The table below shows the sales figures for a brand of shoe over the last 12 months. Months January February March April May June Sales 69 75 86 92 95 100 108 115 125 131 140 150 July August September October November December a. Using the following, forecast the sales for the months up to January the following year:- i. A simple three month moving average. ii. A three period weighted moving average using weights of 1, 2 and 3. Assign the highest weight to the most recent data. iii. Exponential Smoothing when a= .6 and the forecast for March is 350. iv. Determine which of the three forecasting technique is the most accurate using

The table below shows the sales figures for a brand of shoe over the last 12 months. Months January February March April May June Sales 69 75 86 92 95 100 108 115 125 131 140 150 July August September October November December a. Using the following, forecast the sales for the months up to January the following year:- i. A simple three month moving average. ii. A three period weighted moving average using weights of 1, 2 and 3. Assign the highest weight to the most recent data. iii. Exponential Smoothing when a= .6 and the forecast for March is 350. iv. Determine which of the three forecasting technique is the most accurate using

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...

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Under what conditions might a firm use multiple forecasting methods?
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_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_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?
4. The sales (in millions of dollars) for a 15-month period are as follows. Month Sales Month Sales 390 9 470 2 565 10 1000 3 395 11 490 450 12 485 490 13 525 560 14 550 7 420 15 530 8. 565 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.2. Find MSE for both methods. b. Which provides the better forecasts based on MSE? C. Find the forecast for the next month using the best forecast method. 5.
4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 600 600 775 10 775 3 600 11 600 4 650 12 575 700 13 625 800 14 650 550 15 600 8 775 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.1. Which provides the better forecasts based on MSE? b. Find the forecast for the next month using the best forecast method. 2.
B). Using exponential smoothing, calculate the forecasts for months 2, 3, 4, 5, and 6. The smoothing constant is 0.2, and the old forecast for month I is 245. Month Actual Demand Forecast Demand 1 260 2. 230 3 225 4 245 5 250 6. C). Given the following average demand for each month, calculate the seasonal indices for each month. Month Average Demand Seasonal Index January 30 February 50 March 85 April 110 May 125 June 245 July 255 August 135 September 100 October 90 November 50 December 30 Total 3 D). Using the data in part A and the seasonal indices you have calculated, calculate expected monthly demand if the annual forecast is 2000 units. Month Seasonal Forecast Index January February March April May June July August September October November December
Interpret the MAD of the most accurate among the forecasting models below. A. Naïve approach;B. 5-month SMA model;C. WMA model with weights 0.1, 0.3, and 0.6; orD. ES model with α = 0.5 and a forecast of 3,500 liters in the first month.
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
Consider the data below which includes sales data and the forecasts that would have been made using exponential smoothing with a = 0.3. Exp. Smoothing Time Period Sales Forecast 1 90 90 80 90 100 87 4 70 90.9 105 84.6 6. 85 90.7 Report all answers to 1 decimal place. a. Using exponential smoothing with a = 0.3, what is the forecast for time period 7? b. Using the naive value method, what is the forecast for time period 7? c. Using a 3-period moving average, what is the forecast for time period 7?
9 10 38 36 39 Month 1 2 4 5 7 11 12 Flat-Screen Sales 30 32 30 39 33 34 34 30 36 a) Determine the one-step-ahead flat-screen sales forecasts for the first month of next year using 3- and 5-month moving averages. b) Using a 5-month moving average, determine the one-step-ahead flat-screen forecasts for the 7th through 12th months. Compute the MAD. c) Suppose that exponential smoothing is used with a smoothing constant alpha = 0.1 to forecast flat-screen sales for the 7th through 12th months. d) Based on MAD which method did better?