Artificial Intelligence-Driven Supply Chain Collaborative Optimization and Efficiency Improvement

Abstract

Supply chain collaboration has long relied on manual coordination and static planning, producing information lags and slow decision cycles that limit firm competitiveness in volatile markets. This study examines how artificial intelligence (AI) supports three interlocking mechanisms of collaboration. These are information sharing through big data platforms and blockchain ledgers, dynamic resource allocation through machine learning and optimization algorithms, and decision-making upgrades through multi-agent systems and digital twin environments. We demonstrate the framework through a 52-week evaluation on a three-echelon supply chain and a structured case analysis of JD.com drawing on published INFORMS Journal on Applied Analytics data. During demand-spike weeks, a feature-enhanced long short-term memory (LSTM) forecaster cuts mean absolute percentage error by 74 percent compared with Holt-Winters exponential smoothing. On this forecast base, an adaptive inventory policy lowers average stock by 28 percent at equal service level. JD.com’s reported 30.8-day inventory turnover provides industrial-scale corroboration. The study contributes a differentiated framework that positions AI mechanisms against three prior views, quantified simulation evidence, and bias-mitigation guidance grounded in concrete supply chain decisions.