Copyright 2013 John Wiley & Sons, Inc.
Chapter 8 Supplement
Forecasting
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
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
8S-4
Forecasting Methods
Figure 8S.1
8S-5
Qualitative Methods
• Life cycle
• Surveys
• Delphi
• Historical analogy
• Expert opinion
• Consumer panels
• Test marketing
8S-6
Quantitative Methods
• Causal– Input-output– Econometric– Box-Jenkins
• Time series analysis– Simple regression– Exponential smoothing– Moving average
8S-7
Choosing a Forecasting Method
1. Availability of representative data
2. Time and money limitations
3. Accuracy needed
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
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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Common Trend Patterns
Figure 8S.2a
8S-11
Common Trend Patterns
Figure 8S.2b
8S-12
Common Trend Patterns
Figure 8S.2c
8S-13
Moving Averages
8S-14
Four-Period Moving Average of Intel’s Monthly Stock Closing Price
Figure 8S.3
8S-15
Exponential Smoothing
8S-16
Using Exponential Smoothing To Forecast Intel’s Closing Stock Price
Figure 8S.4
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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Fitting Regression Line to Data
Figure 8S.5
8S-19
Example Relationships Between Variables
Figure 8S.8
8S-20
Least Squares Approach to Fitting Line to a Set of Data
Figure 8S.9
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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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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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
