Real-time demand-driven inventory management in a hospital pharmacy

Hospital pharmacies receive thousands of prescriptions per day, consequently, it is crucial that the hospital medication circuit be as optimal as possible. Nevertheless, hospital inventory management is highly complex for various reasons, including space limitations, demand uncertainty, and human resource limitations. Furthermore, the inventory policy must be adaptable to real-time fluctuations in patient demands, with a simultaneous emphasis on preventing backorders and stockouts, given their potential to harm patients' well-being. Additionally, it is not operationally effective for care units (CUs) to repeatedly request new supplies every day to avert situations of insufficient stock. To address this problem, we present a real-time inventory control model based on the characteristics of medication demand while considering uncertainty in demand and space limitations, with the main objective of minimizing CU replenishment frequency. First, we categorize the medications into two groups; fast-moving and slow-moving medications, then, we utilize a continuous and dynamic review inventory control policy for these categories respectively. For optimizing the inventory control parameters we propose a stochastic optimization model for each category. To efficiently solve the proposed inventory policy in real-time, we employ a receding-horizon control (RHC) strategy, where the models are solved iteratively over a predetermined time horizon. To validate the effectiveness of our proposed approach, we use a real-world inventory management setting for a hospital in Montréal and conduct a comparative analysis between the proposed model and the existing state of the inventory policy to demonstrate the advantages of this research.