摘要
电子商务的快速发展显著改变了传统商业模式，同时也对库存管理提出了更高要求。在这一背景下，本研究旨在探讨如何优化电子商务环境下的库存管理策略以提高运营效率和客户满意度。通过结合大数据分析、人工智能算法以及动态需求预测模型，本文提出了一种集成化的库存管理系统框架。该框架能够实时监测销售数据、物流状态及市场趋势，并据此调整库存水平，从而有效降低滞销风险和缺货概率。研究采用定量与定性相结合的方法，选取某知名电商平台作为案例进行实证分析，结果表明所提出的优化方法可显著提升库存周转率并减少成本支出。此外，本研究还创新性地引入了机器学习技术以改进需求预测精度，为库存决策提供了更加科学的依据。总体而言，本研究不仅丰富了电子商务领域中库存管理的理论体系，还为企业实践提供了切实可行的操作指南，具有重要的学术价值和应用意义。
关键词：电子商务；库存管理；大数据分析；机器学习；需求预测
Abstract
The rapid development of e-commerce has significantly transformed traditional business models and simultaneously imposed higher demands on inventory management. Against this backdrop, this study aims to explore the optimization of inventory management strategies in the e-commerce context to enhance operational efficiency and customer satisfaction. By integrating big data analytics, artificial intelligence algorithms, and dynamic demand forecasting models, this paper proposes an integrated inventory management system fr amework. This fr amework is capable of real-time monitoring of sales data, logistics status, and market trends, enabling adjustments to inventory levels accordingly, thereby effectively reducing the risks of overstocking and stockouts. The research employs a combination of quantitative and qualitative methods, selecting a well-known e-commerce platform as a case for empirical analysis. The results indicate that the proposed optimization approach significantly improves inventory turnover rates and reduces cost expenditures. Furthermore, this study innovatively incorporates machine learning techniques to enhance the accuracy of demand forecasting, providing a more scientific basis for inventory decision-making. Overall, this study not only enriches the theoretical fr amework of inventory management in the e-commerce domain but also offers practical operational guidelines for businesses, demonstrating significant academic value and application implications.
Keywords：ECommerce; Inventory Management; Big Data Analysis; Machine Learning; Demand Forecasting
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电子商务环境下库存管理的背景与意义 1
(二) 国内外研究现状分析 1
(三) 研究方法与技术路线 1
二、电子商务库存管理的关键挑战 2
(一) 需求预测的不确定性分析 2
(二) 多渠道销售对库存的影响 2
(三) 数据驱动的库存决策需求 3
三、优化模型与算法设计 4
(一) 库存优化的核心目标设定 4
(二) 基于大数据的预测模型构建 4
(三) 智能算法在库存管理中的应用 5
四、实证研究与案例分析 5
(一) 实证研究的设计与实施 5
(二) 典型电商企业的库存管理实践 6
(三) 优化效果评估与改进建议 6
结 论 8
参考文献 9
