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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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