Analytics Exercise 18-1 (Algo) 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 45 34 32 54 33 32 46 36 33 55 28 20 58 45 36 25 55 41 Boston 58 25 48 41 32 33 34 42 43 46 46 54 19 60 42 32 45 52 Chicago 56 20 68 42 42 45 33 25 52 47 69 65 30 24 96 35 44 47 Dallas 42 35 38 62 43 25 28 34 40 50 62 68 62 48 42 36 43 42 LA 45 40 50 41 38 36 42 45 45 47 66 42 35 39 42 46 53 49 Total 246 154 236 240 188 171 183 182 213 245 271 249 204 216 258 174 240 231 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.)

Analytics Exercise 18-1 (Algo) 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 45 34 32 54 33 32 46 36 33 55 28 20 58 45 36 25 55 41 Boston 58 25 48 41 32 33 34 42 43 46 46 54 19 60 42 32 45 52 Chicago 56 20 68 42 42 45 33 25 52 47 69 65 30 24 96 35 44 47 Dallas 42 35 38 62 43 25 28 34 40 50 62 68 62 48 42 36 43 42 LA 45 40 50 41 38 36 42 45 45 47 66 42 35 39 42 46 53 49 Total 246 154 236 240 188 171 183 182 213 245 271 249 204 216 258 174 240 231 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.)

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

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Analytics Exercise 18-1 (Algo)

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	45	34	32	54	33	32	46	36	33	55	28	20	58	45	36	25	55	41
Boston	58	25	48	41	32	33	34	42	43	46	46	54	19	60	42	32	45	52
Chicago	56	20	68	42	42	45	33	25	52	47	69	65	30	24	96	35	44	47
Dallas	42	35	38	62	43	25	28	34	40	50	62	68	62	48	42	36	43	42
LA	45	40	50	41	38	36	42	45	45	47	66	42	35	39	42	46	53	49
Total	246	154	236	240	188	171	183	182	213	245	271	249	204	216	258	174	240	231

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.)

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