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. (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 42 34 35 57 35 34 46 35 34 55 33 21 55 46 35 27 56 40 Boston 63 24 46 49 31 38 30 44 38 45 48 51 26 61 41 38 42 53 Chicago 52 27 72 35 42 43 35 27 48 46 66 64 33 25 88 36 44 46 Dallas 38 26 36 54 44 27 33 35 38 45 55 65 62 46 38 35 38 42 LA 39 42 45 36 38 39 43 45 46 47 65 43 35 40 40 45 50 50 Total 234 153 234 231 190 181 187 186 204 238 267 244 211 218 242 181 230 231 Consider using a simple moving average model. Experiment with models using five weeks’ and three weeks’ past data. Note: Round your answers to 2 decimal places. 3-week MA 5-week MA 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. Note: 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.
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. (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 | 42 | 34 | 35 | 57 | 35 | 34 | 46 | 35 | 34 | 55 | 33 | 21 | 55 | 46 | 35 | 27 | 56 | 40 |
| Boston | 63 | 24 | 46 | 49 | 31 | 38 | 30 | 44 | 38 | 45 | 48 | 51 | 26 | 61 | 41 | 38 | 42 | 53 |
| Chicago | 52 | 27 | 72 | 35 | 42 | 43 | 35 | 27 | 48 | 46 | 66 | 64 | 33 | 25 | 88 | 36 | 44 | 46 |
| Dallas | 38 | 26 | 36 | 54 | 44 | 27 | 33 | 35 | 38 | 45 | 55 | 65 | 62 | 46 | 38 | 35 | 38 | 42 |
| LA | 39 | 42 | 45 | 36 | 38 | 39 | 43 | 45 | 46 | 47 | 65 | 43 | 35 | 40 | 40 | 45 | 50 | 50 |
| Total | 234 | 153 | 234 | 231 | 190 | 181 | 187 | 186 | 204 | 238 | 267 | 244 | 211 | 218 | 242 | 181 | 230 | 231 |
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Consider using a simple moving average model. Experiment with models using five weeks’ and three weeks’ past data.
Note: Round your answers to 2 decimal places.
3-week MA
5-week MA
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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.
Note: 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.
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