components so less diverse inventory is held. A restaurant can offer four
different fish op�ons and three different potato op�ons, or 12 different meal choices, and it only needs to inventory perch and potatoes. For this approach to work effec�vely, the
lead �mes for making finished products must be short enough that they will be acceptable to customers. That is why process postponement does not work in fast-food restaurants.

Cross Docking

One objec�ve of supply chain management is to reduce inventory throughout the supply chain. Distribu�on centers, which receive shipments from a factory, break down those
shipments into smaller quan��es that are shipped to retailers, who are the customers of distribu�on centers. This represents a major investment in inventory because receipt of
shipments from the factory is not coordinated with shipments to retailers. Thus, a large shipment of a product may be received from the factory. Next, it is placed into inventory
un�l orders are received from retailers that gradually decrease the inventory.

Cross docking seeks to coordinate inbound and outbound shipments so that li�le inventory is kept at the distribu�on center. As a shipment is received from the factory and broken
down, each unit of the inbound shipment is moved to a loca�on awai�ng outbound shipment to a retailer. A�er each outbound shipment is fully assembled, it is sent on to the
retailer. Consequently, the distribu�on center primarily serves as a loca�on for breaking down incoming shipments and redistribu�ng the items into outgoing shipments. Unlike the
tradi�onal approach, the distribu�on center used for cross docking does not serve as a site for storing inventory. Target is one of dozens of retailers that have used cross docking
effec�vely to decrease costs and reduce inventory. Distribu�on centers that use cross docking will have many items that never leave the conveyor system. A pallet of Gatorade may
go from the delivery truck onto a high-speed automated conveyor system to the truck taking the product to the retail store. Scanners, cameras, and bar code readers sort and direct
the pallet through the distribu�on center. As a result, more than 50% of products fed into the system spend a few minutes to a couple of hours in the distribu�on center, and never
leave the conveyor. Much of the product, 70–80%, leaves the distribu�on center in 24 hours or less.

Third Party Logistics (3PL)

To improve the efficiency of supply chains, some companies have developed partnerships with third party logis�cs (3PL). An outside supplier, also known as a third party, handles all
of the logis�cs ac�vi�es between supplier and customer. Third party logis�cs (3PL) is, therefore, the outsourcing of logis�c services. These logis�cs ac�vi�es can include inventory
control, material handling, and transporta�on. United Parcel Service (UPS) is a 3PL provider for health care, retail, and automo�ve opera�ons. It o�en makes good financial sense to
allow specialists who know the best ways to move goods from place to place to handle logis�cs.

Real World Scenarios: Penske and Navistar Strike up a Partnership

Penske Logis�cs and Navistar truck producers have formed a 3PL partnership under which Penske Logis�cs is responsible for reducing supply chain costs and improving
performance. The new approach has included centralizing shipping opera�ons, improving supplier training, establishing new bidding requirements for carriers, and
implemen�ng a proprietary logis�cs management system. Navistar chose 3PL because Penske Logis�cs has exper�se and experience that Navistar does not have. Navistar could
develop this exper�se, but the cost of doing so would be higher than using Penske Logis�cs and the results may not be as good. The advantage of 3PL is that companies such
as Penske Logis�cs have specialized knowledge regarding the best methods and techniques to move parts from one place to another. Furthermore, Penske Logis�cs is able to
combine shipments from many different suppliers and many different customers, taking advantage of opportuni�es for cross docking and reduced transporta�on costs.

Radio Frequency Identification (RFID)

Radio frequency iden�fica�on (RFID) is a wireless system that uses radio frequencies to transmit data from a small device that is a�ached to an item to a receiver that tracks the
item. The small device contains informa�on that can be read from a distance of a few inches to 100 feet or more, depending upon the power output. Hospitals use this to track
medical equipment, pa�ents, and medica�on. While data transmission systems like bar codes require a scanner and a line of sight, RFID does not need to be within a line of sight.

RFID is used on automated toll lanes such as E-ZPass and to pay for gasoline at the gas pump using Speedpass. It is also used to secure items so they cannot be moved without the
owner’s knowledge, such as when securing expensive clothing in a shopping mall, moving cri�cal parts within a chemical supply chain, or storing laptop computers at an office. The
use of RFID in supply chains is growing rapidly.

Enterprise Resource Planning (ERP)

Supply chain management aims to achieve be�er integra�on, coordina�on, and communica�on among members of the supply chain. These efforts, however, are o�en stymied by
the separate databases used by the individual members of the supply chain. Enterprise resource planning (ERP) can eliminate delays caused by separate databases either by
allowing companies to access one another’s databases or, ideally, through the use of one common database.

Radio frequency iden�fica�on (RFID) is used on automated toll lanes like E-ZPass and to pay for gas
using Speedpass.

Daniel Hulshizer/ASSOCIATED PRESS/AP Images

To understand the problems that can be experienced when separate databases are used,
consider a company that directly sells to the final customer. Suppose a customer calls
the company’s marke�ng department to inquire about the status of an order. The
marke�ng database will probably show only informa�on specific to marke�ng, such as
the date the order was entered. If that order is in produc�on, then either someone
from marke�ng or the customer will need to contact the produc�on department to find
out the status of the order. Suppose the order has been completed and shipped. The
produc�on database would probably show only comple�on of the order, but no shipping
informa�on. To obtain the shipping informa�on someone would need to contact
distribu�on or logis�cs. Because the company uses separate databases, no one in any
area of the company has access to all company informa�on. Thus, the customer is
bounced from one department to another to get the answer to a simple ques�on about
order status.

A second problem with separate databases is that they may contain conflic�ng
informa�on. For example, suppose the customer order described above has been
shipped, and the logis�cs database indicates this, but the produc�on database has not
yet been updated, so it shows the order is s�ll at the last processing opera�on.
Produc�on may tell the customer that the order is s�ll in processing when, in reality, it
was already shipped.

The purpose of ERP is to avoid the problems described in this example by combining
databases into one common database for the en�re organiza�on—and possibly for the
en�re supply chain. The advantage of a common database is that all personnel within
the organiza�on have access to all informa�on. For example, someone in marke�ng could see if an order was delayed in produc�on awai�ng a component part from a supplier.
Furthermore, if the ERP system integrates the en�re supply chain, then personnel in marke�ng could determine the loca�on of the part within the supplier’s produc�on system.
Using one common database can effec�vely eliminate problems caused by conflic�ng informa�on among separate databases.

Figure 5.4 shows part of a typical ERP system configura�on. Informa�on is stored centrally in the database servers, which are accessed by individual servers. Users access
informa�on on their personal computers. Newer configura�ons of ERP are Internet-based. The database servers shown in Figure 5.4 can be accessed and updated by members of
the supply chain via the Internet over secure connec�ons. For example, the German company SAP now offers mySAP.com as its e-business pla�orm.

Figure 5.4: Part of a typical ERP system

ERP can be expensive in terms of purchase cost or in terms of the disrup�on that such a major change can have on an organiza�on. For example, it took Owens Corning two years
to install an ERP system at a cost of $100 million. A recent survey found that the average cost of an ERP system was $15 million, although companies in that survey spent a
minimum of $400,000 and a maximum of $300 million. There have also been some high-profile ERP failures. The Hershey’s Company spent $112 million only to find that the ERP
system they had installed delayed shipments to customers. Allied Waste Industries stopped implementa�on of its $130 million ERP system a�er the company decided the system
was too expensive and too complicated to operate.

One complica�on from ERP implementa�on results from using a common database, which o�en requires that procedures be completed differently than they previously were. For
example, in the past, personnel in the marke�ng department may have been responsible for selling a product and then entering orders into the computer system. It was the
produc�on department’s responsibility to meet the promised delivery data, and finance’s responsibility to decide whether to offer the customer credit. With an ERP system,
marke�ng personnel may find that they now are responsible for not only entering orders but also for determining whether a delivery date can be met and whether a customer’s
credit ra�ng is sufficient to jus�fy offering credit. Such changes may require extensive retraining and a long break-in period un�l people can perform their tasks efficiently in the new
way.

While ERP systems are o�en difficult to implement, the advantages of having an integrated, real-�me system to assist customers and to work with suppliers is very appealing. When
success is achieved, the benefits are substan�al.

Service Operations

Because many people think about supply chain management as moving goods from point to point in the produc�on process, these topics are o�en associated with manufacturing.
However, supply chain, logis�cs, inventory, and purchasing are important topics in service opera�ons. Retail and wholesale opera�ons, which are classified as services, move goods
from producers to customers via systems of distribu�on centers, warehouses, brick-and-mortar retail stores, and Internet-based retailers.

Because food has a rela�vely short shelf life, it is essen�al to keep delivery and inventory aligned
with consump�on pa�erns.

iStockphoto/Thinkstock

Supply chain management is cri�cal in restaurants. A restaurant is like a factory that
transforms raw materials into finished goods. It cuts, dices, chops, cooks, and
serves food to customers. Some restaurants focus on specialized high-end food,
while other focus on fast food. In either case, the restaurant faces the same
challenges with supplier quality, delivery reliability, and costs that are found in
manufacturing. Food has a very short shelf life so delivering in a �mely manner and
aligning inventory with consump�on are essen�al tasks.

Health care is also becoming a service business where supply chains and supply
chain management are important. From a tradi�onal perspec�ve, a hospital has
suppliers who provide food, linens, medicine, equipment, and maintenance services
for facili�es. Health care organiza�ons have suppliers, including doctors, who act as
service providers to the hospitals. In this environment, pa�ents, doctors, and
hospital clinical staff exchange informa�on and work together to understand
problems and to develop solu�ons or treatments. This is a highly interac�ve
process where value is created by all of the par�cipants. It is based on trust,
commitment, and a shared vision among the par�cipants. These same elements are
vital in a tradi�onal manufacturing supply chain.

Chapter Summary

Supply chain management is an approach in which all members of the supply chain work together, coordinate their ac�vi�es, and share informa�on.
The bullwhip effect, in which disrup�ons in demand are magnified through the supply chain, is one of the consequences of not sharing informa�on. Some of the informa�on that
can be shared in a supply chain includes demand informa�on, forecasts, planned orders, and sales.
Ver�cal integra�on allows a company to own various components of the supply chain. Under outsourcing, those components of the supply chain are provided by independent
companies.
Supply chain structure includes having many versus fewer suppliers, insourcing versus outsourcing products and services, ver�cal integra�on of the supply chain, and the use of
virtual organiza�ons and disintermedia�on.
Supply chain strategies include agile versus lean supply chains, vendor-managed inventory, postponement, cross docking, 3PL, and radio frequency iden�fica�on.
Agile supply chains focus on quickly ge�ng innova�ve products to market. Efficient supply chains emphasize reducing supply chain costs for func�onal products.
Lean supply chains focus on opera�ng efficiently to minimize costs and keep inventory low. They o�en produce products that have long product life cycles, stable and predictable
demand, and minimal innova�on.
Enterprise resource planning (ERP) is an a�empt to provide integrated, real-�me access to informa�on about the firm and possibly the supply chain. In this way, customers
contac�ng the supply chain can find the answers to ques�ons with one e-mail or phone call.
Supply chains and supply chain management are applicable in service businesses. Retail and wholesale business, restaurants, and health care establishments are three examples.

Case Study

Medical Equipment Devices LLC

Medical Equipment Devices LLC (MED) currently makes a few dozen different sophis�cated, high-quality medical devices that are used in surgical, tes�ng, and treatment procedures.
These items can be customized to the needs of the doctor or hospital and are o�en purchased in small quan��es. MED will some�mes keep a small quan�ty of finished products on
hand, but most sales are made to order. The products are high cost, high profit-margin items that require substan�al follow-up and field support. MED’s suppliers must respond
rapidly to MED’s needs for products because MED’s customers want delivery as fast as possible. In addi�on to speed, high quality, flexibility, and innova�on are essen�al
characteris�cs of MED’s suppliers. Keeping costs low is always an issue, but it is less important than these factors. MED is doing well and has generated substan�al profits, which it
is using to seek addi�onal investment opportuni�es.

