OPTIMIZATION OF DISTRIBUTION MANAGEMENT PROCESSES IN THE CONTEXT OF DIGITAL TRANSFORMATION

Abstract

The article examines the theoretical and applied aspects of optimizing distribution management processes in the context of the digital transformation of the economy. It is proven that the systemic approach serves as a methodological foundation for managing distribution processes, representing a logical and comprehensive study of conditions, events, and objects as a unified system from the standpoint of system analysis. This approach ensures balanced functioning of all elements within the distribution network and enhances its ability to adapt to changes in the external environment. Measures aimed at developing management systems for distribution processes and ensuring their further functioning depend on established norms of current legal regulation. Methodological approaches designed to ensure effective management of distribution processes should be viewed as a set of measures and actions influencing material, financial, informational, labor, and other types of distribution processes aimed at optimizing business objectives. It is established that modern enterprises of various ownership forms are shifting from hierarchical management systems to mixed and decentralized structures integrated with digital platforms such as enterprise resource planning, warehouse management, and transportation management systems, oriented toward ensuring flexibility, customer-centricity, and resilience. The advantages of using digital technologies such as telematics, big data analytics, and cloud services are identified as contributing to the transparency of distribution operations to optimize processes aimed at minimizing costs associated with these operations and enabling more accurate demand forecasting. A predictive model has been developed to optimize routing and delivery planning in order to minimize total costs by integrating analytical, optimization, and forecasting approaches. This model encompasses stages related to data collection and the assessment of potential resource constraints when choosing an optimal distribution route, allowing for a balance between logistical costs associated with distribution processes, transportation speed, and service quality. It is demonstrated that integrating the presented predictive models into corporate information systems enables a transition toward proactive management, which increases the flexibility and stability of distribution networks. The practical value of the results lies in the development of recommendations for improving distribution management through the combination of technological, analytical, and organizational tools. Future research should focus on evaluating the effectiveness of digital transformation in distribution systems and developing models for their sustainable development.