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 $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.