A Framework for Explainable Root Cause Analysis in Manufacturing Systems – Combining Machine Learning, Explainable Artificial Intelligence and the Ishikawa Model for Industrial Manufacturing
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2025-01-07
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1174
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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.
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Data Science and Machine Learning to Support Business Decisions, design science., explainable artificial intelligence, ishikawa model, manufacturing systems, root cause analysis, trace framework
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10
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Proceedings of the 58th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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