Forecasting Stationary Series

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Forecasting
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Forecasting

• Marketing forecasts sales for new and
• existing products.
• Production uses sales forecasts to plan
• production and operations sometimes
• involved in generating sales forecasts.

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Characteristics of Forecasts

• They are usually wrong
• A good forecast is usually more than a single
  number
• Aggregate forecast are more accurate
• The longer the forecasting horizon, the less
  accurate the forecasts will be
• Forecasts should not be used to the exclusion of
  known information

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Forecasting Horizon

• Short term
• (inventory management, production plans..)
• Intermediate term
• (sales patterns for product families..)
• Long term
• (long term planning of capacity needs)

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Forecasting Techniques
Forecasting Technique


Judgmental Models
Time Series Methods
Causal Methods
Delphi Method
Moving Average
Regression Analysis
Exponential Smoothing
Seasonality Models
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Types of forecasting Methods

• Subjective methods
• FREE HAND METHOD
• Objective methods
• SEMI AVERAGE
• EVEN DATA
• ODD DATA
• LEAST SQUARE
• TREND MOMENT

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FREE HAND METHOD
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SEMI AVERAGE EVEN DATA
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Y a bX

No. Year Sales (Y-axis) Base time (X-axis)
1 1988 1850 0 ? 1-6 11520
2 1989 1800 1 Y1 1920
3 1990 1900 2 X1 2.5
4 1991 2000 3
5 1992 1950 4
6 1993 2020 5 a 3514.81 and b 291.72 a 3514.81 and b 291.72 a 3514.81 and b 291.72
7 1994 1980 6 ? 7-12 11979
8 1995 1960 7 Y2 1996.5
9 1996 2000 8 X2 8.5
10 1997 2200 9
11 1998 2240 10
12 1999 2220 11

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SEMI AVERAGE ODD DATA
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Y a bX
No. Year Sales (Y-axis) Base time (X-axis)
1 1988 1850 0 ? 1-5 9500
2 1989 1800 1 Y1 1900
3 1990 1900 2 X1 2
4 1991 2000 3
5 1992 1950 4
6 1993 2020 5 a 1868 and b 16 a 1868 and b 16
7 1994 1980 6 ? 7-11 9980
8 1995 1960 7 Y2 1996
9 1996 2000 8 X2 8
10 1997 2200 9
11 1998 2240 10

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TREND MOMENT METHOD
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LEAST SQUARE METHOD
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EVEN DATA CASE