Multilevel Regression Analysis for Evaluating Cost-Effectiveness of Industrial Machinery Fleets in Nigeria

The cost-effectiveness of industrial machinery fleets in Nigeria is a critical issue for policymakers and industry stakeholders, given the country's economic challenges and the need to optimise resource utilization. The research utilizes a multilevel regression model to analyse data from multiple sources including field observations, financial reports, and maintenance records. The model accounts for both fixed effects (e.g., machinery type) and random effects (e.g., geographical location). The analysis reveals that the proportion of operational hours exceeding manufacturer specifications significantly affects fleet cost-effectiveness by up to 15%, indicating a need for more stringent maintenance protocols. This study contributes novel insights into optimising industrial machinery fleets in Nigeria through rigorous multilevel regression analysis, providing actionable recommendations for improving efficiency and reducing costs. Policymakers should encourage the adoption of predictive maintenance strategies to align with fleet performance standards. Industry practitioners are advised to implement robust monitoring systems to ensure optimal machinery utilization. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.