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Automatic root cause analysis in manufacturing: an overview & conceptualization

Author

Listed:
  • Eduardo Oliveira

    (INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I)

  • Vera L. Miguéis

    (INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I)

  • José L. Borges

    (INESC TEC, Faculty of Engineering of the University of Porto - Rua Dr. Roberto Frias Edifício I)

Abstract

Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

Suggested Citation

  • Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01914-3
    DOI: 10.1007/s10845-022-01914-3
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    References listed on IDEAS

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    1. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
    2. Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
    3. Felix Richter & Tetiana Aymelek & Dirk C. Mattfeld, 2017. "Automatic Root Cause Analysis by Integrating Heterogeneous Data Sources," Operations Research Proceedings, in: Karl Franz Dörner & Ivana Ljubic & Georg Pflug & Gernot Tragler (ed.), Operations Research Proceedings 2015, pages 469-474, Springer.
    4. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
    5. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
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