.png){kind=icon}
返回
Identifying nonconformity root causes using applied knowledge discovery
.png){kind=icon}
OA
AI
DOI：10.1016/j.rcim.2014.12.012.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quality control, failure analysis and improvement are central elements in manufacturing. Total Quality Management (TQM) provides several quality oriented tools and techniques which, in the event of things, are not always applicable. The increased use of Information Technology (IT) in manufacturing means increased data availability and improved potential for knowledge extraction. Exploiting this knowledge requires data storage and processing facilities with demanding, time consuming sessions for interpretation. Without suitable tools and techniques, knowledge remains hidden in databases. This paper presents a method to help identify root causes of nonconformities (NCs) using a pattern identification approach. Hereby, a general framework, Knowledge Discovery in Databases (KDD), is adapted. This adaptation involves incorporating an economic concentration measure, the Herfindahl-Hirschman Index (HHI), as the data mining algorithm. After presenting the theoretical background, a new methodology is proposed. The suggested approach can be regarded as a quality tool to help make root cause identification of failures simpler and more agile. A case study from the automotive industry is examined using this tool. Results are obtained and presented in the form of matrix based patterns. They suggest that concentration indices help indicate possible root causes of NCs, warranting further investigation in this area. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Total Quality Management
Nonconformities
Root cause analysis
Quality tools
Knowledge Discovery in Databases
Herfindahl-Hirschman Index
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
R
IF:
11.4
论文数:
3.3K
被引数:
1.3W
机构
引用论文
Recent Solutions for the Abatement of Hydrocarbon Emissions During the Cold Start of Light Vehicles
.png){kind=icon}
PREAI
分享收藏Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis数据挖掘和工艺知识发现在钣金装配尺寸变化诊断中的应用
PREAI
分享收藏分享收藏
分享收藏
查看更多内容