Given below are the demands of a certain product over the past 10 weeks.Week| 1 2 3 4 | 5 67|89 | 10Demand 50 64 66 68 67 63 69 67 65 66(a) Forecast the demand for the 13th week using the following techniques:i. 3- week weighted moving average with weights 0.2, 0.3, and 0.5ii. Exponential smoothing with a smoothing constant equal to 0.85ii. Adjusted exponential smoothing with a smoothing constant equal to 0.85 andsmoothing constant for trend equal to 0.65(b) Compute for the accuracy of each model using the mean squared error. Based onthe computed MSE, what is the best forecasting technique for this time series?

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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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Under what conditions might a firm use multiple forecasting methods?
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
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.
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
11. Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonald's restaurant: Day Actual Demand Forecast Demand Monday 88.00 88.00 Tuesday 72.00 88.00 Wednesday 68.00 84.00 Thursday 48.00 80.00 Friday − ? Part 2 The forecast for Monday was derived by observing Monday's demand level and setting Monday's forecast level equal to this demand level. Subsequent forecasts were derived by using exponential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, the forecast for Big Mac demand for Friday is __________ Big Macs (round your response to one decimal place).
Weekly demand for dry pasta at a supermarket chain is as follows. Week Demands1 5172 5103 5574 4985 4986 4447 5268 4419 54110 445 a. Forecast the demand for week 11 by using five-period moving average.b. Calculate the same using weighted average methods with weights are 0.3, 0.2, 0.2, 0.1 and 0.2 with start assigning weights to the most recent data
Tools View Week 3 Bonus Activity- DEMAND FORECASTING CASE STUDY After reviewing the forecasting demonstration and looking over the slides, complete the following case activity and transfer your answers to the appropriate questions in the Canvas activity quiz. You have been hired as a demand planning intern for Hawaiian Island Creations (HIC). They want you to de- velop a forecast for their HIC Papanui style of sun- glasses. The goal is to determine how many pair they will produce to meet retailer demand in July 2021. During your first meeting, you were handed some data to work with and the product team talked about the company's upcoming promotional blitz to support Summer Break '21 in major vacation destinations. Month Forecast Demand January 2021 4.000 3,300 February 2021 4,200 3,900 March 2021 4,500 4,300 April 2021 4.800 4,200 May 2021 5 000 5.400 of 4 P Type here to search 立

4-Forecasting using Exponential Smoothing The first five periods of demand data are shown in the following table .Let the smoothing coefficient, alpha, equal 0.2.Compute the exponentially smoothed forecasts for periods one through four .Initialize the procedure with a forecast value for period one of 37. Period Aggregate Demand Forecast demand 0 - - 1 38 37 2 42 3 40 4 36 5 42 Determine the Running Sum of Forecast Errors (RSFE), the Mean Absolute Deviation, MADt-1,and the Tracking Signal(TS) at the end of each period. Let the initial MADt-1 for period 0 be equal to 2.
When forecasting demand for new products, sometimes i rms will use demand data from similar existing products to help forecast demand for the new product. What technique is this an example of?
6. Consider the following data table. (12 Points) a) Forecast demand using exponential smoothing with an apha of 0.25, and an initial forecast of 128.0 for period 1. b) Calculate the MAD and MSE. Period Real demand 130 138 129 140 3 4 5 133
b. Kim’s department at a local department store has tracked the sales of a product over thelast nine weeks. The demand can be seen in the table below.Period Demand1 242 233 264 365 266 307 328 269 25i. Forecast demand using exponential smoothing with an alpha of 0.3, and an initialforecast of 30.0 for period 2 to 9.ii. Use a 5-period moving average, forecast the demand up to period 9.iii. Using MAD, determine which forecasting technique is better.
- Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: 6 10 Week 1 Demand 20 2 3 23 27 4 5 37 26 7 8 9 30 35 22 24 29 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): Week 1 Demand 20 Forecast 20.0 2 3 23 27 4 37 5 26 6 30 7 35 8 22 9 24 10 29

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