Practical Management Science
Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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The Toro Cutlery Company has collected monthly sales information below:
MONTH
January
February
March
April
The company is examining two forecasting methods, moving average and exponential smoothing for forecasting sales.
a. What will the forecast be for January the following year using a three-, four-, and five-month moving averages? Do not round intermediate calculations. Round your answers to the nearest whole number.
Forecast (January, 3-month MA): 87667
Forecast (January, 4-month MA): 72500
Forecast (January, 5-month MA): 69600
b. What will the forecast be for January the following year using exponential smoothing with a = 0.6? Assume the forecast for February this year is 25,000. Do not round intermediate calculations. Round your answer to the nearest
whole number.
SALES
25,000
18,000
43,000
110,000
MONTH
May
June
July
August
SALES
MONTH
84,000 September
28,000
October
97,000 November
58,000 December
SALES
27,000
105,000
82,000
76,000
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Transcribed Image Text:The Toro Cutlery Company has collected monthly sales information below: MONTH January February March April The company is examining two forecasting methods, moving average and exponential smoothing for forecasting sales. a. What will the forecast be for January the following year using a three-, four-, and five-month moving averages? Do not round intermediate calculations. Round your answers to the nearest whole number. Forecast (January, 3-month MA): 87667 Forecast (January, 4-month MA): 72500 Forecast (January, 5-month MA): 69600 b. What will the forecast be for January the following year using exponential smoothing with a = 0.6? Assume the forecast for February this year is 25,000. Do not round intermediate calculations. Round your answer to the nearest whole number. SALES 25,000 18,000 43,000 110,000 MONTH May June July August SALES MONTH 84,000 September 28,000 October 97,000 November 58,000 December SALES 27,000 105,000 82,000 76,000
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