Abstract: Effective supply chain management in retail enterprises entails complex challenges. Among these, proficient inventory management is crucial—not only does it reduce inventory costs and meet customer demands, but also it significantly enhances the efficiency of collaborative management across the supply chain. At the heart of inventory management lies the process of inventory replenishment, which requires a detailed understanding and precise prediction of future product demand. This process must consider various factors, including current inventory levels, order volumes, minimum order requirements and so on. There are problems with traditional manual replenishment methods while widely used, when applied on a large scale. They are susceptible to the influence of personal bias and preference, leading to suboptimal replenishment decisions.
This study presents an intelligent replenishment system that employs statistical and optimization models. It consists of two core components: a demand prediction module and a proactive replenishment module. The demand prediction model incorporates long-term historical sales data to account for irregular demand fluctuations, forecasting future demands through the sales distribution patterns to stabilize inventory levels. We outline two distinct forecasting strategies to support varying replenishment approaches. An aggressive strategy prioritizes demand fulfillment, while the conservative strategy considers the risk of inventory excess. The choice of strategy is user-dependent, allowing for adaptability to the specific conditions of each product.
On this foundation, our study introduces a proactive replenishment optimization model that handles multiple replenishment constraints, ranging from demand constraints to limits on replenishment amounts, quantities, and units. The model aims to achieve dual optimization goals: minimizing replenishment costs and maximizing inventory turnover rate. The relative importance of these goals is adjustable through the assignment of different weights, allowing for a balanced approach to evaluation. To solve the associated integer nonlinear programming challenge effectively, we design a bespoke heuristic algorithm.
The effectiveness of this system is evaluated through simulations and practical trials at the PetroChina Group gas station convenience stores. We test various combinations of demand forecasting and proactive replenishment strategies to facilitate replenishment decisions. These tests allow us to monitor and analyze dynamic shifts in inventory and sales at both the individual store and central warehouse levels over the trial period. Subsequently, we assess key performance indicators, including demand fulfillment rates and inventory turnover rates. The findings suggest that the intelligent replenishment system proficiently meets targeted demand fulfillment objectives while effectively controlling inventory costs. Notably, the system exhibits a low dependence on initial inventory levels and demonstrates the capacity of compensating for initial inventory variances over successive replenishment cycles.
In conclusion, this study offers an advanced framework for decision-making in supply chain management, leveraging operations research optimization models. It facilitates precise demand predictions and intelligent replenishment decisions, reducing the risk of inventory surplus, cutting costs, and enhancing resource utilization efficiency, thereby improving the overall operational performance of the supply chain. Moreover, the study highlights the importance of data-driven technologies in modern business and logistics, emphasizing the critical role of statistical modeling and optimization methods in complex decision-making scenarios.

Key words: chain retailer, supply chain, intelligent replenishment, demand prediction, inventory management, integer nonlinear programming

摘要： 供应链补货任务是连锁零售企业管理中至关重要的一环,需要对未来一段时间商品需求有较为精准的把握和预测,且同时考虑剩余库存、订货单位、起订金额等多重制约因素。传统的人工补货方法在处理大规模商品时存在困难,且容易受到个体主观因素和偏好的干扰,难以实现精准的补货决策。为提高供应链管理效率,本文应用统计和优化模型开发了智能补货系统,包括需求预测模块和主动补货模块。考虑到历史销售数据的不规律波动性,本文从需求分布推断的角度来进行需求预测,能帮助构建合理的商品库存区间。在此基础上,本文构建了主动补货优化模型,综合考虑了多方面补货约束,以最小化补货成本和最大化库存周转率为优化目标,进而设计了相应的启发式算法来高效解决这一整数非线性规划问题。为验证智能补货系统的有效性,进行了仿真模拟测试以及在中石油加油站便利店实际试运行,结果表明该系统能够达到预设的需求满足率,同时有效控制库存成本。本研究应用运筹优化模型提出的智能补货决策系统有助于企业实现准确的商品需求预测和补货决策,避免库存积压和滞销,降低库存和人力成本,提高资源利用效率,从而显著提升整个供应链系统的运营效率。

关键词: 连锁零售企业, 供应链, 智能补货, 需求预测, 库存管理, 整数非线性规划