10.14489/td.2025.03.pp.056-063

2025, 03 March

DOI: 10.14489/td.2025.03.pp.056-063

Lyamkin А. I., Khunuzidi E. I.
AUTOMATION OF ASSESSMENT AND MONITORING OF THE QUALITY MANAGEMENT SYSTEM USING DIGITAL TECHNOLOGIES
(pp. 56-63)

Abstract. The article provides a detailed overview of the applications of quality management systems (QMS) and methods for their automation. Different methods of quality management are considered, which are used to obtain metrics for monitoring the quality of production business processes. Automation of monitoring, analysis, and evaluation of the QMS is becoming an increasingly urgent task in today's market conditions. It allows improving the efficiency of enterprises to develop products and services. Various approaches to automate the process of monitoring and evaluating the QMS, including the use of multidimensional data analysis and neural network technologies, are also analyzed in the article. Multidimensional data analysis helps identify hidden patterns and trends in production and quality management processes. Neural network technologies provide the ability to make informed decisions based on the data obtained. The article also discusses the development of a growth strategy using QMS. The development strategy should be aimed at continuously improving product and service quality, increasing enterprise efficiency, and meeting consumer demands. The concept of an information system for monitoring, measuring, analyzing, and evaluating the QMS is a complex of software and hardware tools that ensure the automation of quality control processes. This system allows obtaining objective data on product and service quality and making informed and qualitative decisions to improve quality based on data analytics. Thus, the article represents a valuable source of information for specialists in the field of quality management, heads of enterprises and organizations, as well as anyone interested in improving product and service quality.

Keywords: information system, quality management system, process approach, neural network, model, organization development strategy, automation, architecture, multidimensional data analysis.

+ - About the Authors Click to collapse

А. I. Lyamkin, E. I. Khunuzidi (MISIS National Research University of Technology. Moscow, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

+ - References Click to collapse

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