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Problem 8 Technique 1 is superior in this comparison because its MAD is smaller, although six observations would generally be too few on which to base a realistic comparison Control chart. Given the demand data that follow. prepare a naive forecast for periods 2 through 10. Then determine each forecast error, and use those values to obtain 2s control limits. If demand in the next two periods turns out to be 125 and 130, can you conclude that the forecasts are in control? Period: 1 2 3 4 5 6 7 8 9 10 Demand 119 126 122 117 123 121 124 118 117 120 Solution 120 --- For a naive forecast, cach period's demand becomes the forecast for the next period. Hence, the forecasts and errors are Period Demand Forecast Error Error 1 11B 2 117 118 -1 1 3 117 9 4 119 120 -1 1 5 126 119 7 49 6 122 126 -4 16 7 117 122 -5 25 8 123 117 6 36 9 123 -2 4 10 124 121 3 9 +6 150 121 Error 150 9- = 4.33 (n= Number of errors) The control limits are 2(4.33)= 8.66. The forecast for period 11 was 124. Demand turned out to be 125, for an error of 125 - 124= +1. This is within the limits of +8.66. If the next demand is 130 and the naive forecast is 125 (based on the period 11 demand of 125), the error is + 5. Again, this is within the limits, so you cannot con clude the forecast is not working property. With more values-at kast five or six-you could plot the errors to see whether you could detect any pattems suggesting the presence of nonrandomness. DISCUSSION AND REVIEW QUESTIONS 1. What are the main advantages that quantitative techniques for forecasting have over qualitative techniques? What limitations do quantitative techniques have? 2. What are some of the consequences of poor forecasts? Explain. 3. List the specific weaknesses of each of these approaches to developing a forecast: a. Consumer surveys. b. Salesforce composite. c. Committee of managers or executives. 1 Forecasts are generally wrong. a. Why are forecasts generally wrong? b. Explain the term "wrong" as it pertains to a good forecast. 5. What is the purpose of establishing control limits for forecast errors? 6 What factors would you consider in deciding whether to use wide or to control limits for forecasts? 7. Contrast the use of MAD and MSE in evaluating forecasts. 8. What advantages as a forecasting tool does exponential smoothing have over moving averages? 9. How does the number of periods in a moving average affect the responsiveness of the forecast? 10. What factors enter into the choice of a value for the smoothing constant in exponential smoothing? 11. How accurate is your local five-day weather forecast? Support your answer with actual data