Basil Diode, the chief financial officer of MED, has requested proposals for opportuni�es to start a new business or acquire a business in the medical field. He does not want to go
outside of the company’s area of exper�se, so he has asked for proposals in the broad area of health care. He has received a proposal to purchase and operate a company called
MedSurgItems Corp. (MSI) that produces several hundred different standard medical and surgical items such as syringes, gowns, gloves, and tongue depressors. These items are
typically made to stock, profit margins are small, and company profits are driven by sales volumes. MSI suppliers must be able to meet these needs.

To evaluate this and other proposals received, Basil has assembled a cross-func�onal team of experts from various disciplines including marke�ng, accoun�ng, finance, and
opera�ons and supply chain management. You are the representa�ve of the opera�ons and supply chain management func�on. Basil has asked you to provide detailed responses
with appropriate jus�fica�on to the following ques�ons. Keep in mind he does not want yes or no answers.

1. Iden�fy the important performance characteris�cs in MED’s current business, and iden�fy them for the MSI business to be acquired.
2. Are these compa�ble? Are there economies of scale in produc�on? Will there be synergy in product design?
3. What, if any, problems do you see in managing the supply chains that support MED’s current business and the MSI business to be acquired?
4. Provide a recommenda�on to Basil with support for that recommenda�on.

Discussion Ques�ons

Click on each ques�on to reveal the answer.

1. List the factors that now require companies to emphasize supply chain management.
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The factors that now require companies to emphasize supply chain management are:
– Increasing globaliza�on
– More intense compe��on
– Shorter product life cycles
– Developments in informa�on technology and data communica�on

2. Explain how the bullwhip effect may occur for a fashion retailer.
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

In general, the bullwhip effect is caused by batching of orders and delays in transmi�ng this informa�on up the supply chain. This is certainly true in retail fashion industries,
but there are other reasons. For a fashion retailer, the bullwhip effect could occur as follows. Suppose the retailer decides to hold a special promo�on of a par�cular item and
places a larger order than usual for that item. The distributor, seeing the larger order may think that demand for this item is suddenly taking off. Because that distributor
probably serves several different retailers, the distributor will increase its order from the manufacturer by enough to cover increased demand from all the retailers it supplies.
The manufacturer, seeing this sudden jump in demand from one of its distributors may also incorrectly assume that increased orders will be coming from all the distributors it
supplies, and thus decide to increase produc�on drama�cally. Because the manufacturer will probably decide to produce this item in a very large quan�ty, it will order enough
materials from its suppliers to meet this an�cipated large increase in demand over an extended period of �me. Consequently, the amount being ordered from suppliers will
suddenly jump by much more than is jus�fied by the one retailer that decided to hold a special promo�on.

3. What ac�ons can firms take to prevent the bullwhip effect from occurring?
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To minimize the bullwhip effect, informa�on must be shared via real �me communica�on rather than �me delayed, sequen�al communica�on. The hub and spoke approach is
one way to do this. Each spoke represents a connec�on to a member of the supply chain. All members of the supply chain transmit informa�on to a central hub, and each
member has access to the informa�on. The informa�on that must be shared in this manner is o�en determined by the focal firm. Electronic Data Interchange (EDI) connects

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the databases of different companies. In supply chain management, EDI is a means of sharing informa�on among all members of a supply chain. Shared databases can ensure
that all supply chain members have access to the same informa�on, providing visibility to everyone and avoiding problems such as the bullwhip effect. Collabora�ve Planning,
Forecas�ng, and Replenishment (CPFR) is more than exchanging data. It seeks to minimize this guessing game through collabora�on among supply chain partners to jointly
develop a plan that specifies what is to be sold, how it will be marketed and promoted, where, and during what �me period. Because these plans and forecasts have been
jointly agreed upon, considerable uncertainty is removed from the process.

4. O�en forecasts of future demand are not accurate. How can firms address this problem in its supply chains?
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Keys to coping with forecas�ng uncertainty are to move informa�on about actual demand to the suppliers as quickly as possible and to ask the suppliers to reduce lead �me by
building flexible produc�on systems that can produce what is in demand. Flexibility allows the supplier to switch produc�on cheaply and quickly to high demand items. The
Walmart feature in this chapter illustrates these points.

5. Describe the supply chain that might exist for an automobile manufacturer and discuss some informa�on that might flow through the supply chain. Do the same for a fast-food
restaurant.
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An automobile manufacturer’s supply chain would involve the following. Consumers buy the product from dealers, who receive it from the manufacturer’s factories. Depending
upon the loca�on of those factories, the logis�cs used for distribu�on could include trucks, railroads, and ships. Manufacturing facili�es would include assembly plants that
assemble the final product. In addi�on, the manufacturer might operate its own plants to fabricate parts, such as engines or body panels. Other parts are obtained from
suppliers. Tier 1 suppliers provide components such as electronics, interiors, and �res. These �er 1 suppliers obtain their component parts from �er 2 suppliers, which could
include steel companies or chemical companies. Tier 3 suppliers, which serve the �er 2 suppliers, could include companies that mine the iron ore for making steel.

A fast-food restaurant works with raw materials like an automobile manufacturer. It sells its products directly to the final consumer, so there is no distribu�on system for the
product. However, there can s�ll be several �ers of suppliers. For example, hamburger buns are probably purchased from a bakery, which would be a �er 1 supplier. That
bakery buys its flour from a milling company, which is a �er 2 supplier. The wheat for the flour comes from farmers who would be �er 3 suppliers. The logis�cs in this system
could include railways and barges that move the grain and flour, and trucks that transport the hamburger buns to the fast-food restaurant.

6. Iden�fy some organiza�ons that are ver�cally integrated and some that use extensive outsourcing.
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An example of a ver�cally integrated company would be an oil company such as ChevronTexaco. ChevronTexaco sells its products directly to consumers through its gasoline
sta�ons. The company also operates its own refineries, which process crude oil into finished products. Furthermore, ChevronTexaco operates its own wells, which extract oil
from the earth, and even has its own drilling and explora�on teams to find more oil.

Apple is an example of a company that uses outsourcing extensively. It outsources the produc�on of most of its components on worldwide basis. Apple and others are
reexamining global outsourcing because of the rising cost of transporta�on and the nega�ve impacts on the environment.

7. Should a firm a�empt to have fewer or more suppliers? What are the advantages and disadvantages of each approach?
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By using many suppliers, a company can take advantage of compe��on among those suppliers to meet the company’s demands for cost, quality, and delivery. If one supplier
goes out of business or is unable to provide the good or service as requested, it is a simple ma�er to use another supplier. On the other hand, there are some advantages to
having only a few suppliers or even one supplier for a good or service. Chief among these is the long-term partnership arrangements that can be developed. Such rela�onships
enable both par�es to work together for greater integra�on of the supply chain and for development of methods that can improve quality and lower costs. These close
partnerships o�en lead to high levels of dependency between the customer and the supplier.

8. Describe agile supply chains, including the characteris�cs of the products they produce.
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Markets like fashion and technology are characterized by frequent innova�on, making product demand unpredictable and requiring the en�re supply chain to respond quickly
as new products are introduced and demand changes. The supply chain must be able to transmit customer responses to new products and informa�on about what customers
would like to see in future products. An agile supply chain, can respond to these requirements. Members of such a supply chain are selected based on their speed and
flexibility and their capacity to transit informa�on reliably, accurately, and quickly from the marketplace to supply chain members. An agile supply chain a�empts to assess in
great detail the needs of its customers so it can provide customized products that be�er meet the customers’ expecta�ons. An agile supply chain is a coopera�ve rela�onships
where supplies help to design and develop new products that can meet individual customer needs be�er, create a flexible and responsive produc�on process that allow the
supply chain to deliver differen�ated products, work on quality improvement projects that affect the company and its suppliers, and work together to keep costs in line with
customer expecta�ons.

9. Describe lean supply chains, including the characteris�cs of the products they produce.
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Products with long life cycles, stable and predictable demand, and minimal innova�on o�en require a lean supply chain. They are also o�en characterized by low profit margins.
For these products, the supply chain must focus on opera�ng efficiently to minimize costs. Supply chain members are chosen based on their ability to keep costs down and to
minimize inventory in the system. Black and Decker produces a variety of small appliances and hand tools. Success depends on manufacturing standard products that have high
quality and low cost and a modest amount of variety. Designs for these appliances and tools change slowly and demand for these products can be characterized as slow and
steady. A lean supply chain focuses on opera�ng issues as it a�empts to eliminate non-value added opera�ons. A lean supply chain supports the reduc�on of setup �mes to
enable the economic produc�on of small quan��es. This enables the supply chain to keep inventory costs low and achieve manufacturing cost reduc�ons, in part, by enabling
opera�ons to switch quickly among products.

10. Outsourcing, especially to low labor-cost countries, has grown substan�ally. What are the advantages and disadvantages of outsourcing?
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One important reason companies decide to outsource is that the goods or services can o�en be obtained less expensively from outside suppliers. Outside suppliers may
specialize in producing that good or service, enabling them to maintain high quality while keeping costs low. Suppliers may have proprietary technology that gives them a
compe��ve advantage. In the past decade or more, global outsourcing has grown drama�cally as firms seek to find low cost suppliers. This push, driven primarily by low labor
costs in developing economies such as Mexico, China, India, and Vietnam, has lengthened the supply chain, which increases transporta�on and inventory holding costs. With
longer supply chains as well as poli�cal uncertainty and cultural differences there is also an increased risk of supply chain disrup�on. Yet, the allure of lower costs is a powerful
force. As some of the disadvantages of global sourcing are being examined, including concerns about quality and rising labor costs in some developing countries, there are signs
of produc�on returning to the U.S.

11. Is Amazon.com a virtual organiza�on? Find as much informa�on as you can about the company, and then use that informa�on to support your argument.
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Some aspects of Amazon.com certainly are very close to being a virtual organiza�on. The company uses technology to handle fulfillment, distribu�on and other logis�c
ac�vi�es, and it does not manufacture its own products. However, it does maintain its own distribu�on centers. In some ways Amazon is very similar to a standard retailer,
except that all customer transac�ons take place over the Internet. Amazon is also offering marke�ng services for many small companies that cannot afford the sophis�cate web
site to display its products and collect the payment. Amazon provides access to millions of customers worldwide. Amazon will sell the product, no�fy the company who
arranges shipment, collects the money, takes a por�on for its services, and send the balance to the small company.

12. List some products that would be most appropriate for an agile supply chain. Do the same for a lean efficient supply chain.
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Products appropriate for an agile supply chain include any items with short product lives and vola�le demand. These could include many electronics items, including computers,
as well as fashion goods. Lean supply chains deal with products that have fairly constant demand and for which price is an important considera�on. Such products can include
grocery items, building materials, or gasoline.

13. Explain how companies that make the materials used in the apparel industry (e.g., denim) may use postponement.
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Several approaches can be used in the fashion industry. For example, the popularity of different colors can o�en change quickly. In the past, fabric makers o�en dyed cloth
ahead of �me, then stored the dyed cloth in inventory. However, as fashions changed, some colors could go out of style, leaving the fabric maker with a large supply of
material with li�le demand. Today, many companies store undyed cloth in inventory, then wait un�l orders are received to dye the material.

