Analyzing and Predicting Material Flow Networks Using Stochastic Block Models and Statistical Graph Embeddings

Abstract

Manufacturing and logistics systems consist of many complexly interacting elements. Starting from social science, the field of complex networks has developed concepts and methods to analyze and predict networks, such as friendship networks or protein interactions. However, although these examples have equivalents in the form of company networks and interactions within manufacturing processes, more sophisticated methods have not yet been transferred to manufacturing and logistics. We propose to apply methods from clustering and graph embedding on representations of machine interactions to analyze the structural stability of manufacturing systems and to predict structural changes of such systems.