Master Thesis Student Supply Chain Science (m/w/d)

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Our 4flow research team conducts exciting research projects, develops innovative digitalization solutions and creates market-relevant studies in the field of supply chain management and logistics. Support us full-time as a Master's student as part of your thesis project! What your new challenge will look like Support the 4flow research team in application-oriented studies. Work on scientific questions from data preparation and analysis to modeling, evaluation, and interpretation. Always in close collaboration with our experienced logistics researchers. Process data from various sources and expert interviews and integrate them into a scalable analysis pipeline. Implement models in Python ( Codex ) and evaluate their quality using established metrics. Collaborate closely with our data scientists and logistics researchers, exchange ideas regularly and ensure a smooth knowledge transfer within the team. Current thesis topics include: Data-Driven Transparency for Global Supply Chains The true origin of materials, intermediates and even consumer products is often unknown, as re-export and processing across multiple stages obscure where a material actually comes from. This lack of transparency makes it hard to understand dependencies of companies, industries and entire economies, and to react appropriately when the true source is unknown. The aim of the research is to develop a data-driven framework that estimates the true origin of materials and intermediates from publicly available data, combining the research, integration and processing of heterogeneous public sources, with the resulting model validated empirically using real-world data. In addition to static estimation, the identified origin and processing nodes provide a foundation for monitoring supply risks. This involves mapping public event signals, such as those from news streams, fire detection data or weather feeds, onto the estimated node distribution. Probabilistic Transport Corridor Analysis for Resilient Response Strategies Visibility into global supply chains remains limited beyond direct suppliers, even when the origin of a material is known. Therefore, it often remains unclear which routes , hubs, and chokepoints it passes through on its way to the destination. Without this insight, dependencies stay hidden, and informed responses are difficult where no direct visibility exists. The aim of the research is to develop a path inference engine that estimates likely transport routes from publicly available data, exploring methods such as probabilistic geographic routing and link prediction, to reconstruct plausible paths and supply structures with explicitly stated uncertainty. The reconstructed corridor topology also enables ongoing observations, such as the detection of chokepoint congestion or passage disruptions, by combining the estimated route structure with maritime traffic signals . The precise focus of the work w ill be sp ecified together with the candidate. Why you belong at 4flow You're studying logistics , business informatics, business mathematics, or a comparable field in a Master's program with very good performance. You want to support us for 5-6 months as a Master's student. You have solid programming skills, e.g., in Python or R. You've already gained some practical experience in logistics or supply chain management. You're interested in logistics-related questions and enjoy scientific work. You have very strong English skills . German is a plus .

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