{ "background": "Asset management for electrical power-distribution networks in developing economies requires robust, data-driven risk-assessment frameworks. A previously proposed Bayesian hierarchical model offered a novel approach for quantifying risk reduction from maintenance interventions on equipment such as transformers and circuit breakers.", "purpose and objectives": "This study aimed to replicate the methodological evaluation of the Bayesian hierarchical model for power-distribution asset management, assessing its reproducibility and refining its application to enhance operational decision-making for infrastructure in Ghana.", "methodology": "We conducted a computational replication using the originally specified model structure: $\\lambda{ij} \\sim \\text{Gamma}(\\alphai, \\betai)$, where $\\lambda{ij}$ is the failure rate for asset $j$ in category $i$, with hyperpriors on $\\alphai$ and $\\betai$. The model was re-implemented and tested on an expanded, proprietary dataset of equipment condition and failure records. Sensitivity analyses were performed on prior distributions.", "findings": "The replication confirmed the model's core functionality but revealed a critical sensitivity: posterior estimates for transformer failure rates were highly sensitive to the choice of hyperprior for $\\alpha_i$, with credible interval widths varying by up to 40% under different weakly informative priors. This underscores a previously unreported specification risk.", "conclusion": "The original model is methodologically sound but requires careful prior elicitation to produce reliable, actionable risk metrics. The replication validates its potential while highlighting a crucial refinement need for practical deployment.", "recommendations": "Asset managers should adopt the refined model with empirically informed hyperpriors, developed through expert judgement and historical data analysis. Future work should focus on integrating real-time sensor data to dynamically update the model parameters.", "key words": "asset management, Bayesian statistics, power distribution, reliability engineering, risk assessment, replication study", "contribution statement": "This study provides a verified and refined methodological tool for quantifying risk reduction, directly contributing to more resilient infrastructure management by identifying and mitigating