Resilient production planning in supply chain

Project coordination & management

Herr Prof. Dr. rer. pol. habil. Thorsten Claus
Herr Prof. Dr.-Ing. Frank Herrmann (Ostbayerische Technische Hochschule Regensburg)

Project implementation

Herr M.Sc. Chenghao Dai
Herr M.Sc. Maximilian Schön

Period:

01.05.2023 to 31.04.2026

Short description:

Global production and supply chains are increasingly exposed to disruptions, which lead to significant losses in delivery performance and due-date reliability, particularly in multi-site production networks. Since production planning and control (PPC) is hierarchically structured and capacity-oriented, disruptions do not remain local but propagate across multiple planning levels and production sites. Classical optimization approaches provide important decision support in this context; however, they are often limited to individual planning levels or static scenarios and therefore cannot adequately capture the dynamic interactions that arise under disruptive conditions.

The objective of this project is to develop a hierarchical PPC simulator for analyzing the delivery performance of multi-site production and supply chains under disruptions. The PPC simulator integrates optimization models across multiple planning levels—ranging from aggregate production planning to material and capacity planning and detailed scheduling—within a rolling simulation environment. This allows optimization-based planning decisions to be analyzed not in isolation, but with respect to their operational execution and dynamic feedback effects over time.

Within the PPC simulator, different disruption profiles, such as capacity reductions or supply quantity constraints, can be deliberately introduced at specific production sites or planning levels. The simulation enables the analysis of disruption propagation throughout the production and supply network and the evaluation of operational resilience measures—such as subcontracting or alternative capacity allocations—in interaction with hierarchical planning decisions. The proposed approach can be understood as the operational core of a digital supply chain twin.

Partial results of the project have already been published as peer-reviewed conference papers at the ECMS International Conference on Modelling and Simulation (38th and 39th ECMS).