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3.1 Introduction 76
3.2 Features Common to All
Forecasts 78
3.3 Elements of a Good
Forecast 78
3.4 Forecasting and the Supply
Chain 79
3.5 Steps in the Forecasting
Process 79
3.6 Approaches to
Forecasting 80
3.7 Qualitative Forecasts 80
Executive Opinions 80
Salesforce Opinions 81
Consumer Surveys 81
Other Approaches 81
3.8 Forecasts Based on Time-
Series Data 82
Naive Methods 82
Techniques For Averaging 84
Other Forecasting Methods 88
Techniques For Trend 89
Trend-Adjusted Exponential
Smoothing 92
After completing this chapter, you should be able to:
LO3.1 List features common to all forecasts.
LO3.2 Explain why forecasts are generally wrong.
LO3.3 List the elements of a good forecast.
LO3.4 Outline the steps in the forecasting process.
LO3.5 Describe four qualitative forecasting techniques.
LO3.6 Use a naive method to make a forecast.
LO3.7 Prepare a moving average forecast.
LO3.8 Prepare a weighted-average forecast.
LO3.9 Prepare an exponential smoothing forecast.
LO3.10 Prepare a linear trend forecast.
LO3.11 Prepare a trend-adjusted exponential smoothing forecast.
LO3.12 Compute and use seasonal relatives.
LO3.13 Compute and use regression and correlation coefficients.
LO3.14 Summarize forecast errors and use summaries to makedecisions.
LO3.15 Construct control charts and use them to monitor forecasterrors.
LO3.16 Describe the key factors and trade-offs to consider when choosing a forecasting technique.
Forecasting
LEARNING OBJECTIVES
3
CHAPTER
CHAPTER OUTLINE
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Weather forecasts are one of the many types of forecasts used by some business organizations. Although some busi-
nesses simply rely on publicly available weather forecasts, others turn to firms that specialize in weather-related fore-
casts. For example, Home Depot, Gap, and JCPenney use such firms to help them take weather factors into account for
estimating demand.
Many new car buyers have a thing or two in common. Once they make the decision to buy a new car, they want it
as soon as possible. They usually don’t want to order it and then have to wait six weeks or more for delivery. If the car
dealer they visit doesn’t have the car they want, they’ll look elsewhere. Hence, it is important for a dealer to anticipate
buyer wants and to have those models, with the necessary options, in stock. The dealer who can correctly forecast
buyer wants, and have those cars available, is going to be much more successful than a competitor who guesses
instead of forecasting—and guesses wrong—and gets stuck with cars customers don’t want. So how does the dealer
know how many cars of each type to stock? The answer is, the dealer doesn’t know for sure, but by analyzing previ-
ous buying patterns, and perhaps making allowances for current conditions, the dealer can come up with a reasonable
approximation of what buyers will want.
Planning is an integral part of a manager’s job. If uncertainties cloud the planning horizon, managers will find it diffi-
cult to plan effectively. Forecasts help managers by reducing some of the uncertainty, thereby enabling them to develop
Photodisc/Getty Images
Techniques For
Seasonality 93
Techniques For Cycles 98
3.9 Associative Forecasting
Techniques 98
Simple Linear Regression 98
Comments On The Use Of
Linear Regression Analysis 102
Nonlinear And Multiple
Regression Analysis 104
3.10 Forecast Accuracy 104
Summarizing Forecast
Accuracy 106
3.11 Monitoring Forecast Error 107
3.12 Choosing a Forecasting
Technique 111
3.13 Using Forecast Information 112
3.14 Computer Software in
Forecasting 113
3.15 Operations Strategy 113
Cases: M&L Manufacturing 136
Highline Financial Services,
Ltd., 137
continued
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3.1 INTRODUCTION
Forecasts are a basic input in the decision processes of operations management because they
provide information on future demand. The importance of forecasting to operations manage-
ment cannot be overstated. The primary goal of operations management is to match supply
to demand. Having a forecast of demand is essential for determining how much capacity or
supply will be needed to meet demand. For instance, operations needs to know what capacity
will be needed to make staffing and equipment decisions, budgets must be prepared, purchas-
ing needs information for ordering from suppliers, and supply chain partners need to make
their plans.
Businesses make plans for future operations based on anticipated future demand. Antici-
pated demand is derived from two possible sources, actual customer orders and forecasts. For
businesses where customer orders make up most or all of anticipated demand, planning is
straightforward, and little or no forecasting is needed. However, for many businesses, most or
all of anticipated demand is derived from forecasts.
Two aspects of forecasts are important. One is the expected level of demand; the other is
the degree of accuracy that can be assigned to a forecast (i.e., the potential size of forecast
error). The expected level of demand can be a function of some structural variation, such as a
trend or seasonal variation. Forecast accuracy is a function of the ability of forecasters to cor-
rectly model demand, random variation, and sometimes unforeseen events.
Forecasts are made with reference to a specific time horizon. The time horizon may be fairly
short (e.g., an hour, day, week, or month), or somewhat longer (e.g., the next six months, the
next year, the next five years, or the life of a product or service). Short-term forecasts pertain
to ongoing operations. Long-range forecasts can be an important strategic planning tool. Long-
term forecasts pertain to new products or services, new equipment, new facilities, or something
else that will require a somewhat long lead time to develop, construct, or otherwise implement.
