Monthly sales for a six month period are as follows: Month Sales Jan 18,000 Feb 22,000 Mar 16,000 Apr 18,000 May 20,000 June 24,000 Compute the sales forecast for July using the following approaches: (1) Four-month moving average; (2) Weighted three-month moving average using .50 for June, .30 for May and .20 for April; (3) Exponential smoothing with α (smoothing constant) equal to .40, assuming a February forecast of 18,000

Monthly sales for a six month period are as follows: Month Sales Jan 18,000 Feb 22,000 Mar 16,000 Apr 18,000 May 20,000 June 24,000 Compute the sales forecast for July using the following approaches: (1) Four-month moving average; (2) Weighted three-month moving average using .50 for June, .30 for May and .20 for April; (3) Exponential smoothing with α (smoothing constant) equal to .40, assuming a February forecast of 18,000

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 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.
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 owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The table shows the values found in the error analysis. What method of forecasting would be best to use? What is the forecasted value? MSE forecast for week 13 Time Series 1.08 14.76 3-week MA 3.04 13.67 exp smoothing 11.04 9.5 Group of answer choices A.) Time Series, forecasted value = 14.76 B.) 3-week MA, forecasted value = 13.67 C.) exp smoothing, forecasted value = 9.5 D.) Time Series, forecasted value = 1.08 E.) 3-week MA, forecasted value = 3.04 F.) exp smoothing, forecasted value = 11.04
q 1 An analyst must decide between two different forecasting techniques for weekly sales of roller blades. Based on the following data, answer the below questions. time Units sold 1 147 2 148 3 151 4 145 5 155 6 152 7 155 8 157 9 160 10 165 What's the MAD if the exponential smoothing technique with alpha 0.4 is used?
Q1 Given the Supply chain cost for a service is PKR 195 and the customer value is PKR 225. Determine supply chain surplus and consumer surplus. Q2 PizzaHut is a franchise based model which serves customers a variety of pizzas and related products. The service is purely customized. Identify whether the model is push-based supply chain or pull based supply chain. Can we a have push-pull boundary in PizzaHut case? How? Q3 Discuss Qualitative forecasting technique. Explain the situations where we use Qualitative methods. Discuss Delphi forecasting method and its challenge.
q1(a)Imagine that you need to introduce a new gadget such as the Apple Watch. Determine which forecasting method is appropriate for projecting the future demand of such this gadget. b)ARIMA is mostly used to forecast future values using historical time series data, as we all know. Its primary use is in short-term forecasting with at least 38-40 historical data points and a small number of outliers. If we don't have at least 38 data points, we should consider using another strategy.
Exercise # 2 –Calculating MADt, Revised MADt,Error,and the Revised Error :Has the Forecast Improved ? How much has it improved in % terms ? Franklin Henry forecasts demand for an auditing and accounting services group, for an estate planning group ,and a business consulting group.His group forecasts for the preceding six-month(M) period, which were made six months prior to that time, are each based on a level, patternless projection.The following data table shows these forecasts(F) and the actual demand (AD) for each group : Estate Planning Auditing and Accounting Business Consulting (Client –days) (Client –days) (Client –days) M AD F M AD F M AD F 1 120 130 1 260 230 1 120 120 2 80 130…
Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
16- Statistical forecasting models have the following weaknesses, except __________. a. Can be costly and time consuming to model “soft” information b. Can process large amounts of data c. Slow to react to changing environments d. Only as good as the model formulation and the data it is based upon