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

The provision of high-quality and cost-effective healthcare is a major global issue, and managing medication supply chains in hospital pharmacies is a complex task due to factors such as space limitations, demand uncertainty, and medication shelf-life. This research addresses the pharmacy inventory control problem at Centre Hospitalier de l'Université de Montréal (CHUM), Canada. This research aims to enhance the efficiency of medication inventory management within the hospital by optimizing the inventory management policy and choosing the best inventory control parameters for each group of medicines in real-time. The project involved analyzing demand and categorizing items in the care unit into fast and slow-moving products. A novel real-time data-driven inventory policy is proposed for medications at the point-of-use location in the hospital, considering demand uncertainty and space constraints. The proposed stochastic optimization model is based on the (s,S) policy, with fast-moving drugs continuously reviewed and the only ones that can initiate the CU's replenishment process, while slow-moving drugs are only reviewed when a fast-moving item needs replenishment. To solve the large stochastic problem, the model is decomposed based on the categories derived from the data, and a rolling-horizon method is implemented to capture demand fluctuations and update inventory control parameters.