Order forecasts 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.

Are you interested in your own use cases?


An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.


There will be a interactive and Flexible application, including of different maps with two different views implemented.


Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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