Chair of Logistics and Quantitative Methods


Data-driven Logistics and Supply Chain Management

In today’s world, many operational decisions (e.g., regarding capacity sizing and utilization, order quantities or job scheduling) have to be taken under conditions in which relevant planning data such as demand, order sizes, machine outages or maintenance needs are uncertain. With the rise of affordable sensors, smart production equipment, cheap storage for very detailed transactional data and tracking systems for data regarding customer behavior, e.g., from E-Commerce applications, organizations have started to collect and consolidate data sources that are related to their processes and their customers’ behavior. We develop new methodology to use this (potentially big) data to make better decisions regarding capacity, inventory levels and pricing, e.g., by identifying patterns, understanding relationships between different uncertain parameters and their drivers, and, more importantly: by incorporating this knowledge into existing and new models. In our research, we combine techniques from AI/Machine Learning and mathematical optimization to develop new methods and tools that exploit rich data sets in order to improve and automate decision making in logistics and supply chain management. Our research results contribute to the digital transformation of supply chains and we also study how this will impact the future of global supply chain operations and organizations.

Our research is motivated by specific problems that arise from our collaborations with industry. Members of the chair team have worked on research projects in the past with many well-known companies, including SAP, McKinsey & Co., Alcatel-Lucent, Lufthansa, Celanese, Deutsche Post DHL, and many more. Furthermore, a multitude of professional training programs have been held for companies (e.g., Henkel, Philipps, Porsche, Deutsche Bank). From these activities, long-term collaborations have often emerged as well as interesting topics for research and diverse practical examples and teaching applications. We enjoy working with companies – but only when we are faced with real challenges. We are not consultants and funding from company projects is used only for the good of our university.
More information about our collaborations with industry are available here.

Research Areas

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Previous Work