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Forecasting
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Planning Forecast Customer Production Process Finished Goods Inputs
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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 Intermediate term Long 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
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
∑ 1-6 = 11520 2 1989 1800 Y1 1920 3 1990 1900 X1 2.5 4 1991 2000 5 1992 1950 6 1993 2020 a= and b= 7 1994 1980 ∑ 7-12 = 11979 8 1995 1960 Y2 1996.5 9 1996 X2 8.5 10 1997 2200 11 1998 2240 12 1999 2220
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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
∑ 1-5 = 9500 2 1989 1800 Y1 1900 3 1990 X1 4 1991 2000 5 1992 1950 6 1993 2020 a= 1868 and b= 16 7 1994 1980 ∑ 7-11 = 9980 8 1995 1960 Y2 1996 9 X2 10 1997 2200 11 1998 2240
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TREND MOMENT METHOD
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LEAST SQUARE METHOD
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EVEN DATA CASE
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