Generative AI in Supply Chains: Utilising Opportunities, Managing Risks

Global supply chains are under pressure. Generative artificial intelligence accelerates planning, improves forecasting and facilitates coordination. Companies are realising initial efficiency gains, but at the same time dependencies on platforms, data access and proprietary models are growing. The pharmaceutical industry is characterised by opportunities and risks, from which forecasts are derived. Existing regulatory fragmentation can lead to gaps in corporate governance. In addition, data sets and simulation-based approaches prove that efficiency and vulnerability indicators can be empirically measured and support the diagnosis of this work. The study develops an analytical model that captures efficiency and vulnerability in a common logic. It is conceptually and theoretically based and combines literature analyses with scenario-based stress tests. The results illustrate three mechanisms for improving performance. Information processing becomes more precise, routine decisions are relieved and coordination along complex networks is accelerated. In contrast, three areas of structural risks emerge. Platform concentration shifts power, model-related lock-ins make adaptation more difficult and governance gaps increase information asymmetries. The central diagnosis is clear. Efficiency gains without reconfigurability turn speed into fragility. In the long term, competitive advantages arise from the implementation of open architectures that enable networking between institutions. Keywords: supply chain, operations management, resilience, resource-based view, dynamic capabilities, governance, platform power, interoperability, data portability, lock-in, stress test, Generative AI,