Forecast method: exponential smoothingLN calculates the demand forecast according to the Exponential Smoothing forecast method as follows: The relevant parameters for this forecast method are:
You can maintain these parameters in the Plan Items - Forecast Settings (cpdsp1110m000) session. If the Automatic Update of Forecast Parameters check box is selected, LN first recomputes the smoothing factors for the exponential smoothing method. Using an iterative process, with step sizes of 0.2 and subsequently 0.05, LN produces an optimum combination of smoothing factors for the demand, the season, and the trend. This combination gives the smallest mean absolute deviation (MAD). Next, LN calculates a demand forecast starting from the first period with demand history to the last forecast period via the exponential smoothing method. The various variables for the demand forecast are computed as follows: Average demand Without seasonal influence: AV(t) = FD(t) + a (AD(t) - FD(t)) With a constant seasonal influence: AV(t) = (FD(t) + a (AD(t) - FD(t))) - SF(t) With a progressive seasonal influence: AV(t) = (FD(t) + a (AD(t) - FD(t))) / SF(t) Where:
(*) For the current period and later periods, the forecast demand is taken as the actual demand. Trend factor With a linear trend influence: TF(t) = TF(t-1) + b ((AV(t)-AV(t-1)) - TF(t-1)) With a progressive trend influence: TF(t) = TF(t-1) + b (1.0 + ((AV(t)-AV(t-1))/AV(t)) - TF(t-1)) Where:
Seasonal factor With a constant seasonal influence: SF(t+L) = SF(t) + g ((AD(t) - AV(t)) - SF(t)) With a progressive seasonal influence: SF(t+L) = SF(t) + g ((AD(t) / AV(t)) - SF(t)) Where:
(*) For the current period and later periods, the forecast demand is taken as the actual demand. Demand forecast Without trend influence: FD(t+1) = AV(t) With a linear trend influence: FD(t+1) = FD(t+1) + TF(t) With a progressive trend influence: FD(t+1) = FD(t+1) * TF(t) With a constant seasonal influence: FD(t+1) = FD(t+1) + SF(t+1) With a progressive seasonal influence: FD(t+1) = FD(t+1) * SF(t+1) Where:
Mean forecast error Where:
The tracking signal is calculated as follows: TS(t) = abs(SE(t)/AE(t)) Where:
Note If the forecast demand (FD) is always greater than the actual demand (AD), the value of (SE(t)/AE(t)) is 1. If the forecast demand (FD) is always less than the actual demand, the value of (SE(t)/AE(t)) is -1. The tracking signal is a number between 0 and 1. The tracking signal indicates whether the forecast demand is systematically above or below the actual demand. If the Tracking Signal for Demand Forecast check box is selected, the smoothing factor for the demand is dependent on the forecast error. If the tracking signal is greater than the value of the Critical Tracking Signal field, LN makes the smoothing factor for the demand equal to the tracking signal.
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