@inproceedings{KieferStraubBitschetal.2025,
  author    = {Kiefer, Daniel and Straub, Tim and Bitsch, G{\"u}nter and van Dinther, Clemens},
  title     = {A framework for explainable root cause analysis in manufacturing systems - combining machine learning, explainable artificial intelligence and the Ishikawa model for industrial manufacturing},
  booktitle = {Proceedings of the 58th Hawai'i International Conference on System Sciences (HICSS) : 7-10 January 2025, Hawai'i},
  issn      = {2572-6862},
  doi       = {10.24251/HICSS.2025.140},
  institution = {ESB Business School},
  pages     = {1178 -- 1187},
  year      = {2025},
  abstract  = {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.},
  language  = {en}
}