AI与大数据在电商供应链优化中的应用效果及经济价值评估

Application Effects and Economic Value Evaluation of AI and Big Data in E-Commerce Supply Chain Optimization

摘要: 本文围绕“AI与大数据在电商供应链优化中的应用效果及经济价值”展开研究，以动态能力理论为支撑，构建“数据采集–智能分析–决策优化–价值落地”的供应链升级路径，并从技术适配性、数据安全合规、成本约束、行业适配差异四个维度刻画其边界条件。研究采用文献综述与机制分析相结合的方法，辅以典型场景(如智能库存管理、动态物流调度)进行理论映射与实践剖析。研究表明：数据整合深度与算法迭代效率是AI与大数据赋能企业动态能力构建的核心；需求预测精准度与资源配置效率的提升是应用效果的关键体现；数据安全合规、技术投入成本与行业特性适配度直接影响模式的可持续性。在此基础上，本文提出平台侧的数据治理与算法优化建议、企业侧的技术投入与人才储备策略、行业侧的标准共建与资源共享路径，为电商供应链借助AI与大数据构建动态能力、实现高质量发展提供理论框架与管理启示。

Abstract: This study focuses on the application effects and economic value of AI and big data in the optimization of e-commerce supply chains, with the dynamic capability theory as the theoretical support, constructing a supply chain upgrading path of “data collection—intelligent analysis—decision optimization—value implementation”, and delineating its boundary conditions from four dimensions: technical adaptability, data security and compliance, cost constraints, and industry adaptation differences. Methodologically, this study combines literature review with mechanism analysis, supplemented by typical scenarios (such as intelligent inventory management and dynamic logistics scheduling) for theoretical mapping and practical analysis. The findings indicate that the depth of data integration and the efficiency of algorithm iteration are the core for AI and big data to empower enterprises to build dynamic capabilities; the improvement of demand forecasting accuracy and resource allocation efficiency are the key manifestations of application effects; data security and compliance, technical investment costs, and industry characteristic adaptability directly affect the sustainability of the model. Based on these insights, this study proposes recommendations for data governance and algorithm optimization on the platform side, strategies for technical investment and talent reserve on the enterprise side, and paths for standard co-construction and resource sharing on the industry side, providing a theoretical framework and management implications for e-commerce supply chains to build dynamic capabilities and achieve high-quality development with the help of AI and big data, and puts forward 3 theoretical propositions for subsequent testing.