6th Edition

ISBN: 9781337406659

Author: WINSTON, Wayne L.

Publisher: Cengage,

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Forecasting is the process of predicting the future based on the past and the present data and circumstances. In other words, forecasting is the act of making a detailed analysis of future events based on the decisions made in the past as well as the pre…

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Textbook Question

Chapter 10.5, Problem 17P

In Problem 11 from the previous section, we stated that the damage amount is normally distributed. Suppose instead that the damage amount is triangularly distributed with parameters 500, 1500, and 7000. That is, the damage in an accident can be as low as $500 or as high as $7000, the most likely value is $1500, and there is definite skewness to the right. (It turns out, as you can verify in @RISK, that the mean of this distribution is $3000, the same as in Problem 11.) Use @RISK to simulate the amount you pay for damage. Run 5000 iterations. Then answer the following questions. In each case, explain how the indicated event would occur.

a. What is the probability that you pay a positive amount but less than $750?
b. What is the probability that you pay more than $600?
c. What is the probability that you pay exactly $1000 (the deductible)?

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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Chapter 10.5, Problem 16P

Chapter 10.5, Problem 18P

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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…

Chapter 10 Solutions

Practical Management Science

Ch. 10.2 - Use the RAND function and the Copy command to...Ch. 10.2 - Use Excels functions (not @RISK) to generate 1000...Ch. 10.2 - Use @RISK to draw a uniform distribution from 400...Ch. 10.2 - Use @RISK to draw a normal distribution with mean...Ch. 10.2 - Use @RISK to draw a triangular distribution with...Ch. 10.2 - Use @RISK to draw a binomial distribution that...Ch. 10.2 - Use @RISK to draw a triangular distribution with...Ch. 10.2 - We all hate to keep track of small change. By...Ch. 10.4 - Prob. 11P Ch. 10.4 - In August of the current year, a car dealer is...

Ch. 10.4 - Prob. 13P Ch. 10.4 - Prob. 14P Ch. 10.4 - Prob. 15P Ch. 10.5 - If you add several normally distributed random...Ch. 10.5 - In Problem 11 from the previous section, we stated...Ch. 10.5 - Continuing the previous problem, assume, as in...Ch. 10.5 - In Problem 12 of the previous section, suppose...Ch. 10.5 - Use @RISK to analyze the sweatshirt situation in...Ch. 10.5 - Although the normal distribution is a reasonable...Ch. 10.6 - When you use @RISKs correlation feature to...Ch. 10.6 - Prob. 24P Ch. 10.6 - Prob. 25P Ch. 10.6 - Prob. 28P Ch. 10 - Six months before its annual convention, the...Ch. 10 - Prob. 30P Ch. 10 - A new edition of a very popular textbook will be...Ch. 10 - Prob. 32P Ch. 10 - W. L. Brown, a direct marketer of womens clothing,...Ch. 10 - Assume that all of a companys job applicants must...Ch. 10 - Lemingtons is trying to determine how many Jean...Ch. 10 - Dilberts Department Store is trying to determine...Ch. 10 - It is surprising (but true) that if 23 people are...Ch. 10 - Prob. 40P Ch. 10 - At the beginning of each week, a machine is in one...Ch. 10 - Simulation can be used to illustrate a number of...Ch. 10 - Prob. 43P Ch. 10 - Prob. 46P Ch. 10 - If you want to replicate the results of a...Ch. 10 - Suppose you simulate a gambling situation where...Ch. 10 - Prob. 49P Ch. 10 - Big Hit Video must determine how many copies of a...Ch. 10 - Prob. 51P Ch. 10 - Prob. 52P Ch. 10 - Why is the RISKCORRMAT function necessary? How...Ch. 10 - Consider the claim that normally distributed...Ch. 10 - Prob. 55P Ch. 10 - When you use a RISKSIMTABLE function for a...Ch. 10 - Consider a situation where there is a cost that is...

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Solution Summary: The author explains that simulation model is the digital prototype of the physical model that helps to forecast the performance of a system in the real world.

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Chapter 10 Solutions