14. How do supply chains and supply chain management impact service opera�ons?
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Supply chain, logis�cs, inventory, and purchasing are important topics in services opera�ons. Retail and wholesale opera�ons, which are classified as services, move goods from
producers to the customers via systems of distribu�on centers, warehouses, brick and mortar retail stores, and Internet based retailers. Supply chain management is cri�cal in
restaurants, which is like a factory that transforms raw materials into finished goods. It cuts, dices, chops, cooks, and serves food to customers. Some restaurants focus on
specialized high-end food while other focus on fast food. In either case, the restaurant has the same issues with supplier quality, delivery reliability, and costs that are found in
manufacturing. Healthcare is also becoming a service business where supply chains and supply chain management are important. From a tradi�onal perspec�ve, a hospital has
suppliers who provide food, linen, medicine, equipment, and maintenance service for facili�es. Healthcare organiza�ons have suppliers, including doctors who act as service
providers to the hospitals. In this environment, pa�ents, doctors, and hospital clinical staff exchange informa�on and work together to understand the problem and to develop
solu�ons or treatments.

Key Terms

Click on each key term to see the defini�on.

agile

supply chain
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A type of supply chain that focuses on quickly responding to changes in demand for various products.

backward

ver�cal integra�on
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When a company owns the organiza�ons that perform ac�vi�es in the upstream supply chain.

bullwhip effect
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An example of what can happen when informa�on is not shared in a supply chain. It occurs when a slight increase in demand at the retailer level increases nearly exponen�ally,
resul�ng in a huge increase in demand at the raw material supplier level.

collabora�ve planning, forecas�ng, and replenishment (CPFR)
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Facilitates coordina�on among supply chain partners by jointly developing plans and schedules for what is to be sold, produced, and delivered.

cross docking
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Coordina�on between inbound and outbound shipments so that li�le, if any, inventory must be kept at a distribu�on center.

disintermedia�on
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The process of elimina�ng some func�ons in a supply chain to improve its efficiency, such as when a manufacturer sells directly to the final consumer.

downstream
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A designa�on for the part of the supply chain through which a company’s products are sold, such as distributors, retailers, dealers, or final consumers.

electronic data interchange (EDI)
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The use of electronic transmissions, such as telephone lines or the Internet, to share data among members of a supply chain.

enterprise resource planning (ERP)
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The use of one common database for all func�ons of an organiza�on, or all members of a supply chain.

focal firm
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The most important organiza�on in the supply chain, and o�en the firm that interfaces with the final consumer. The focal firm designs and manages the supply chain.

forward ver�cal integra�on
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

When a company owns the organiza�ons that cons�tute the downstream side of the supply chain.

insourced
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

When a company internally produces the goods or services that it uses in its own opera�ons.

lean supply chain
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A type of supply chain that emphasizes cost minimiza�on and efficiency.

logis�cs
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The management of the movement of materials and components from point to point in the supply chain.

outsourced
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Contrac�ng with another company to do work that was once done by the organiza�on itself.

point-of-sale (POS)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Data collected directly from the cash registers in a store.

process postponement
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

When certain steps in the produc�on process are delayed un�l the last possible moment such that the finished product will be produced only a�er customer orders have been
received.

product postponement
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Producing a generic product at the central manufacturing facility, then producing specific components needed to customize the product for the final consumer, which are added at
the latest possible point in the distribu�on system.

radio frequency iden�fica�on (RFID)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A wireless, contact-less system that uses radio frequencies to transfer data from a tag a�ached to an object to a system that tracks the item.

reverse logis�cs
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The ability to return a product to the manufacturer for repair or replacement. It is also being employed to recycle products at the end of their useful life.

supply chain
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

All ac�vi�es associated with the flow and transfer of goods and services from raw material extrac�on through use by the organiza�on that sells to the final consumer.

supply chain management
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The integra�on of supply chain ac�vi�es through improved supplier rela�onships to achieve sustainable compe��ve advantage for all members in the supply chain.

third party logis�cs (3PL)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

An outside supplier that handles all the logis�cs ac�vi�es between supplier and customer; the outsourcing of logis�cs services.

�er 1 suppliers
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Companies in a supply chain that sell component parts to the company that makes the finished product.

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�er 2 suppliers
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Companies in a supply chain that sell component parts or raw materials to a �er 1 supplier.

�er 3 suppliers
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Companies in a supply chain that usually sell raw materials to a �er 2 supplier.

upstream
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A designa�on for the part of the supply chain that includes suppliers, produc�on planning, and purchasing.

vendor managed inventory (VMI)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

An inventory replenishment approach in which a supplier makes inventory management decisions about the products it sells for the company that buys those products.

ver�cal integra�on
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

When a firm owns the producing assets up and down the supply chain. The more assets owned, the greater the degree of ver�cal integra�on.

virtual corpora�ons
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Companies that provide only coordina�on ac�vi�es and outsource all other ac�vi�es involved in producing and distribu�ng a product.

https://content.ashford.edu/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover/books/AUBUS644.13.2/sections/cover#

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6

©

iStockphoto/Thinkstock

Models and Forecasting

Learning Objec�ves
A�er comple�ng this chapter, you should be able to:

Define a model and describe how models can be used to analyze opera�ng problems.
Discuss the nature of forecas�ng.
Explain how forecas�ng can be applied to problems.
Describe methods of forecas�ng, including judgment and experience, �me-series analysis, and regression and
correla�on.
Construct forecas�ng models.
Es�mate forecas�ng errors.

Processing math: 0%

Model airplanes and buildings have physical characteris�cs similar to full-scale versions and can be
used to test design characteris�cs.

Uwe Lein/ASSOCIATED PRESS/AP Images

6.1 Introduction to Models and Decision Making

In order for an organiza�on to design, build, and operate a produc�on facility that is capable of mee�ng customer demand for services (such as health care) or goods (such as
ceiling fans), it is necessary for management to obtain an es�mate or forecast of demand for its products. A forecast is a predic�on of the future. It o�en examines historical data to
determine rela�onships among key variables in a problem and uses those rela�onships to make statements about the future value of one or more of the variables. Once an
organiza�on has a forecast of demand, it can make decisions regarding the volume of product that needs to be produced, the number of workers to hire, and other key opera�ng
variables. A model is an abstrac�on from the real problem of the key variables and rela�onships in order to simplify the problem. The purpose of modeling is to provide the user
with a be�er understanding of the problem and with a means of manipula�ng the results for what-if analyses. Forecas�ng uses models to help organiza�ons predict important
parameters. Demand is one of those parameters, but cost, revenue, profits, and other variables can also be forecasted. The purpose of this chapter is to discuss models and describe
how they can be applied to business problems, and to explain forecas�ng and its role in opera�ons.

Stages in Decision Making

Organiza�onal performance is a result of the decisions that management makes over a period of �me: decisions about what markets to enter, what products to produce, what types
of equipment and facili�es to acquire, and where to locate facili�es. The quality of these decisions is a func�on of how well managers perform (see Table 6.1).

Table 6.1: Stages in decision making

Stage Example

Define the problem and the
factors that influence it

A hospital is having difficulty maintaining high-quality, low-cost food service. The quality and cost of incoming food and the training of
staff are influencing factors.

Select criteria to guide the
decision; establish objec�ves

The hospital selects cost per meal and pa�ent sa�sfac�on as the criteria. The objec�ves are to reduce meal cost by 15% and improve
pa�ent sa�sfac�on to 90%, based upon the hospital’s weekly surveys.

Formulate a model or models The model includes mathema�cal rela�onships that indicate how materials (food) and labor are converted into meals. This model
includes an analysis of wasted food and the standard amount of labor required to prepare a meal.

Collect relevant data Data on food costs, the amount of food consumed, the number of meals served, and the amount of labor are collected. Pa�ent
preferences are inves�gated so that meals meet nutri�onal requirements and taste good.

Iden�fy and evaluate
alterna�ves

Alterna�ves include subcontrac�ng food prepara�on, considering new food suppliers, establishing be�er training programs for the
staff, and changing management.

Select the best alterna�ve One of the alterna�ves or some combina�on of alterna�ves is selected.

Implement the alterna�ve, and
reevaluate

The selected alterna�ve is implemented, and the problem is reevaluated through monitoring costs and the pa�ent survey data to see
if the objec�ves have been achieved.

A model is a way of thinking about a problem. Decision makers use models to increase their understanding of the problem because it helps to simplify the problem by focusing on
the key variables and rela�onships. The model also allows managers to try different op�ons quickly and inexpensively. In these ways, decision making can be improved.

Types of Models

Models are commonly seen for airplanes, cars, dams, or other structures. These
models can be used to test design characteris�cs. Model airplanes can be tested in
wind tunnels to determine aerodynamic proper�es, and a model of a hydroelectric
dam can help architects and engineers find ways of integra�ng the structure with
the landscape. These models have physical characteris�cs similar to those of the
real thing. Experiments can be performed on this type of model to see how it may
perform under opera�ng condi�ons. With technology, such as computer simula�on
systems, virtual models can be rendered and tested quickly and less expensively.
The aerodynamic proper�es of an airplane can be tested in a virtual wind tunnel
that exists only inside the memory of a computer. Models also include the drawings
of a building that display the physical rela�onships between the various parts of the
structure. All of these models are simplifica�ons of the real thing used to help
designers make be�er decisions.

Computer-based technology has been used for many years to design cars, buildings,
furniture, and other products. It is moving quickly into the field of medicine.
Medical schools teach students about anatomy using 3-D computer generated
models. Students can see the nervous system, the blood vessels, the lymph nodes,
and glands along with the skeleton. The so�ware can show each separately and put
them all together in one 3-D picture. The so�ware can take input from various
medical tests and generate 3-D models of a pa�ent to diagnose medical condi�ons
faster and be�er.

In addi�on to these physical and virtual models, managers use mathema�cal abstrac�on to model important rela�onships. The break-even point calcula�on that is taught in
accoun�ng and finance is an example of applying a mathema�cal model. The use of drawings and diagrams is also modeling. The newspaper graph that illustrates stock market price

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changes in the last six months is a way to help the reader see trends in the market. Models do not have to be sophis�cated to be useful. Most models can be grouped into four
categories, and computers play a cri�cal role in the development and use of each type.

Mathema�cal models include algebraic models such as break-even analysis, sta�s�cal models used in forecas�ng and quality control, mathema�cal programming models, and
calculus-based models.
Graphs and charts are pictorial representa�ons of mathema�cal rela�onships. They include a visual representa�on of break-even analysis, a pie chart that illustrates market share,
a graph of stock prices over �me, or a bar graph that indicates the demand for energy for the last five years.
Diagrams and drawings are pictorial representa�ons of conceptual rela�onships. They include a precedence diagram that represents the sequence required to assemble a
building, a drawing of a gear that is part of a transmission in a car, a diagram that represents the logic of a computer program, and a drawing of an aircra� carrier.
Scale models and prototypes are physical representa�ons of an item. They include a scale model of an airplane and the first part produced (prototype), which is normally used for
tes�ng purposes. These models are o�en built and analyzed inside a computer system. Three-dimensional technology called stereolithography allows computers to create solid
models of parts. This is done by successively “prin�ng” very thin layers of a material, which cures quickly to form a sold part.

Mathema�cal models, graphs and charts, and diagrams are most commonly used by business and management professionals, so the discussion in this chapter focuses on these
types of models.

Application of Models

Many people use models frequently without realizing it. At a pizza party, the host will probably determine how much pizza to order by mul�plying the number of people expected
to a�end by the amount each person is expected to consume. The host is likely to then mul�ply the an�cipated cost per pizza by the number ordered to determine the cost. This is
a simple mathema�cal model that can be used to plan a small party or major social event.

In mathema�cal models, symbols and algebra are used to show rela�onships. Mathema�cal models can be simple or complex. For example, suppose a family is planning a trip to
Walt Disney World in Orlando, Florida. To es�mate gasoline costs for the trip, family members check a road atlas (one type of model), or go online to get direc�ons and a map
(another type of model). They determine that Orlando is approximately a 2,200-mile round trip from their home. From knowledge of the family car (a database), the family
es�mates that the car will achieve 23 miles per gallon (mpg) on the highway. The average cost of a gallon of gasoline is es�mated at $3.80. Using the following model, they make an
es�mate of gasoline cost.

