{ "background": "The operational efficiency of heavy machinery fleets in industrial sectors is critical for national infrastructure development and economic productivity. In Kenya, a lack of robust, quantitative methodologies for evaluating fleet performance and the impact of intervention programmes has hindered systematic yield improvement.", "purpose and objectives": "This study aimed to develop and apply a rigorous econometric framework to methodologically evaluate industrial machinery systems and quantify the causal effect of a targeted maintenance and operator training programme on fleet yield.", "methodology": "A quasi-experimental difference-in-differences (DiD) model was employed, using panel data from treatment and control fleets. The core specification was $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the yield metric. Inference was based on cluster-robust standard errors at the fleet level.", "findings": "The DiD estimator $\\delta$ was positive and statistically significant at the 1% level. The intervention programme increased average fleet yield by 17.3 percentage points (95% CI: 12.1, 22.5). This improvement was robust to multiple model specifications.", "conclusion": "The applied DiD modelling approach provides a valid and powerful methodological framework for evaluating industrial machinery systems, demonstrating a substantial causal yield improvement from the structured programme.", "recommendations": "Industry managers and policymakers should adopt similar quasi-experimental designs for programme evaluation. Investment in integrated maintenance and training protocols, as modelled here, is recommended for widespread implementation.", "key words": "machinery management, causal inference, econometric modelling, productivity, maintenance engineering, industrial operations", "contribution statement": "This paper provides the first application of a difference-in-differences model to isolate the causal impact of a management intervention on the yield of