Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: Week 2 4 8 9. 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 18 16 20 18 22 20 15 22 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.2 (round your responses to two decimal places). Week 3 4 8 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 17.80 18.04 19.03 18.83 18.26 18.61 18.49 19.19 19.35 18.48 b) The MAD for this model = 2.38' (round your response to two decimal places). c) Compute the cumulative forecast errors, cumulative MAD, and tracking signals (round your responses to two decimal places). Cumulative Cumulative Forecast Tracking Signal Forecast Tracking Signal Week Errors MAD Week Errors MAD 0.00 0.00 8.04 2.25 3.57 4.00 2.00 2.00 7.43 2.05 3.63 5.20 1.73 3.00 10.94 2.21 4.96 4 10.16 2.54 4.00 10 11.75 2.07 5.68 9.13 2.24 4.08 11 7.40 2.28 3.25 6.30 2.34 2.70 12 10.92 2.38 4.59 The control limits for the tracking signal are set at ±4MADS. The tracking signal is not within acceptable limits.

Passenger miles flown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: Week 2 4 8 9. 10 11 12 Actual Passenger Miles (in thousands) 17 21 19 23 18 16 20 18 22 20 15 22 a) Assuming an initial forecast for week 1 of 17,000 miles, use exponential smoothing to compute miles for weeks 2 through 12. Use a = 0.2 (round your responses to two decimal places). Week 3 4 8 10 11 12 Forecasted Passenger Miles (in thousands) 17.00 17.00 17.80 18.04 19.03 18.83 18.26 18.61 18.49 19.19 19.35 18.48 b) The MAD for this model = 2.38' (round your response to two decimal places). c) Compute the cumulative forecast errors, cumulative MAD, and tracking signals (round your responses to two decimal places). Cumulative Cumulative Forecast Tracking Signal Forecast Tracking Signal Week Errors MAD Week Errors MAD 0.00 0.00 8.04 2.25 3.57 4.00 2.00 2.00 7.43 2.05 3.63 5.20 1.73 3.00 10.94 2.21 4.96 4 10.16 2.54 4.00 10 11.75 2.07 5.68 9.13 2.24 4.08 11 7.40 2.28 3.25 6.30 2.34 2.70 12 10.92 2.38 4.59 The control limits for the tracking signal are set at ±4MADS. The tracking signal is not within acceptable limits.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 28P: The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present...

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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_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_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.
Under what conditions might a firm use multiple forecasting methods?
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 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?
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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?
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?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
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