AI-Integrated Risk Management for Healthcare Supply Chains

This paper presents an AI-driven framework for identifying and prioritizing supply chain risks in the healthcare sector, focusing on factors influencing operational efficiency and sustainability. The proposed model leverages data from various risk indicators, including supplier performance, shipping delays, compliance issues, and geopolitical events, to assess and classify risks such as 'Critical', 'High', 'Medium', and 'Low'. By utilizing machine learning algorithms, specifically Random Forests, the model automatically processes and analyzes historical supply chain data to predict risk factors and prioritize interventions. A key feature of the model is its ability to handle missing data and generate synthetic values for missing columns, ensuring comprehensive risk assessment. The framework also incorporates advanced data visualization techniques to enhance decision-making, offering stakeholders clear insights into risk distributions, correlations, and prioritization. This approach aims to optimize risk management practices, improve resilience in healthcare supply chains, and ensure timely responses to potential disruptions, ultimately enhancing healthcare logistics' overall reliability and efficiency.