{ "background": "Manufacturing systems in developing economies face complex, multi-faceted risks that are often poorly quantified. Traditional risk assessment methods frequently lack the flexibility to integrate diverse data sources and account for variability across different plants and operational contexts.", "purpose and objectives": "This study presents a methodological evaluation of a novel Bayesian hierarchical model designed to measure and predict risk reduction within manufacturing systems. The objective is to provide a robust, adaptable framework for engineering risk management that can handle sparse and heterogeneous operational data.", "methodology": "The proposed model, $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigmay^2), \\; \\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\sigma_{\\alpha}^2)$, was applied to operational performance and incident data from multiple plants. Parameters were estimated using Hamiltonian Monte Carlo sampling, with model fit assessed via posterior predictive checks and Watanabe-Akaike information criterion.", "findings": "The model successfully quantified the reduction in operational risk associated with specific maintenance interventions. A key finding was that plants implementing structured predictive maintenance protocols showed a median reduction in high-risk event probability of 34% (95% credible interval: 28% to 41%). The hierarchical structure effectively captured plant heterogeneity.", "conclusion": "The Bayesian hierarchical model provides a statistically rigorous and operationally informative methodology for evaluating risk reduction strategies in complex manufacturing environments. It offers a superior alternative to conventional aggregate analyses.", "recommendations": "Manufacturing engineers and plant managers should adopt hierarchical modelling approaches to better inform capital allocation for safety and reliability upgrades. Further research should integrate real-time sensor data into the model's observational layer.", "key words": "Bayesian inference, hierarchical modelling, risk assessment, manufacturing engineering, operational reliability, predictive maintenance", "contribution statement": "This paper introduces a novel application of Bayesian hierarchical modelling to manufacturing risk quantification in an industrialising context, providing a new method for evidence-based decision-making that