Prof. Dr. Stefan Minner
TUM School of Management, Technical University of Munich
Stefan Minner is a Full Professor for Logistics and Supply Chain Management at the School of Management, Technical University of Munich (TUM) and a core-member of the Munich Data Science Institute (MDSI). Currently, he is the acting Vice Dean of Research and Innovation at TUM School of Management. His research interests using methods of operations research, artificial intelligence and machine learning are in global supply chain design, transportation optimization and inventory management. His work was published in many peer-reviewed journals, including Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management, Transportation Science, Transportation Research Part B, and European Journal of Operational Research. He serves on several editorial boards of logistics and operations research journals. Currently, Stefan Minner is the Editor-in-Chief of the International Journal of Production Economics. Stefan Minner is a fellow of the International Society for Inventory Research (ISIR) and the International Foundation of Production Research (IFPR). He received the PhD supervisory award at TUM School of Management in 2024 and the Science Award for his lifetime achievements by the German Operations Research Society in 2025.
Keynote
Circular Intelligence – Business models, data-driven optimization, and coordination
Merging two popular streams of industry challenges and their research areas, artificial intelligence and circular economy, brings new opportunities for production economics. The additional complexity when moving from linear to circular business models, the multi-sourcing nature of repair, refurbish, and remanufacturing strategies, and the increased variability and uncertainty in circular supply chains require adjustments of traditional stochastic and data-driven decision models, and can use artificial intelligence for complexity reduction, advanced learning, and automation of decision-making. We present different data-driven prediction and prescription models and respective solution algorithms to support circular intelligence.