{ "background": "Process-control systems in industrial settings are critical for safety and efficiency. A previously proposed Bayesian hierarchical model offered a novel framework for quantifying risk reduction in such systems, but its methodological robustness and applicability in specific regional contexts, particularly within West African industrial infrastructure, remained untested.", "purpose and objectives": "This study aimed to conduct a rigorous, independent replication and validation of the specified Bayesian hierarchical model. The objective was to evaluate its methodological soundness and practical utility for assessing risk reduction in process-control systems within the Ghanaian industrial sector.", "methodology": "The replication involved re-implementing the core model, defined as $y{ij} \\sim \\text{Normal}(\\mu + \\alphai, \\sigma^2)$, $\\alphai \\sim \\text{Normal}(0, \\tau^2)$, where $y{ij}$ represents risk metrics. We used original and newly collected operational data from Ghanaian processing plants. Model performance was assessed through posterior predictive checks, sensitivity analyses, and comparison of inferred parameter distributions against the original study.", "findings": "The replication confirmed the model's structural validity but revealed a substantive discrepancy: the estimated group variance ($\\tau^2$) was 40% lower in the Ghanaian context, indicating less heterogeneity in risk profiles across different plants than previously modelled. Posterior distributions for key risk reduction coefficients were notably narrower, suggesting more precise inference with the new data.", "conclusion": "The Bayesian hierarchical model is methodologically sound but its parameterisation is context-sensitive. The replication underscores that while the framework is generalisable, prior distributions and variance structures require careful calibration to local operational conditions for accurate risk assessment.", "recommendations": "Future applications of the model in the region should employ empirically informed priors derived from local data. Engineers and risk managers should incorporate this calibrated model into periodic safety audits to prioritise maintenance interventions.", "key words": "Bayesian replication, process safety, hierarchical modelling, risk assessment, industrial engineering", "contribution