DOI: 10.18503/1995-2732-2025-23-3-187-194
Abstract
The article examines current challenges in the development of predictive analytics at industrial enterprises, considering the specificities and implementation possibilities within the Russian Federation. A key challenge is the necessity to collect and process vast amounts of reliable data with minimal load on the computational infrastructure. As an alternative to purely cloud-native solutions the use of alternative technical solutions architecture is proposed. The article discusses fog and edge computing which enable data processing closer to its source reducing latency and costs. Based on practical experience a set of problems hindering the development of predictive analytics in Russia is highlighted: the lack of a systematic approach to implementation, inflated expectations from ready-made solutions, unpreparedness of enterprises for large-scale digital transformation, and difficulties with data exchange and the use of cloud solutions. In conclusion, solutions are proposed, including the standardization of approaches to developing low level devices and data transmission protocols, strategic partnerships between industrial enterprises, and the creation of anonymized industrial data banks with state support for the joint development and refinement of predictive models.
Keywords
predictive analytics of industrial equipment, Internet of Things (IoT), big data analysis, forecasting algorithms, fog and edge computing, artificial intelligence, machine learning, predictive maintenance (PdM), Industry 4.0, cyber-physical systems, data collection, data reliability, data anomalies, data processing, predictive model, mathematical modeling, digital transformation, domestic development, systematic approach, industrial data banks.
For citation
Ershov A.N. Current Issues in the Development of Predictive Analytics in Industry. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical Uni-versity]. 2025, vol. 23, no. 3, pp. 187-194. https://doi.org/10.18503/1995-2732-2025-23-3-187-194
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