*Throughout this text, to enlarge the size of the math equa�ons, please right click on the equa�on and choose “se�ngs” then “scale all math” to increase the viewing percentage.

Cost = (trip miles)(cost per gallon)/miles per gallon
=
= $363.48

A mathema�cal model can be used to answer what-if ques�ons. In the previous example, costs could be es�mated with a $.30 increase in the price of a gallon of gas, as shown in
the following:

Cost =

= $392.17

The model could also be used to es�mate the cost of the trip if the car averaged only 20 miles per gallon, as shown in the following:

Cost =

= $418.00

Models cannot include all factors that affect the outcome because many factors cannot be defined precisely. Also, adding too many variables can complicate the model without
significantly increasing the accuracy of the predic�on. For example, on the trip to Florida, the number of miles driven is affected by the number of rest stops made, the number of
unexpected detours taken, and the number of lane changes made. The number of miles per gallon is influenced by the car’s speed, the rate of accelera�on, and the amount of �me
spent idling in traffic. These variables are not in the model. The model builder should ask if adding the variables would significantly improve the model’s accuracy and usefulness.

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Technology Forecas�ng; TEDTalks: Chris Anderson—
Technology’s Long Tail

6.2 Forecasting

Forecas�ng is an a�empt to predict the future. Forecasts are usually the result of examining past experiences
to gain insights into the future. These insights o�en take the form of mathema�cal models that are used to
project future sales, product costs, adver�sing costs, and more. The applica�on of forecas�ng is not limited to
predic�ng factors needed to operate a business. Forecas�ng can also be used to es�mate the cost of living,
housing prices, the federal debt, and the average family income in the year 2025. For organiza�ons, forecasts
are an essen�al part of planning. It would be illogical to plan for tomorrow without some idea of what could
happen.

The cri�cal word in the last sentence is “could.” Any competent forecaster knows that the future holds many
possibili�es and that a forecast is only one of those possibili�es. The difference between what actually happens
and what is predicted is forecas�ng error, which is discussed later in this chapter. In spite of this poten�al
error, management should recognize the need to proceed with planning using the best possible forecast and
should develop con�ngency plans to deal with the possible error. Management should not assume that the
future is predetermined, but should realize that its ac�ons can help to shape future events. With the proper
plans and execu�on of those plans, an organiza�on can have some control over its future.

Stages of Forecast Development

The forecas�ng process consists of the following steps: determining the objec�ves of the forecast, developing
and tes�ng a model, applying the model, considering real-world constraints on the model’s applica�on, and
revising and evalua�ng the forecast (human judgment). Figure 6.1 illustrates these steps.

Figure 6.1: Steps in forecas�ng

Determining the objec�ves. What kind of informa�on does the manager need? The following ques�ons should be considered:

1. What is the purpose of the forecast?
2. What variables are to be forecast?
3. Who will use the forecast?
4. What is the �me frame of the forecast—long or short term?
5. How accurate should the forecast be?
6. When is the forecast needed?

Developing and tes�ng a model. A model should be developed and then tested to ensure that it is as accurate as possible. Several techniques including moving average, weighted
moving average, exponen�al smoothing, and regression analysis for developing forecas�ng models are discussed later in this chapter. In addi�on to these quan�ta�ve approaches, it
is o�en useful to consider qualita�ve factors, which are also discussed later in this chapter.

Applying the model. A�er the model is tested, historical data about the problem are collected. These data are applied to the model, and the forecast is obtained. Great care should
be taken so that the proper data are used and the model is applied correctly.

Real-world constraints. Applying any model requires considera�on of real-world constraints. A model may predict that sales will double in the next three years. Management,
therefore, adds the needed personnel and facili�es to produce the service or good, but does not consider the impact this increase will have on the distribu�on system. A so�ware
company expands its product offerings by hiring addi�onal programmers and analysts, but it does not provide the capability to install the so�ware on customers’ systems. If a
manufacturer is planning to expand produc�on to address an increase in demand: Should it consider raw-material availability? Will compe�tors react by cu�ng prices so that

Technology
Forecasting
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demand is less than expected? Where can the firm find the skilled labor to do the work? Forecast should not be taken as fact. A forecast is one scenario that managers must ground
in reality. A forecast is not a complete answer, but rather one more piece of informa�on.

Revising and evalua�ng the forecast. The technical forecast should be tempered with human judgment. What rela�onships may have changed? In the case of the electric u�lity
industry, a fundamental change in the rate of growth greatly affected the accuracy of es�mates for future consump�on. Forecasts should not be treated as complete or sta�c.
Revisions should be made as changes take place within the firm or the environment. The need for revision may be occasioned by changes in price, product characteris�cs,
adver�sing expenditures, or ac�ons by compe�tors. Evalua�on is the ongoing exercise of comparing the forecast with the actual results. This control process is necessary to a�ain
accurate forecasts.

Highlight: Forecas�ng for Quarry-Front Ice Cream Stand

In a small Midwest town, the Quarry-Front Ice Cream Stand operates in a small spot of land that is adjacent to an old stone quarry now used for swimming, and baseball fields
used for T-ball, Pee Wee, Li�le League, and PONY league baseball. The owner is preparing a plan to operate the stand for the coming summer months, which she is basing
upon informa�on gathered about prior years of opera�on.

1. Objec�ve: The owner needs to forecast demand, so she can order enough milk product, sprinkles, and other items as well as schedule enough staff to meet demand. As
expected for an ice cream stand in the Midwest, the demand is highly seasonal, so the �me period for the forecast is from early in May when baseball begins un�l Labor Day.
This stand closes for the rest of the year.

2. Developing and Tes�ng the Model: The owner has sales receipts by day for the last five summers. The owner decides to use a simple average to project demand for the coming
year. She averages the daily receipts for the 5-year period. As she tests her forecast with the actual sales data over the past five years, she finds that her projec�ons are not very
good. As she examines the data, she sees that there are major differences among the days of the week. For example, demand on Sunday is much lower. She recalculates the
averages by day of the week, so she has a projec�on for Monday based upon the average of all Mondays, for Tuesdays based upon all Tuesdays, etc. Demand on Mondays,
shows big differences; some Mondays are very busy, but others are not. She is unsure how to u�lize this data, but she moves forward with a plan based upon the daily forecast.

3. Applying the Model: As the ice cream stand opens, the owner decides to ask her staff to keep a simple tally for the first month of opera�ons. She provides each of them with a
sheet that is has a single column with the rows designated by 30-minute increments star�ng at 11:00 a.m. when the Quarry-Front Ice Cream Stand opens, and ending when it
closes at night 10:00 p.m. The staff is to place a tally mark for each customer served. As she studies the results, she no�ces strong demand in the early a�ernoon, which she
deduces is most likely driven by kids from the quarry who want lunch or a snack. She also no�ces a strong demand in the evenings, which is associated with teams and baseball
players’ parents purchasing a postgame ice cream treat. There is also a very big demand in early June when the small town has its homecoming parade and fes�val. The owner
gets the opera�ng schedule from the quarry and for the Baseball Associa�on to use that data to adjust her inventory and staffing to be�er meet the pa�erns of demand.

4. Real World Constraints: The quarry and the baseball leagues are part of real world constraints, but there are other factors as well. Weather greatly reduces demand because
the quarry may be closed and the baseball games rained out. Games scheduled before school is dismissed also cut demand because parents want their kids home early on
weeknights.

Real World Scenarios: 1973 Oil Embargo

In 1973, an oil embargo hit the United States, and energy prices climbed substan�ally in only a few weeks. The costs of all forms of energy increased, including gasoline,
natural gas, and electricity. The embargo caused a na�onwide effort to conserve energy. The demand for fiberglass insula�on soared; fiberglass companies did not have
sufficient capacity because their planning models were based upon much slower growth rates. Higher energy prices made spending money to conserve energy an a�rac�ve
investment. Conversely, the growth in demand for electricity dropped from about 3% annually, to near zero. In a rela�vely short �me it rebounded to about 1% per year. The
embargo changed the pa�ern of growth in the industry. Electrical u�li�es had planned for a significantly higher growth rate and did not react quickly enough to the change.
Many u�li�es con�nued to build new power plants. The result was a surplus of electrical genera�on capacity and the cancella�on of orders for nuclear power plants.

In the 1990s, the growth rate for electricity rebounded in part because of the growing demand for computer technology, including the prolifera�on of computer servers. Once
again, the forecas�ng models, this �me using the slower growth rates of the late 1970s and 1980s, underes�mated the need for electricity. This resulted in a brownout in some
parts of the United States in the late 1990s and early 2000s.

Application of Models

Before becoming immersed in the details involved with preparing a forecast, it is important to know that forecas�ng requires more than developing the model and performing an
analysis. The results from the model should be tempered with human judgment. The future is never perfectly represented by the past, and rela�onships change over �me. Thus, the
forecast should take into account judgment and experience.

Many techniques exist for developing a forecast. It is impossible to cover all the techniques effec�vely in a short �me. En�re books are devoted to forecas�ng, and some university
students major in forecas�ng as others major in marke�ng, accoun�ng, or supply chain management. In the following sec�ons, qualita�ve, �me-series, and regression analysis
methods of forecas�ng are discussed. Regression analysis can be used to project �me-series and cross-sec�onal data. There are several varia�ons of these methods:

Qualita�ve methods
Buildup method
Survey method
Test markets
Panel of experts (Delphi Technique)

Time-series methods
Simple moving average
Weighted moving averageProcessing math: 0%

Forecas�ng involves more than developing a model and conduc�ng analysis. Because the future may
not accurately represent the past, the results from a model should take into account the forecaster’s
judgment and experience.

©Tyler E Nixon/Flickr/Ge�y Images

Exponen�al smoothing
Regression and correla�on analysis (simple and mul�ple regression)

Qualitative Methods

Mathema�cal models are known as quan�ta�ve methods, while more subjec�ve
approaches are referred to as qualita�ve. Although mathema�cal models are useful
because they help management make predic�ons, qualita�ve approaches can also be
helpful. Qualita�ve forecasts that are based upon subjec�ve interpreta�on of historical
data and observa�ons are frequently used. A homeowner who decides to refinance his
or her home has made an implicit predic�on that home mortgage rates cannot be
lower, and are likely to remain constant or to increase in the future. Similarly, a manager
who decides to purchase extra materials because of uncertainty in supply has made an
implicit predic�on that a strike or other ac�on may disrupt the flow of materials. There
are many different qualita�ve methods for making forecasts. The buildup method,
surveys, test markets, and the panel of experts are discussed briefly, next.

Buildup Method

The buildup method requires star�ng at the bo�om of an organiza�on and making an
overall es�mate by adding together es�mates from each element. For example, a
brokerage firm could use this approach to forecast revenues from stock market
transac�ons. If the buildup method is used for predic�ng revenue, the first step is to ask
each representa�ve to es�mate his or her revenue. These es�mates are passed on to
the next-higher level in the organiza�on for review and evalua�on. Es�mates that
appear too high or too low are discussed with the representa�ve so that management
can understand the logic that supports the predic�on. If the representa�ve cannot convince the supervisor, a new predic�on based upon this discussion is made. The predic�on is
then passed on to the next level in the organiza�on.

As these subjec�ve judgments are passed up the organiza�on, they are reviewed and refined un�l they become, in total, the revenue forecast for the en�re organiza�on. It is top
management’s responsibility to make the final judgment about the forecast’s validity. Once top management has decided on the forecast, it becomes an input used in making
capacity, produc�on planning, and other decisions.

Survey Method

In some cases, organiza�ons use surveys to gather informa�on from external sources. A survey is a systema�c effort to elicit informa�on from specific groups and is usually
conducted via a wri�en ques�onnaire, a phone interview, or the Internet. The target of the survey could be consumers, purchasing agents, economists, or others. A survey may
a�empt to determine how many consumers would buy a new flavor of toothpaste, or consider a maintenance service that comes to their home to complete minor repairs on their
car. Currently, surveys of purchasing agents are conducted to assess the health of the economy. Surveys are o�en used to prepare forecasts when historical data are not available, or
when historical data are judged not to be indica�ve of the future. Surveys can also be used to verify the results of another forecas�ng technique.

