Methodology of optimization of the industrial production process

Abstract

This article presents a new methodology for optimizing technological processes in industrial production, aimed at increasing efficiency and reducing costs. The research is relevant in a dynamically changing market environment and increasingly complex product quality requirements. The introduction justifies the need to develop an integrated approach that can take into account a variety of production parameters and adapt to changes in equipment operating conditions. The research methodology is based on the integration of mathematical modeling methods, statistical analysis and machine learning algorithms. During the work, data was systematically collected from production lines, pre-processed and then analyzed to identify the main factors affecting the efficiency of the equipment. The development of the model included the construction of an optimization scheme to reduce energy consumption and reduce equipment downtime. At the same time, the experimental verification was carried out at a real industrial enterprise using modern software, which ensured the reliability and objectivity of the results obtained. The results of the study demonstrate a 15% increase in productivity and a 10% reduction in engineering maintenance costs. The application of the developed methodology also made it possible to improve the quality of products by optimizing technological modes and increasing the accuracy of the elements of the production line. Comparison with traditional optimization methods has shown significant advantages of the proposed approach in terms of adaptability and scalability. It is noted in the discussion that the developed methodology has high practical significance and can be successfully applied in various branches of industrial production. The authors conclude that further research is promising in the direction of integrating artificial intelligence and intelligent production process management systems to achieve even better results. Additionally, the analysis showed that the integrated approach significantly reduces the interval between planning and performing production operations, minimizing the reaction time to changes in working conditions. The use of machine learning algorithms provides an opportunity to predict potential failures, which paves the way for timely prevention and improved system reliability. The developed methodology facilitates a comprehensive analysis of key parameters, which ensures optimal resource allocation and improved production cycles. This research lays the foundation for future innovations in production process management, contributing to the formation of a competitive and dynamic production environment.