Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 2013 were: Month demand Month demand January 89 July 223 February 57 August 286 March 144 September 212 April 221 October 275 May June 177 November 188 280 December 312 a) Using a four-month moving average, determine the one-step-ahead forecast for July through December 2013. b) Using a four-month moving average, determine the two-step-ahead forecast for July through December 2013. c) Obtain the MAD for the forecasts obtained in the above sections. Which method gave better results? Based on forecasting theory, which method should have given better results?

Observations of the demand for a certain part stocked at a parts supply depot during the calendar year 2013 were: Month demand Month demand January 89 July 223 February 57 August 286 March 144 September 212 April 221 October 275 May June 177 November 188 280 December 312 a) Using a four-month moving average, determine the one-step-ahead forecast for July through December 2013. b) Using a four-month moving average, determine the two-step-ahead forecast for July through December 2013. c) Obtain the MAD for the forecasts obtained in the above sections. Which method gave better results? Based on forecasting theory, which method should have given better results?

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

See similar textbooks

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 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_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 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 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_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?
1. The number of bushels of apples sold at a roadside fruit stand over a 12-day period were PROBLEMS as follows: Day Numbar Sold Day Number Sold 25 35 31 29 33 32 38 10 40 37 32 34 11 37 12 If a two-period moving average has been used to forecast sales, what were the daily forecasts starting with the forecast for day 3? If a four-period moving average has been used, what were the forecasts for eacn uay starting with day 5? Plot the original data and each set of forecasts on the same graph. Which forecast has the greater tendency to smooth? Which forecast has the better ability to respond quickly to changes? What does use of the term sales instead of demand imply? b. C. 2. If exponential smoothing with a = .4 had been used to forecast daily sales for apples in Problem 1, determine what the daily forecasts would have been. Then, plot the original data, the exponential forecasts, and a set of naive forecasts on the same graph. Based on a visual comparison, is the naive more accurate or…
4.23 Sales of vegetable dehydrators at Bud Banis's dis- count department store in St. Louis over the past year are shown below. Management prepared a forecast using a combination of exponential smoothing and its collective judgment for the 4 months (March, April, May, and June of 2010): Management's Forecast 2009-2010 Month Unit Sales 100 July August September October November 93 96 110 124 December 119 92 January February March 83 101 120 96 114 April May 89 110 June 108 108 a) Compute MAD and MAPE for management's technique. b) Do management's results outperform (i.e., have smaller MAD and MAPE than) a naive forecast? Which forecast do you recommend, based on lower forecast error?
The monthly sales for Yazici Batteries, Inc., were as follows: Month Sales Jan 19 Feb Mar 17 21 Apr May Jun Jul Aug Sept Oct 12 15 18 15 19 20 20 This exercise contains only parts b and c. b) The forecast for the next month (Jan) using the naive method = 22 sales (round your response to a whole number). Nov Dec 20 22 The forecast for the next period (Jan) using a 3-month moving average approach = 20.67 sales (round your response to two decimal places). The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are applied to the most recent month = sales (round your response to one decimal place).
Explain the distinction between short- and long-term forecasts?