"background": "Industrial machinery fleets are critical capital assets, yet systematic methodologies for evaluating their operational efficiency in developing economies are lacking. In Uganda, ad-hoc maintenance and heterogeneous usage patterns complicate performance assessment, hindering evidence-based asset management. ", "purpose and objectives": "This study aims to develop and apply a robust methodological framework for evaluating the efficiency of industrial machinery fleets. The primary objective is to quantify efficiency gains using a multilevel regression model that accounts for operational heterogeneity. ", "methodology": "A novel two-stage methodology was employed. First, a systematic evaluation protocol was developed to standardise data collection on fleet utilisation, maintenance logs, and fuel consumption across multiple industrial sites. Second, a multilevel linear regression model was fitted to the panel data. The core statistical model is y{ij = \0 + \1x1ij + uj + eij, where yij is the efficiency metric for machine i in fleet j, x1ij denotes standardised operational hours, uj represents random fleet-level effects, and eij is the residual error. Robust standard errors were used for inference. ", "findings": "The multilevel analysis revealed that implementing the standardised evaluation protocol was associated with a significant 18. 2% average improvement in fuel-use efficiency across monitored fleets (95% CI: 14. 5% to 21. 9%). The random effects structure confirmed that approximately 30% of the variance in efficiency outcomes was attributable to differences between fleets, rather than individual machinery. ", "conclusion": "The study demonstrates that a structured methodological approach, combined with multilevel modelling, can effectively isolate and measure efficiency gains in heterogeneous industrial machinery operations, moving beyond descriptive performance reporting. ", "recommendations": "Fleet managers should adopt standardised evaluation protocols to generate comparable performance data. Policymakers and industry bodies are encouraged to integrate such methodologies into national equipment management guidelines to
Kato et al. (Fri,) studied this question.