African Structural Engineering | 12 September 2009
Bayesian Hierarchical Model for Cost-Effectiveness Evaluation of Industrial Machinery Fleets in Nigeria: A Comparative Study
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Abstract
Industrial machinery fleets play a critical role in Nigeria's industrial sector, influencing productivity and economic growth. However, cost-effectiveness evaluations of these fleets remain underexplored. A Bayesian hierarchical model is employed to analyse fleet performance data across different industries. The model accounts for variability within and between industries, providing robust estimates of cost-effectiveness metrics such as return on investment (ROI). The analysis revealed significant variations in ROI among industrial sectors, with manufacturing showing the highest average ROI at 30% compared to utilities at 15%. These findings highlight sector-specific challenges and opportunities for cost optimization. Bayesian hierarchical models offer a nuanced approach to assessing cost-effectiveness in industrial machinery fleets, enabling tailored strategies for resource management across sectors. Policy makers should consider industry-specific cost-effectiveness metrics when designing support programmes, promoting targeted interventions that enhance fleet efficiency and economic performance. cost-effectiveness, Bayesian hierarchical model, industrial machinery, Nigeria, return on investment The maintenance outcome was modelled as $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.