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 18 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 35 34 33 58 32 32 46 35 33 54 30 20 58 46 35 26 57 42 Boston 58 29 49 45 33 34 33 45 42 46 49 55 21 64 45 33 43 53 Chicago 53 24 62 40 40 45 33 26 50 47 65 65 30 25 95 34 44 48 Dallas 36 30 34 55 40 28 28 35 38 47 60 68 62 45 40 35 46 43 LA 42 42 46 38 36 36 42 44 46 46 66 42 35 39 42 45 50 50 Total 224 159 224 236 181 175 182 185 209 240 270 250 206 219 257 173 240 236 a. Consider using a simple moving average model. Experiment with models using five weeks’ and three weeks’ past data. (Round your answers to 2 decimal places.) 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 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. (Round your answers to 2 decimal places. Negative values should be indicated by a minus sign.) ATL BOS CHI DAL LA Avg of DCs 3-week MA MAD MAPE TS 5-week MA MAD MAPE TS

Name: Starbucks has a large, global supply chain that must efficiently suppl
Uploaded: 2024-03-20T06:57:05.000Z
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Description: 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 th

Question

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 18 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	35	34	33	58	32	32	46	35	33	54	30	20	58	46	35	26	57	42
Boston	58	29	49	45	33	34	33	45	42	46	49	55	21	64	45	33	43	53
Chicago	53	24	62	40	40	45	33	26	50	47	65	65	30	25	95	34	44	48
Dallas	36	30	34	55	40	28	28	35	38	47	60	68	62	45	40	35	46	43
LA	42	42	46	38	36	36	42	44	46	46	66	42	35	39	42	45	50	50
Total	224	159	224	236	181	175	182	185	209	240	270	250	206	219	257	173	240	236

a. Consider using a simple moving average model. Experiment with models using five weeks’ and three weeks’ past data. (Round your answers to 2 decimal places.)

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 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. (Round your answers to 2 decimal places. Negative values should be indicated by a minus sign.)