Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy最新文献
{"title":"A System Approach for Creating Employee-Oriented Quality Control Loops in\u0000 Production for Smart Failure Management System in SMEs","authors":"Turgut Refik Caglar, R. Jochem","doi":"10.54941/ahfe1002835","DOIUrl":"https://doi.org/10.54941/ahfe1002835","url":null,"abstract":"The basis of effective processes for failure elimination and prevention\u0000 is formed by quality control loops, which aim to detect and analyse\u0000 deviations/failures from quality specifications and to take appropriate\u0000 measures to correct them in time. Quality methods can either be used as\u0000 controllers (e.g. statistical process control, failure mode and effects\u0000 analysis) in quality control loops or as a special form of these (e.g.\u0000 Plan-Do-Check-Act cycle, 8D Report, Six Sigma methodology). For the\u0000 successful introduction of quality control loops, relevant data should be\u0000 systematically collected, evaluated and interpreted in order to derive\u0000 targeted measures as a consequence. Small and Medium Enterprises (SME)\u0000 rarely have a systematic quality management system for the comprehensive\u0000 collection and analysis of quality data and their embedding in quality\u0000 control loops. On the other hand, the increasing complexity of production\u0000 systems requires the digitalisation and expansion of quality control loops\u0000 already in use, although they have delivered good results so far. At this\u0000 point, Artificial Intelligence (AI) is a future key technology that holds\u0000 significant potential for future value creation. AI-driven data science\u0000 methods (e.g. machine learning) enable the explanation of complex,\u0000 correlationally directed relationships in large amounts of data and\u0000 accordingly contribute to process improvement as well as failure management.\u0000 In this context, the expansion of quality control loops through digitalised\u0000 elements and AI methods can help to achieve a smart failure management\u0000 system.In terms of content, failure management also includes the term\u0000 \"failure prevention\" and is not a one-time process, but a continuous process\u0000 that requires the motivation and understanding of all employees.\u0000 Furthermore, the concept of \"Total Productive Management\" also aims at\u0000 defect-free products and effective production processes and involves all\u0000 employees in improvement activities to maximise plant efficiency and\u0000 minimise losses. At this point, SMEs need intelligent, digital and\u0000 employee-oriented error management systems. The core objective of the paper\u0000 is to present the conceptual development of a smart failure management\u0000 system that is in a continuous learning process through interaction with the\u0000 employee and in this way learns human cognitive problem-solving skills. This\u0000 approach is intended to detect failures on the shop floor at an early stage\u0000 in order to identify possible causes of problems and derive measures. If the\u0000 defect type, cause or measure are not known, the system suggests suitable\u0000 methods/tools of quality and data science to support employees in problem\u0000 solving process. In order for the assistance system to have human cognitive\u0000 problem-solving capabilities, the system must be trained in advance by\u0000 qualified employees who have extensive technical and methodological\u0000 knowledge and can apply it confidently. With this in mind, the failu","PeriodicalId":269162,"journal":{"name":"Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems, February 22–24, 2023, Venice, Italy","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125968630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}