A Bayesian Hierarchical Model for System Reliability in Kenyan Transport Maintenance Depots: A Methodological Evaluation and Data Descriptor

{ "background": "The reliability of transport maintenance depot systems is critical for infrastructure integrity and economic activity. Current reliability assessments in such contexts often lack formal frameworks to integrate sparse, multi-level operational data and quantify uncertainty, limiting predictive maintenance planning.", "purpose and objectives": "This data descriptor presents and methodologically evaluates a Bayesian hierarchical model for quantifying system reliability in transport maintenance depots. The objective is to provide a robust, probabilistic framework that accounts for heterogeneity across depot subsystems and informs data collection standards.", "methodology": "The methodology centres on a Bayesian hierarchical Weibull reliability model. The core statistical model is Tij \~ (\, \λij), log(\λij) = \β0 + \β1 xij + uj, where Tij is time-to-failure for component i in subsystem j, \ is the shape, \λij is the scale, xij is a covariate, and uj \~ N(0, \σ²u) is a random intercept. Inference uses Hamiltonian Monte Carlo, with model fit assessed via posterior predictive checks.", "findings": "The methodological evaluation, applied to a novel dataset from multiple depots, demonstrates the model's capacity to pool information and yield precise subsystem reliability estimates. A key finding is that incorporating hierarchical structure reduced the 95% credible interval width for mean time to failure estimates by an average of 23% compared to non-hierarchical models, indicating substantially improved precision.", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous and operationally useful framework for reliability analysis in maintenance depots, effectively handling the inherent data structure and uncertainty.", "recommendations": "Future data collection for depot reliability should record component-level covariates and subsystem groupings to fully leverage hierarchical modelling. Practitioners should adopt this framework for prioritising maintenance interventions on the least