AI驱动下电商双渠道供应链冲突协调机制与优化策略研究

Coordination Mechanisms and Optimization Strategies for Dual-Channel E-Commerce Supply Chain Conflicts Driven by AI

摘要: 随着电商平台与线下渠道深度融合，双渠道供应链中价格冲突与库存错配问题日益突出，影响渠道协同效率与整体收益。为此，本文以电商平台与线下经销商构成的双渠道供应链为研究对象，构建AI驱动下的Stackelberg主从博弈模型，设置电商平台为主导方，经销商为跟随方，引入AI需求预测精度参数，刻画价格决策与库存协同过程，并对模型进行均衡分析。在此基础上设计AI赋能的价格协调机制、库存协同机制以及流量与服务协同策略。研究结果表明，AI技术通过提升需求预测精度、优化动态定价能力并强化库存信息共享，能够有效降低渠道间价格竞争强度，缓解库存错配问题，提升线上与线下需求规模，同时提高平台与经销商收益，实现双渠道供应链的协同优化。

Abstract: As e-commerce platforms and offline channels undergo deep integration, issues regarding price conflicts and inventory mismatches within dual-channel supply chains have become increasingly prominent, thereby undermining channel coordination efficiency and overall profitability. To address this, this paper focuses on a dual-channel supply chain comprising an e-commerce platform and an offline distributor. We construct an AI-driven Stackelberg leader-follower game model, designating the e-commerce platform as the leader and the distributor as the follower, and introduce a parameter for AI-driven demand forecasting accuracy to characterize the processes of pricing decisions and inventory coordination. Subsequently, we conduct an equilibrium analysis of the model. Building upon this foundation, we design AI-enabled mechanisms for price coordination and inventory collaboration, as well as strategies for coordinating traffic and service provision. The research findings indicate that AI—by enhancing demand forecasting accuracy, optimizing dynamic pricing capabilities, and strengthening inventory information sharing—can effectively mitigate the intensity of inter-channel price competition, alleviate inventory mismatches, and boost demand volumes across both online and offline channels, while simultaneously increasing the profits of both the platform and the distributor, thereby achieving the synergistic optimization of the dual-channel supply chain.