Convergent Technological and Hyperconvergent Forms of Productivity Improvement in the Extractive Sector of Economy

Abstract

The advancement of the extractive sector is affected by a combination of external and internal factors, such as the volatility of world prices with demand for raw materials and the peak of productivity due to technological limitations. In this context, the global demand for raw materials may lead to different issues and an increase in productivity is dependent on the widespread adoption of technologies, which form the essence of Industry 4.0. Therefore, this research aimed to identify the influence of convergent technologies on productivity in the extractive sector of the economy and determine the role of national technological platforms in facilitating the process. The research methodology was formed by the works of Russian and foreign economists in the field of raw materials markets analysis, mineral sector productivity, perspectives and conditions of Industry 4.0 development, technological convergence, as well as regulation of innovation interaction between science and production. The results showed that a recurring cycle of long-term developmental challenges, "productivity stagnation -slowdown of investments inflow -further productivity stagnation" was identified by analyzing prices in the global raw materials market, investment patterns, incomes, and productivity trends within the Russian extractive sector. In addition, this cycle was supported by reliance on equipment imports and the replication of outdated technologies. Due to the advancements of Industry 4.0, a convergent technological approach to modernization was proposed to increase productivity. In this context, a platform was developed for the diffusion of convergent technologies based on the analysis of international practices of using NBIC-convergence in the activities of mineral complex enterprises. Hyperconvergent technologies were particularly considered to mediate the transition from the "Internet of Things" to the "Internet of Everything", facilitating the evolution of automated to fully unmanned production processes. The results could be used in the formation of strategic programs for the modernization of the extractive sector.