Generative AI-Driven Optimization of Intelligent Supply Chain Decision-Making: Mechanisms and Applications

Abstract

Modern supply chains face mounting complexity due to volatility in demand, frequent disruptions, and fragmented data systems. Conventional decision-making approaches, which are often rule based, siloed, and reactive, lack the adaptability required in today’s dynamic environment. Generative artificial intelligence (GenAI) offers a transformative solution by integrating heterogeneous data, generating realistic counterfactual scenarios, and enabling continuous, context-aware optimization. This paper outlines how GenAI enhances supply chain intelligence through four key mechanisms: probabilistic forecasting under uncertainty, proactive risk simulation, adaptive resource allocation using reinforcement learning, and automated generation of strategic plans from both structured and unstructured data. Real-world applications span procurement (e.g., resilient supplier selection), warehouse management (demand-responsive inventory planning), logistics (dynamic routing), and after-sales service (predictive maintenance). Looking forward, the convergence of GenAI with digital twins and multi-enterprise collaboration platforms will enable more autonomous, transparent, and resilient operations. However, effective deployment requires human-AI governance that ensures explainability, ethical alignment, and strategic oversight. By shifting supply chains from reactive problem-solving to anticipatory intelligence, GenAI paves the way for next-generation operational excellence.