A framework for explainable root cause analysis in manufacturing systems – combining machine learning, explainable artificial intelligence and the Ishikawa model for industrial manufacturing
- This paper proposes a novel framework – “Transparent Reasoning in Artificial intelligence Cause Explanation” (TRACE) – that combines root cause analysis, explainable artificial intelligence, and machine learning in an understandable way for the worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. The paper outlines the need of such a framework, describes the design process, and shows a preliminary mockup, a possible underlying software architecture as well as an evaluation and integration plan in an industrial environment.
| Author of HS Reutlingen | Kiefer, Daniel; Straub, Tim; Bitsch, Günter |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-55401 |
| DOI: | https://doi.org/10.24251/HICSS.2025.140 |
| ISSN: | 2572-6862 |
| Erschienen in: | Proceedings of the 58th Hawai'i International Conference on System Sciences (HICSS) : 7-10 January 2025, Hawai'i |
| Publisher: | University of Hawai'i at Manoa |
| Place of publication: | Manoa |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2025 |
| Tag: | design science; explainable artificial intelligence; ishikawa model; manufacturing systems; root cause analysis; trace framework |
| Page Number: | 10 |
| First Page: | 1178 |
| Last Page: | 1187 |
| DDC classes: | 004 Informatik |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
