数字化dm:制造业持续数字化的可持续数据挖掘模型

Christian Weber, P. Czerner, M. Fathi
{"title":"数字化dm:制造业持续数字化的可持续数据挖掘模型","authors":"Christian Weber, P. Czerner, M. Fathi","doi":"10.1109/eIT57321.2023.10187390","DOIUrl":null,"url":null,"abstract":"Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing\",\"authors\":\"Christian Weber, P. Czerner, M. Fathi\",\"doi\":\"10.1109/eIT57321.2023.10187390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.\",\"PeriodicalId\":113717,\"journal\":{\"name\":\"2023 IEEE International Conference on Electro Information Technology (eIT)\",\"volume\":\"160 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Conference on Electro Information Technology (eIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/eIT57321.2023.10187390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Electro Information Technology (eIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eIT57321.2023.10187390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

制造业作为一个行业面临着持续的压力,要求以正确的质量、数量和时间交付正确的产品。为了做到这一点,在短时间内发现制造问题的根源以及防止已知问题的进一步发生变得越来越重要。数据挖掘的重点是识别问题模式并推断出正确的解释,从而及时跟踪和解决问题的根本原因。然而,吸取的经验教训很少被传输到数字解决方案中,然后彻底实现自动检测和解决事件。数据挖掘模型已经存在,但没有结构化的方法来数字化地转换和维持已发现的解决方案。我们正在引入digital - dm作为数字化分析结果的结构化和战略性过程。digital - dm是建立在现有数据挖掘模型之上的,但它定义了一个持续数字化的战略过程,利用分析经验教训,实现可持续的数字化制造支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing
Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信