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On the influence of overlap in automatic root cause analysis in manufacturing.

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Title: On the influence of overlap in automatic root cause analysis in manufacturing.
Authors: e Oliveira, Eduardo1 (AUTHOR) eduardo.l.oliveira@inesctec.pt, Miguéis, Vera L.1 (AUTHOR), Borges, José L.1 (AUTHOR)
Source: International Journal of Production Research. Nov2022, Vol. 60 Issue 21, p6491-6507. 17p. 3 Diagrams, 5 Charts, 3 Graphs.
Subjects: Root cause analysis, Manufacturing processes
Abstract: To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: On the influence of overlap in automatic root cause analysis in manufacturing.
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  Data: <searchLink fieldCode="AR" term="%22e+Oliveira%2C+Eduardo%22">e Oliveira, Eduardo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> eduardo.l.oliveira@inesctec.pt</i><br /><searchLink fieldCode="AR" term="%22Miguéis%2C+Vera+L%2E%22">Miguéis, Vera L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Borges%2C+José+L%2E%22">Borges, José L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Nov2022, Vol. 60 Issue 21, p6491-6507. 17p. 3 Diagrams, 5 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Root+cause+analysis%22">Root cause analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink>
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  Label: Abstract
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  Data: To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2021.1992680
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        Text: English
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      – SubjectFull: Root cause analysis
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      – SubjectFull: Manufacturing processes
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      – TitleFull: On the influence of overlap in automatic root cause analysis in manufacturing.
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              Text: Nov2022
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