Every day, managers in operations and supply chain management struggle with two issues: their world is highly uncertain – more so in the most recent future, and they are overwhelmed by an abundance of data that may or may not be relevant to their decisions. Our team develops and analyzes new approaches and tools to convert supply chain data into better decisions in an uncertain (supply chain) world.
In today’s world, many decisions in operations and supply chain management 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 access to abundant data that may help to take better supply chain decisions under uncertainty. We develop new methodology to use supply chain-related data to make better decisions in inventory, capacity and transportation management, e.g., by identifying patterns, understanding relationships between different uncertain parameters and their drivers, and, more importantly: by incorporating this knowledge into new models and tools.

In our research, we combine techniques from AI/Machine Learning and mathematical optimization to develop new “end-to-end“ methods and tools that exploit rich data sets in order to improve and automate decision making in operations and supply chain management. These end-to-end models require a minimum of human intervention, as they learn to prescribe good decisions directly from the available data. 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.