Driving Operational Performance Through Predictive Maintenance: Evidence from Industrial Condition Monitoring and Fault Diagnosis Data

Predictive maintenance enabled by condition monitoring and fault diagnosis (CMFD) has emerged as a critical operational capability for improving equipment reliability and production continuity. However, empirical evidence based on real operational data linking CMFD to performance outcomes remains limited within the operations and supply chain management literature. This study examines how condition monitoring intensity and fault diagnosis effectiveness influence maintenance efficiency, unplanned downtime, and operational performance using secondary industrial datasets from manufacturing environments. Drawing on maintenance strategy and operations performance theory, a causal framework is developed connecting CMFD capabilities to productivity, cost efficiency, and service reliability through maintenance effectiveness. The analysis employs panel regression and mediation techniques to evaluate performance changes associated with predictive maintenance interventions across multiple equipment units and time periods. The findings demonstrate that enhanced monitoring frequency and improved fault detection accuracy significantly reduce unplanned downtime and maintenance costs while increasing throughput and delivery reliability. Maintenance effectiveness is shown to mediate the relationship between CMFD capabilities and operational performance outcomes. This research contributes to operations and supply chain management literature by empirically establishing predictive maintenance as a strategic operational capability rather than solely a technical tool. Managerially, the results provide evidence-based justification for investments in CMFD technologies as drivers of operational efficiency, resilience, and sustainable performance.