USING PROBABILITY THEORY TO ASSESS RISKS AND MITIGATE UNCERTAINTY IN PROCUREMENT PROCESSES

This study explores the application of probability theory in assessing risks and mitigating uncertainties in procurement processes. The primary objective was to identify key procurement risks, evaluate the effectiveness of probabilistic models, and develop actionable recommendations for integrating probability-based approaches into procurement strategies. A mixed-methods approach was employed, utilizing Monte Carlo simulations, Bayesian networks, and regression analysis to analyze procurement data from 2020 to 2024. Key findings reveal that supply chain disruptions and price volatility were the most significant risks, with their probability increasing from 15% to 35% and 10% to 25%, respectively, over the five-year period. The Monte Carlo simulation estimated an expected financial loss of $3.5 million due to supply chain disruptions, with a 12% probability of losses exceeding $5 million. Regression analysis demonstrated a strong predictive relationship (R² = 0.78, p < 0.001), indicating that procurement risk probabilities increase at an average annual rate of 4%. The overall correlation coefficient (-0.55) confirmed that procurement risks negatively impact procurement efficiency, while quality assurance measures exhibited a positive correlation (0.60) with procurement performance. Based on these findings, the study recommends the integration of AI-driven risk analytics, supplier diversification, and continuous probabilistic modeling updates to enhance procurement resilience and efficiency.