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 lets 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. (week 1 is the week before week 1 in the table, 2 is two weeks before week 1, etc.).

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.

WEEK 5 4 3 2 1 1 2 3 4 5 6 7 8 9 10 11 12 13 Atlanta 48 34 33 55 32 32 46 36 33 55 28 20 58 45 36 25 55 41 Boston 55 25 45 40 31 33 34 42 43 46 46 54 24 60 45 32 45 52 Chicago 54 20 65 40 40 45 33 25 52 47 69 65 30 24 96 35 44 49 Dallas 40 35 35 60 42 28 28 34 40 50 62 68 62 48 42 36 43 42 LA 44 40 50 40 35 36 42 45 45 47 66 42 35 39 42 46 53 49 Total 241 154 228 235 180 174 183 182 213 245 271 249 209 216 261 174 240 233

a. Consider using a simple exponential smoothing model. In your analysis, test two alpha values, 0.2 and 0.4. When using an alpha value of 0.2, assume that the forecast for week 1 is the past three-week average (the average demand for periods 3, 2, and 1). For the model using an alpha of 0.4, assume that the forecast for week 1 is the past five-week average. (Round your answers to 2 decimal places.)

ES, = 0.2

ES, = 0.4

b. 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. (Negative values should be indicated by a minus sign. Round all answers to 2 decimal places. Enter "MAPE" answers as a percentage rounded to 2 decimal places.)