Although the unpredicted, yet disruptive, impact of the CoViD-19 pandemic is global and has negatively affected all organizations, identify a business organization that you think has been affected the most. In the new normal, which forecasting time horizon (short-range, medium-range, or long-range) would you suggest should be adopted by this organization in forecasting the demand for its main product? Justify your answer. Cite your assumptions. Cite 2 to 3 factors that this business organization should use as a basis for forecasting the demand for its main product. Justify your answer

Although the unpredicted, yet disruptive, impact of the CoViD-19 pandemic is global and has negatively affected all organizations, identify a business organization that you think has been affected the most. In the new normal, which forecasting time horizon (short-range, medium-range, or long-range) would you suggest should be adopted by this organization in forecasting the demand for its main product? Justify your answer. Cite your assumptions. Cite 2 to 3 factors that this business organization should use as a basis for forecasting the demand for its main product. Justify your answer

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.3: Simple Regression Models

Problem 9P

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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.
What forecasting techniques are used in the management of technology and innovation?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
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_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
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 file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
Forecasting is critical in modern times. Business organizations manifested more concern with anticipating the future, and as a consequence, a number of highly successful consulting firms emerged to provide forecasting help for governments and businesses. You are required: iii. Identify the different levels that forecasting of business trends takes place. iv. Give 3 examples where long-range forecasting can be used v. Do you think forecasting is always correct and accurate? Discuss this assertion in detail
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Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows: PREDICTED DEMAND Period Demand F1 F2 1 68 61 60 2 75 67 66 3 70 75 70 4 74 69 67 5 69 74 72 6 72 67 78 7 80 74 78 8 78 76 80 Click here for the Excel Data File a. Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 2 decimal places.)