A Bayesian Hierarchical Model for System Reliability in Ethiopian Transport Maintenance Depots: A Methodological Case Study, 2000–2026

{ "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) = exp(-(\ t)\), where the scale parameter \ followed a log-normal distribution, log(\λᵢ) \~ N(\μ, \σ²), 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