Abstract
Modern road freight transport faces increasing demands for speed and economic efficiency, rendering traditional planning methods insufficient. Concurrently, growing transit flows in Kazakhstan necessitate an urgent review of infrastructure and logistics strategies. The research niche of this article is the integration of digital platforms into existing logistics systems, specifically focusing on the interaction between different participants under various management models in road freight transport. The primary aim of the study was to justify the effectiveness of digital tools for optimising the management of logistics processes. The central research question evaluated the effectiveness of centralised, decentralised, and hybrid logistics flow management models under the implementation of digital solutions. The methodology relied on transport flow modelling, big data analysis, and the simulation of logistics processes. Data spanning 2020-2025 from official transport portals and meteorological resources in Kazakhstan were utilised to recreate authentic logistics structures. The quantitative simulation results demonstrated that adopting a hybrid management model integrated with geographic information systems and IoT reduced average vehicle downtime by 18% and improved peak-hour delivery reliability to 94%. Furthermore, pilot scenarios using a decentralised strategy reduced peak-hour delivery times by 12% and unplanned stops by 17%. Concurrently, based on the synthesis of secondary literature, the adoption of digital solutions within such models can potentially reduce transportation costs by 15-25% and improve demand forecasting accuracy by 45%. In conclusion, within the modelled Kazakhstan-specific road, climatic, and infrastructure conditions, the results suggest that hybrid digital logistics management may improve selected operational indicators compared with centralised and decentralised models, providing actionable directions for logistics policy specifically for the analysed routes.