Forecasts are the basis for budgeting, planning capacity, sales, production and inventory,
personnel, purchasing, and more. Forecasts play an important role in the planning process
because they enable managers to anticipate the future so they can plan accordingly.
Forecasts affect decisions and activities throughout an organization, in accounting,
finance, human resources, marketing, and management information systems (MIS), as well as
in operations and other parts of an organization. Here are some examples of uses of forecasts
in business organizations:
Accounting. New product/process cost estimates, profit projections, cash management.
Finance. Equipment/equipment replacement needs, timing and amount of funding/bor-
rowing needs.
Human resources. Hiring activities, including recruitment, interviewing, and training;
layoff planning, including outplacement counseling.
more meaningful plans. A forecast is an estimate about the future value of a variable
such as demand. The better the estimate, the more informed decisions can be. Some
forecasts are long range, covering several years or more. Long-range forecasts are
especially important for decisions that will have long-term consequences for an organization or for a town, city, country,
state, or nation. One example is deciding on the right capacity for a planned power plant that will operate for the next
40 years. Other forecasts are used to determine if there is a profit potential for a new service or a new product: Will
there be sufficient demand to make the innovation worthwhile? Many forecasts are short term, covering a day or week.
They are especially helpful in planning and scheduling day-to-day operations. This chapter provides a survey of business
forecasting. It describes the elements of good forecasts, the necessary steps in preparing a forecast, basic forecasting
techniques, and how to monitor a forecast.
Forecast A statement about
the future value of a variable
of interest.
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Marketing. Pricing and promotion, e-business strategies, global competition strategies.
MIS. New/revised information systems, internet services.
Operations. Schedules, capacity planning, work assignments and workloads, inventory
planning, make-or-buy decisions, outsourcing, project management.
Product/service design. Revision of current features, design of new products or services.
In most of these uses of forecasts, decisions in one area have consequences in other areas.
Therefore, it is very important for all affected areas to agree on a common forecast. How-
ever, this may not be easy to accomplish. Different departments often have very different
perspectives on a forecast, making a consensus forecast difficult to achieve. For example,
salespeople, by their very nature, may be overly optimistic with their forecasts, and may want
to “reserve” capacity for their customers. This can result in excess costs for operations and
inventory storage. Conversely, if demand exceeds forecasts, operations and the supply chain
may not be able to meet demand, which would mean lost business and dissatisfied customers.
Forecasting is also an important component of yield management, which relates tothe per-
centage of capacity being used. Accurate forecasts can help managers plan tactics (e.g.,offer
discounts, don’t offer discounts) to match capacity with demand, thereby achieving high-
yield levels.
There are two uses for forecasts. One is to help managers plan the system, and the other
is to help them plan the use of the system. Planning the system generally involves long-range
plans about the types of products and services to offer, what facilities and equipment to
have, where to locate, and so on. Planning the use of the system refers to short-range and
intermediate-range planning, which involve tasks such as planning inventory and workforce
levels, planning purchasing and production, budgeting, and scheduling.
Business forecasting pertains to more than predicting demand. Forecasts are also used to
predict profits, revenues, costs, productivity changes, prices and availability of energy and
raw materials, interest rates, movements of key economic indicators (e.g., gross domestic
product, inflation, government borrowing), and prices of stocks and bonds. For the sake of
simplicity, this chapter will focus on the forecasting of demand. Keep in mind, however, that
the concepts and techniques apply equally well to the other variables.
The Walt Disney World forecasting
department has 20 employees who
formulate forecasts on volume and
revenue for the theme parks, water
parks, resort hotels, as well as
merchandise, food, and beverage
revenue by location.
Peter Cosgrove/AP Images
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Despite of its use of computers and sophisticated mathematical models, forecasting is not
an exact science. Instead, successful forecasting often requires a skillful blending of science
and intuition. Experience, judgment, and technical expertise all play a role in developing use-
ful forecasts. Along with these, a certain amount of luck and a dash of humility can be helpful,
because the worst forecasters occasionally produce a very good forecast, and even the best fore-
casters sometimes miss completely. Current forecasting techniques range from the mundane to
the exotic. Some work better than others, but no single technique works all the time.
3.2 FEATURES COMMON TO ALL FORECASTS
A wide variety of forecasting techniques are in use. In many respects, they are quite different
from each other, as you shall soon discover. Nonetheless, certain features are common to all,
and it is important to recognize them.
• Forecasting techniques generally assume that the same underlying causal system that
existed in the past will continue to exist in the future.
Comment A manager cannot simply delegate forecasting to models or computers and then
forget about it, because unplanned occurrences can wreak havoc with forecasts. For instance,
weather-related events, tax increases or decreases, and changes in features or prices of competing
products or services can have a major impact on demand. Consequently, a manager must be alert
to such occurrences and be ready to override forecasts, which assume a stable causal system.
• Forecasts are not perfect; actual results usually differ from predicted values; the pres-
ence of randomness precludes a perfect forecast. Allowances should be made for fore-
cast errors.
LO3.1 List features com-
mon to all forecasts.
LO3.2 Explain why fore-
casts are generally wrong.
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