IMPROVING ENTERPRISE RISK MANAGEMENT SYSTEMS: THE CASE OF "UZAUTOMOTORS" JSC

Abstract

This paper addresses the transformation of Enterprise Risk Management (ERM) systems in large-scale automotive manufacturing entities, focusing on the empirical case of "UzAutoMotors" JSC. Operating within highly volatile macroeconomic environments and shifting global supply chains, traditional fragmented ("siloed") risk management approaches fail to ensure operational resilience. This study proposes an integrated four-block automation framework that bridges specialized ERM platforms with core Enterprise Resource Planning (ERP) databases. By incorporating parametric Value-at-Risk (VaR) modeling for foreign exchange exposures and stochastic Monte Carlo simulations for supply chain disruptions, the research transitions risk management from qualitative heuristics to rigid quantitative cost-based metrics. The financial validation of the proposed framework indicates a 78.9% reduction in data aggregation latency and a contraction of expected annual corporate losses from $10.33 million to $3.82 million, yielding a net first-year economic effect of $5,965,000.