THE IMPACT OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES ON PRODUCT QUALITY MANAGEMENT SYSTEM
DOI:
https://doi.org/10.32782/2311-844X/2024-2-7Keywords:
technologies, artificial intelligence, management, personnel, management system, product quality management, enterpris, product qualityAbstract
This paper substantiates the significance of product quality management for the success of modern enterprises. It ensures that goods or services comply with established standards, thereby enhancing consumer trust and fostering customer retention. The study aimed to characterize the peculiarities of using artificial intelligence to improve product quality management in enterprises. In accordance with the set objectives, the research identified directions for improving the efficiency of product quality management systems and justified the integration of artificial intelligence systems. A comprehensive analysis of scientific and practical literature revealed that a number of theories and concepts related to artificial intelligence systems and their impact remain insufficiently explored. This prompted the choice of this topic as a relevant one. A range of methods were employed in the study, including induction and deduction, comparison and systematization, synthesis and analysis, morphological analysis, graphical and tabular evaluation, and abstract-logical analysis. The key features of a product quality management system were presented. It was demonstrated that the implementation of artificial intelligence systems in this area is a pressing task, as it allows for improving the efficiency of quality control and decision-making. The introduction of digital technologies has made it possible to automate many quality control processes, increasing the accuracy and speed of data acquisition. As a result of the study, key areas for improving the efficiency of quality management systems through artificial intelligence were identified, and the need for the integration of these technologies was substantiated. The main types of artificial intelligence technologies that can be used in this field were highlighted, and the potential risks of their application were analyzed. A list of threats and obstacles posed by artificial intelligence systems for product quality management in enterprises was identified. The obtained results indicate that digitalization and the application of artificial intelligence significantly impact product quality management processes, enabling the automation of control, increased accuracy of data analysis, and more informed decision-making. Future research will focus on analyzing international practices of applying artificial intelligence technologies in product quality management systems.
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