Inventory management of storage: an optimization and sensitivity analysis

Effective inventory management plays a vital role in optimizing operational efficiency and ensuring cost competitiveness within storage environments. This study, titled “Inventory Management of Storage: An Optimization and Sensitivity Analysis Approach,” focuses on developing a mathematical optimization model designed to minimize the Total Annual Cost (TAC) associated with inventory decisions. The model integrates fundamental inventory parameters such as ordering, holding, and shortage costs under stochastic demand and lead-time conditions. By extending the classical Economic Order Quantity (EOQ) framework, the study captures the dynamic nature of storage operations, where uncertainty in demand and supplier lead times can significantly affect replenishment strategies and service performance.The research methodology employs mixed-integer linear programming (MILP) to determine the optimal order quantity (Q*) and reorder point (R*) for two operational scenarios: backlog and lost-sale cases. The backlog case considers situations where customer orders are delayed but eventually fulfilled, while the lost-sale case represents irreversible demand losses due to stockouts. By comparing the outcomes of both cases, the study identifies how different customer service environments influence inventory cost structures and policy decisions. The optimization model serves as a decision-support tool for balancing the trade-offs among cost efficiency, service reliability, and inventory responsiveness. To evaluate the robustness of the proposed model, a comprehensive sensitivity analysis is performed on key parameters, including variations in demand and supplier lead time. The results reveal that fluctuations in demand exert a greater influence on total cost and reorder decisions than equivalent changes in lead time, highlighting the importance of accurate demand forecasting and supplier coordination. Overall, the findings contribute to advancing practical inventory management strategies for storage operations, offering a systematic approach to minimize cost while maintaining service quality in uncertain and dynamic environments.