Approach Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naïve Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naïve Forecast had the Forecast Error closest to zero with a -15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below. Naïve Month Period Actual Demand Naïve Forecast Error 3- Month Moving Forecast 3- Month Moving Forecast Error Exponential Exponential Exponential Exponential Smoothing Smoothing Smoothing Smoothing Seasonal Forecast Seasonal Forecast for .2 Forecast Forecast for .5 Forecast Error .2 Error .5 Error Year 1 JAN FEB MAR APR MAY 12345 74 70 4 74 0 73 1 78 -4 79 74 5 74 5 74 5 93 -14 74 79 -5 74 0 75 -1 77 -3 82 -8 102 74 28 76 26 75 27 76 26 99 3 107 102 5 85 22 80 27 89 18 82 25 JUN 6 112 107 5 94 18 85 27 98 14 92 20 JUL 7 93 112 -19 107 -14 90 3 105 -12 82 11 AUG 8 112 93 19 104 8 91 21 99 13 101 11 SEPT 9 79 112 -33 106 -27 95 -16 106 -27 76 3 OCT 10 70 79 -9 95 -25 92 -22 93 -23 83 -13 NOV 11 79 70 9 87 -8 88 -9 82 -3 93 -14 DEC 12 70 79 -9 76 -6 86 -16 81 -11 91 -21 Year 2 JAN 13 70 FEB 14 MAR 15 APR 16 MAY 17 JUN 18 JUL 19 AUG 20 SEPT 21 OCT 22 NOV 23 DEC 24 Activity 1: Year 2 Forecast Forecast next period Year 2 MAPE% Average Actual Demand MAPE Month Year 1 Year 2 Seasonal Index JAN JAN 74 0.954 FEB 79 0.848 FEB MAR MAR 74 0.901 APR APR 102 1.033 MAY MAY 107 1.298 JUN JUN 112 1.219 JUL JUL 93 1.139 AUG AUG 112 1.113 SEPT OCT NOV DEC SEPT 79 1.033 OCT 70 0.848 NOV 79 0.848 DEC 70 0.768 Activity 2: Forecast Technique Analysis Forecasting technique that best fits the data: Seasonal Forecast

Approach Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naïve Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naïve Forecast had the Forecast Error closest to zero with a -15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below. Naïve Month Period Actual Demand Naïve Forecast Error 3- Month Moving Forecast 3- Month Moving Forecast Error Exponential Exponential Exponential Exponential Smoothing Smoothing Smoothing Smoothing Seasonal Forecast Seasonal Forecast for .2 Forecast Forecast for .5 Forecast Error .2 Error .5 Error Year 1 JAN FEB MAR APR MAY 12345 74 70 4 74 0 73 1 78 -4 79 74 5 74 5 74 5 93 -14 74 79 -5 74 0 75 -1 77 -3 82 -8 102 74 28 76 26 75 27 76 26 99 3 107 102 5 85 22 80 27 89 18 82 25 JUN 6 112 107 5 94 18 85 27 98 14 92 20 JUL 7 93 112 -19 107 -14 90 3 105 -12 82 11 AUG 8 112 93 19 104 8 91 21 99 13 101 11 SEPT 9 79 112 -33 106 -27 95 -16 106 -27 76 3 OCT 10 70 79 -9 95 -25 92 -22 93 -23 83 -13 NOV 11 79 70 9 87 -8 88 -9 82 -3 93 -14 DEC 12 70 79 -9 76 -6 86 -16 81 -11 91 -21 Year 2 JAN 13 70 FEB 14 MAR 15 APR 16 MAY 17 JUN 18 JUL 19 AUG 20 SEPT 21 OCT 22 NOV 23 DEC 24 Activity 1: Year 2 Forecast Forecast next period Year 2 MAPE% Average Actual Demand MAPE Month Year 1 Year 2 Seasonal Index JAN JAN 74 0.954 FEB 79 0.848 FEB MAR MAR 74 0.901 APR APR 102 1.033 MAY MAY 107 1.298 JUN JUN 112 1.219 JUL JUL 93 1.139 AUG AUG 112 1.113 SEPT OCT NOV DEC SEPT 79 1.033 OCT 70 0.848 NOV 79 0.848 DEC 70 0.768 Activity 2: Forecast Technique Analysis Forecasting technique that best fits the data: Seasonal Forecast

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

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