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Forecasting Guide
Selecting the Approach
Combining Approaches
Judgmental Models
Delphi Method
Curve Fitting
Analogous Data
Time Series Models
Moving Average
Exponential Smoothing
Decomposition Models
Box-Jenkins Models
Leading Indicator
Input-Output Models
TECHNIQUE #6: Exponential Smoothing

BASIC IDEA: Values in more recent time periods have more impact on the forecast and therefore should be given more weight. This method overcomes the drawback of moving averages which assigns equal weights.



Y(t+1) = X S(t) + (1 - X) Y(t)


Y(t+1) = Forecasts sales for next period

Y(t) = Previously forecasted sales of current period

S(t) = Current period sales

X is a smoothing constant with values between 0 and 1.

How to determine the value of X? Use trial and error and choose a number that gives the best forecast.



• Finding the best smoothing constant can be difficult.

• Usually a poor model for medium or long range forecasts.

• Large random fluctuation can severely hurt forecasts. Try double exponential smoothing model here too. The exact formulas for this procedure are given in the references.


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