A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach

In light of the intense need for quality health and well-being, the healthcare industry must improve its operations by strengthening its service delivery and reducing overall costs. A smart inventory management system for managing medicines in a hospital supply chain (HSC) is one of the best solutions as its advantages ensure availability of affordable medicines as well as inventory cost reduction. Significant effort is made on inventory management from literature; however, smart inventory systems for HSC considering the various complexities and uniqueness of healthcare systems are limited. This paper attempts to fill this gap by developing a stochastic semi-Markov decision process model which is solved by a multi-agent reinforcement learning method. Data collected from a multispecialty hospital in India's eastern region serves to validate the proposed model. The results present the optimal order quantities and optimal inventory control policy for the HSC considering the uncertain medication demand at the hospital pharmacy and multiple point-of-care units, and demand dependencies among medicines prescribed to the patients admitted in the hospital. The study also has significant managerial implications for enhancing HSC's inventory performance, improving the healthcare service delivery, reducing healthcare cost, thereby ensuring the affordable, accessible, and quality healthcare to the society.