The COVID-19 pandemic has revealed the fragility of healthcare supply chains, necessitating a strategic shift toward robust inventory control and disruption management plans within the industry. To address this, professionals are increasingly turning to advanced modeling techniques that integrate data analytics, optimization algorithms, simulation tools, and risk assessment frameworks specifically tailored for the unique challenges faced by healthcare supply networks. Here, data was gathered from an Eye Hospital, focusing on four medicines classified into four distinct classes, based on demand variability and diffusion. Among these medicines, a particular medicine emerged as a Class 1 drug with low demand variability and high diffusion, making it the focal point for supply chain management strategies. The collected data includes crucial metrics such as order quantity, cost of medicine, stockout cost, holding cost, and lead time. The ARENA simulation software is used in this work to simulate the baseline models that show how the supply chain normally works and then add different disruption scenarios. These scenarios are then subjected to multiple variations to test different mitigation strategies, aiding in the identification of the best recovery strategy in the face of supply chain disruptions.