{ "background": "The management of industrial machinery fleets represents a significant capital and operational expenditure for engineering and construction firms in Nigeria. Current evaluation practices often rely on cross-sectional or aggregated financial data, which fail to account for unobserved heterogeneity and dynamic efficiency changes across firms and time.", "purpose and objectives": "This article presents a novel panel-data methodology to rigorously evaluate the cost-effectiveness of heavy machinery fleets. The objective is to provide a robust analytical framework that isolates the impact of utilisation, maintenance regimes, and fleet composition on total cost of ownership.", "methodology": "The proposed methodology employs a fixed-effects panel regression model. The core specification is $C{it} = \\alphai + \\beta1 U{it} + \\beta2 M{it} + \\beta3 A{it} + \\gamma Z{it} + \\epsilon{it}$, where $C_{it}$ is the total cost per operating hour for firm $i$ in period $t$, $U$ is utilisation, $M$ is maintenance expenditure, $A$ is average fleet age, and $Z$ is a vector of controls. Inference is based on cluster-robust standard errors to account for serial correlation.", "findings": "Application of the methodology to a simulated dataset, reflecting typical industry conditions, demonstrates its utility. A key finding is that the marginal cost of poor maintenance escalates non-linearly, with a 10% reduction in scheduled maintenance spend leading to a 15–22% increase in total cost per hour, a relationship obscured in pooled analyses.", "conclusion": "The panel-data approach provides a superior framework for cost-effectiveness analysis by controlling for time-invariant firm-specific factors, leading to more accurate identification of causal drivers of machinery costs.", "recommendations": "Practitioners and analysts should adopt panel-data techniques for fleet evaluation. Future research should focus on collecting standardised, time-series data on machinery performance to facilitate broader application of this methodology.", "key words": "panel