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论文编号：	15446
作者编号：	2320224046
上传时间：	2025/6/11 16:55:47
中文题目：	大数据背景下C电商企业价值链成本管理优化研究
英文题目：	Research on Optimization of Value Chain Cost Management for E-commerce Enterprise C in the Context of Big Data
指导老师：	李姝
中文关键字：	成本管理；价值链；大数据；电商企业
英文关键字：	Cost Management; Value Chain; Big Data; Ecommerc e Enterpris
中文摘要：	随着数字经济时代的全面来临，电商企业依托大数据技术重构传统价值链成本管理模式已成为提升核心竞争力的关键路径。相比于传统的成本管理而言，基于价值链架构的成本管理则是通过解构采购、物流、仓储及销售等全周期运营节点，系统识别资源消耗轨迹与价值生成节点，进而锁定战略增值环节实施动态管控。本研究以C电商企业为案例，结合价值链理论与价值链成本管理理论，通过文献分析、案例对比及定量研究，探讨企业应用大数据技术优化成本管理的实践路径。研究发现，企业在采购、物流、仓储及营销等环节存在显著管理短板：采购环节因需求预测偏差导致库存积压，预付资金占用规模过大；物流运输与逆向退货环节成本控制粗放，缺乏精细化调度；仓储管理依赖传统人工模式，智能化水平不足；营销活动中过度依赖高投入推广，实体门店运营效率低下。 针对上述症结，研究提出全链路优化框架：第一、采购环节升级。建立数字化采购平台，实时跟踪市场动态和销售数据，加强与供应商的信息共享，利用数据分析预测商品需求量，同时制定清晰的岗位考核标准，避免责任推诿；第二、物流仓储提升效率。通过智能系统监控商品从入库到配送的全过程，整合运输路线和车辆资源，减少重复运输；根据消费区域分布调整仓库位置，优先覆盖高频配送地区，缩短送货距离；第三、实现技术与运营的优化。投入自动化设备提升仓储分拣效率，降低人工成本。在广告投放中分析用户偏好数据，精准匹配目标客群；结合线下人流热度和租金成本，科学选择门店位置，关闭低效门店集中资源。通过以上措施，企业可打通采购、物流、仓储、销售等环节的数据链条，减少库存积压和资源浪费，实现成本精细化管理，最终形成快速响应市场、高效控制成本的核心竞争力。
英文摘要：	With the comprehensive advent of the digital economy era, e-commerce enterprises relying on big data technology to reconstruct traditional value chain cost management models has become a key path to enhance their core competitiveness. Compared to traditional cost management, cost management based on value chain architecture deconstructs the full cycle operation nodes such as procurement, logistics, warehousing, and sales, systematically identifies resource consumption trajectories and value generation nodes, and then locks in strategic value-added links to implement dynamic control. This study takes C e-commerce enterprise as a case, combines value chain theory and value chain cost management theory, and explores the practical path of applying big data technology to optimize cost management through literature analysis, case comparison, and quantitative research. Research has found that there are significant management shortcomings in procurement, logistics, warehousing, and marketing for enterprises: inventory backlog in the procurement process due to demand forecasting deviations, and excessive use of prepaid funds; The cost control of logistics transportation and reverse returns is extensive, lacking refined scheduling; Warehouse management relies on traditional manual methods and lacks sufficient level of intelligence; Excessive reliance on high investment promotion in marketing activities results in low operational efficiency of physical stores. To address the above issues, a full chain optimization framework is proposed: firstly, upgrading the procurement process. Establish a digital procurement platform, track market dynamics and sales data in real-time, strengthen information sharing with suppliers, use data analysis to predict product demand, and develop clear job assessment standards to avoid responsibility shifting; Secondly, logistics and warehousing can improve efficiency by monitoring the entire process of goods from storage to distribution through intelligent systems, integrating transportation routes and vehicle resources, and reducing duplicate transportation; Adjust warehouse locations based on the distribution of consumption areas, prioritize coverage of high-frequency delivery areas, and shorten delivery distances; Thirdly, optimize technology and operations. Invest in automated equipment to improve warehouse sorting efficiency and reduce labor costs. Analyze user preference data in advertising placement and accurately match target customer groups; Based on the popularity of offline foot traffic and rental costs, scientifically select store locations and close inefficient stores to concentrate resources. Through the above measures, enterprises can connect the data chain of procurement, logistics, warehousing, sales and other links, reduce inventory backlog and resource waste, achieve refined cost management, and ultimately form a core competitiveness of rapid response to the market and efficient cost control.
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