{ "background": "The adoption of advanced manufacturing systems in developing economies is a critical driver of industrialisation, yet robust methodological frameworks for evaluating its progress are lacking. Existing assessments often rely on aggregate indicators that mask regional and sectoral heterogeneity.", "purpose and objectives": "This paper develops and assesses a novel Bayesian hierarchical modelling framework to evaluate the adoption rates of manufacturing plant systems. The objective is to provide a methodologically rigorous tool for capturing spatial and temporal variations in technological uptake.", "methodology": "A Bayesian hierarchical model is constructed, integrating plant-level survey data with regional economic covariates. The core adoption rate for plant $i$ in region $j$ is modelled as $\\text{logit}(p{ij}) = \\alphaj + \\beta X{ij}$, with $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\sigma^2{\\alpha})$. Inference uses Markov chain Monte Carlo simulation, with posterior distributions summarising parameter uncertainty.", "findings": "The model reveals significant regional disparities, with adoption rates in the industrial corridor approximately 2.3 times higher than in other regions (95% credible interval: 1.8 to 2.9). The hierarchical structure effectively captured unobserved regional heterogeneity, with the hyperparameter $\\sigma_{\\alpha}$ having a posterior median of 0.42.", "conclusion": "The proposed Bayesian hierarchical model offers a superior methodological framework for evaluating manufacturing systems adoption, providing probabilistic estimates that transparently communicate uncertainty and regional variation.", "recommendations": "Policymakers should utilise such probabilistic models to target regional interventions. Future research should integrate this framework with longitudinal data to analyse adoption pathways.", "key words": "Bayesian statistics, hierarchical model, technology adoption, manufacturing systems, industrial policy, probabilistic modelling", "contribution statement": "This paper introduces a novel Bayesian hierarchical modelling framework, specifically tailored to the context of developing economies, which quantifies regional adoption disparities and associated uncertainties more effectively than prior deterministic methods."