USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR SUPPLY CHAIN MANAGEMENT IN GLOBAL CRISES: EXPERIENCE OF TRANSNATIONAL CORPORATIONS

The purpose of this article is to study the practice of foreign transnational corporations in the field of using artificial intelligence and machine learning to manage supply chains in the context of global crises. The methodological basis of this article was an integrated approach that made it possible to use a set of methods (analysis, classification and generalization) that allowed us to systematically and consistently study the experience of transnational corporations in solving the problem of using the latest achievements in the field of machine learning and the use of artificial intelligence in supply chain management in the context of global crises. As a result of the study, the author identified and systematized the main reasons requiring the use of machine learning of artificial intelligence in supply chain management in the context of global crises. The practical value of this article is determined by the possibility of using the obtained results for the purpose of further development of the problem related to the use of artificial intelligence and machine learning to manage supply chains in the context of global crises. It is concluded that the machine learning system, which allows expanding the capabilities of artificial intelligence, enables multinational companies to make decisions based on historical and current information on supply and demand, forming accurate forecasts regarding the functioning of supply chains in a turbulent external environment. Intelligent machine learning systems (IMLS) provide multinational corporations with a tool that helps reduce costs and increase revenues, and helps maintain the image of a reliable supplier.