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 Atlanta 46 35 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 13 32 54 34 33 46 36 33 55 28 20 58 45 36 25 55 41 Boston 59 26 48 41 33 35 34 43 43 46 46 54 24 60 45 32 45 52 Chicago 55 22 68 42 44 46 33 25 52 47 69 65 30 24 96 35 44 47 Dallas 43 36 38 62 45 26 28 34 40 50 62 68 62 48 42 36 43 42 LA 44 41 52 42 37 35 40 45 45 47 66 42 35 39 42 46 53 49 Total 247 160 238 241 193 175 181 183 213 245 271 249 209 216 261 174 240 231

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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A Use a simple moving average model. Experiment with the models using five weeks and three weeks past data. 3 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 5 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 B evaluate the forecast that would have been made over the 13 weeks using overall mean absolutely deviation, mean absolute percent error, and tracking signal as criteria.

Transcribed Image Text: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 Atlanta 46 35 Boston Chicago 55 22 -3 -2 32 54 -1 1 2 3 4 5 6 7 8 9 10 11 12 13 34 33 46 36 33 55 28 20 58 45 36 25 55 41 59 26 48 41 33 35 34 43 43 46 46 54 24 60 45 32 45 52 68 42 44 46 33 25 52 47 69 65 30 24 96 35 44 47 Dallas LA 43 36 38 62 45 26 28 34 40 50 62 68 62 48 42 36 43 42 44 41 52 42 37 35 40 45 45 47 66 42 35 39 42 46 53 49 Total 247 160 238 241 193 175 181 183 213 245 271 249 209 216 261 174 240 231

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