Test Markets

Test marke�ng is a special kind of survey. In a test market, the forecaster arranges for the placement of a new or redesigned product in a city believed to be representa�ve of the
organiza�on’s overall market. For example, an organiza�on that wants to test the “at-home” and “at-work” market for an oil change service could offer the service in one or two
ci�es to determine how customers may respond. The analyst examines the sales behavior in the test market and uses it to predict sales in other markets. Test marke�ng can be
expensive, but the results tend to be more accurate than those complied from a survey because the consumers in a test market actually use the product.

Highlight: Assessing Demand for Car Repair Services

Jordan’s car repair service center is planning to launch its “At-Home – Car Services” business beginning in the summer of the coming year. The business model is based upon
providing car repairs and rou�ne service at a customer’s home or work place instead of at a repair shop. Before launching the new business, Jordan would like to know
something about demand such as the kind of at-home services customers want, the level of demand for these desired services, if there is a seasonal or other pa�ern to the
demand, and whether customers would be willing to pay a small premium for this convenient service. Using mathema�cal modeling to project demand will not likely provide a
good forecast because Jordan has no history of demand for this new business and there are no other businesses like it; therefore there is no demand data. Jordan has decided
to design a short survey to collect data about demand from three different groups of poten�al customers. First, he will seek input from his ac�ve customers to see if they
would like to use the new service. While this group is easy to access because they use the service center regularly, the group provides only li�le, if any, new revenue because
they are already supplying Jordan with their business. He may a�ract, at best, a small increase in business from this group, or he may prevent them from choosing a
compe�tor in the future. Second, and more financially lucra�ve, Jordan would like to iden�fy people who are not currently using his services. This is new business that is likely
to support the at-home service, and if the new customers like the at-home service, they may bring their vehicle to the service center for work that cannot be easily performed
at-home. This creates synergy between the two parts of his business. Third, if the business is ini�ally successful, Jordan would like to expand the at-home service to include
neighboring towns. If he can build an at-home service in these towns, he may be able to open an addi�onal service center there.

If Jordan decided to launch this at-home service, he would do this in a limited way. For example, he could limit the geography to provide only rou�ne maintenance to part of
his current service area. He could also limit the services offered to oil changes, air filters, and lubrica�on. This would allow him to keep his ini�al investment low and also
gather data about demand, which could be used to project demand for his full-service opera�on. A smaller investment reduces his risk.

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Organiza�ons o�en employ subject experts who a�empt to make forecasts by building consensus.

©Creatas/Thinkstock

Panel of Experts

A panel of experts is comprised of people who are knowledgeable about the subject being considered. This group a�empts to make a forecast by building consensus. In an
organiza�on, this process may involve execu�ves who are trying to predict the level of informa�on technology applied to banking opera�ons, or store managers who are trying to
es�mate labor costs in retail opera�ons. The panel can be used for a wide variety of forecasts, and with this method, forecasts can o�en be made very quickly.

The Delphi Technique uses a panel of experts and surveys in a par�cular manner.
The members of the panel provide a sequence of forecasts through responses to
ques�onnaires. This sequence of ques�onnaires is directed at the same item or set
of items. A�er each forecast, results are compiled, and the individuals are given
summary sta�s�cs such as the median response and the 50th percen�le of the
item or items being forecasted. This provides a reference point for the par�cipants,
who can decide whether or not to change their es�mate based upon this
informa�on. Because responses are gathered by ques�onnaire rather than by group
interac�on, the par�cipants do not meet face-to-face. As a result, a few
par�cipants, who may be overly conserva�ve or overly op�mis�c, cannot dominate
the discussion and bias the results. The Delphi process assumes that as each
forecast is conducted and the results disseminated among the panel members, the
range of responses diminishes and the median represents the “true” consensus of
the group.

Time-Series Methods

The historical data used in forecas�ng can be cross-sec�onal data, �me-series data,
or a combina�on of the two. Cross-sec�onal data samples across space, such as
height of adults in the United States, Europe, and Asia. The simplest way to
illustrate the differences in these data is with an example. One Pacific Coast Bank
wants to project usage of its automated teller service. It has collected data from
ATM systems in Stockton, San Jose, Santa Cruz, and Berkeley for the last two years.

The study has both �me-series and cross-sec�onal elements, as shown in Table 6.2. The �me-series data are the two years of data that are available for the banks. The cross-
sec�onal element is represented by the data from more than one bank.

Table 6.2: Time-series and cross-sec�onal data

Jan. Feb. Mar. . . . Dec. Jan. Feb. Mar. . . . Dec.

Stockton

San Jose

Santa Cruz

Berkeley

Forecas�ng sales, costs, and other relevant es�mates usually involves �me-series data, and the techniques discussed here are useful in predic�ng such data. See Figure 6.2 for the
�me line and nota�on used in forecas�ng. Each point on the �me line has associated with it an actual value, which is represented by x and a subscript. Each point on the line also
has a forecasted value, represented by f and a subscript. Every period has a forecasted value when it is in the future; as �me passes, it will have an actual value.

Figure 6.2: Forecas�ng �me line

Simple Moving Average

One approach to forecas�ng is to use only the most recent �me period to project the next �me period. This system, however, can introduce a significant error into a forecast
because any odd occurrence in the previous period will be completely reflected in the predic�on. Suppose that in one month a temporary price cut caused sales to be significantly
greater than normal. If these ac�ons are not repeated in the next month, then using the previous month’s sales as the forecast will provide a biased predic�on.

The purpose of the simple moving average is to smooth out the peaks and valleys in the data. In the data set shown in Figure 6.3, the data fluctuate significantly. Basing a
projec�on on the prior quarter’s result could provide a significant error. A moving average will smooth these peaks and valleys and provide a more reasoned predic�on. In theProcessing math: 0%

ft+1 = the forecast for �me period t + i, that is, the next �me period when i = 1

xt–i = the observed value for period t – i, where t is the last period for which data are available and i = 0, . . ., n–1

n = the number of �me periods in the average

moving average model, the forecast for the next period is equal to the average of recent periods.

where

The longer the �me—that is, the greater the n—the more smoothing that will take place. The selec�on of n is a management decision based upon the amount of smoothing
desired. A small value of n will put more emphasis on recent predic�ons and will more completely reflect fluctua�ons in actual sales. In fact, if n = 1, then the most recent �me
period’s actual results will become the next period’s forecast.

Figure 6.3: Graph of imports

Example: Following are the data shown in Figure 6.3:

Year: Quarter Imports ($000,000)

2012:1 4,100

2012:2 2,000

2012:3 5,700

2012:4 2,500

2013:1 7,300

2013:2 9,200

2013:3 6,300

To calculate a seven-quarter moving average for imports, sum the most recent seven quarters, and divide by seven. Please observe that the nota�on “year: quarter” is used in
the subscript here. The fourth quarter of 2013 is noted as “13:4.”

f13:4 =

= 5,300

A three-quarter moving average is calculated as follows:

f13:4 =

= 7,600

Which es�mate is likely to be�er represent the future? Which predic�on should be used? It depends upon whether the forecaster feels the last three quarters be�er predict
what is to come than the prior seven months. If so, use the 3-month moving average. If the last three months reflect some unusual condi�ons that are unlikely to recur, use
the 7-month moving average to smooth the high values in the last three quarters. Forecas�ng models do not provide complete answers to ques�ons. Managerial judgment
plays a cri�cal role.

This technique is called a moving average because to forecast the next quarter, the most recent quarter’s actual imports are added ande the oldest quarter’s actual imports are
subtracted from the total. In a way, the average moves. Refer again to the import example. Assume that actual imports for the fourth quarter of 2013 are $7,500 million. A
three-quarter moving average for the first quarter of 2014 would drop the $7,300 million, which is the actual value for the first quarter of 2013, and add the most recent
quarter. The following illustrates the calcula�on for the first quarter of 2014:

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f14:1 =

= 7,667

Weighted Moving Average

In a simple moving average, each �me period has the same weight. With a weighted moving average, it is possible to assign different weights to each period. The equa�on for
determining the weighted moving average is:

where

wt–i = the weight for period t–i, where t is the last period for which data are available and i = 0,. . . , n–1. The weights for all n periods must sum to 1.0.

Example: Five-period weighted moving average for the fourth quarter of 2013

Year: Quarter Weight Imports ($000,000)

2012:1 — 4,100

2012:2 — 2,000

2012:3 0.10 5,700

2012:4 0.15 2,500

2013:1 0.20 7,300

2013:2 0.25 9,200

2013:3 0.30 6,300

If the weights for each period are set at 0.20, then the weighted moving average and the simple moving average using five quarters will be equal. Try this for yourself.

The weights for each period need to be selected in some logical way. Usually the most recent periods are weighted more heavily because these periods are thought to be more
representa�ve of the future. If there is an up or down trend in the data, a weighted moving average can adjust more quickly than a simple moving average. S�ll, this form of the
weighted moving average is not as accurate as regression analysis (discussed later in this chapter) is in adap�ng to trends.

Exponen�al Smoothing

Exponen�al smoothing is another form of a weighted moving average. It is a procedure for con�nually revising an es�mate to include more recent data. The method is based upon
averaging (smoothing) past values. To start a forecast using exponen�al smoothing, the forecast for the first period, Exponen�al smoothing is another form of a weighted moving
average. It is a procedure for con�nually revising an es�mate to include more recent data. The method is based upon averaging (smoothing) past values. To start a forecast using
exponen�al smoothing, the forecast for the first period, ft+1  would be based upon the actual value for the most recent period, xt. (See equa�on 6.1.) The forecast for the second

period, ft+2 is equal to the actual value of the previous period, xt+1 �mes the smoothing constant, A, plus (1 – A) �mes the prior period’s forecast, ft+1. (See equa�on 6.2.)

Remember, the prior forecast, ft+1, is simply the actual value from period t. The forecast in equa�on 6.2 is A �mes the prior period’s actual value plus (1 – A) �mes the prior

period’s forecast, ft+1.

ft+1 = xt (6.1)

ft+2 = A(xt+1) + (1 – A)ft+1 (6.2)

ft+3 = A(xt+2) + (1 – A)ft+2 (6.3)

.       .         .

.       .         .
.       .         .

.       .         .
ft+n = A(xt+n – 1) + (1 – A)ft+n – 1

where

n = some number of periods in the future

Consider one more equa�on in detail. Equa�on 6.3 uses the prior period’s actual value �mes the weigh�ng factor, A, plus (1 – A) �mes the prior period’s forecast. Exponen�al
smoothing carries all the historical actual data in the prior period’s forecast.

How should the smoothing constant A be selected? First, A must be greater than or equal to zero and less than or equal to one. Within this range, a manager has discre�on. What
will happen if a manager selects a smoothing constant at an extreme? If A = 1, then according to equa�on 6.2, the forecast will be based solely on the actual value from the prior
period. In this case, no smoothing takes place. The forecast for the next period is always the last period’s actual value. If the smoothing constant is set to 0, then the prior period’s
actual value is ignored. Once the forecas�ng pa�ern gets started, the forecast is so smooth that it will never change. No actual amounts can enter the equa�on because A = 0.
Neither of these alterna�ves is acceptable.

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There are no specific rules about choosing the value of A. If the forecaster wants to put more weight on the most recent �me period, then A should be set closer to 1. If the
manager desires a smoother forecast that will not react dras�cally to short-term change, A should be set closer to 0. Values between 0.1 and 0.3 are most commonly used. Typically,
values in this range are selected so the forecast does not overcompensate for sudden changes in the data. For example, if weather was extremely warm in the last �me period and
demand was high, the forecast for the next �me period would not be pushed to an extreme level if a value of A is selected that is within this range. The forecast would be
smoothed because more weight is placed on the historical data, meaning the data that are prior to the sales value for the most recent �me period.

