UDC 004.94
DOI: 10.36871/2618-9976.2022.10.003
Authors
Leonid S. Zvyagin
Candidate of Economic Sciences, Associate Professor, Associate Professor of the Department of System
Analysis in Economics, Financial University under the Government of Russia, Moscow, Russia
Abstract
Information technologies have long spread to all spheres of human life, and even more so to companies. Their use provides greater labor productivity and automation of both production and business processes. The relevance of this article is due to the fact that a large number of pieces of equipment of modern companies need daily maintenance and troubleshooting of technical means is a significant process in any organization, and its optimization will inevitably bring a positive economic effect. The purpose of the article is to model an approach to decisionmaking, which includes predicting breakdowns and assessing the impact of breakdowns on revenue using machine learning methods. Modeling the impact of breakdowns on revenue using machine learning methods can be used to determine the development of a maintenance policy for technical facility breakdowns in order to maximize revenue in conditions of limited resources, which is a significant practical result.
Keywords
Modeling of information systems, Machine learning, Data dynamics, System architecture
