平衡需求与供应:快速消费品的库存分配
摘要: 本研究深入探讨了快速消费品 (FMCG) 行业供应链管理的关键环节——最优库存配置。FMCG 企业面临着满足动态客户需求的同时,最大限度地降低过剩库存成本的挑战。本摘要重点介绍了成功的 FMCG 企业在客户满意度和高效库存管理之间取得平衡的关键策略。此外,对于季节性产品,量身定制的库存配置方法至关重要,它使企业能够调整库存水平以适应高峰需求并防止库存过剩。先进的库存管理软件的集成有助于实时跟踪、分析和决策,从而简化库存配置流程。通过实施这些策略并根据实际数据不断改进,FMCG 企业可以实现最优库存配置,从而提高客户满意度、降低成本并全面提升供应链效率。本研究全面概述了库存优化技术,旨在帮助 FMCG 企业应对复杂的库存管理,并在快速发展的市场中保持竞争力。
Abstract: This study delves into the critical aspect of supply chain management—optimal inventory allocation—for the fast-moving consumer goods (FMCG) industry. FMCG companies face the challenge of meeting dynamic customer demands while minimizing excess inventory costs. This abstract highlights key strategies employed by successful FMCG businesses to strike a balance between customer satisfaction and efficient inventory management. Moreover, for seasonal products, a tailored approach to inventory allocation is vital, allowing companies to adjust stock levels to match peak demand and prevent overstocking. The integration of advanced inventory management software facilitates real- time tracking, analysis, and decision-making, streamlining the inventory allocation process. By implementing these strategies and continually refining them based on real-world data, FMCG businesses can achieve optimal inventory allocation, leading to improved customer satisfaction, reduced costs, and an overall boost in supply chain efficiency. This study offers a comprehensive overview of inventory optimization techniques, aiming to assist FMCG companies in navigating the complexities of inventory management and remaining competitive in a fast-paced market.
文章引用:郑丽. 平衡需求与供应:快速消费品的库存分配[J]. 经济管理与实践, 2025, 3(2): 1-5.
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参考文献

[1] Huang T, Van Mieghem J A. Clickstream Data and Inventory Management: Model and Empirical Analysis. Prod Oper Manag, 2014, 23: 333-347. DOI: 10. 1111/poms. 12046.

[2] Williams, B D, Tokar T. A review of inventory management research in major logistics journals: Themes and future directions, The International Journal of Logistics Management, 2008, 19(2): 212-232.

[3] Nemtajela N, Mbohwa C. Inventory management models and their effects on uncertain demand, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 2016: 1046-1049.

[4] Nemtajela N, Charles Mbohwa C. Relationship between Inventory Management and Uncertain Demand for Fast Moving Consumer Goods Organisations, Procedia Manufacturing, 2017, 8: 699-706.

[5] Wang C H, Chen T Y. Combining biased regression with machine learning to conduct supply chain forecasting and analytics for printing circuit board. International Journal of Systems Science: Operations & Logistics, 2022, 9(2): 143-154.

[6] Cao J, Jiang Z, Wang K. Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm. Engineering Optimization, 2017, 49: 7: 1197-1210.

[7] Ghods L Kalantar M. Methods for long-term electric load demand forecasting; a comprehensive investigation, IEEE International Conference on Industrial Technology, Chengdu, China, 2008: 1-4.

[8] Anna-Lena B, Minner S. Safety stock planning under causal demand forecasting, International Journal of Production Economics, 2012, 140(2): 637-645.