Causal Discovery in Manufacturing: A Structured Literature Review
Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Journal of Manufacturing and Materials Processing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4494/6/1/10 |
| _version_ | 1797479070641946624 |
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| author | Matej Vuković Stefan Thalmann |
| author_facet | Matej Vuković Stefan Thalmann |
| author_sort | Matej Vuković |
| collection | DOAJ |
| description | Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed. |
| first_indexed | 2024-03-09T21:40:34Z |
| format | Article |
| id | doaj.art-c82fc8b4147549c8b388b52c7657cc26 |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-09T21:40:34Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-c82fc8b4147549c8b388b52c7657cc262023-11-23T20:33:39ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942022-01-01611010.3390/jmmp6010010Causal Discovery in Manufacturing: A Structured Literature ReviewMatej Vuković0Stefan Thalmann1Pro2Future GmbH, Inffeldgasse 25F, 8010 Graz, AustriaBusiness Analytics and Data Science Center, University of Graz, 8010 Graz, AustriaIndustry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.https://www.mdpi.com/2504-4494/6/1/10manufacturingcausal discoverymachine learningindustry 4.0artificial intelligence |
| spellingShingle | Matej Vuković Stefan Thalmann Causal Discovery in Manufacturing: A Structured Literature Review Journal of Manufacturing and Materials Processing manufacturing causal discovery machine learning industry 4.0 artificial intelligence |
| title | Causal Discovery in Manufacturing: A Structured Literature Review |
| title_full | Causal Discovery in Manufacturing: A Structured Literature Review |
| title_fullStr | Causal Discovery in Manufacturing: A Structured Literature Review |
| title_full_unstemmed | Causal Discovery in Manufacturing: A Structured Literature Review |
| title_short | Causal Discovery in Manufacturing: A Structured Literature Review |
| title_sort | causal discovery in manufacturing a structured literature review |
| topic | manufacturing causal discovery machine learning industry 4.0 artificial intelligence |
| url | https://www.mdpi.com/2504-4494/6/1/10 |
| work_keys_str_mv | AT matejvukovic causaldiscoveryinmanufacturingastructuredliteraturereview AT stefanthalmann causaldiscoveryinmanufacturingastructuredliteraturereview |