Order forecast for spare parts orders

Using machine learning techniques, a proof of concept was carried out for the creation of order forecasts for volume and express orders of several warehouse areas.

Successful proof of concept and foundation stone for further analyses within 8 weeks

Preparation of 7 different data sources

Calculation of over 20 individual models


Forecast accuracies up to 91%


For a logistics company, it is of interest to be able to predict the spare parts orders for volume and express orders in the near future for better control.
A detailed order forecast is to be used, among other things, to derive the personnel requirements.


Data selection, exploration and preparation of 7 different data sources. Creation of meaningful influencing variables (features) to predict spare parts orders. Calculation of a GBM for each storage area for express orders and a GBM for volume orders.
Produce forecasts on a daily basis for 20 days as well as on time points for 2 days in advance.


Expansion of the forecasts in terms of quality and granularity. Consideration of important influencing variables (e.g. public holidays). Derivation of staffing requirements possible on the basis of the forecasts.

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