Data Science and Machine Learning
Focus on developing decision-support methods that transform supply chain data into quantitative models and analytical intelligence. This includes approaches for diagnosing operational challenges, forecasting future supply chain dynamics, identifying emerging risks and developing data-driven mitigation strategies, and supporting effective operational and strategic decision-making through predictive and prescriptive analytics.
Simulation
Focus on developing simulation-based methods for analysing complex supply chain operations under dynamic and uncertain conditions. By modelling material and information flows, resource constraints, and decision rules, simulation enables the evaluation of inventory management policies, capacity planning approaches, warehouse and logistics operations, and supply chain network configurations. This research supports the quantitative assessment of performance, resilience, and the effectiveness of alternative planning and optimisation strategies.
Data Governance for Sustainable Supply Chains
Focus on developing knowledge on how data governance frameworks, data quality, and digital technologies enable sustainable supply chains. Research investigates the full data journey across supply networks, examining how data is generated, shared, governed, and transformed into decision support. Particular attention is given to how reliable and transparent data enables more accurate quantitative analyses, supports sustainability monitoring and due diligence, strengthens risk management capabilities, and improves coordination across multi-tier supply chain networks.