Concept explainers
a.
To determine: The appropriate
Introduction: Forecasting includes quantitative methods apart from the qualitative methods. One basic type of quantitative category is Time series models. The time series models depict the connection between dependent and independent variables based on past observed data.
b.
To determine: The appropriate forecasting method to be usedto predict the use of new outdoor paint keeping the sale of new houses as the basis for forecasting.
Introduction: The casual models are one basic type of quantitative methods of forecasting that attempt to build the finest statistical relation of dependent and explanatory variables. When forecasting attempt is made using a single explanatory variable in causal forecasting models, it is regarded as simple regressionmodel.
c.
To determine: The appropriate forecasting method to be used in case whenCompany I tries to find the cost of a stock-out of a critical tape driver component.
Introduction: Delphi method is a subjective forecasting method based on soliciting the experts’ opinions. Here, a few rounds of questionnaires are answered by the specialists, and after each round, the anonymous answers are aggregated and shared with the group.
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Production and Operations Analysis, Seventh Edition
- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe 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?arrow_forwardThe 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.arrow_forward
- 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?arrow_forwardThe 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?arrow_forwardThe 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?arrow_forward
- 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?arrow_forwardThe 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?arrow_forwardExplain the difference between qualitative and quantitative approaches to forecasting. Describe three (3) qualitative methods used in forecasting. Given the following data of demand for shopping carts at a leading supermarket. Prepare a forecast for period 6 using each of the following approaches: Period 1 2 3 4 5 Demand 60 65 55 58 64 A three-period moving average. A weighted average using weights of .50 (most recent), .20 and .30. Exponential smoothing with a smoothing constant of .40. The manager of a large cement production factory in Road Town, Tortola has to choose between two alternative forecasting techniques. His production staff used both techniques in order to prepare forecasts for a six-month period (See table below). Using MAD as a criterion, which technique has the better performance record? FORECAST MONTH DEMAND TECHNIQUE 1 TECHNIQUE 2 1 492 488 495 2 470 484 482 3 485…arrow_forward
- Cinema HD an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 Membership 17 16 16 21 20 20 23 25 24 a. What are the issues associated with qualitative forecasting, and how are these overcome? b.Provide an example of qualitative forecasting and explain the shortcomings.arrow_forwardThe manager of a large manufacturer of industrial pumps must choose between two alternative forecasting techniques. Both techniques have been used to prepare forecasts for a six month period. Using MAD as a criterion, which technique has the better performance record? Month Demand Technique1 Technique 2 1 492 488 495 2 470 484 482 3 485 480 478 4 493 490 488 5 498 497 492 6 492 493 493arrow_forwardAt the ABC Floral Shop, an argument developed between two of the owners, Bob and Henry, over the accuracy of forecasting methods. Bob argued that exponential smoothing with α = .1 would be the best method. Henry argued that the shop would get a better forecast with α = .3.a. Using F1 = 100 and the data from problem 3, which of the two managers is right?b. Graph the two forecasts and the original data using Excel. What does the graph reveal?c. Maybe forecast accuracy could be improved. Try additional values of α = .2, .4, and .5 to see if better accuracy is achieved.arrow_forward
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