Data Driven Operations Management

This course explores when and how the availability of large amounts of relevant data (“big data”) affects decision making in operations management. As such, the course combines elements of artificial intelligence (AI)/machine learning (ML) with traditional approaches in operations management. Thereby, it addresses the pertinent question of how new models in operations and supply chain management will evolve, or traditional models have to be modified, to leverage extensive auxiliary data. The course will first introduce participants to the most relevant AI/ML techniques for operations management. After an in-depth discussion of the traditional paradigm of operations management (“sequential estimation and optimization”), its critical assumptions and potential shortfalls, participants will experience and discuss how decision making in operations and supply chain management may change when “big data” is available. The latter will be based on a number of new and relevant publications in the field of data-driven operations management (see references below), as well as selected practical cases and datasets that are currently used in research projects of the organizers of the course. While the primary focus of the course lies on recent developments in operations and supply chain management, and not the development of hands-on skills in implementing ML techniques, the course will include a number of lab session to illustrate how “novel” models in data-driven operations management leveraging ML techniques can or should be implemented and evaluated. The course will also include contributions of international guest lecturers who will share their view of how data-driven operations management will evolve, and how it will shape the research agenda in the field.


October 4 - 7, 2022




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Dr. Richard Pibernik,
Professor of Logistics and Quantitative Methods,
Julius-Maximilians-Universität Würzburg, Germany

Dr. Christoph M. Flath,
Chair of Information Systems and Management,
Julius-Maximilians-Universität Würzburg, Germany

Dr. Nikolai Stein,
Julius-Maximilians-Universität Würzburg, Germany


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Registration Deadline: September 11, 2022