{ "background": "Transport maintenance depots are critical infrastructure for road network reliability, yet systematic risk assessment methodologies for these facilities in developing contexts are underdeveloped. This creates challenges for prioritising maintenance investments and ensuring fleet operational readiness.", "purpose and objectives": "This case study presents and evaluates a novel Bayesian hierarchical modelling framework designed to quantify and reduce systemic risk within transport maintenance depot systems. The objective is to provide a robust, evidence-based tool for engineering decision-making under uncertainty.", "methodology": "A Bayesian hierarchical model was developed, integrating data on depot operational capacity, spare parts inventory, technician skill levels, and equipment failure rates. The core model structure is $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, with $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau{\\alpha}^2)$, where $y_{ij}$ represents a risk metric for component $i$ in depot $j$. Posterior distributions were estimated using Markov chain Monte Carlo simulation, with inference based on 95% credible intervals.", "findings": "The model application to a network of depots identified that procedural and inventory-related factors accounted for approximately 70% of the modifiable systemic risk. A key quantitative finding was that improving standardisation of repair protocols yielded the highest posterior probability (0.92) for significant risk reduction per unit of investment.", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous and adaptable framework for diagnosing and prioritising risk mitigation strategies in complex maintenance systems, moving beyond heuristic approaches.", "recommendations": "Implement the modelling framework as a decision-support tool for national depot network audits. Future work should integrate real-time sensor data to transition from periodic to continuous risk assessment.", "key words": "Bayesian hierarchical model, infrastructure risk, maintenance engineering, decision support, asset management", "contribution statement": "This study provides the first application of a Bayesian hierarchical model to the risk assessment of transport