ASSESSMENT OF FACTORS INFLUENCING THE RATIONALIZATION OF BUSINESS PROCESS MANAGEMENT BASED ON SMART TECHNOLOGIES

Abstract

The constructed system of quantitative analysis of factors influencing the rationality of business process management based on SMART technologies ensures the transition from a conceptual framework to an empirically measurable model. It is demonstrated that the operationalization of twenty-four factors through open statistical indicators from the EU and Ukraine provides a foundation for comparative monitoring of enterprises’ digital maturity and enables the formation of an integral SMART-BPM index. It is substantiated that normalization of indicators using the min–max method and the harmonization of measurement scales ensure the comparability of results, while the use of European benchmarks enhances the analytical accuracy of the model. It is argued that the dynamics observed during 2021–2024 indicate a gradual transition of Ukraine from fragmented digitalization to a comprehensive SMART-based management environment. The increase in the share of enterprises using cloud services from 24% to 36%, ERP systems from 20% to 32%, IoT from 10% to 22%, AI from 2% to 8%, as well as the growth of the digitalization index from 30 to 61 points, confirms the coordinated development of technological, analytical, and organizational components. Within this trajectory, a convergence of system components in terms of maturity level has been identified, which resulted in an increase of the integral SMART-BPM index from 0.35 to 0.89, i.e., more than 2.5 times. It is proven that the acceleration of intelligent technologies, expansion of network bandwidth, and strengthening of cybersecurity have become the key drivers of improving the rationality of business process management. The combination of technological, analytical, and managerial factors has ensured the consolidation of SMART systems within the national economy, enhancing decision-making efficiency and increasing enterprise resilience to risks. It is further substantiated that the developed analytical model and the obtained statistical data series can be applied to improve public digitalization policies, support corporate planning, and serve as a basis for building econometric models to forecast the effects of digital transformation in 2025–2030.