Using Probability Theory to Assess and Mitigate Risks in Global Supply Chains for Internationally Traded Goods and Services
Abstract
Global supply chains that support internationally traded goods and industrial services have become increasingly vulnerable to disruptions arising from geopolitical conflicts, economic uncertainty, climate-related disasters, cybersecurity threats, and transportation bottlenecks. These disruptions have exposed the limitations of conventional risk management approaches and created a growing need for quantitative methods capable of predicting uncertainty and supporting evidence-based decision making. Probability theory offers a rigorous analytical framework for assessing disruption likelihoods and improving supply chain resilience. This study investigates the application of probability theory in assessing and mitigating risks across global supply chains for internationally traded goods and services. A mixed-methods research design was employed, integrating Monte Carlo simulation, Bayesian networks, chi-square testing, Pearson correlation analysis, and multiple regression analysis to evaluate major supply chain risk factors and their operational impacts. The findings indicate that supply chain disruptions are not evenly distributed across regions, with North America and Europe experiencing the highest disruption frequencies, confirmed by the Chi-square test (χ² = 43.27, p < 0.01). Monte Carlo simulation showed that high-risk disruption events occur with probabilities ranging from 20% to 60%. Multiple regression analysis identified geopolitical risks (β = 1.50), economic downturns (β = 1.20), and supply chain congestion (β = 1.80) as the strongest predictors of supply chain disruptions (R² = 0.78, p < 0.001). Pearson correlation analysis further demonstrated a strong positive relationship between disruption frequency and economic losses (r = 0.85, p < 0.001). The study concludes that integrating probability-based analytical models significantly improves risk prediction, decision quality, and supply chain resilience. It recommends the adoption of real-time data analytics, artificial intelligence-driven forecasting, digital technologies, and collaborative risk-sharing mechanisms to strengthen the resilience of global supply chains for internationally traded goods and services.