Supply Chain Management with APO

4.4 Statistical Forecasting 57
The fixed key figure does not need to be assigned explicitely to the planning
area. The notes 409181, 410680, 643517, 681451 and 687074 provide additional
information on fixing values.
It is not necessary any more to have a persistent aggregate to fix key
figure values on aggregated level. With this solution the fixing information
on aggregated level exists only at the detailed level, and the fixed values of
the underlying details are aggregated and are displayed as fixed values at
aggregate level.
4.4 Statistical Forecasting
4.4.1 Basics of Forecasting
Statistical forecasting is usually applied to forecast sales quantities. There
are two major groups of statistical methods, one is the univariate methods,
where a key figure is forecasted from its past values, and the other group is
causal models, where a key figure is forecasted according to the history of
other key figures (also referred to as ‘causal factors’). For the latter the
most common method is multiple linear regression (MLR).
The quality of the statistical forecast depends on the successful approximation
of inherent regularities. One basic fact of statistics is that these
regularities are analysed and predicted the better the larger the number of
data is. At the determination of the forecast level the trade off between
increasing the data amount by using a higher aggregation level (e.g. product
group instead of product) and losing information due to an inadequate
disaggregation has to be considered. A common scenario is to carry out the
forecast on the more detailed level (e.g. product level) for the short term
horizon, when the accuracy of the operative data has priority, and to switch
to forecasting on an aggregated level for longer horizons. This of course
depends on what the data is used for. The logical prerequisite to forecast
on aggregated level is that the history of each product is similar.
A further issue is the selection of the data basis. A typical question is
whether to use third party sales or a mix of third party and inter-company
sales for forecasting. To avoid a distortion of the inherent regularities of
the sales data by local inventory policies and lot sizes, only third party
sales should be used as data history whenever possible.
Another crucial factor is the application of the appropriate forecast
model, which is able to model these regularities. Therefore an analysis of
the data history has a significant impact on the accuracy of the statistical
forecast. In some cases the data history is already examined and appropriate