Markov chain simulation for pharmaceutical medicine inventory optimization amidst pandemic conditions: analyzing ABC and VED inventory policies

This study investigates the integration of Markov chain simulations with the ABC and VED inventory policies to optimize pharmaceutical inventory management during pandemics. By leveraging historical data and predictive analytics, decision-makers refine inventory policies, enhance demand forecasting accuracy, and streamline procurement processes. The hybrid ABC-VED model classifies inventory by value and criticality, ensuring essential medicines like antivirals and antibiotics are prioritized while optimizing costs for non-urgent items. Markov Chain simulations provide a probabilistic framework to predict inventory shifts, allowing for dynamic responses to fluctuating demand. This proactive approach strengthens operational resilience and ensures continuity of care during global health crises. By exploring these hybrid models, the study offers robust strategies to enhance pharmaceutical inventory management, enabling pharmaceutical companies to meet healthcare demands efficiently and fortify healthcare infrastructure resilience. The findings contribute valuable insights into improving inventory practices, supporting better preparedness during pandemics.