An (R, Q) inventory model under limited storage with backlog: a case study of medical supply

Efficient inventory management is crucial in healthcare facilities, particularly for high-demand medical supply items, such as medical gloves, to ensure the item availability, while minimizing costs of managing the item’s inventory. This independent study presents a simulation-based optimization for the inventory management of medical gloves using the (R, Q) inventory policy with backlog. The study focuses on a constant lead time scenario and limited storage capacity. Spreadsheet simulations of the (R, Q) inventory system were conducted to evaluate the policy performance using a combination of Microsoft Excel and Python. The cyclic coordinate method embedded with the golden section search was employed to determine the optimal reorder point (R) and order quantity (Q) that result in the minimum total annual cost, which consists of ordering, inventory holding, backlog, and over-storage costs. Results from computational study based on actual demand data show that this simulation-based optimization approach can help healthcare facilities effectively manage medical glove inventory to meet demand, while minimizing the total annual cost.