{ "background": "Industrial machinery fleet management is critical for productivity in developing economies, yet systematic methodological evaluations of its impact on operational yield are scarce. In Kenya, the performance of such fleets in sectors like manufacturing and agro-processing is often suboptimal, with a lack of quantitative frameworks to diagnose inefficiencies and guide improvement.", "purpose and objectives": "This study aims to develop and apply a novel multilevel regression framework to methodologically evaluate industrial machinery fleet systems, with the primary objective of quantifying their impact on yield improvement. It seeks to identify key operational and maintenance factors influencing system-level output.", "methodology": "A cross-sectional dataset was constructed from technical audits, maintenance logs, and production records of 127 machinery fleets across multiple industrial sectors. A three-level hierarchical linear model was specified: $Y{ij} = \\beta{0j} + \\beta{1j}X{1ij} + \\epsilon{ij}$, with $\\beta{0j} = \\gamma{00} + \\gamma{01}Zj + u{0j}$, where $i$, $j$ index machines and fleets, respectively. Model estimation used restricted maximum likelihood with robust standard errors.", "findings": "The multilevel analysis revealed that preventive maintenance compliance was the strongest positive predictor of yield at the machine level, with a one standard deviation increase associated with a 15.7% yield improvement (95% CI: 12.3% to 19.1%). At the fleet level, the integration of telematics for real-time monitoring significantly moderated this relationship.", "conclusion": "The methodological framework confirms that yield is a function of interdependent factors operating at both machine and fleet management levels. A siloed focus on individual machine performance is insufficient for optimal system output.", "recommendations": "Industry practitioners should adopt integrated fleet management systems that prioritise data-driven preventive maintenance schedules. Policymakers are advised to support technical training programmes focused on systemic operational analysis.", "key words": "fleet management,