September 19, 2025Production Planning & Control

Machine learning-based predictive maintenance: empirical insights of challenges and countermeasures

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Machine learning adoption for predictive maintenance in industry is notably low, suggesting a need for improved strategies.
Key challenges, such as training employees, are overlooked in existing literature, revealing gaps in current understanding.
A Delphi study was employed to rank challenges and countermeasures, providing a structured approach to address these issues.
Implementing effective countermeasures may enhance machine learning adoption for predictive maintenance across industrial settings.

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Implication

This research identifies challenges and proposes countermeasures for machine learning in predictive maintenance, highlighting critical adoption issues.

Cite This Study

Leoni et al. (2025) studied this question.

synapsesocial.com/papers/68d437f7713b0b5dfea77a37 https://doi.org/10.1080/09537287.2025.2555924

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Implication

This research identifies challenges and proposes countermeasures for machine learning in predictive maintenance, highlighting critical adoption issues.

Machine learning-based predictive maintenance: empirical insights of challenges and countermeasures

Key Points

Discussion

Implication

Cite This Study

Machine learning-based predictive maintenance: empirical insights of challenges and countermeasures

Key Points

Discussion

Implication

Cite This Study

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