Data-Driven Robust Optimization in Supply Chain Inventory Management Considering Demand Fluctuations

Demand fluctuation is a major challenge in supply chain inventory management because it affects order quantity, safety stock, shortage risk, and total inventory cost. Traditional inventory models often use average demand, which may lead to unreliable decisions under uncertain market conditions. This study proposes a data-driven robust optimization approach to improve inventory decisions under demand uncertainty. Historical demand data are used to construct an uncertainty set, and a robust optimization model is developed to determine optimal order quantities and safety stock levels while minimizing holding and shortage costs. The proposed model is compared with a deterministic inventory model over a ۱۲-period planning horizon. The results show that the robust model eliminated shortages, improved the service level from ۹۶.۵۴% to ۱۰۰%, and reduced total inventory cost from ۲,۳۸۴ to ۱,۸۲۴, equal to a ۲۳.۴۹% cost reduction.

کلیدواژه ها:

Supply chain management ، Inventory management ، Demand fluctuation ، Data-driven robust optimization ، Safety stock