Fynn Oldenburg is a PhD candidate at the Kühne Logistics University (KLU), Department of Operations & Technology, since May 2023 under the supervision of Prof. Dr. Kai Hoberg and Prof. Dr. Henrik Leopold. His research explores the application of process intelligence in supply chain analytics in collaboration with Celonis. In particular, he investigates how upstream disruptions amplify in impact and propagate downstream across supply chain actors, focusing on impact measurement, visualization, and predictive modeling.
Fynn holds a Master of Science in Business Analytics from Nova School of Business and Economics in Lisbon and a Bachelor of Science in Business Administration with a specialization in Supply Chain Management from KLU. His research has been published in the International Journal of Production Research and the proceedings of the IEEE International Conference on Data Science and Advanced Analytics. He has presented his work at leading international conferences, including the AAAI Conference on Artificial Intelligence, the Production and Operations Management Society (POMS) Annual Meeting, and the Celosphere Practitioner Conference. Furthermore, Fynn gained practical experience during work placements in product management at Celonis, technical consulting at Deloitte and research associate positions at NOVA SBE and KLU.
Contact
Networks
Education
| Since 2023 | PhD candidate at Kühne Logistics University, Hamburg, Germany |
| 2021 - 2023 | Master of Science in Business Analytics, Nova School of Business and Economics, Lisbon, Portugal |
| 2019 - 2019 | Exchange Semester, Heriot-Watt University, Edinburgh, United Kingdom |
| 2017 - 2020 | Bachelor of Science in Business Administration, Kühne Logistics University, Hamburg, Germany |
Professional Experience
| 2023 - 2023 | Research Associate, Kühne Logistics University, Hamburg, Germany |
| 2021 - 2022 | Data Research Scientist, Nova School of Business & Economics, Lisbon, Portugal |
| 2019 - 2021 | Internship & Working Student, Deloitte Germany GmbH, Hamburg & Munich, Germany |
| 2018 - 2018 | Working Student Business Development, Cargonexx GmbH, Hamburg, Germany |
Publications
Recent advances in process mining technology have extended its applicability beyond traditional domains such as healthcare, finance and manufacturing, making it increasingly relevant for addressing problems in supply chain management. In our research, we explore the integration of process mining techniques within the domain of supply chain management, focusing on uncovering inefficiencies, ensuring compliance, and identifying opportunities for improvement. Therefore, we first review the technological advances in process mining relevant to supply chain management and outline six relevant approaches. We then combine the identified methodologies with expert interviews to derive and validate six specific use cases where process mining can significantly contribute to supply chain efficiency and resilience. The paper presents a detailed description of these use cases, demonstrating how process mining can provide actionable insights for a wide range of supply chain
problems. We discuss the implications of our findings for practitioners, who benefit from enhanced visibility and optimisation opportunities, and researchers, who are provided with a roadmap for exploration of this promising interdisciplinary field. To the best of our knowledge, this is the first work to explore potential use cases for process mining in a supply chain context, providing a comprehensive perspective on the potential benefits and challenges.
