"background": "Industrial machinery fleet reliability is a critical determinant of productivity and economic output in developing economies. However, systematic, longitudinal assessments of fleet system performance in such contexts are scarce, limiting evidence-based maintenance and replacement strategies. ", "purpose and objectives": "This study aims to methodologically evaluate approaches for assessing industrial machinery fleet reliability and to develop a robust panel-data model for estimating system-wide reliability trends within a major industrial sector. ", "methodology": "A longitudinal dataset of operational and failure events from a large, multi-plant fleet was constructed. Reliability was modelled using a generalised linear mixed model for panel data. The core specification was = \ (\ X{it + \ +), where is the failure rate for unit i in period t, Xit are time-varying covariates, and \ᵢ captures unobserved unit-specific heterogeneity. Estimation used maximum likelihood with robust standard errors clustered at the plant level. ", "findings": "The methodological evaluation identified significant bias in cross-sectional approaches. The panel estimation revealed a statistically significant declining trend in aggregate fleet reliability, with a mean annual increase in failure rate of 2. 7% (95% CI: 1. 9% to 3. 5%). Unobserved heterogeneity between individual machines accounted for over 30% of the variance in failure rates. ", "conclusion": "The analysis confirms that industrial machinery fleet reliability has deteriorated systematically. The panel-data approach provides a superior methodological framework for capturing dynamic reliability trends and unobserved heterogeneity compared to static models. ", "recommendations": "Industry practitioners should adopt panel-data methodologies for fleet reliability analysis. Policy should incentivise the collection of standardised, high-frequency operational data to support predictive maintenance and capital planning. ", "key words": "reliability engineering, panel data, fleet management, maintenance, generalised linear mixed model, industrial machinery", "contribution statement": "This paper provides
Sani et al. (Mon,) studied this question.