FORCASTING AND
DEMAND PLANNING
CHAPTER 11
DAVID A. COLLIER AND JAMES R. EVANS
OM3 Chapter 11 Forecasting and Demand Planning
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
LO1 Describe the importance of forecasting to the
value chain.
LO2 Explain basic concepts of forecasting and time
series.
LO3 Explain how to apply single moving average and
exponential smoothing models.
LO4 Describe how to apply regression as a forecasting
approach.
LO5 Explain the role of judgment in forecasting.
LO6 Describe how statistical and judgmental
forecasting techniques are applied in practice.
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t
CHAPTER 11
FORECASTING AND DEMAND PLANNING
he demand for rental cars in Florida and other warm
climates peaks during college spring break season. Call
centers and rental offices are flooded with customers
wanting to rent a vehicle. National Car Rental took a
unique approach by developing a customer-identification
forecasting model, by which it identifies all customers who
are young and rent cars only once or twice a year. These
demand analysis models allow National to call this target
market segment in February, when call volumes are lower,
to sign them up again. The proactive strategy is designed
to both boost repeat rentals and smooth out the peaks and
valleys in call center volumes.
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
What do you think?
Think of a pizza delivery
franchise located near a
college campus. What
factors that influence
demand do you think
should be included in
trying to forecast
demand for pizzas?
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Forecasting and Demand Planning
Forecasting is the process of projecting the
values of one or more variables into the future.
Types of forecasts:
• Long-range forecasts in total sales dollars (top
management level)
• Aggregate forecasts of sales volume (middle
management level)
• Forecasts of individual units (operational level)
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.1 The Need for Forecasts in a Value Chain
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Basic Concepts in Forecasting
• The planning horizon is the length of time on
which a forecast is based.
 This spans from short-range forecasts with a
planning horizon of under 3 months to longrange forecasts of 1 to 10 years.
• The time bucket is the unit of measure for the
time period used in a forecast.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Basic Concepts in Forecasting
• A time series is a set of observations measured
at successive points in time or over successive
periods of time.
• A time series pattern may have one or more of
the following five characteristics:





Trend
Seasonal patterns
Cyclical patterns
Random variation (or noise)
Irregular (one time) variation
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Basic Concepts in Forecasting
•
•
•
•
•
A trend is the underlying pattern of growth or decline in
a time series.
Seasonal patterns are characterized by repeatable
periods of ups and downs over short periods of time.
Cyclical patterns are regular patterns in a data series
that take place over longer periods of time.
Random variation (sometimes called noise) is the
unexplained deviation of a time series from a predictable
pattern, such as a trend, seasonal, or cyclical pattern.
Irregular variation is a one-time variation that is
explainable.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.2 Example Linear and Nonlinear Trend Patterns
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.3 Seasonal Pattern of Home Natural Gas Usage
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit Extra Trend and Business Cycle Characteristics
(each data point is 1 year apart)
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.4
Call Center Volume
Example of a time
series with trend
and seasonal
components:
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.5 Chart of Call Volume
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Basic Concepts in Forecasting
•
Forecast error is the difference between the observed
value of the time series and the forecast, or At – Ft .
Mean Square Error (MSE)
MSE =
Σ(At – Ft )2
[11.1]
T
Mean Absolute Deviation Error (MAD)
MAD =
‫׀‬At – Ft ‫׀‬
[11.2]
T
Mean Absolute Percentage Error (MAPE)
MAPE = Σ‫(׀‬At – Ft )/At ‫׀‬
T
X
100
[11.3]
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.6 Forecast Error of Example Time Series Data
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Basic Concepts in Forecasting
• MSE is influenced much more by large forecasts
errors than by small errors (because the errors are
squared).
• The measurement scale factor in MAPE is eliminated
by dividing the absolute error by the time-series data
value, making it easier to interpret.
• The selection of the best measure of forecast
accuracy is not a simple matter; indeed, forecasting
experts often disagree on which measure should be
used.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Statistical Forecasting Models
• Statistical forecasting is based on the
assumption that the future will be an
extrapolation of the past.
• Judgmental forecasting relies upon
opinions and expertise of people in developing
forecasts.
OM3 Chapter 11 Forecasting and Demand Planning
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Single Moving Average
• A moving average (MA) forecast is an average
of the most recent “k” observations in a time series.
Ft+1 = ∑(most recent “k” observations)/k
= (At + At–1 + At–2 + ... + At–k+1)/k
[11.4]
 MA methods work best for short planning horizons
when there is no major trend, seasonal, or
business cycle pattern.
