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a. Compute the MAD of forecast errors. (Round your answers to 2 declmal places.) Week MAD 1 2 3 4 7 8

a. Compute the MAD of forecast errors. (Round your answers to 2 declmal places.) Week MAD 1 2 3 4 7 8

Practical Management Science
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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Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown below along with the actual demand that occurred: The following eight weeks show the forecast (based on last year) and the demand that actually occurred: | Week | Forecast Demand | Actual Demand | |------|-----------------|---------------| | 1 | 130 | 127 | | 2 | 138 | 123 | | 3 | 146 | 149 | | 4 | 144 | 159 | | 5 | 146 | 179 | | 6 | 156 | 169 | | 7 | 158 | 184 | | 8 | 145 | 205 | a. Compute the MAD of forecast errors. (Round your answers to 2 decimal places.) | Week | MAD | |------|------| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | b. Using the RSFE, compute the tracking signal. (Round your answers to 2 decimal places. Negative values should be indicated by a minus sign.) | Week | Tracking Signal | |------|-----------------| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | c. Based on your answers to parts a and b, comment on Harlen’s method of forecasting. - The forecast should be considered poor. - The forecast should be considered good.
Transcribed Image Text:Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown below along with the actual demand that occurred: The following eight weeks show the forecast (based on last year) and the demand that actually occurred: | Week | Forecast Demand | Actual Demand | |------|-----------------|---------------| | 1 | 130 | 127 | | 2 | 138 | 123 | | 3 | 146 | 149 | | 4 | 144 | 159 | | 5 | 146 | 179 | | 6 | 156 | 169 | | 7 | 158 | 184 | | 8 | 145 | 205 | a. Compute the MAD of forecast errors. (Round your answers to 2 decimal places.) | Week | MAD | |------|------| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | b. Using the RSFE, compute the tracking signal. (Round your answers to 2 decimal places. Negative values should be indicated by a minus sign.) | Week | Tracking Signal | |------|-----------------| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | c. Based on your answers to parts a and b, comment on Harlen’s method of forecasting. - The forecast should be considered poor. - The forecast should be considered good.
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