A Bayesian Hierarchical Model for Manufacturing Systems Efficiency Diagnostics in the Ethiopian Industrial Sector (2000–2026)
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2003
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| _version_ | 1866901797798412288 |
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| author | Assefa, Tewodros Gebrehiwot, Meklit |
| author_facet | Assefa, Tewodros Gebrehiwot, Meklit |
| contents | <p>{ "background": "The industrial sector in Ethiopia has undergone significant expansion, yet systematic, data-driven diagnostics of manufacturing systems efficiency remain underdeveloped. Existing methods often fail to account for plant-level heterogeneity and the hierarchical structure of industrial data, limiting actionable insights for engineering management.", "purpose and objectives": "This study aims to develop and validate a novel Bayesian hierarchical model to diagnose efficiency in manufacturing systems, quantifying gains and identifying key drivers of performance variation across plants and subsectors.", "methodology": "We formulate a Bayesian hierarchical model where the efficiency $\eta{ij} = \\exp(-u{ij})$ for plant $i$ in group $j$ is modelled with $u{ij} \\sim \\text{Half-Normal}^{+}(\\sigmaj)$, and group-level parameters $\\sigmaj$ follow a hyperprior $\\sigmaj \\sim \\text{Inverse-Gamma}(\\alpha, \\beta)$. Inference uses Hamiltonian Monte Carlo, with model fit assessed via posterior predictive checks and Watanabe-Akaike information criterion.", "findings": "The model identified substantial inter-plant efficiency variation, with a posterior probability of 0.92 that the textile subsector's efficiency dispersion parameter exceeded that of agro-processing. Estimated median efficiency gains from targeting the worst-performing decile of plants exceeded 15 percentage points.", "conclusion": "The proposed model provides a robust diagnostic framework, successfully capturing multi-level efficiency dynamics within the manufacturing sector and offering a superior fit compared to conventional, non-hierarchical approaches.", "recommendations": "Industrial policy and plant management should adopt hierarchical diagnostic tools to prioritise interventions. Future research should integrate real-time operational data into the modelling framework for dynamic efficiency monitoring.", "key words": "Bayesian inference, efficiency diagnostics, hierarchical modelling, industrial engineering, manufacturing systems, stochastic frontiers", "contribution statement": "This paper introduces a novel Bayesian hierarchical stochastic frontier model, specifically tailored for the diagnostic analysis of manufacturing systems, and provides the first application yielding plant- and subsector-level efficiency estimates</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18972549 |
| institution | Zenodo |
| language | eng |
| publishDate | 2003 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Bayesian Hierarchical Model for Manufacturing Systems Efficiency Diagnostics in the Ethiopian Industrial Sector (2000–2026) Assefa, Tewodros Gebrehiwot, Meklit Bayesian hierarchical modelling manufacturing systems efficiency industrial diagnostics Sub-Saharan Africa data envelopment analysis stochastic frontier analysis productivity measurement <p>{ "background": "The industrial sector in Ethiopia has undergone significant expansion, yet systematic, data-driven diagnostics of manufacturing systems efficiency remain underdeveloped. Existing methods often fail to account for plant-level heterogeneity and the hierarchical structure of industrial data, limiting actionable insights for engineering management.", "purpose and objectives": "This study aims to develop and validate a novel Bayesian hierarchical model to diagnose efficiency in manufacturing systems, quantifying gains and identifying key drivers of performance variation across plants and subsectors.", "methodology": "We formulate a Bayesian hierarchical model where the efficiency $\eta{ij} = \\exp(-u{ij})$ for plant $i$ in group $j$ is modelled with $u{ij} \\sim \\text{Half-Normal}^{+}(\\sigmaj)$, and group-level parameters $\\sigmaj$ follow a hyperprior $\\sigmaj \\sim \\text{Inverse-Gamma}(\\alpha, \\beta)$. Inference uses Hamiltonian Monte Carlo, with model fit assessed via posterior predictive checks and Watanabe-Akaike information criterion.", "findings": "The model identified substantial inter-plant efficiency variation, with a posterior probability of 0.92 that the textile subsector's efficiency dispersion parameter exceeded that of agro-processing. Estimated median efficiency gains from targeting the worst-performing decile of plants exceeded 15 percentage points.", "conclusion": "The proposed model provides a robust diagnostic framework, successfully capturing multi-level efficiency dynamics within the manufacturing sector and offering a superior fit compared to conventional, non-hierarchical approaches.", "recommendations": "Industrial policy and plant management should adopt hierarchical diagnostic tools to prioritise interventions. Future research should integrate real-time operational data into the modelling framework for dynamic efficiency monitoring.", "key words": "Bayesian inference, efficiency diagnostics, hierarchical modelling, industrial engineering, manufacturing systems, stochastic frontiers", "contribution statement": "This paper introduces a novel Bayesian hierarchical stochastic frontier model, specifically tailored for the diagnostic analysis of manufacturing systems, and provides the first application yielding plant- and subsector-level efficiency estimates</p> |
| title | A Bayesian Hierarchical Model for Manufacturing Systems Efficiency Diagnostics in the Ethiopian Industrial Sector (2000–2026) |
| topic | Bayesian hierarchical modelling manufacturing systems efficiency industrial diagnostics Sub-Saharan Africa data envelopment analysis stochastic frontier analysis productivity measurement |
| url | https://doi.org/10.5281/zenodo.18972549 |