A company which installs swimming pools has recently merged with another regional installer. It is becoming evident that prices over the next 24 months will increase substantially, and the supply manager is considering forward buying or some other option to take advantage of what is expected to be an expensive and tight market. In particular, there is a great need for pool chemicals. In terms of forecasting, which of the following would be most useful in this situation? O Seasonal Forecasting O Future sales based on past sales O Judgment sampling
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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.
- 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_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_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?The Nuts’n’Bolts hardware store stocks some high-value items and a large number of rel- atively low-value components. Inventory is reviewed weekly, and orders are placed with suppliers when inventory levels indicate that current levels are “too low.” Most suppliers deliver the low-value items within two weeks; some of the high-value items may take four weeks for delivery. How would you develop a forecasting system to predict the sales of such items at the individual (SKU ? stock-keeping unit) level? How would you use such fore- casts to plan future purchases?Use PHIVE to go through the necessary steps. For purposes of the discussion, assume that that any historical data you might need can be made available.The following data show the number of liters of gasoline sold by Mackrin's in Colorado for the past 12 weeks. Which forecasting model would you recommend to Mackrin's manager? Why? What demand forecast would you recommend for week 13? Week Actual Demand (1000 liters) Forecasting (1000 liters) FSMA /A-F/ FWMA /A-F/ FES, α=0.3 /A-F/ 1 17 2 21 17 4 3 19 18.2 0.8 4 23 19 4 18.44 4.56 5 18 21 3 20.8 2.8 19.81 1.81 6 16 20 4 20 4 19.3 3.3 7 20 19 1 18.3 2.3 18.31 1.69 8 18 18 0 18.7 0.7 18.82 0.82 9 22 18 4 18.2 4.2 18.6 3.4 10 20 20 0 19.8 0.2 19.62 0.38 11 15 20 5 20.2 5.2 19.73 4.73 12 22 19 3 18.2 3.8 18.3 3.7 13 19 19.5 19.41 MAD 2.67 2.9 2.7 MSE 10.22…
- What does the term "adaptive forecasting" mean?Explain why forecasting devices such as moving averages, weighted moving averages, and exponential smoothing are not good for trendy data series.All forecasts are subject to error. Do you think topmanagers would be concerned about the effectson the firm if sales revenues or unit costs, forexample, turned out to be different from the forecasted level? How could you provide informationon the effects of such errors?