Abstract
Material flows in logistics and manufacturing become increasingly dynamic and flexible and at the same time those systems grow with increasing number of goods and partners. Existing forecasting solutions require detailed information to create an appropriate prediction about future states of the system. Therefore, we propose a new approach using the Stochastic Block Model (SBM), which needs only the aggregated representation of the considered system as a material flow network. Based on an inferred clustering of the investigated material flow network, we are able to predict the usage of specific paths and the system as a whole.
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