Example: Exponen�al Smoothing

Use exponen�al smoothing to forecast imports from the previous example. To illustrate the impact of the smoothing constant, use A = 0.1 and A = 0.6. To begin, there can be
no forecast for the first quarter of available data because no history is available. The forecast for the second quarter is the prior quarter’s actual value because no forecast is
available for the first quarter. In Figure 6.4, 4,100 is the forecast for both A = 0.1 and A = 0.6. A�er that, the forecasts are significantly different because of the large difference
in smoothing constants and the large fluctua�ons in demand. The third quarter’s forecast follows the equa�ons described previously because an actual value and a forecasted
value are available for the prior quarter. Despite that the actual demand is available through the third quarter of 2013, the forecasted values were calculated to illustrate the
difference between the two forecasts and the poten�al for inaccurate forecasts when historical demand varies significantly.

For A = 0.1

f12:3 = A(x12:2) + (1 – A)f12:2

= 0.1(2,000) + 0.9(4,100)

= 3,890

For A = 0.6

f12:3 = 0.6(2,000) + 0.4(4,100)

= 2,840

Year: Quarter Imports ($000,000) Forecast A = 0.1 Forecast A = 0.6

2012:1 4,100

2012:2 2,000 4,100 4,100

2012:3 5,700 3,890 2,840

2012:4 2,500 4,071 4,556

2013:1 7,300 3,914 3,322

2013:2 9,200 4,253 5,709

2013:3 6,300 4,748 7,804

2013:4 4,903 6,902

The forecasts are significantly different. The forecast with A = 0.1 does not react abruptly to sudden changes. The forecast with A = 0.6 does respond but the response is
delayed. This can be seen graphically in Figure 6.4 where the actual value and the two forecasts are plo�ed.

Figure 6.4: Exponen�al smoothing examples

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6.3 Advanced Statistical Methods

Correla�on analysis measures the degree of rela�onship between two variables, and regression analysis is a method to predict the value of one variable based upon the value of
other variables. The coefficient of correla�on is a measure of the strength of linear rela�onship between variables. If there is no rela�onship, then the coefficient of correla�on is 0.
Perfect posi�ve correla�on is 1.0, and perfect nega�ve correla�on is –1.0 (see Figure 6.5). Between the limits of perfect posi�ve and perfect nega�ve correla�on, there are many
levels of strength. Examples are shown in Figures 6.5 and 6.6.

Figure 6.5: Sca�er diagrams showing zero, perfect
posi�ve, and perfect nega�ve correla�ons

Figure 6.6: Sca�er diagrams showing examples of
correla�on

Regression analysis can be used to forecast both �me-series and cross-sec�onal data. Regression analysis is o�en used to es�mate the slope of a trend line for �me-series data.
Regression analysis can be either simple or mul�ple. Simple regression analysis involves the predic�on of only one variable (the dependent variable) and uses only one variable for
predic�on (the independent variable). Mul�ple regression analysis has only one dependent variable, but can have more than one independent variable.

Regression and Correlation Analysis

The equa�on for simple regression follows. Y is the dependent variable, and X is the independent variable. The variable b is the slope of the line, which is es�mated by equa�on
6.4, and variable a is the Y-intercept, which is es�mated by equa�on 6.5.

where

Y = the dependent variable. It depends on the variables X, a, and b.

X = the independent variableProcessing math: 0%

n = the number of data points in the sample

r = the coefficient of correla�on

b = (6.4)
a = (6.5)
r = (6.6)

Example: Regression

When working with �me-series data, it is usually easier to convert the �me variable from the month/day/year format to simpler numbers. There are many possible ways of
coding. Here, the number 1 is used to represent the first �me period for which data are available. Following periods will be consecu�vely numbered. In this example, the
assump�on is that demand (Y) depends on �me (X), the independent variable. The import data from an earlier example are used for analysis.

Year: Quarter Coded Value for Year: Quarter (X) Imports ($000,000) (Y) XY X2 Y2

2012:1 1 4,100 4,100 1 16,810,000

2012:2 2 2,000 4,000 4 4,000,000

2012:3 3 5,700 17,100 9 32,490,000

2012:4 4 2,500 10,000 16 6,250,000

2013:1 5 7,300 36,500 25 53,290,000

2013:2 6 9,200 55,200 36 84,640,000

2013:3 7 6,300 44,100 49 39,690,000

Sum 28 37,100 171,000 140 237,170,000

Interpre�ng the results of the model requires an understanding of the original units of the data as well as the slope/intercept method of represen�ng a straight line. The last
quarter of 2013 is coded as “8” because the quarters are consecu�vely numbered. The imports are given in millions of dollars. As a result, the imports are projected to
increase $807.1 million per quarter. The intercept is $2,072 million, and it represents the point on the regression line for the quarter prior to the first quarter of 2012. Project
the imports for the last quarter of 2013 where the es�mated value is represented by Ye. The predic�ve model follows.

Thus, the projec�on for imports is $8,528 million.

Goodness of Fit

How well does the equa�on determined by regression analysis fit the data? The principles on which simple regression analysis and mul�ple regression analysis are constructed are
similar. The regression model es�mates the Y-intercept (a) and the slope of the line (b) that best fits the data. The criterion that is used to determine the “best fit” line minimizes
the squared distance from each point to the line. This is o�en called the least squares method. These distances are labeled di in Figure 6.7, with i equal to 1, . . ., n. The method

used to derive the parameters of the best fit line is based upon differen�al calculus and is not covered in this text. The equa�ons that determine the parameters of the slope (b)
and the Y- intercept (a) are 6.4 and 6.5, respec�vely.

Figure 6.7: Regression line

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The coefficient of correla�on calculated in the prior example (r = 0.671) indicates a high degree of rela�onship between the dependent and independent variables. The higher the
coefficient of correla�on (closer to 1), the more confident the forecaster can be that varia�on in the dependent variable (imports) is explained by the independent variable (�me).
This can be observed by looking at the sca�er diagram in Figure 6.8. A measurement of this varia�on about the regression line is the standard error of the es�mate, sy/x. It is the

difference between each observed value, Yo, and the es�mated value, Ye. The equa�on for the standard error of the es�mate follows. An alterna�ve formula that is easier to use

with a calculator is provided in Figure 6.8.

Simple regression models can be constructed for cross-sec�onal data. The mechanics are similar.

Figure 6.8: Sca�er diagram and regression line for import
problem

Computer Applica�on of Simple Regression Analysis

Many different computer so�ware packages are available for doing both simple and mul�ple regression analyses. Table 6.3 is the computer-based output for regression analysis. The
coefficients calculated by so�ware are the same (with allowances for rounding) as the coefficients calculated by hand. The standard error of the es�mate is also the same as the
value calculated by hand. The computer output also provides addi�onal informa�on. The standard error of the coefficients, 1,784.800 and 399.093, are standard devia�ons for the
coefficients. They can be used to test the null hypotheses that the actual values of the coefficients are equal to zero. The t-values are the calculated t-sta�s�cs for the hypothesis
tests. The two-sided significant probabili�es are the levels that alpha or Type 1 error would have to be set at in order to fail to reject the null hypothesis. In this example, the trend
coefficient would be significant if alpha error is set at 0.1 or higher. On the other hand, the coefficient for the intercept would be significant if alpha error is set at 0.3 or higher.

Table 6.3: Regression coefficients for imports

Variable Coefficient Std. Error t-value Two-sided Sig. Prob.

Constant 2,071.42900 1,784.8000 1.16059 0.298204

YRS/OUT 807.14290 399.09340 2.02244 0.099061

Standard error of es�mate = 2111.804Processing math: 0%

Mul�ple Regression Model

Mul�ple regression has only one dependent variable, but can have many independent variables.

Y = a + b1 x1 + b2 x2 + . . . + bk xk

where

Y = the dependent variable. It depends on the variables X1 through Xk and the model parameters a, b1, b2. . ., bk , where k is the number of independent variables.

(Equa�ons for the parameters are not given here. There are many available computer packages, such as EXCEL, SPSSX, SAS, or MINITAB to do the necessary
calcula�ons.)

xi = an independent variable, with i = 1, . . ., k. Each independent variable will have n observa�ons or data points.

Minimizing squared distances from each observed point to the best fit regression line is s�ll useful. However, because mul�ple regression requires more than two dimensions, two-
dimensional graphs cannot be used. Computerized sta�s�cal models are used to make the calcula�ons.

Problem

The prior examples use only one independent variable (�me) to predict imports. Most rela�onships are not that simple, because other factors will also affect the dependent
variable. To expand the previous example, disposable income and the consumer price index are added to the model.

Imports ($000,000) Year: Quarter Code Value For Year: Quarter
(Xi)

Disposable Income (Billions of
$) (X2)

Consumer Price Index (X3)

4,100 2012:1 1 65 110

2,000 2012:2 2 60 111

5,700 2012:3 3 73 113

2,500 2012:4 4 61 113

7,300 2013.1 5 70 117

9,200 2013.2 6 77 118

6,300 2013:3 7 78 117

The mul�ple regression output is shown in Figure 6.9. The equa�on for predic�ng imports is

ye = –141,000 – 1,387.6×1 + 206.35×2 + 1,205.4×3

Figure 6.9: Mul�ple regression output

To predict imports for the fourth quarter of 2013, assume that disposable income is $78 billion and the consumer price index is 118 for the fourth quarter.

ye = –141,000 – 1,387.6(8) + 206.35(78) + 1,205.4(118) = 6,232

The predic�on is $6,232 million worth of imports in the fourth quarter of 2013.

Measuring Forecasting Error

Regardless of which forecas�ng model is used, it is important to have some way to determine the model’s propensity for error. If an organiza�on has been using a par�cular model
to forecast sales for some �me, has the model been performing well? How large is the error? One approach is to simply subtract the forecast for one �me period from the actual

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When using a forecas�ng model, it is cri�cal to have a way to determine the model’s propensity for
error.

iStockphoto/Thinkstock

value for the same �me period. This can be repeated so that the forecaster has
differences for many periods. Differences are posi�ve when the forecast is less than the
actual value, and nega�ve when the forecast is greater than the actual value. In raw
form, these differences tell the forecaster li�le. If these are added, the nega�ve errors
and the posi�ve error will cancel and therefore underes�mate the error. A common
method used by forecasters to avoid this problem is to calculate the mean squared
error. The mean squared error (MSE) is the average of all the squared errors. The
differences are squared and added together, and then that total is divided by the
number of observa�ons. The following calcula�ons help illustrate the method.

Problem

Month Actual Sales ($) Forecasted Sales ($) Error ($) Squared Error

January 419,000 448,000 – 29,000 841,000,000

February 480,000 481,000 – 1,000 1,000,000

March 601,000 563,000 + 38,000 1,444,000,000

April 505,000 525,000 – 20,000 400,000,000

May 462,000 490,000 – 28,000 784,000,000

June 567,000 519,000 + 48,000 2,304,000,000

TOTAL 5,774,000,000

Some�mes the square root of the mean squared error is used to measure the error. This is analogous to the standard error of the es�mate, which is discussed in the sec�on
on regression analysis.

It is also possible to use mean absolute devia�on (MAD), which is similar to MSE, to es�mate forecas�ng error. MAD is calculated by adding together the differences between
the actual and forecasted value once the nega�ve and posi�ve signs are removed. If MAD is not calculated, a large nega�ve error would offset a large posi�ve error, so the
total error would be greatly underes�mated. Try summing the “Error” column in this example with the signs included. The total error is $8,000 because the nega�ve and
posi�ve errors cancel each other. Once the signs are removed, the total error is $164,000, which is divided by the number of data points n, as was done for MSE. The MAD is
$164,000/6, which equals $27,333. The MAD is easier to interpret than MSE because MAD is the average error for the prior six forecasts. As a result, MAD can be used to
es�mate future forecas�ng errors.

