Digital transformation of supply and distribution policy: from traditional methods to ai-driven solutions

Abstract

Abstract. Purpose. The research aims to analyze the process of digital transformation of enterprise supply and distribution policy and develop practical recommendations for implementing AI-driven solutions in business processes. Methods. The study employs methods of systematic analysis to examine the evolution of supply and distribution management methods, comparative analysis to contrast traditional and AI-driven approaches, synthesis to form a comprehensive model of an intelligent ecosystem, and graphical visualization methods for presenting research results. Results. The evolution of supply and distribution management methods from traditional approaches to AI-driven systems has been analyzed, identifying four key transformation stages: traditional methods with manual information processing, basic automation through ERP and CRM systems implementation, Big Data technologies utilization for analytics and forecasting, and AI solutions implementation with automated decision-making. Key technologies of digital transformation have been systematized, including machine learning for pattern recognition with 40-60% accuracy improvement in demand forecasting, predictive analytics for future event prediction, neural networks for processing non-linear dependencies, IoT sensors for real-time monitoring, blockchain for supply chain transparency ensuring, and RPA robotization for routine operations automation. An architecture of AI-driven supply and distribution management system has been developed, integrating four functional modules: demand forecasting module with accuracy increase of 40-60%, inventory optimization module with frozen capital reduction of 15-25%, logistics routing module with cost savings of 15-25%, and offer personalization module with conversion rate improvement of 20-40%. Key advantages and challenges of AI technology implementation have been identified, including high initial investments requirements, personnel training necessity and organizational culture transformation demands, need for large volumes of quality data, integration complexity with existing information systems, cybersecurity risks, and ethical issues concerning customer personal data utilization. Conclusions. Digital transformation of supply and distribution policy through AI-driven solutions implementation ensures significant business process efficiency improvement, however requires a comprehensive approach to project planning and execution considering technological, organizational, and human factors for successful implementation and sustainable competitive advantage achievement.