DIGITAL TRANSFORMATION OF SUPPLY CHAIN PLANNING: OPPORTUNITIES, BENEFITS, AND CHALLENGES
DOI:
https://doi.org/10.29038/2786-4618-2026-01-140-150Keywords:
artificial intelligence; Big Data; machine learning; supply chain planning; supply chain management; digital transformation; demand forecasting; supply chain resilience.Abstract
Introduction.Planning is a fundamental component of effective supply chain management, as it ensures the coordination of actions among all participants and supports decision-making aimed at supply continuity, cost optimization, and service level improvement.
The purpose of the study is to generalize the theoretical and applied aspects of using artificial intelligence, Big Data technologies, and machine learning in supply chain management, as well as to identify their role in enhancing transparency, efficiency, resilience, and competitiveness of enterprises.
Methods.The research methods include analysis and synthesis of scientific publications by domestic and foreign authors, comparative analysis of traditional and intelligent approaches to supply chain management, generalization of statistical and analytical data from international studies, as well as systems approach and logical generalization methods for drawing conclusions.
Results.The study finds that the application of intelligent algorithms enables more accurate demand forecasting through the analysis of large volumes of historical and streaming data, thereby reducing the risks of excess inventory or product shortages. It is demonstrated that the use of Big Data analytics allows enterprises to achieve end-to-end visibility of logistics processes in real time, improve coordination among supply chain participants, and promptly identify bottlenecks. This creates a foundation for enhancing the resilience of logistics systems, especially under conditions of an unstable external environment. Machine learning algorithms increase the efficiency of inventory management, transportation, and warehousing operations by accounting for nonlinear relationships, temporal patterns, and factors that cannot be processed by traditional statistical models. This makes it possible to optimize logistics routes, reduce transportation costs, minimize delivery delays, and improve customer satisfaction. It is substantiated that the effectiveness of artificial intelligence and machine learning applications depends on the quality, timeliness, and consistency of data, as well as on the level of digital competencies of personnel.
Conclusion.Artificial intelligence, Big Data, and machine learning are key drivers of the digital transformation of logistics and supply chain management.
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