TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Kiefer, Daniel
A1  - Straub, Tim
A1  - Bitsch, Günter
A1  - van Dinther, Clemens
T1  - A framework for explainable root cause analysis in manufacturing systems – combining machine learning, explainable artificial intelligence and the Ishikawa model for industrial manufacturing
T2  - Proceedings of the 58th Hawai'i International Conference on System Sciences (HICSS) : 7-10 January 2025, Hawai'i
N2  - 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.
KW  - design science
KW  - ishikawa model
KW  - manufacturing systems
KW  - root cause analysis
KW  - trace framework
KW  - explainable artificial intelligence
Y1  - 2025
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-55401
SN  - 2572-6862
SS  - 2572-6862
U6  - https://doi.org/10.24251/HICSS.2025.140
DO  - https://doi.org/10.24251/HICSS.2025.140
SP  - 1178
EP  - 1187
S1  - 10
PB  - University of Hawai'i at Manoa
CY  - Manoa
ER  -