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Chapter Summary

A model is an important way to think about problems. It is an abstrac�on from a real problem of the key variables and rela�onships in order to simplify the problem and improve
understanding.
There are many different types of models, including prototypes used in product design; scale models used in architecture; diagrams and drawings used by scien�sts, engineers,
managers, and others; and mathema�cal models used in many disciplines.
Models are used to assist managers and others in answering what-if ques�ons by changing a parameter in the model.
There are qualita�ve and quan�ta�ve methods for developing a forecast.
Forecas�ng is a type of mathema�cal model that can be used to predict the future. It is an important part of the planning process in an organiza�on
The forecas�ng process consists of determining the objec�ves of the forecast, developing and tes�ng a model, applying the model, considering real-world constraints in the
applica�on of the model, and revising and evalua�ng the forecast.
Forecas�ng techniques typically use historical data to develop the model that is used to make the projec�on. If the rela�onships in the data change over �me, the model may no
longer predict the future accurately.
Forecasters require a way to measure the amount of forecas�ng error.

Case Studies

Blast-Away Housecleaning Service

Blast-Away Housecleaning Service uses powerful water jets to clear loose paint from residen�al buildings and to clean aluminum siding. The company is trying to arrive at a fast and
accurate way of es�ma�ng cleaning jobs. The following simple formula is its first a�empt. It includes a fixed charge for coming to the job plus �me requirements, which are a
func�on of the exterior of the house measured in square feet (sf).

Es�mated cost = $15 + ($.06/sf)(sf)

A�er one year of experience, Blast-Away has lost $50,000 on sales of $250,000. At first, the owner, Hadley Powers, could not understand the reasons for his losses. His employees
worked hard, and Blast-Away could barely meet with demand. In fact, Powers was planning to add another crew this year, but if he cannot determine the reason for the losses and
find a solu�on, his investors would be reluctant to provide him with addi�onal capital. What caused the loss?

He learned from his accountant that the model he used had not included a recovery of his investment in the equipment used on the jobs. Powers had invested $60,000 in
equipment at the beginning of the first year and expected it to last three years. His accountant recommended that Powers increase the price charged per job to generate an extra
$20,000 per year to cover equipment costs. If Powers were able to do this, his losses would be $30,000 if all other factors remained the same. He had to look further for answers to
the problem.

Powers has hired you to carefully examine last year’s job �ckets, which contain the quoted price; distance from headquarters; size of the house; type of exterior, such as painted
wood, aluminum, or brick; and style of the house, such as ranch, two-story, or split-level. You also have the operator’s logbook that lists travel �me and the �me necessary to
complete each house. As you analyze the job �ckets, you no�ce that a substan�al number of the jobs that Blast-Away gets are for small, split-level or two-story homes located in
the suburbs and surrounding rural area. Many of the homes have wooden siding, which is the most difficult type of siding to clean to the customer’s sa�sfac�on.

1. In addi�on to the equipment recovery problem, what is causing Blast-Away to lose money?
2. What would you recommend Powers do to correct the problem?
3. What data would you want to collect to verify your recommenda�ons?

Lucy’s Lamps-R-Us

Lucy Mertz has opened a specialty lamp shop in a suburban shopping mall. Mertz’s shop has an excellent loca�on next to the entrance to the largest and most popular department
store in the five-county area. A�er a slow beginning, business picked up nicely, and the lamp shop had made a nice profit. To plan for the next year, Mertz decided to use sales for
the last eight months to forecast next year’s sales. She has asked you to use the following data to project sales. The forecast listed here, which is for last year, was based upon
judgment. Mertz wants you to use a quan�ta�ve approach.

Time Period Forecasted Sales Actual Sales
May $5,000   $8,300
June   5,200   10,200
July   5,600   9,900
August   6,200   10,200
September   6,900   9,800
October   7,800   11,400
November   8,500   12,800
December   9,000   14,500

1. How much error existed in the old forecast?
2. Project the sales for January, February, and March of next year.

It is now the end of March, and the actual sales for the first three months are available. The results are disappoin�ng. In January, sales declined because of returns from the
Christmas buying season and an increase in bargain hun�ng. Also, the large department store that anchored Mertz’s end of the shopping mall closed at the end of January because
of opera�ng losses by the parent company.

Time Period Actual Sales
January 7,500
February 6,000

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March 6,100

3. Why did the model give Mertz a poor forecast?
4. What would you recommend to Mertz regarding the forecast for the next three months?

Discussion Ques�ons

Click on each ques�on to reveal the answer.

1. What is model building, and why is model building important for managers?
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

A model is an abstrac�on from the real problem of the cri�cal variables and rela�onships in order to simplify the problem. Managers should use models to gain a be�er
understanding of the problem. Models also permit the manager to analyze a given situa�on by asking “what if” ques�ons.

A model is an important way of thinking about a problem. Decision makers use models to increase their understanding of a problem. A model helps managers simplify the
problem by focusing on the key variables and rela�onships in the problem.

2. Discuss the different types of models.
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

Many different types of models are possible. Models can be used to test design characteris�cs such as tes�ng the aerodynamic proper�es of an airplane in a wind tunnel.
Mathema�cal and graphical models are also possible.

a. Mathema�cal models include algebraic models such as break-even analysis, sta�s�cal models used in forecas�ng and quality control, and calculus based models such as the
economic order quan�ty.

b. Graphs and Charts are pictorial representa�ons of mathema�cal rela�onships. They include a graphical representa�on of break-even analysis, a pie chart that illustrates
market share in an industry, and a graph of stock prices for the past four months.

c. Diagrams and drawings are pictorial representa�ons of conceptual rela�onships. They include such things as precedence diagrams that represent the sequence required to
assemble a building, a drawing of a gear that might be part of a transmission in a car, and a diagram that represents the logic of a computer program.

d. Scale models and prototypes are physical representa�ons of an item. They include a scale model of an airplane and the first part produced (prototype) which is used for
tes�ng purposes.

3. Describe how models can be used to answer what-if ques�ons.
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

A model can be used to answer “what if” ques�ons by changing one of the assump�ons in the model. For example, in the model of gasoline costs on the trip to Florida, an
increase in the cost per gallon of gasoline caused the cost of the trip to increase and a decrease in the miles per gallon, which the car would a�ain, caused the cost to increase.
In a physical model, such as the airplane example, the designers could change the angle or shape of the wing to determine the impact of the change on the performance of
the plane.

4. How are models used in business and opera�ons?
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

Models are widely used in opera�ons and in other areas within the organiza�on. Financial managers use net present value and internal rate of return models to analyze
investment alterna�ves. Informa�on systems personnel use flow diagrams to understand the logic required to develop a computerized inventory control system. Accountants
use ra�os such as the current ra�o to measure an organiza�on’s ability to pay its short-term bills. Opera�ons managers use models extensively to forecast sales, understand the
cost-volume-profit rela�onship, assign tasks to work sta�ons, decide to make or buy a product, and to set the order size.

5. What is forecas�ng, and why is it important to an organiza�on?
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

Forecas�ng is an a�empt to predict the future. Forecasts are usually the result of examining past experience to gain insight into the future. These insights o�en take the form
of mathema�cal models. Forecas�ng is essen�al to the success of an organiza�on because organiza�ons must prepare for the future and a forecast provides some insights as to
the condi�ons, which the organiza�ons might encounter.

6. Describe the forecas�ng process.
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644

To begin the forecas�ng process, the modeler should determine the objec�ves of the forecast. This should lead to a discussion of the purpose of the forecast, the variables to
be forecast, who will use the forecast, the �me frame of the forecast (long, medium, or short term), the level of accuracy needed, and when the forecast is needed. A
technique or method for doing the forecast must be selected and the forecas�ng model should be developed and tested. When the model is applied, the results should be
considered with respect to constraint in the “real-world.” For example, a forecast of sales that predicts a 20 percent increase in sales must be balanced against a company’s
ability to produce at that level. Finally, the forecast should be evaluated to determine how well it is predic�ng the future. Have assump�ons upon which the forecas�ng model
was built changed to the point that the model is no longer valid? If the assump�ons have changed, then the model should be revised.

7. Discuss the qualita�ve approaches to forecas�ng.
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Qualita�ve approaches to forecas�ng are usually based on the judgment and experience of people who are knowledgeable in the area being forecast. Qualita�ve approaches
are usually based on historical data because the judgment and experience of the people involved are affected by events in the past. The buildup method for predic�ng sales
involves star�ng at the bo�om of an organiza�on. Sales representa�ves from each sales territory predict their sales. These es�mates are passed up to the next level of the
organiza�on for review and evalua�on. A�er agreement is reached at that level, the sales are passed up to the next level in the organiza�on. When the sales figures reach the
top of the organiza�on, they can become the target for the en�re organiza�on.

A survey is a systema�c effort to elicit informa�on from specific groups and is usually conducted via a wri�en ques�onnaire, Internet survey, or phone interview. The target of a
survey could be consumers, purchasing agents, or economists. A survey could a�empt to determine the demand for a new or exis�ng product. The use of a survey is not
limited to es�ma�ng demand for product. It could be used to measure the overall health of the economy or the support level of a poli�cal candidate on a par�cular issue.

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In a test market the actual product is distributed in a small geographical area and demand for the product is carefully monitored. The test market area should be representa�ve
of the overall market if the results are to be useful. Test markets are used to es�mate sales.

A panel of experts involves people who are knowledgeable about a subject. This group a�empts to make a forecast by building consensus. The Delphi Technique uses a panel of
experts in a par�cular manner. The panel of experts can be used to predict a wide variety of items from the cost of a raw material to the chance for nuclear disarmament.

8. How does the Delphi Technique work? What are its advantages?
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The Delphi Technique uses a panel of experts and surveys. The members of the panel do not interact directly with one another. They provide a sequence of forecasts through
responses to ques�onnaires. A�er each forecast, results are compiled and the individuals on the panel are informed as to the 25th, 50th, 75th percen�le and/or other relevant
data points for the item being forecast. Because the response is by ques�onnaire rather than by group interac�on, which can be dominated by a few individuals, undue
conserva�sm or op�mism and argumenta�on are avoided. The Delphi Technique assumes that as each forecast is conducted and the results disseminated among the panel
members, the range of responses diminishes and the median moves to a posi�on represen�ng the “true” consensus of the group.

9. How is regression analysis different from the moving average, the weighted moving average, and exponen�al smoothing?
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There are obvious differences between the way regression analysis parameters are calculated and the ways that moving average, weighted moving average, and exponen�al
smoothing are calculated. The difference, which the ques�on is a�emp�ng to address, is that regression analysis actually es�mates the amount of trend in the data. In simple
regression analysis, this is the slope coefficient b. The other techniques predict the next �me periods value without es�ma�ng the trend. As a result, these techniques will
consistently underes�mate sales when sales are rising at a constant or accelera�ng rate period a�er period and will consistently overes�mate sales when sales are declining in
this manner.

Regression analysis (simple or mul�ple regression) is a cause and effect model that considers independent variable(s) to determine the dependent variable. The (simple) moving
average, weighted moving average, and exponen�al smoothing models use �me series data to predict the next period. However, �me can be a dependent variable in regression
analysis as well. Regression analysis can es�mate a trend.

10. What is forecas�ng error, and why should it be measured?
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Forecas�ng error is the difference between the forecasted value for an item and the actual value. There are many different ways to es�mate the error, and Mean Squared Error
(MSE) and Mean Absolute Devia�on (MAD) are common ways. Both methods take the difference between the forecast and the actual values for all observa�on. For MSE, these
differences are squared and summed; then, the sum is divided by the total number of observa�ons. For MAD, the absolute value of these differences is determined and the
errors are summed. The sum is divided by the total number of observa�ons. Without a way to measure forecas�ng error, it would be difficult to determine when a forecast is
no longer representa�ve of what is actually driving the factor being forecasted.

