Operations and Supply Chain Management 9th edition
9th Edition
ISBN: 9781119320975
Author: Roberta S. Russell, Bernard W. Taylor III
Publisher: WILEY
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Chapter 12, Problem 20Q
Selecting from MAD, MAPD, MSE, E, and
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Selecting from MAD, MAPD, MSE, E, and , which measure of forecast accuracy do you consider superior? Why?
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Chapter 12 Solutions
Operations and Supply Chain Management 9th edition
Ch. 12 - List some of the operations and functions in a...Ch. 12 - What is the difference between quantitative...Ch. 12 - Describe the difference between short- and...Ch. 12 - Prob. 4QCh. 12 - Why is accurate forecasting so important to...Ch. 12 - Discuss the relationship between forecasting and...Ch. 12 - Prob. 7QCh. 12 - Describe the Delphi method for forecasting.Ch. 12 - What is the difference between a trend and a cycle...Ch. 12 - How is the moving average method similar to...
Ch. 12 - In the chapter examples for time series methods,...Ch. 12 - What effect on the exponential smoothing model...Ch. 12 - How does adjusted exponential smoothing differ...Ch. 12 - What determines the choice of the smoothing...Ch. 12 - How does the linear trend line forecasting model...Ch. 12 - Of the time series models presented in this...Ch. 12 - What advantages does adjusted exponential...Ch. 12 - Describe how a forecast is monitored to detect...Ch. 12 - Explain the relationship between the use of a...Ch. 12 - Selecting from MAD, MAPD, MSE, E, and E, which...Ch. 12 - What is the difference between linear and multiple...Ch. 12 - Define the different components (y, x, a, and b)...Ch. 12 - A company that produces video equipment, including...Ch. 12 - The Hartley-Davis motorcycle dealer in the...Ch. 12 - The manager of the I-85 Carpet Outlet needs to be...Ch. 12 - The LawnPlus Fertilizer Company distributes...Ch. 12 - Graph the demand data in Problem 12.3. Can you...Ch. 12 - The chairperson of the department of management at...Ch. 12 - The manager of the Excom Service Station wants to...Ch. 12 - The Intrepid mutual fund of growth stocks has had...Ch. 12 - The Oceanside Hotel is adjacent to City Coliseum,...Ch. 12 - Mary Hernandez has invested in a stock mutual fund...Ch. 12 - Globetron manufactures components for use in small...Ch. 12 - The Bee Line Caf is well known for its popular...Ch. 12 - For the demand data in Problem 12.11, develop a...Ch. 12 - Develop a seasonally adjusted forecast for the...Ch. 12 - Backstreets Pizza delivery service has randomly...Ch. 12 - The Willow River Mining Company mines and ships...Ch. 12 - The Great Northwest Outdoor Company is a catalog...Ch. 12 - Townside Food Vending operates vending machines in...Ch. 12 - The town aquatic center has an indoor pool that...Ch. 12 - Develop an adjusted exponential smoothing forecast...Ch. 12 - During the past five months the emergency room at...Ch. 12 - At its craft store and through its website, the...Ch. 12 - A group of business students at Tech organized a...Ch. 12 - Temco Industries has developed a forecasting model...Ch. 12 - Monitor the forecast in Problem 12.23 for bias...Ch. 12 - Develop a statistical control chart for the...Ch. 12 - Monitor the adjusted exponential smoothing...Ch. 12 - Develop an exponential smoothing forecast with =...
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- 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_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_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
- 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?arrow_forwardThe 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_forward
- Please solve it within 30 minutes i really need helparrow_forwardConsider the following time series data: Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 2) Refer to the time series data in Exercise 1. Using the average of all the historical data as a forecast for the next period. Compute the following measures of forecast accuracy i)Mean absolute error ii)Mean squared error iii)Mean absolute percentage error iv)What is the forecast for week 7?arrow_forwardThe manager of a travel agency asked you to come up with a forecasting technique that will best fit to the actual demand for packaged tours. You have observed and recorded the actual demand for the last 10 periods. You also identified two possible techniques for consideration: 2-month moving averages (F1), and exponential smoothing (F2) with a smoothing constant of 0.25. Using Cumulative Forecasting Error (CFE) and Mean Absolute Deviation (MAD) as your performance measures you will determine the technique that will best fit to the actual demand data provided in the following table.STEP 1: Given start forecast values in period 3, compute forecast values from period 4 to 10. You are asked to provide the forecast values for period 6 and 10 for both techniques. 2-Month MA Exponential Period Demand F1 F2 1 115 -- -- 2 176 -- -- 3 97 146 129 4 141 5 98 6 132 7 114 8 129 9 107…arrow_forward
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