Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the numberof fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weeklyforecast for the same month this year. This technique was used to forecast eighnt weeks for this year, which are shown below alongwith the actual demand that occurred.The following eight weeks show the forecast (based on last year) and the demand that actually occurred:ITTFORECASTACTUALWEEKDEMANDDEMAND13011272130123314614941441595146179156169150184145205a. Compute the MAD of forecast errors. (Round your answers to 2 decimal places.)WeekMAD1247b. Using the RSFE, compute the tracking signal. (Round your answers to 2 decimal places. Negative values should be Indicated by amlnus sign.)TrackingWeekSignal12478c. Based on your answers to parts a and b, comment on Harlen's method of forecasting.O The forecast should be considered poor.O The forecast should be considered good.

Answered: a. Compute the MAD of forecast errors.…

Business

Operations Management

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

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 25P: The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a...

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The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
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The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
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