文章基本信息

标题：Quality 4.0. Time of revolutionary changes in the QMS
本地全文：下载
作者：Dmitry Yurin ; Antonina Deniskina ; Boris Boytsov 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：244
页码：11010
DOI：10.1051/e3sconf/202124411010
出版社：EDP Sciences
摘要：The article examines the tendencies and prospects for constructing adaptive mechanisms for the functioning of active systems, taking into account the influence of the tendencies of the fourth industrial revolution, called “Industry 4.0”, on the quality management system of enterprises. The main components of “Industry 4.0” is the global digitalization of all enterprise processes, including management processes, which makes the process of creating learning mechanisms of functioning (MFF) of active systems, which are able to improve their functioning over time, considered relevant. The described construction of the GFM is based on the use of learning processes carried out using probabilistic iterative (recurrent) algorithms. These algorithms make it possible, as a result of processing current information, to make up for the lack of a priori data and, ultimately, to achieve the best, from a certain point of view, performance indicators.
其他摘要：The article examines the tendencies and prospects for constructing adaptive mechanisms for the functioning of active systems, taking into account the influence of the tendencies of the fourth industrial revolution, called “Industry 4.0”, on the quality management system of enterprises. The main components of “Industry 4.0” is the global digitalization of all enterprise processes, including management processes, which makes the process of creating learning mechanisms of functioning (MFF) of active systems, which are able to improve their functioning over time, considered relevant. The described construction of the GFM is based on the use of learning processes carried out using probabilistic iterative (recurrent) algorithms. These algorithms make it possible, as a result of processing current information, to make up for the lack of a priori data and, ultimately, to achieve the best, from a certain point of view, performance indicators.