An explainable artificial intelligence model for predictive maintenance and spare parts optimization

Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utiliz...

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Bibliographic Details
Published in:Supply Chain Analytics 2024-12, Vol.8, p.100078, Article 100078
Main Authors: Dereci, Ufuk, Tuzkaya, Gülfem
Format: Article
Language:English
Subjects:
Explainable Artificial Intelligence
Human-centric Maintenance
Machine Learning
Predictive Maintenance
Spare Parts Management
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Summary:Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.
ISSN:2949-8635
2949-8635
DOI:10.1016/j.sca.2024.100078