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

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Presentation on theme: "Copyright 2013 John Wiley & Sons, Inc. Chapter 8 Supplement Forecasting."— Presentation transcript:

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

2 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

3 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

4 8S-4 Forecasting Methods Figure 8S.1

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

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

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

8 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

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

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

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

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

13 8S-13 Moving Averages

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

15 8S-15 Exponential Smoothing

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

17 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

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

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

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

21 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

22 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:

23 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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