The basic ingredient of any demand plan is a statistical forecast.
Statistical models and resulting forecasts are the building blocks
of the planning process.
Although consensus and collaboration are key ingredients of a
successful demand management program, statistical forecasting is the
first-step to create the baseline plan. To this end, a good
process and software technologies become important. One of the
key things you look for when you prepare a Request for Proposal
(RFP) is to ensure that you cover all of the modeling algorithms and
techniques which are relevant for your process. This depends
on your industry and your specific business model.
Forecasting techniques can be broadly classified as:
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Time Series Forecasting models
consisting of exponential smoothing, Holt-Winters Multiplicative
Smoothing, ARIMA models and Box-Jenkins Models, Logarithmic
regression models.
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Promotional Planning
Models that typically use event modeling methodologies and
indicator variable models.
-
Causal models that include a
variety of Multiple Linear Regression Models and transfer
function models.
-
Probabilistic
Models that often forecast the probability of a particular event
happening in the future and these include Logit, Probit and
Tobit models.
-
Croston's
Models to forecast intermittent demand. Here is a link to
a
semi-technical explanation of Croston's Method.
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