UDC 658.7; 658.5
DOI: 10.36871/ek.up.p.r.2025.03.13.026
Authors
Shamil N. Vagapov,
Valentina M. Repnikova,
Plekhanov Russian University of Economics, Moscow, Russia
Abstract
The article explores theoretical, methodological, and practical aspects of applying Big Data technologies to optimize enterprises’ logistic operations in the context of digital transformation. The relevance of the study is driven by the rapid development of Industry 4.0, the expansion of digitalization, and the growing need to improve the efficiency of transport and logistics processes through the integration of next-generation analytical tools. The role of Big Data as a key resource is substantiated, enabling the development of predictive management models, route optimization, demand forecasting, inventory control, and the integration of logistics chains into a unified informational and analytical system. The article analyzes the infrastructural, technical, and organizational components required for the implementation of Big Data technologies. Special attention is paid to the justification of optimization directions using predictive analytics, machine learning, IoT sensors, and routing algorithms. Examples of functional logistics blocks that demonstrate the potential of Big Data are presented, along with identified limitations caused by imbalances between data volume, infrastructure, and the interpretability of data in the context of managing logistics operations. The article clarifies the value, applicability, and conditions for the effective use of Big Data technologies in logistics. It emphasizes the necessity of developing a conceptual model that ensures the integration of Big Data into the management of logistic operations at both strategic and tactical levels, taking into account the enterprise's digital maturity, the qualifications of specialists, and development goals.
Keywords
big data, digital transformation, logistic operations, predictive analytics, machine learning, logistics optimization, inventory management, demand forecasting, route optimization, analytical systems, logistics chains, information infrastructure, accounting systems, transport logistics, data integration, intelligent algorithms, digital economy, enterprise processes, data processing, decision-making
