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As we discussed at the beginning of the chapter, Starbucks has a large, global supply chain that must efficiently supply over 17,000 stores. Although the stores might appear to be very similar, they are actually very different. Depending on the location of the store, its size, and the profile of the customers served, Starbucks management configures the store offerings to take maximum advantage of the space available and customer preferences.
Starbucks’ actual distribution system is much more complex, but for the purpose of our exercise let’s focus on a single item that is currently distributed through five distribution centers in the United States. Our item is a logo branded coffeemaker that is sold at some of the larger retail stores. The coffeemaker has been a steady seller over the years due to its reliability and rugged construction. Starbucks does not consider this a seasonal product, but there is some variability in demand. Demand for the product over the past 13 weeks is shown in the following table.
The demand at the distribution centers (DCs) varies between about 40 units on average per week in Atlanta and 48 units in Dallas. The current quarter’s data are pretty close to the demand shown in the table.
Management would like you to experiment with some forecasting models to determine what should be used in a new system to be implemented. The new system is programmed to use one of two forecasting models: simple moving average or exponential smoothing.

Consider using a simple moving average model. Experiment with models using five weeks’ and three weeks’ past data. The past data in each region are given below (week −1 is the week before week 1 in the table, −2 is two weeks before week 1, etc.). Evaluate the forecasts that would have been made over the 13 weeks using the overall (at the end of the 13 weeks) mean absolute deviation, mean absolute percent error, and tracking signal as criteria.

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