Professor Wu’s research has focused on quantitative approaches to risk analysis, supply chain optimisation, and decision support under uncertainty, with particular emphasis on trade credit, service recommendation, lead-time planning, and the integration of network analysis with service-location optimisation. By combining operations research, game theory, machine learning, and computational methods, he has developed analytical frameworks for examining asymmetric information in supply chains, optimising planned lead times in multi-level production systems, and designing intelligent recommendation models for digital and online-to-offline services. He has authored 150 peer-reviewed publications, with an h-index of 44 and more than 6,600 citations. His selected works address trade-credit models under asymmetric competition and information, customer behaviour and market information in trade-credit systems, time-aware cloud-service recommendation using similarity-enhanced collaborative filtering, planned lead-time optimisation for multi-level assembly systems under uncertainty, and recommendation models for online-to-offline services based on customer networks and service locations. Collectively, these contributions reflect a sustained research programme connecting rigorous quantitative analysis with the practical challenges of supply chain management, digital service platforms, risk-informed decision-making, and resilient business operations.