HY公司在实际库存管理过程中面临较为突出的缺料与呆料问题。一方面，关键物料缺料频发，导致生产线中断、交付延迟，影响客户满意度与订单履约；另一方面，大量物料长期未使用形成呆滞库存，占用仓储空间与资金资源，降低库存周转率。现有库存管理体系响应滞后、分类粗放，缺乏针对性的预警机制与动态管理工具，难以适应市场波动和供应链变化的需求，库存效率亟待提升。 为解决上述问题，研究基于实地访谈与业务数据，系统分析了HY公司库存管理现状，识别出需求预测失准、生产计划频繁变更、供应链联动滞后等核心成因，并以此为基础构建了缺料与呆料双预警机制。缺料预警机制中，采用基于“物料价值-周转率”的二维ABC分类模型，实现库存精细化管理；引入VMI模式优化补货响应路径，并结合指数平滑法提升需求预测的时效性和准确性。呆料预警机制方面，构建以存货周转率、库存占比、入库时间等为核心的预警指标体系，并结合ERP系统实现实时监测与动态阈值调整，确保不同类别物料的精准识别和有效处置。两套机制均设计了从数据监测、风险识别、预警触发到响应闭环的全流程预警路径，形成库存动态管控能力。 本文不仅为HY公司建立库存预警机制提供了可行的理论支撑和实践路径，也为制造企业提升库存管理数字化水平与供应链协同效率提供了参考范式。在库存管理向智能化、柔性化转型的背景下，该研究对同类型企业提升库存决策科学性与资源配置效率具有一定推广价值。

In the process of actual inventory management, HY company faces prominent problems of material shortage and stupedness. On the one hand, the frequent shortage of key materials leads to the interruption of production line and delivery delay, which affects customer satisfaction and order fulfillment. On the other hand, a large number of materials are not used for a long time to form inert inventory, which takes up storage space and capital resources and reduces inventory turnover. The existing inventory management system has lagged in response, extensive classification, lack of targeted early warning mechanism and dynamic management tools, which is difficult to adapt to the needs of market fluctuations and supply chain changes, and inventory efficiency needs to be improved urgently.

In order to solve the above problems, based on field interviews and business data, the research systematically analyzed the current situation of inventory management of HY Company, identified the core causes such as inaccurate demand forecast, frequent production plan changes, and lag of supply chain linkage, and constructed the double early warning mechanism of missing materials and frozen materials on this basis. In the early warning mechanism of material shortage, a two-dimensional ABC classification model based on "material value-turnover rate" was used to realize fine inventory management. The VMI model was introduced to optimize the replying response path, and the exponential smoothing method was combined to improve the timeliness and accuracy of demand forecasting. In terms of early warning mechanism, an early warning index system with inventory turnover rate, inventory proportion and warehousing time as the core was constructed, and real-time monitoring and dynamic threshold adjustment were realized combined with the ERP system to ensure the accurate identification and effective disposal of different types of materials. Both mechanisms are designed with a full-process early-warning path from data monitoring, risk identification, early-warning trigger to closed-loop response, forming a dynamic inventory control capability.

This study not only provides a feasible theoretical support and practical path for HY company to establish an inventory early warning mechanism, but also provides a reference paradigm for manufacturing enterprises to improve the digital level of inventory management and supply chain synergy efficiency. Under the background of the transformation of inventory management to intelligent and flexible, this study has certain promotion value for similar enterprises to improve the scientific inventory decision-making and resource allocation efficiency.

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