基于大数据分析的电商供应链韧性评价体系构建与优化策略

Construction and Optimization Strategy of E-Commerce Supply Chain Resilience Evaluation System Based on Big Data Analysis

摘要: 在数字经济与消费升级双重驱动下，电商供应链面临极端天气、地缘冲突、需求波动等多重不确定性冲击，传统以“效率为核心”的管理模式已难以应对风险挑战，构建科学的韧性评价体系成为电商企业可持续发展的关键。本文结合大数据技术的实时性、关联性与预测性优势，首先界定电商供应链韧性的核心维度，通过文献梳理与企业调研筛选出“抗扰能力、恢复速度、重构潜力、协同效率”四大一级指标及16项二级指标，运用层次分析法(AHP)与熵权法确定组合权重，形成多维度、可量化的韧性评价体系。其次，针对当前电商供应链存在的“数据孤岛导致风险预警滞后”“多级协同不足削弱恢复能力”“动态调整机制缺失限制重构效率”等实际问题，提出基于大数据的“实时监测–智能预警–协同响应–动态优化”全流程优化策略，并以京东物流、菜鸟网络等企业实践案例验证策略可行性。研究结果可为电商企业提升供应链风险应对能力提供理论支撑与实践参考，助力其在不确定性环境中实现韧性与效率的平衡。

Abstract: Driven by the dual impetus of the digital economy and consumption upgrading, e-commerce supply chains are exposed to multiple uncertain shocks, including extreme weather, geopolitical conflicts, and demand fluctuations. The traditional “efficiency-centered” management model can hardly address these risk challenges, making the construction of a scientific resilience evaluation system critical for the sustainable development of e-commerce enterprises. Leveraging the real-time, correlational, and predictive advantages of big data technology, this paper first defines the core dimensions of e-commerce supply chain resilience. Through literature review and enterprise research, four primary indicators—“Disturbance Resistance Capability, Recovery Speed, Reconstruction Potential, and Collaboration Efficiency”—as well as 16 secondary indicators are selected. Combined weights are determined using the Analytic Hierarchy Process (AHP) and entropy weight method, forming a multi-dimensional and quantifiable resilience evaluation system. Secondly, targeting practical problems in current e-commerce supply chains such as “delayed risk early warning caused by data silos”, “weakened recovery capacity due to insufficient multi-level collaboration”, and “limited reconstruction efficiency resulting from the lack of dynamic adjustment mechanisms”, a full-process optimization strategy of “real-time monitoring-intelligent early warning-collaborative response-dynamic optimization” based on big data is proposed. Practical cases of enterprises like JD Logistics and Cainiao Network are cited to verify the strategy’s feasibility. The research findings provide theoretical support and practical reference for e-commerce enterprises to enhance their supply chain risk response capabilities, helping them achieve a balance between resilience and efficiency in an uncertain environment.