 As the value of “k” increases, the forecast reacts
slowly to recent changes in the time series data.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Solved Problem
Develop three-period and four-period moving-average forecasts
and single exponential smoothing forecasts with a = 0.5. Compute
the MAD, MAPE, and MSE for each. Which method provides a
better forecast?
Period
Demand
Period
Demand
1
86
7
91
2
93
8
93
3
88
9
96
4
89
10
97
5
92
11
93
6
94
12
95
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Solved Problem
98
96
94
92
90
Moving
Average
Forecasts
88
86
84
82
80
1
2
3
4
5
6
7
8
9
10
11
12
Period
Based on these error metrics (MAD, MSE, MAPE), the 3-month
moving average is the best method among the three.
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.7 Summary of 3-Month Moving-Average Forecasts
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.8 Milk-Sales Forecast Error Analysis
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Single Exponential Smoothing
• Single Exponential Smoothing (SES) is a
forecasting technique that uses a weighted
average of past time-series values to forecast the
value of the time series in the next period.
Ft+1 = At + (1 – )Ft
= Ft +  (At – Ft)
[11.5]
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.9 Summary of Single Exponential Smoothing Milk-Sales
Forecasts with α = 0.2
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.10 Graph of Single Exponential Smoothing Milk-Sales Forecasts
with α = 0.2
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Regression as a Forecasting Approach
• Regression analysis is a method for building a
statistical model that defines a relationship between
a single dependent variable and one or more
independent variables, all of which are numerical.
Yt = a + bt
(11.7)
 Simple linear regression finds the best values of a and
b using the method of least squares.
 Excel provides a very simple tool to find the bestfitting regression model for a time series by selecting
the Add Trendline option from the Chart menu.
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FORECASTING AND DEMAND PLANNING
Exhibit 11.11 Factory Energy Costs
OM3 Chapter 11 Forecasting and Demand Planning
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FORECASTING AND DEMAND PLANNING
Exhibit 11.12
Format Trendline
Dialog Box
OM3 Chapter 11 Forecasting and Demand Planning
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Exhibit 11.13 Least-Squares Regression Model for Energy Cost Forecasting
OM3 Chapter 11 Forecasting and Demand Planning
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FORECASTING AND DEMAND PLANNING
Causal Forecasting with Multiple Regression
• A linear regression model with more than one
independent variable is called a multiple linear
regression model.
 Multiple regression models can include other
independent variables such as economic
indexes or demographic factors that may
influence the time series.
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FORECASTING AND DEMAND PLANNING
Exhibit 11.14 Gasoline Sales Data
OM3 Chapter 11 Forecasting and Demand Planning
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FORECASTING AND DEMAND PLANNING
Exhibit 11.15 Chart of Sales versus Time
OM3 Chapter 11 Forecasting and Demand Planning
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FORECASTING AND DEMAND PLANNING
Exhibit 11.16 Multiple Regression Results
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FORECASTING AND DEMAND PLANNING
Judgmental Forecasting
• Judgmental forecasting relies upon opinions and
expertise of people in developing forecasts.
 Grass Roots forecasting is simply asking those
who are close to the end consumer, such as
salespeople, about the customers’ purchasing
plans.
 The Delphi method consists of forecasting by
expert opinion by gathering judgments and
opinions of key personnel based on their
experience and knowledge of the situation.
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FORECASTING AND DEMAND PLANNING
Forecasting in Practice
• Managers use a variety of judgmental and
quantitative forecasting techniques.
• Statistical methods alone cannot account for such
factors as sales promotions, competitive
strategies, unusual economic disturbances, new
products, large one-time orders, labor
complications, etc.
• Statistical forecasts are often adjusted to account
for qualitative factors.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
Forecasting in Practice
A tracking signal provides a method for monitoring a
forecast by quantifying bias—the tendency of
forecasts to consistently be larger or smaller than
the actual values of the time series.
Tracking signal = Σ(At – Ft)
MAD
[11.8]
Tracking signals between plus and minus 4 indicate
an adequate forecasting model.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
OM3 Chapter 11 Forecasting and Demand Planning
© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
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CHAPTER 11
FORECASTING AND DEMAND PLANNING
BankUSA: Forecasting Help Desk Demand by Day
Case Study
1. What are the service management
characteristics of the CSR job?
2. Define the mission statement and strategy of
the Help Desk contact center. Why is the Help
Desk important? Who are its customers?
3. How would you handle the customer affected
by the inaccurate stock price in the banks trust
account system? Would you take a passive or
proactive approach? Justify your answer.
4. Using the data on Call Volume in the
accompanying table, how would you forecast
short-term demand?
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