Abstract
{ "background": "Industrial machinery fleet management in developing economies is often characterised by high failure rates and costly downtime due to reactive maintenance strategies. In the Ethiopian context, a lack of robust, data-driven frameworks for risk assessment has historically impeded operational efficiency and capital planning for critical infrastructure projects.", "purpose and objectives": "This case study presents and evaluates a novel Bayesian hierarchical modelling framework designed to quantify and reduce operational risks in industrial machinery fleets. The primary objective is to demonstrate a methodological shift from deterministic to probabilistic risk assessment, enabling more informed maintenance and replacement decisions.", "methodology": "A case study methodology was employed, applying a Bayesian hierarchical model to fleet data. The core model structure is $y{ij} \\sim \\text{Weibull}(\\alphaj, \\betaj)$, with $\\alphaj \\sim \\text{Normal}(\\mu\\alpha, \\sigma\\alpha)$ and $\\betaj \\sim \\text{Normal}(\\mu\\beta, \\sigma\\beta)$, where $y{ij}$ is time-to-failure for machine $i$ in fleet $j$. Parameters were estimated using Hamiltonian Monte Carlo, with posterior credible intervals used for inference on failure rate reductions.", "findings": "The application of the model demonstrated a quantifiable reduction in predicted operational risk. A key finding was a central estimate of a 22% reduction in the annual probability of critical fleet-wide failure events when the model's recommendations were simulated. Uncertainty in this estimate was captured by an 80% posterior credible interval of 15% to 28% reduction.", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous and adaptable framework for risk-informed machinery management. It successfully synthesises disparate data sources to yield probabilistic insights superior to traditional averaging methods.", "recommendations": "Fleet operators should adopt probabilistic risk models to prioritise capital expenditure. National engineering institutions are encouraged to integrate such methodologies into asset management guidelines to improve lifecycle cost efficiency.", "key words": "Bay