Operations Management: Processes and Supply Chains (12th Edition) (What's New in Operations Management)
12th Edition
ISBN: 9780134741062
Author: Lee J. Krajewski, Manoj K. Malhotra, Larry P. Ritzman
Publisher: PEARSON
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Chapter 8, Problem 21P
Using the data in Problem 20 and the Time-Series Solver of OM Explorer, find the best exponential smoothing parameter alpha that minimizes MAD. Let the
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Chapter 8 Solutions
Operations Management: Processes and Supply Chains (12th Edition) (What's New in Operations Management)
Ch. 8 - Figure 8.9 shows summer air visibility...Ch. 8 - Kay and Michael Passe publish What‘s...Ch. 8 - Demand for oil changes at Garcia’s Garage has...Ch. 8 - Prob. 2PCh. 8 - Ohio Swiss Milk Products manufactures and...Ch. 8 - A manufacturing firm has developed a skills test,...Ch. 8 - The materials handling manager of a manufacturing...Ch. 8 - Marianne Kramer, the owner of Handy Man Rentals,...Ch. 8 - Sales for the past 12 months at Computer Success...Ch. 8 - Bradley’s Copiers sells and repairs photocopy...
Ch. 8 - Consider the sales data for Computer Success given...Ch. 8 - A convenience store recently started to carry a...Ch. 8 - Community Federal Bank in Dothan, Alabama,...Ch. 8 - The number of heart surgeries performed at...Ch. 8 - The following data are for calculator sales in...Ch. 8 - Prob. 14PCh. 8 - Forrest and Dan make boxes of chocolates for which...Ch. 8 - The manager of Alaina’s Garden Center must make...Ch. 8 - The manager of a utility company in the Texas...Ch. 8 - Franklin Tooling, Inc., manufactures specialty...Ch. 8 - Create an Excel spreadsheet on your own that can...Ch. 8 - Prob. 20PCh. 8 - Using the data in Problem 20 and the Time-Series...Ch. 8 - Prob. 22PCh. 8 - Cannister, Inc., specializes in the manufacture of...Ch. 8 - The Midwest Computer Company serves a large number...Ch. 8 - A certain food item at P=0.20 (with a combination...Ch. 8 - Prob. 26PCh. 8 - Prob. 27PCh. 8 - A manufacturing firm seeks to develop a better...Ch. 8 - How much does the forecasting process at Deckers...Ch. 8 - Prob. 2VCCh. 8 - What factors make forecasting at Deckers...Ch. 8 - Prob. 4VCCh. 8 - Prob. 5VCCh. 8 - Comment on the forecasting system being used by...Ch. 8 - Develop your own forecast for bow rakes for each...
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- 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?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_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_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_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_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_forward
- Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.arrow_forwardThe 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_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_forward
- The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?arrow_forwardManagement of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.arrow_forwardThe file P13_19.xlsx contains the weekly sales of a particular brand of paper towels at a supermarket for a one-year period. a. Using a span of 3, forecast the sales of this product for the next 10 weeks with the moving averages method. How well does this method with span 3 forecast the known observations in this series? b. Repeat part a with a span of 10. c. Which of these two spans appears to be more appropriate? Justify your choice.arrow_forward
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