Enhancing Industrial Maintenance : A Critical Examination of Predictive Analytics for Asset Reliability

Eko Sunarno, Rizky Andrika, Agus Budiyono, Ary Setijadi Prihatmanto

Abstract


This paper critically examines the role of predictive analytics in enhancing industrial maintenance and asset reliability. As industries face increasing pressure to improve efficiency and reduce downtime, the adoption of predictive analytics offers a transformative approach to maintenance strategies. By leveraging historical and real-time data, predictive models can forecast equipment failures, optimize maintenance schedules, and extend asset lifespans. This paper explores various predictive analytics techniques, their implementation in industrial settings, and the resulting impact on maintenance practices. Through a review of current literature and case studies, we highlight the benefits, challenges, and future directions of integrating predictive analytics in industrial maintenance. Our findings suggest that while predictive analytics significantly improves asset reliability and operational efficiency, successful implementation requires overcoming technical, organizational, and data-related barriers.

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