Autor: Darja Wagner

The network analysis is a promising approach for assessing the behavior of material flow systems based on large data volumes. Previous studies focus mainly on static network analysis. This means that all the events that occur in a specific time period are aggregated to a single material flow network. As material flow systems are rapidly changing systems, a static view is not sufficient. The aim of this paper is to present existing concepts to obtain such structural changes and to assess their suitability for material flow networks by using real data.

The use of cyber-physical systems in production and logistics is an important component of Industry 4.0. The merger of production and logistics systems with information technology facilitates the transition from centralized to decentralized production planning and control. However, the need to equip logistic objects with the necessary technology is associated with high investment costs for production companies. This paper presents a method using dynamic clustering, which is suitable for identifying those parts of the production system, where an investment in technology for autonomous control leads to the largest increase in performance of the overall system.