"background": "The reliability of transport maintenance depot systems is critical for national infrastructure, yet quantitative, system-level reliability assessments in developing contexts are scarce. Existing approaches often fail to account for hierarchical data structures and inherent uncertainties in operational performance data. ", "purpose and objectives": "This case study presents and evaluates a novel Bayesian hierarchical modelling framework for assessing the system reliability of transport maintenance depots. The objective is to provide a robust methodological tool for integrating sparse, multi-level operational data to infer system-wide reliability metrics. ", "methodology": "A case study methodology was employed, applying a Bayesian hierarchical model to depot performance data. The core reliability metric for a depot system i was modelled as Ri (t) = \ (- (\ t) ^{\), where the scale parameter \ followed a log-normal distribution, \ (\ᵢ) \ N (\, \²), pooling information across all depots. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. ", "findings": "The model successfully quantified system reliability and its uncertainty, revealing substantial variability in depot performance. A key finding was that the posterior mean reliability for a typical depot at a specified mission time was 0. 78, with a 95% credible interval of 0. 72, 0. 83. This indicates a moderate but uncertain level of systemic performance, with the hierarchical structure identifying several depots as outliers. ", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous framework for system reliability assessment in data-constrained environments. It effectively quantifies both central estimates and uncertainty, offering a superior alternative to aggregated or isolated analyses. ", "recommendations": "Adopt the proposed modelling framework for ongoing performance monitoring and resource allocation. Future work should integrate predictive maintenance data and covariate information to enhance the model's explanatory power. ", "key words": "System reliability, Bayesian hierarchical modelling, maintenance depots, infrastructure management, uncertainty quantification", "contribution
M Assefa (Tue,) studied this question.