AI-Enabled Supply Chain: Theoretical Logic and Practical Pathways of Technological Reconfiguration and Value Creation

This study takes Huawei's supply chain as a case to reveal the mechanism of AI-driven supply chain reconfiguration based on multidisciplinary theories. Huawei has achieved breakthroughs such as reducing demand forecasting error rate to 8%, optimizing inventory turnover ratio by 25%, etc., through cognitive network architecture and multi-technology integration. Technologies such as spatiotemporal graph neural networks and adversarial learning have demon-strated significant effectiveness in demand perception and resilient supply chain construction. Reparameterization of production functions has reduced capacity costs by 22%, while hyper-heuristic algorithms have optimized logis-tics costs by 15%. The research proposes innovative frameworks such as data middleware platform and double-loop learning, where knowledge graphs have shortened project cy-cles by 40% and smart contracts have reduced transaction costs by 70%. At the governance level, a transparency framework has been established, federated learning balances data sovereignty, and ESG embedding has achieved a compli-ance rate of 95%. In the future, quantum machine learning may break through real-time optimization bottlenecks, with the synergy between humanistic AI and ethical governance being key to development. This study provides theoreti-cal insights for intelligent supply chain research and practical references for corporate transformation.