INTELLECTUAL DECISION SUPPORT IN INFORMATION SYSTEMS OF PRODUCT QUALITY MANAGEMENT IN THE AI ECONOMY - Argen K. Syiabekov; Zhenishbek K. Belekov; Guzal A. Alimova; Fedor P. Potapov; Olga A. Iurlova (Proceedings on Engineering Sciences)
This paper studies theoretical and methodological aspects of using artificial intelligence (AI) in the information systems of product quality management in the conditions of the AI economy development. The essence and specifics of AI technology are considered from the position of its use in product quality management. The state and dynamics of the development of the AI economy are analysed, its advantages, features, and structure are determined, and examples and perspectives of implementing the tools of AI in the information systems of product quality management by leading technological companies are given. Emphasis is placed on the potential of AI technology in combination with other digital technologies to overcome the restrictions and problems of intellectual support of product quality management systems in modern conditions. The methodology of this research is based on the complex application of the system and interdisciplinary approaches. The system approach allows considering information systems of quality management as interconnected unity of internal elements in view of external effects. The interdisciplinary approach ensures the integration of knowledge on management, economics, digital technologies, and organisational interaction. The research methodology is formed by empirical & analytical methods, expert methods, observation, generalisation, and extrapolation. The theoretical basis of the research includes the theory of general-purpose technologies, decision-making, quality management, development of information systems, etc., which ensures the comprehensive consideration of the problem in the context of modern scientific paradigms. The research results demonstrate a substantial potential of AI in overcoming cognitive limitations of the traditional systems of quality management, in particular in processing large volumes of data, forecasting defects, optimising production processes, and raising the effectiveness of decision-making. Practical examples of the implementation of AI in the systems of quality management by leading technological companies are considered. This paper has scientific and practical value because it discloses the mechanisms and perspectives of using AI technologies in systems of management decision support for product quality management in the context of the AI economy development.