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

Abstract

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 demonstrated 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 logistics costs by 15%.

The research proposes innovative frameworks such as data middleware platform and double-loop learning, where knowledge graphs have shortened project cycles 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 compliance 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 theoretical insights for intelligent supply chain research and practical references for corporate transformation.