This engineering education enables graduates to make data-driven decision-making to optimise supply chains, apply advanced analytics and machine learning techniques to enhance visibility and predictive capabilities, use simulation at different levels, and leverage technology to drive innovation and increase supply chain resilience.
The programme is designed to run in close collaboration with industry, exploring projects that reflect the current challenges within digitalisation and taking mostly a quantitative approach.
The center operates under three main pillars:
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Data science and machine learning research provide decision‑support tools that convert supply chain data into managerial insights, capable of diagnosing operational problems, predicting future system behavior, and prescribing effective business policies.
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Simulation research offers a formal framework for representing, analyzing, and evaluating supply chain systems under dynamic and uncertain conditions. By modelling material and information flows, resource constraints, and decision rules, simulation supports the systematic assessment of operational policies and system behavior over time.
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Sustainable supply chain management research investigates how digitalisation and end-to-end visibility enable more effective, sustainable, and resilient supply chain design and decision-making. We examine how multi-tier data governance and digital value chain strategies enable coordination, risk-based due diligence, and data-driven managerial action to advance sustainability across complex global supply chains.