Problems

1. Blast-Away Housecleaning Service uses powerful water jets to clear loose paint from residen�al buildings and to clean aluminum siding. The company is trying to arrive at a fast
and accurate way of es�ma�ng cleaning jobs. The following simple formula is its first a�empt. It includes a fixed charge for coming to the job plus �me requirements, which are
a func�on of the exterior of the house measured in square feet (sf).

Es�mated cost = $15 + ($0.06/sf)(number of sf)

a. How much should Blast-Away charge to clean a house that is a rectangular 40-by-28 feet? The distance from the roof line to the bo�om of the siding is 9 feet.
b. Suppose Blast-Away’s labor costs increase and the cost per square foot increases to $0.064. How much should it charge for the house in Part a?
c. What other factors may Blast-Away include in the pricing model to improve the precision of the model?

2. As a service to its customers, Turbo Natural Gas Company will es�mate the amount of natural gas required (NGR) in hundreds of cubic feet (CCF) to heat your home. This is
done by a mathema�cal model that considers the square footage on the first floor (sf1), the square footage on the second floor (sf2), and the temperature se�ng on the

thermostat. The temperature se�ng entered into the model should be the difference between the temperature se�ng in the home and 65 degrees (td). Make sure to keep the
minus sign if the se�ng is less than 65 degrees. The model builder assumed that the homes have 8-foot ceilings, an average number of good-quality windows, 3.5 inches of
insula�on in each wall, 6 inches of insula�on in the a�c, and a typical Midwestern winter.

a. How much natural gas will an 1,800-square-foot ranch home (one floor only) use if the thermostat is set at 70 degrees?
b. How much natural gas will a two-story home with a total of 2,400 square feet use if the thermostat is set at 63 degrees? There is 1,000 square feet on the second floor.
c. What happens to the natural gas cost in Parts a and b if the model is revised and the usage for the first floor increases to 0.60 CCF/sf from 0.50 CCF/sf.

3. It appears that the imports of beef have been increasing about 10% annually on the average. Project the 2002 imports using linear regression.

Year Imports of Beef
(Thousands of Tons)

2003 82
2004 101
2005 114
2006 126
2007 137
2008 151
2009 164
2010 182
2011 189

4. Mighty-Maid Home Cleaning Service has been in opera�on for eight months, and demand for its products has grown rapidly. The owner, who is also the manager, of Mighty-Maid is
trying to keep pace with demand, which means hiring and training more workers. She believes that demand will con�nue at the same pace. She needs an es�mate of demand so she
can recruit and train the workforce. The following represents the history of Mighty-Maid:

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Time Hours of Service Rendered
December 300
January 750
February 650
March 920
April 1,300
May 1,400
June 1,200
July 1,500

Es�mate the trend in the data using regression analysis.

5. Use the regression model calculated in the Mighty-Maid problem to es�mate the hours of service for December through July. Now that both the actual and the forecasted values are
available, answer the following ques�ons:

a. What are the MSE and MAD for the forecast?
b. Is the forecas�ng model a “good” model?

6. The figures below indicate the number of mergers that took place in the savings and loan industry over a 12-year period.

Year Mergers Year Mergers
2000 46 2006 83
2001 46 2007 123
2002 62 2008 97
2003 45 2009 186
2004 64 2010 225
2005 61 2011 240

a. Calculate a 5-year moving average to forecast the number of mergers for 2012.
b. Use the moving average technique to determine the forecast for 2005 to 2011. Calculate measurement error using MSE and MAD.
c. Calculate a 5-year weighted moving average to forecast the number of mergers for 2012. Use weights of 0.10, 0.15, 0.20, 0.25, and 0.30, with the most recent year weighted

being the largest.
d. Use regression analysis to forecast the number of mergers in 2012.

7. Find the exponen�ally smoothed series for the series in Problem 6, (a) using A = 0.1 and then (b) using A = 0.7, and plot these �me series along with the actual data to see the impact
of the smoothing constant.

8. The �me series below shows the number of firms in an industry over a 10-year period.

Year Firms Year Firms
2002 441 2007 554
2003 468 2008 562
2004 481 2009 577
2005 511 2010 537
2006 551 2011 589

a. Find the 5-year moving average for this series.
b. Find the 3-year weighted moving average for this series. Use the following scheme to weight the years:

Weight
Most recent year 0.5
Two years ago 0.3
Three years ago 0.2

c. Determine the amount of measurement error in the forecast. Use the weighted moving average technique (with the weights from Part b to forecast for 2005 to 2011.) Then use
that 7-year period to calculate measurement error (both MSE and MAD).

d. Find the exponen�ally smoothed forecast for this series with A = 0.2.
9. The quarterly data presented here show the number of appliances (in thousands) returned to a par�cular manufacturer for warranty service over the last five years.

1st Quarter 2nd Quarter 3rd Quarter 4th Quarter
5 years ago 1.2 0.8 0.6 1.1
4 years ago 1.7 1.2 1.0 1.5
3 years ago 3.1 3.5 3.5 3.2
2 years ago 2.6 2.2 1.9 2.5
1 year ago 2.9 2.5 2.2 3.0

a. Find the equa�on of the least squares linear trend line that fits this �me series. Let t = 1 be the first quarter five years ago.
b. What would be the trend-line value for the second quarter of the current year—that is, two periods beyond the end of the data provided?

10. The following are AJV Electric’s sales of model EM-5V circuit assemblies over the last 16 months (in thousands of units):

Month Sales (Thousands of
Units)

Month Sales (Thousands of
Units)

Sept. 2010 55 May 2011 63
Oct. 2010 53 June 2011 53
Nov. 2010 60 July 2011 51
Dec. 2010 49 Aug. 2011 60
Jan. 2011 48 Sept. 2011 58

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Feb. 2011 61 Oct. 2011 52
Mar. 2011 61 Nov. 2011 51
Apr. 2011 53 Dec. 2011 63

Use the moving average technique to forecast sales of AJV’s model EM-5V for January 2012 (use a 3-month base). Does the model appear to be appropriate? Why or why not?

11. Employ the single exponen�al smoothing technique to forecast sales of AJV’s model EM-5V for January 2012 (use A = 0.8). Does the model appear to be appropriate?
12. U�lize the single exponen�al smoothing technique to forecast sales of AJV’s model EM-5V for January 2012 (use A = 0.1). How do the results compare with those from problem 11?

Is one be�er than another? Why or why not?
13. Using linear regression, forecast the sales of AJV’s model EM-5V for January 2012 through June 2012.
14. Thri�y Bank and Trust is trying to forecast on-the-job performance by its employees. The bank administers an ap�tude test to new employees. A�er the employee training period

and an addi�onal six months on the job, the bank measures on-the-job performance. The following data have been gathered from the last eight people hired:

Employee Number Transac�ons Score per Hour
1 90 36
2 70 29
3 85 40
4 80 32
5 95 42
6 60 23
7 65 29
8 75 33

a. Fit a line to the data using regression analysis. What is the meaning of the parameters that were es�mated by the regression analysis model?
b. How well does the model fit the data?
c. How many transac�ons per hour would you expect from someone who scored 87 on the ap�tude test?

15. The data in the following table were collected during a study of consumer buying pa�erns.

Observa�on X Y
1 154 743
2 265 830
3 540 984
4 332 801
5 551 964
6 487 955
7 305 839
8 218 478
9 144 720
10 155 782
11 242 853
12 234 878
13 343 940

a. Fit a linear regression line to the data using the least squares method.
b. Calculate the coefficient of correla�on and the standard error of the es�mate.
c. How could the coefficient of correla�on and the standard error of the es�mate be used to make a judgment about the model’s accuracy?

16. Perfect Lawns intends to use sales of lawn fer�lizer to predict lawn mower sales. The store manager feels that there is probably a six-week lag between fer�lizer sales and mower
sales. The per�nent data are shown below.

Period
Fer�lizer Sales
(Tons)

Number of
Mowers Sold
(Six-Week Lag)

1 1.7 11
2 1.4 9
3 1.9 11
4 2.1 13
5 2.3 14
6 1.7 10
7 1.6 9
8 2.0 13
9 1.4 9
10 2.2 16
11 1.5 10
12 1.7 10

a. Use the least squares method to obtain a linear regression line for the data.
b. Calculate the coefficient of correla�on and the standard error of the es�mate.
c. Predict lawn mower sales for the first week in August, if two tons of fer�lizer sold six weeks earlier

Click here to see solu�ons to the odd-numbered problems.
(h�ps://media.thuze.com/MediaService/MediaService.svc/constella�on/book/AUBUS644.13.2/{pdf}bus644_ch06_odd_problem_solu�ons )

Key Terms
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Click on each key term to see the defini�on.

buildup method
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

An approach to forecas�ng that starts at the bo�om of an organiza�on and makes an overall es�mate by adding together es�mates from each element.

coefficient of correla�on
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A measure of the strength of a rela�onship between variables. If there is no rela�onship, the coefficient of correla�on will be zero. A perfect posi�ve correla�on is 1.0 and a perfect
nega�ve correla�on is 1.0.

correla�on analysis
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A measure of the degree of rela�onship between two variables.

Delphi Technique
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A forecas�ng procedure that uses a panel of experts and surveys to build consensus regarding future events. It is an itera�ve process for consensus building.

dependent variable
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The variable in regression analysis that is being predicted.

exponen�al smoothing
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Another form of a weighted moving average. It is a procedure for con�nually revising an es�mate to include more recent data. The method is based on averaging (smoothing) past
values.

forecast
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

An es�mate of future events.

forecas�ng
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The process of a�emp�ng to predict the future.

forecas�ng error
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The difference between the forecasted value and the actual value.

independent variable
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A variable in regression analysis which is used to predict the dependent variable.

mean absolute devia�on (MAD)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The average of absolute error. The differences between the actual value of a variable and the forecasted value are added a�er the plus and minus signs are removed. This total is
divided by the number of observa�ons.

mean squared error (MSE)
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The average of all the squared errors. The differences between the actual value of a variable and the forecasted value are squared, added together and divided by the number of
observa�ons.

model
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

An abstrac�on from the real problem of the key variables and rela�onships in order to simplify the problem. The purpose of modeling is to provide the user with a be�er
understanding of the problem, and with a way to manipulate the results for “what if” analysis.

mul�ple

regression analysis
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Regression analysis that uses two or more (independent variables) to predict one dependent variable.

panel of experts
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

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An approach to forecas�ng that involves people who are knowledgeable about the subject. This group a�empts to make a forecast by building consensus.

regression analysis
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A method to predict the value of one variable based upon the value of one or more variables. It is based upon minimizing squared distances from the data points to the es�mated
regression line.

simple moving average
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A method to smooth out the peaks and valleys in the data by using the most recent actual values to predict the next period. The average moves because as �me passes the next
period becomes the current period so the actual value for the oldest period is dropped and the most recent actual value is added.

simple regression analysis
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Regression analysis that uses only one variable (independent variable) to predict a single dependent variable.

survey
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A systema�c effort to elicit informa�on from specific groups and is conducted via a wri�en ques�onnaire, phone interview or the Internet.

t-sta�s�c
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

Measures the distance from the mean to a point in the t-distribu�on represented by standard devia�ons.

t-value
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

The calculated t-sta�s�c used in hypothesis tes�ng.

test market
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A special kind of survey in which the forecaster arranges for the placement of a new product or an exis�ng product that has been modified and data on actual sales are collected.

weighted moving average
(h�p://content.thuzelearning.com/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/sec�ons/cover/books/AUBUS644.13.2/

A method that is similar to the simple moving average. In the simple moving average, the weight for each historical �me period is equal. In the weighted moving average, different
weights can be assigned to each historical period. The weights assigned must sum to 1.0.

Processing math: 0%

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