1. Chapter 8 Supplement
Forecasting

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

4. Forecasting Methods
Figure 8S.1

5. Qualitative Methods Life cycle Surveys Delphi Historical analogy
Expert opinion
Consumer panels
Test marketing

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

7. Choosing a Forecasting Method
Availability of representative data
Time and money limitations
Accuracy needed

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. Components of Time Series
Trend T
Seasonal variation S
Cyclical variation C
Random variation R

10. Common Trend Patterns
Figure 8S.2a

11. Common Trend Patterns
Figure 8S.2b

12. Common Trend Patterns
Figure 8S.2c

13. Moving Averages

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

15. Exponential Smoothing

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

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. Fitting Regression Line to Data
Figure 8S.5

19. Example Relationships Between Variables
Figure 8S.8

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

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. 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. Developing Regression Models
Identify candidate independent variables to include in the model
Transform the data
Select the variables to include in the model
Analyze the residuals