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Greenfield's Tannery supplies leather to the South Fork Boot Company on a monthly basis for manufacturing western style boots. South Fork does not keep a large stock of inventory on hand, thus the tannery must provide sufficient leather to meet demand on a timely basis. Furthermore, the quality of the leather must be of the highest grade. The demand for western boots is lowest in the late winter, increases during the summer and fall and reaches its peak during the holiday season. The demand for leather follows the same general pattern. Greenfield's Tannery has experienced the following monthly demand for the past fourteen months. |Month January | February Demand (in sq yards) 1300 1100 March 900 April May 1600 1500 June 2100 July August September October November 2400 2000 2900 3200 3700 December 2400 January February 1600 1000 1. Calculate a forecast of the above demand using a three-month and a five-month moving average in a table. 2. In your table, include a column for computing the forecast errors for each of the moving average forecasts. Compute the error per period and then be sure to include the cumulative forecast error for each. You can find help with this in the instructor video in Content for the week.

Greenfield's Tannery supplies leather to the South Fork Boot Company on a monthly basis for manufacturing western style boots. South Fork does not keep a large stock of inventory on hand, thus the tannery must provide sufficient leather to meet demand on a timely basis. Furthermore, the quality of the leather must be of the highest grade. The demand for western boots is lowest in the late winter, increases during the summer and fall and reaches its peak during the holiday season. The demand for leather follows the same general pattern. Greenfield's Tannery has experienced the following monthly demand for the past fourteen months. |Month January | February Demand (in sq yards) 1300 1100 March 900 April May 1600 1500 June 2100 July August September October November 2400 2000 2900 3200 3700 December 2400 January February 1600 1000 1. Calculate a forecast of the above demand using a three-month and a five-month moving average in a table. 2. In your table, include a column for computing the forecast errors for each of the moving average forecasts. Compute the error per period and then be sure to include the cumulative forecast error for each. You can find help with this in the instructor video in Content for the week.

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
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Greenfield's Tannery supplies leather to the South Fork Boot Company on a monthly basis for manufacturing western style boots. South Fork does not keep a large stock of inventory on hand, thus the tannery must provide sufficient leather to meet demand on a timely basis. Furthermore, the quality of the leather must be of the highest grade. The demand for western boots is lowest in the late winter, increases during the summer and fall and reaches its peak during the holiday season. The demand for leather follows the same general pattern. Greenfield's Tannery has experienced the following monthly demand for the past fourteen months. Demand (in sq yards) Month January February 1300 1100 March 900 April Мay 1600 1500 June 2100 July August September October November 2400 2000 2900 3200 3700 December 2400 January February 1600 1000 1. Calculate a forecast of the above demand using a three-month and a five-month moving average in a table. 2. In your table, include a column for computing the forecast errors for each of the moving average forecasts. Compute the error per period and then be sure to include the cumulative forecast error for each. You can find help with this in the instructor video in Content for the week.
Transcribed Image Text:Greenfield's Tannery supplies leather to the South Fork Boot Company on a monthly basis for manufacturing western style boots. South Fork does not keep a large stock of inventory on hand, thus the tannery must provide sufficient leather to meet demand on a timely basis. Furthermore, the quality of the leather must be of the highest grade. The demand for western boots is lowest in the late winter, increases during the summer and fall and reaches its peak during the holiday season. The demand for leather follows the same general pattern. Greenfield's Tannery has experienced the following monthly demand for the past fourteen months. Demand (in sq yards) Month January February 1300 1100 March 900 April Мay 1600 1500 June 2100 July August September October November 2400 2000 2900 3200 3700 December 2400 January February 1600 1000 1. Calculate a forecast of the above demand using a three-month and a five-month moving average in a table. 2. In your table, include a column for computing the forecast errors for each of the moving average forecasts. Compute the error per period and then be sure to include the cumulative forecast error for each. You can find help with this in the instructor video in Content for the week.
Greenfield's Tannery Template #1 - Average Forecast: 3 Month Moving Average #2 - Average Forecast: 5 Month Moving Average At 5 Month Moving Avg At Ft 3 Month Moving Avg Ft 5 Month Forecast Cumulative Dt Forecast Cumulative Dt Demand 3 Month Forecast Error Period Forecast Forecast Error Period Demand Error Forecast Error 1 1300 1300 1100 2 1100 3 900 3 900 4 1600 4 1600 1500 1500 2100 6 2100 7 2400 2400 8 2000 8 2000 9 2900 9 2900 10 3200 10 3200 11 3700 11 3700 12 2400 12 2400 13 1600 13 1600 14 1000 14 1000
Transcribed Image Text:Greenfield's Tannery Template #1 - Average Forecast: 3 Month Moving Average #2 - Average Forecast: 5 Month Moving Average At 5 Month Moving Avg At Ft 3 Month Moving Avg Ft 5 Month Forecast Cumulative Dt Forecast Cumulative Dt Demand 3 Month Forecast Error Period Forecast Forecast Error Period Demand Error Forecast Error 1 1300 1300 1100 2 1100 3 900 3 900 4 1600 4 1600 1500 1500 2100 6 2100 7 2400 2400 8 2000 8 2000 9 2900 9 2900 10 3200 10 3200 11 3700 11 3700 12 2400 12 2400 13 1600 13 1600 14 1000 14 1000
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