Application of (s, S) inventory policy with backlog: a case study of chemical products

This research paper examines inventory optimization using the (s, S) backlog policy for chemical products in a company. The products were examimed under the condition of uncertain and fluctuating demand and lead time. The research presents a simulation utilizing Excel spreadsheets to optimize inventory levels by evaluating total annual costs based on sample demand and lead time data that were collected from historical data. The methodology involves multiple Excel sheets. The first generatessample demand and lead times using Excel functions; the second contains simulationparameters including review period, reorder points (s), order up to level (S), and unlimited warehouse capacity, along with cost components. The simulation applys a golden section search (GSS) method to search for the optimal values for s and S. The study examines two chemical products under different conditions: one with a fastmoving product with a short lead time, and another with a slow-moving product with longer and more variable lead time. The findings aim to identify optimal inventory levels, which is reorder points (s), order up to level (S), that minimize total annual cost. This is to enhance inventory management and cost savings. Through this detailed simulation approach, the research studies on inventory optimization under uncertainty, offering practical solutions for real world inventory management challenges.

本研究针对某企业的化工产品，采用(s, S)积压库存策略开展库存优化相关研究。本次研究针对需求与前置期均存在不确定性及波动性的场景，对相关化工产品展开分析。本研究借助Excel电子表格构建仿真模型，基于从历史数据中采集的样本需求与前置期数据，通过评估年度总成本来优化库存水平。该研究方法依托多张Excel工作表实现：第一张工作表通过Excel内置函数生成样本需求与前置期数据；第二张工作表则包含仿真参数，具体涵盖盘点周期、再订货点(s)、最高订货库存水平(S)、仓库容量无约束，以及各类成本构成项。该仿真采用黄金分割搜索(Golden Section Search, GSS)算法，求解可使年度总成本最低的最优s与S取值。本研究针对两类不同工况下的化工产品展开分析：一类为需求周转较快、前置期较短的产品，另一类为需求周转缓慢、前置期更长且波动性更强的产品。本研究的目标为确定可使年度总成本最低的最优库存参数，即再订货点(s)与最高订货库存水平(S)，以此提升库存管理效率并实现成本节约。通过本次精细化的仿真研究方法，本研究针对不确定性环境下的库存优化问题展开探讨，为现实场景中的库存管理难题提供切实可行的解决方案。