2 In the text, the example of Walmart's collaboration with Sara Lee during hurricaneseason enables them to determine 'pop tarts, beer and flashlights' sell at 7 times thenormal rate in this season. This sort of forecasting system would be considered:A collaborative system built and wholesaled but also looking at detail into atspecific forecasts before assessing future demand.A collaborative planning, forecasting and replenishment system.A strategic weapon utilized to forecast for supply chain well in advance of itscompetitors.

Demand forecasting is the method involved with utilizing prescient investigation of verifiable…

Answered: In the text, the example of Walmart's…

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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Similar questions

Under what conditions might a firm use multiple forecasting methods?
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 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_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_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_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 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?
It wouldnt let me take a screenshot of the table so I had to copy and paste it in here. My question is: How do you Calculate a moving average forecast using the last 2 and 4 dates of stay. What is the prediction for 7/1/2021 for each technique? Discussion Question 1 - 200 room hotel Forecasts MA 2 Errors MA 4 Errors ES (0.8) Errors ES (0.5) Errors Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) AE APE AE APE AE APE AE APE 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 6/4/2021 Friday 199 6/5/2021 Saturday 179 6/6/2021 Sunday 140 6/7/2021 Monday 117 6/8/2021 Tuesday 132 6/9/2021 Wednesday 108 6/10/2021 Thursday 151…
The 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 6 492 493 493
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 3 485 480 478 4 493 490 488 5 498 497 492 6 492 493 493
At the Rhorer Car Dealership, two salespersons deployed different forecast techniques. The Dealership's son, Joe uses exponential smoothing with alpha of 0.15; while the Sales Manager, Jack conducted focus groups, and market research. The data shown below. Determine which technique (using MAD analysis) gives a better forecast. # 1 2 3 4 5 6 7 8 9 10 11 12 | 13 14 15 16 17 18 19 20 21 22 23 24 [Select] [Select] [Select] Month - Yr. Sales 1/1/2016 234 345 3/1/2016 125 1-Apr 259 5/1/2016 315 278 7/1/2016 401 231 368 268 11/1/2016 214 159 1/1/2017 200 2/1/2017 398 1-Feb 1-Jun 1-Aug 9/1/2016 1-Oct 1-Dec 1-Mar 4/1/2017 250 410 398 501 8/1/2017 327 219 10/1/2017 348 209 12/1/2017 276 1-May 6/1/2017 1-Jul 1-Sep 199 1-Nov Joe Exp Smooth alpha 0.15 234 234 251 232 236 248 252 275 268 283 281 271 254 246 269 258 257 280 298 328 328 312 317 301 Jack Focus Group 269 397 144 298 362 320 461 266 423 308 246 183 240 478 239 300 492 478 601 392 263 418 251 331 What is the average error in the forecast…

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