Economic justification for implementing predictive analytics and artificial intelligence in supply chain management

Abstract

Subject. Supply chain management as a key factor in a company's economic stability in conditions of high external market volatility, sanctions, and the need for import substitution. Objectives. A systematic analysis of the economic efficiency of implementing predictive analytics and artificial intelligence tools in supply chain management. Justifying the need to reduce operating costs and optimize warehouse inventory to minimize business operation risks. Methods. Methods of comparative analysis, systems analysis, statistical analysis, and expert evaluations were used. Results. Key "inefficiency zones" have been identified in the traditional supply chain management system, leading to either excess inventory buildup or shortages. The structure of the economic impact of implementing artificial intelligence has been defined, including reducing storage costs, lowering losses from writing off unsellable stock, and improving working capital turnover. A set of measures for integrating smart systems into the existing management framework has been proposed. Conclusions. Implementing the suggested approaches allows not only minimizing logistics risks but also freeing up financial resources for developing production capacities.