ANALYSIS AND MODELING OF RISKS OF LOGISTICS SYSTEMS USING ARTIFICIAL INTELLIGENCE IN THE CONDITIONS OF GLOBAL INSTABILITY

Abstract

This article investigates the risks faced by logistics systems in the context of global instability and explores the potential of artificial intelligence (AI) for their prediction, prevention, and effective management. The study emphasizes the role of advanced technologies—machine learning (ML), deep learning (DL), neural networks, expert systems, big data analytics, and the Internet of Things (IoT)—in developing intelligent and adaptive logistics models. The research identifies and categorizes key types of risks—economic, geopolitical, technological, ecological, and operational—and describes how AI-based approaches can be employed for their detection, classification, modeling, and mitigation. The application of AI allows for the real-time monitoring of supply chain vulnerabilities, enhances predictive capabilities, and supports strategic decision-making under uncertainty. Special attention is given to the integration of digital twins, AR (augmented reality), and AI-powered autonomous transport in creating virtual models of logistics systems for scenario analysis. These technologies enable logistics operators to simulate potential risk situations, predict disruptions, and optimize resource allocation. The article reviews global and domestic practices, including the experience of Maersk and DHL in applying machine learning to minimize transportation delays, as well as Nova Poshta’s use of big data for forecasting seasonal demand surges in Ukraine. These examples highlight the growing reliance on AI to increase resilience and responsiveness in logistics operations. The paper also discusses the importance of ethical AI in logistics, stressing the need for transparent algorithms, data security, and the ethical implications of automated decision-making. A step-by-step framework for implementing AI in logistics risk management is proposed, including risk auditing, algorithm selection, IoT and data integration, decision automation, and continuous model adaptation. Overall, the research argues that AI is not just a technological enhancement but a critical strategic tool for building robust, flexible, and competitive logistics systems in a rapidly changing world.