"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 = \0 + \1 + \2 + \ (\) +, where Yit is the yield metric. Inference was based on cluster-robust standard errors at the fleet level. ", "findings": "The DiD estimator \ 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
Wanjiku Mwangi (Sun,) studied this question.