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Forecast Management

 

PART II. 

 

Many factors influence a forecast's accuracy and, once identified, they should be used to adjust the forecast. The factors outside the control of a company include: the economy, competitive activity and circumstances of nature. In addition, there are activities within a company that will affect the sales of a product, including promotions and sales of other related products. What is needed and, fortunately, readily available is a method of enhancing the present mathematical forecast to include some adjustment for known inside and outside influences. This enhancement is reached by adjusting the seasonal indices in a simple exponential smoothing model.

Figure 1 represents a sales pattern that displays three fundamental patterns. These are: a base (or magnitude), a regular variation (or seasonality) and a trend. The dotted lines represent a forecast that may have been developed using the historical data. It claims that what happened in the past will happen in the future since the pattern is a virtual repetition of the solid line pattern.

The formulas for the forecast are as follows:

/ActualDemand,\

Base Value, = ( ————————— )+(l-ot)*(Base Value,, i + Trend, i) v Oldlndexs /

Trend, = (3 (Base Value, - Base Value ,., ) + (1-0) * (Trend

profile, i.e., it will always react to any change after the change has occurred. This is shown by the dotted line, which does not rise fast enough during the beginning of the promotion and predicts higher sales after the promotion is over. Increased backorders during the promotion and excess inventories after its close can result.

 

/ActualDemand,\

Seasonal Index s = Y (————————-) + U-Y) * (Old Indexs) v Base Value, '

Forecast t+x = (Base Value, -t- X * Trend ,) * Index ,+x-m

where

a = Smoothing Constant for the Base

P = Smoothing Constant for the Trend

Y = Smoothing Constant for the Seasonality

Indexs = Seasonal Index for the Seasons

Trend = Trend estimate per period calculated at the end of period t

X = Number of periods beyond period t for which the forecast is desired

m = The number of periods in the seasonal cycle, (i.e., 12 months per year)

The seasonal index used in this method adjusts the trend-enhanced forecast with a multiplier that is based on an average month (i.e., an index of 1 is an average sales month and an index of 1.2 indicates a month in which sales are 20% above average).

To be Continued


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