Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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Chapter 13.4, Problem 15P
Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual
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Scenario
You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill.
Accurate forecasts are important for many reasons, including for the company to ensure they have the materials
they need to create the products required in a certain period of time. Your objective is to minimize the forecast
error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%.
You have historical monthly sales data for the past year and access to software that provides forecasts based on
five different forecasting techniques (Naïve, 3-Month Moving Average, Exponential Smoothing for .2, Exponential
Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given
data, you will identify trends and patterns to create a more accurate forecast.
Approach
Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next…
Approach
Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next
month.
Remember to select the forecasting technique that produces the forecast error nearest to zero. For example:
a. Naïve Forecast is 230 and the Forecast Error is -15.
b. 3-Month Moving Forecast is 290 and the Forecast Error is -75.
c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93.
d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64.
e. Seasonal Forecast is 297 and the Forecast Error is -82.
The forecast for the next month would be 230 as the Naïve Forecast had the Forecast Error closest to zero with a
-15. This forecasting technique was the best performing technique for that month. You do not need to do any
external analysis-the forecast error for each strategy is already calculated for you in the tables below.
Naïve
Month Period
Actual
Demand
Naïve Forecast
Error
3-
Month
Moving
Forecast
3-
Month
Moving…
Scenario
You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill.
Accurate forecasts are important for many reasons, including for the company to ensure they have the materials
they need to create the products required in a certain period of time. Your objective is to minimize the forecast
error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%.
You have historical monthly sales data for the past year and access to software that provides forecasts based on
five different forecasting techniques (Naïve, 3-Month Moving Average, Exponential Smoothing for .2, Exponential
Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given
data, you will identify trends and patterns to create a more accurate forecast.
Approach
Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next…
Chapter 13 Solutions
Practical Management Science
Ch. 13.3 - The file P13_01.xlsx contains the monthly number...Ch. 13.3 - The file P13_02.xlsx contains five years of...Ch. 13.3 - The file P13_03.xlsx contains monthly data on...Ch. 13.3 - The file P13_04.xlsx lists the monthly sales for a...Ch. 13.3 - Management of a home appliance store wants to...Ch. 13.3 - Do the sales prices of houses in a given community...Ch. 13.3 - Prob. 7PCh. 13.3 - The management of a technology company is trying...Ch. 13.3 - Prob. 9PCh. 13.3 - Sometimes curvature in a scatterplot can be fit...
Ch. 13.4 - Prob. 12PCh. 13.4 - A trucking company wants to predict the yearly...Ch. 13.4 - An antique collector believes that the price...Ch. 13.4 - Stock market analysts are continually looking for...Ch. 13.4 - Suppose that a regional express delivery service...Ch. 13.4 - The owner of a restaurant in Bloomington, Indiana,...Ch. 13.6 - The file P13_19.xlsx contains the weekly sales of...Ch. 13.6 - The file P13_20.xlsx contains the monthly sales of...Ch. 13.6 - The file P13_21.xlsx contains the weekly sales of...Ch. 13.6 - The file P13_22.xlsx contains total monthly U.S....Ch. 13.7 - You have been assigned to forecast the number of...Ch. 13.7 - Simple exponential smoothing with = 0.3 is being...Ch. 13.7 - The file P13_25.xlsx contains the quarterly...Ch. 13.7 - The file P13_26.xlsx contains the monthly number...Ch. 13.7 - The file P13_27.xlsx contains yearly data on the...Ch. 13.7 - The file P13_28.xlsx contains monthly retail sales...Ch. 13.7 - The file P13_29.xlsx contains monthly time series...Ch. 13.7 - A version of simple exponential smoothing can be...Ch. 13 - Prob. 31PCh. 13 - Prob. 32PCh. 13 - Management of a home appliance store would like to...Ch. 13 - A small computer chip manufacturer wants to...Ch. 13 - The file P13_35.xlsx contains the amount of money...Ch. 13 - Prob. 36PCh. 13 - Prob. 37PCh. 13 - Prob. 39PCh. 13 - The Baker Company wants to develop a budget to...Ch. 13 - Prob. 41PCh. 13 - The file P13_42.xlsx contains monthly data on...Ch. 13 - Prob. 43PCh. 13 - Prob. 44PCh. 13 - Prob. 45PCh. 13 - Prob. 46PCh. 13 - Prob. 49P
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