DEVELOPMENT OF A METHODOLOGY FOR APPLYING BIG DATA AND IOT TECHNOLOGIES IN SUPPLY CHAIN MANAGEMENT

Abstract

The digitalization of supply chain management is shifting managerial logic from retrospective accounting toward continuous observation, forecasting, and predictive-prescriptive control. In this context, the Internet of Things provides real-time primary data on the condition of objects, cargo, transport, warehouses, and production assets, whereas Big Data ensures the integration, storage, processing, and analysis of high-volume, heterogeneous, and fast-arriving data for decision-making. The purpose of this article is to develop a methodology for applying Big Data and IoT technologies in supply chain management on the basis of current scientific and industry evidence. The study relies on analytical synthesis of literature on Supply Chain 4.0, Big Data analytics, IoT-enabled visibility, digital logistics, and technology implementation risks. The results show that Big Data and IoT are complementary rather than alternative technologies: IoT generates factual event streams, while Big Data converts them into managerial decisions. The most mature application domains are demand forecasting, transport monitoring, inventory management, cargo condition control, route optimization, and risk management. A layered data architecture and a nine-stage implementation methodology are proposed, including diagnosis, use-case prioritization, data mapping, IoT deployment, Big Data platform creation, model development, managerial integration, piloting, and governance. The article also systematizes operational, economic, visibility, resilience, and ESG indicators for performance assessment. It is concluded that the key effect is achieved not by data collection alone, but by embedding analytics into the operational contour of supply chain management.