USING PROBABILITY THEORY TO ASSESS AND MITIGATE RISKS IN GLOBAL SUPPLY CHAIN OPERATIONS

This study explores the application of probability theory in assessing and mitigating risks in global supply chain operations. The research aims to develop probabilistic models that quantify uncertainties and predict disruptions, enhancing decision-making for businesses and policymakers. A mixed-methods approach was employed, combining Monte Carlo simulations, Bayesian inference, and regression analysis on case studies of multinational corporations and industry reports from 2020 to 2024. Key findings indicate that supply chain disruptions increased annually, with total risk exposure rising from $50 billion in 2020 to $70 billion in 2024. Monte Carlo simulations estimated a 16% probability of severe disruptions (impact > 20%), while a regression model revealed a strong positive correlation (R2=0.89) between time and risk exposure, projecting costs to exceed $80 billion by 2026 if proactive measures are not implemented. The chi-square test confirmed regional disparities in supply chain risks, emphasizing the need for tailored mitigation strategies. The overall Pearson correlation analysis indicated a negative relationship (−0.45) between GDP growth and supply chain risks, highlighting economic stability as a critical factor in risk reduction. The study concludes that diversified supplier networks (85% effectiveness), AI-driven predictive analytics, and enhanced cyber security (80% effectiveness) are the most impactful risk mitigation strategies. It recommends integrating real-time monitoring through IoT and blockchain, financial hedging mechanisms, and adaptive probability-based risk management frameworks to enhance global supply chain resilience.