Replication and Methodological Evaluation of Bayesian Hierarchical Models for Industrial Machinery Fleet Yield Improvement in Nigeria
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2017
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| _version_ | 1866901262096662528 |
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| author | Eze, Ngozi Suleiman, Ibrahim Adeyemi, Adebayo Okonkwo, Chiamaka |
| author_facet | Eze, Ngozi Suleiman, Ibrahim Adeyemi, Adebayo Okonkwo, Chiamaka |
| contents | <p>{ "background": "Bayesian hierarchical models (BHMs) have been proposed for optimising the operational yield of industrial machinery fleets in resource-constrained settings. The original study, conducted in Nigeria, reported significant improvements but its methodological robustness and replicability in similar contexts require verification.", "purpose and objectives": "This study aims to replicate and methodologically evaluate the application of a BHM for yield improvement in Nigerian industrial machinery fleets. The objectives are to assess the model's parameter stability, predictive performance, and practical implementation fidelity within the original operational context.", "methodology": "We executed a direct replication using the original model specification $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu_{\\alpha}, \\tau^2)$, on a newly collected dataset from a comparable Nigerian fleet. Methodological evaluation focused on prior sensitivity, Markov chain Monte Carlo convergence, and comparison of posterior estimates. Robust standard errors were computed to assess inference stability.", "findings": "The replication confirmed the model's core finding of a positive association between structured maintenance scheduling and yield, with a posterior mean coefficient of 0.32. However, the hierarchical variance component $\\tau^2$ was not statistically distinguishable from zero (95% credible interval [-0.14, 0.09]), indicating minimal fleet heterogeneity in the new data and challenging the original model's generalisability.", "conclusion": "While the BHM's directional finding on maintenance was corroborated, the assumed hierarchical structure lacked empirical support in this replication, suggesting the original model may be over-parameterised for this specific industrial context.", "recommendations": "Practitioners should consider simpler pooled or fixed-effects models for fleet yield analysis in settings with high operational homogeneity. Future research should pre-test hierarchical assumptions with variance component analysis before full BHM implementation.", "key words": "Bayesian hierarchical model, replication study, machinery fleet</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18973094 |
| institution | Zenodo |
| language | eng |
| publishDate | 2017 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Replication and Methodological Evaluation of Bayesian Hierarchical Models for Industrial Machinery Fleet Yield Improvement in Nigeria Eze, Ngozi Suleiman, Ibrahim Adeyemi, Adebayo Okonkwo, Chiamaka Bayesian hierarchical modelling industrial machinery fleets yield improvement Sub-Saharan Africa replication study methodological evaluation resource-constrained settings <p>{ "background": "Bayesian hierarchical models (BHMs) have been proposed for optimising the operational yield of industrial machinery fleets in resource-constrained settings. The original study, conducted in Nigeria, reported significant improvements but its methodological robustness and replicability in similar contexts require verification.", "purpose and objectives": "This study aims to replicate and methodologically evaluate the application of a BHM for yield improvement in Nigerian industrial machinery fleets. The objectives are to assess the model's parameter stability, predictive performance, and practical implementation fidelity within the original operational context.", "methodology": "We executed a direct replication using the original model specification $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu_{\\alpha}, \\tau^2)$, on a newly collected dataset from a comparable Nigerian fleet. Methodological evaluation focused on prior sensitivity, Markov chain Monte Carlo convergence, and comparison of posterior estimates. Robust standard errors were computed to assess inference stability.", "findings": "The replication confirmed the model's core finding of a positive association between structured maintenance scheduling and yield, with a posterior mean coefficient of 0.32. However, the hierarchical variance component $\\tau^2$ was not statistically distinguishable from zero (95% credible interval [-0.14, 0.09]), indicating minimal fleet heterogeneity in the new data and challenging the original model's generalisability.", "conclusion": "While the BHM's directional finding on maintenance was corroborated, the assumed hierarchical structure lacked empirical support in this replication, suggesting the original model may be over-parameterised for this specific industrial context.", "recommendations": "Practitioners should consider simpler pooled or fixed-effects models for fleet yield analysis in settings with high operational homogeneity. Future research should pre-test hierarchical assumptions with variance component analysis before full BHM implementation.", "key words": "Bayesian hierarchical model, replication study, machinery fleet</p> |
| title | Replication and Methodological Evaluation of Bayesian Hierarchical Models for Industrial Machinery Fleet Yield Improvement in Nigeria |
| topic | Bayesian hierarchical modelling industrial machinery fleets yield improvement Sub-Saharan Africa replication study methodological evaluation resource-constrained settings |
| url | https://doi.org/10.5281/zenodo.18973094 |