基于人工智能预测性库存管理的电商平台运营效率变革与策略研究

Research on the Transformation and Strategy of E-Commerce Platform Operation Efficiency Based on AI Predictive Inventory Management

摘要: 随着电子商务进入高质量发展阶段，精细化运营成为平台提升竞争力的核心路径，而库存管理作为连接供应链与消费者的关键环节，其效率直接影响运营成本与客户体验。传统库存管理模式在应对海量SKU、需求波动剧烈等复杂场景时面临高库存与高缺货并存的困境。人工智能技术凭借其强大的数据处理与模式识别能力，推动预测性库存管理的兴起，通过融合多源内外部数据，构建高精度需求预测模型，实现库存的前瞻性布局与动态优化。本文系统探讨了人工智能在电商库存管理中的应用现状，分析其在降低库存成本、提升履约率与优化供应链协同方面的显著成效，同时指出在数据质量、模型可解释性、组织协同与环境适应性等方面面临的现实挑战，并提出强化数据治理、构建人机协同机制、推动供应链智能化演进等策略。研究表明，AI驱动的库存管理不仅是技术工具的升级，更是企业运营范式向数据驱动、系统协同与动态适应转型的重要体现。

Abstract: As e-commerce enters a high-quality development stage, refined operation has become the core path for platforms to enhance their competitiveness. Inventory management, as a key link connecting the supply chain and consumers, directly affects operational costs and customer experience. Traditional inventory management models face the dilemma of high inventory and high stockouts when dealing with complex scenarios such as a vast number of SKUs and sharp fluctuations in demand. With its powerful data processing and pattern recognition capabilities, artificial intelligence technology has driven the rise of predictive inventory management. By integrating multi-source internal and external data, it builds high-precision demand prediction models to achieve forward-looking inventory layout and dynamic optimization. This article systematically explores the current application status of artificial intelligence in e-commerce inventory management, analyzes its significant achievements in reducing inventory costs, improving fulfillment rates, and optimizing supply chain collaboration, and points out the practical challenges in data quality, model interpretability, organizational collaboration, and environmental adaptability. It also proposes strategies such as strengthening data governance, building human-machine collaboration mechanisms, and promoting the intelligent evolution of the supply chain. Research shows that AI-driven inventory management is not only an upgrade of technical tools but also an important manifestation of the transformation of enterprise operation paradigms towards data-driven, system collaboration, and dynamic adaptation.