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Copyright 2013 John Wiley & Sons, Inc. Chapter 8 Supplement Forecasting

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8S-2 Forecasting Purposes and Methods Must forecast future to plan An accurate estimate of demand is crucial to the efficient operation of a system Not only demand can be forecasted –New technology –Economic conditions –Changes in lead time, scrap rates, and so on

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8S-3 Primary Uses of Forecasting 1.To determine if sufficient demand exists 2.To determine long-term capacity needs 3.To determine midterm fluctuations in demand to avoid short-sighted decisions 4.To determine short-term fluctuations in demand for production planning, workforce scheduling, and materials planning

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8S-4 Forecasting Methods Figure 8S.1

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8S-5 Qualitative Methods Life cycle Surveys Delphi Historical analogy Expert opinion Consumer panels Test marketing

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8S-6 Quantitative Methods Causal –Input-output –Econometric –Box-Jenkins Time series analysis –Simple regression –Exponential smoothing –Moving average

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8S-7 Choosing a Forecasting Method 1.Availability of representative data 2.Time and money limitations 3.Accuracy needed

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8S-8 Time Series Analysis Time series is a set of values measured either at regular points in time or over sequential intervals of time Can be collected over short or long periods of time

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8S-9 Components of Time Series 1.Trend T 2.Seasonal variation S 3.Cyclical variation C 4.Random variation R

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8S-10 Common Trend Patterns Figure 8S.2a

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8S-11 Common Trend Patterns Figure 8S.2b

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8S-12 Common Trend Patterns Figure 8S.2c

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8S-13 Moving Averages

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8S-14 Four-Period Moving Average of Intel’s Monthly Stock Closing Price Figure 8S.3

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8S-15 Exponential Smoothing

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8S-16 Using Exponential Smoothing To Forecast Intel’s Closing Stock Price Figure 8S.4

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8S-17 Simple Regression: The Linear Trend Multiplicative Model Y = α + βX + ε Where: X = Independent variable Y = Dependent variable α and β are the parameter of the model

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8S-18 Fitting Regression Line to Data Figure 8S.5

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8S-19 Example Relationships Between Variables Figure 8S.8

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8S-20 Least Squares Approach to Fitting Line to a Set of Data Figure 8S.9

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8S-21 Regression Analysis Assumptions The residuals are normally distributed The expected value of the residuals is zero The residuals are independent of one another The variance of the residuals is constant

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8S-22 The Multiple Regression Model Simple regression uses one independent variable Using more than one independent variable is called multiple regression Form of the model is:

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8S-23 Developing Regression Models 1.Identify candidate independent variables to include in the model 2.Transform the data 3.Select the variables to include in the model 4.Analyze the residuals

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