Abstract. In an era marked by global supply chain disruptions, optimizing risk management processes in logistics is essential for improving operational resilience and decision-making efficiency. This paper investigates the implementation of digital technologies—specifically predictive analytics, digital twins, and AI-driven risk assessment models—in the identification, evaluation, and mitigation of risks in logistics. A case study based on a mid-sized logistics provider operating in Central Asia is presented to demonstrate the quantitative impact of digital integration. The study employs a hybrid methodology combining Failure Mode and Effects Analysis (FMEA) with Monte Carlo simulation to assess the probabilistic consequences of supply delays, vehicle breakdowns, and warehouse bottlenecks. The findings indicate a 37% reduction in risk exposure and a 21% increase in supply chain responsiveness after the deployment of an AI-powered predictive platform. Additionally, the average delay time per delivery was reduced from 3.5 to 2.2 hours, and the Risk Priority Number (RPN) for key logistical hazards dropped from 216 to 136. This demonstrates the significant value of digitization in enhancing the accuracy of risk assessments and optimizing logistics operations under uncertainty. The study concludes with strategic recommendations for integrating digital tools into logistics workflows, emphasizing scalability and adaptability for companies facing complex risk environments.

Keywords: risk management, logistics, digital technologies, predictive analytics, digital twin, Monte Carlo simulation, supply chain optimization.