CHAPTER 11
MANAGING THE FORECASTING PROCESS
ANSWERS TO PROBLEMS AND CASES
1. a. One response: Forecasts may not be right, but they improve the odds of being
close to right. More importantly, if there are no agreed upon set of forecasts to
b. One response: Analogy If you think education is expensive, try ignorance.
CASE 11-1: BOUNDARY ELECTRONICS
1. This case invites students to think about how to use some of the forecasting techniques
discussed in Chapter 11. Guy Preston is trying to get his managers to think about the
long-range position of the company, as opposed to the short range thinking that most
2. The instructor should point out that the purpose of Guy’s retreat is to expand the
planning horizon of his managers. He should be prepared to continue this effort after
3. There are two possible benefits from Guy’s retreat. First, he may gain valuable insights
into the company’s future to use in his own long range thinking. Second, and
CASE 11-2: BUSBY ASSOCIATES
1.
case and forecast better here.
2. Jill should definitely update her historical data as new data points arrive. Since she
3. After the results for a few additional quarters (say 4) become available, the analysis
4. Box-Jenkins ARIMA methodology is not well suited for small sample sizes and
can be difficult to explain to a non-statistician.
This case illustrates the practical problems that are typically encountered when
attempting to forecast a time series in a business setting. Among the problems Jill
encounters are:
She chooses to forecast a national variable for which data values are available
CASE 11-3: CONSUMER CREDIT COUNSELING
Students should summarize the results of the analyses of these data in the cases at the
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CASE 11-4: MR. TUX
We collected the data from the Mr. Tux rental shop so that real data could be used at the
end of each chapter instead of contrived data. We didn’t know what would happen when we tried
to forecast this variable, but we think it turned out well because no one method was superior.
The case in Chapter 11 summarizes the different ways John used to forecast his monthly
sales, and asks students to comment on his efforts. We think a key point is that a lot of real data
sets do not lend themselves to accurate forecasting, and that continually trying different methods is
required. For the Mr. Tux data, there are fairly simple seasonal models (see the cases in Chapters
8 and 9) that represent the data well and provide reasonable forecasts.
What advice should we give to John Mosby for the future? Some suggestions to offer
might include:
3. Try to develop a useful relationship between monthly sales and regional
economic variables. Perhaps the area unemployment rate or an economic activity
CASE 11-5: ALOMEGA FOOD STORES
1.
2. Having students, either individually or in teams, prepare a memo to Julie outlining
their analysis and choice of forecasting method is an alternative to class
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3. Other forecasting methods are certainly possible in this case. An assignment
beyond a consideration of choosing between decomposition and multiple
CASE 11-6: SOUTHWEST MEDICAL CENTER
residual analyses and forecasts. Moreover, they should point out the apparent difficulty in