{ "background": "Transport maintenance depots in Kenya face systemic inefficiencies, leading to suboptimal resource utilisation and yield. Existing diagnostic approaches often lack structured, evidence-based frameworks tailored to the operational constraints of such depots. ", "purpose and objectives": "This study aimed to empirically evaluate a novel diagnostic framework designed to identify and rectify yield-limiting factors in transport maintenance depot systems. The primary objective was to measure the framework's causal impact on yield improvement through a randomised field trial. ", "methodology": "A randomised controlled trial was conducted across multiple depots. Depots were randomly assigned to either a treatment group, implementing the diagnostic framework, or a control group, continuing standard practice. Yield was measured as the ratio of productive maintenance hours to total available hours. The impact was estimated using a linear regression model: Yi = \0 + \1 Ti + \\ + \, where Yi is yield, Ti is the treatment indicator, and ᵢ is a vector of depot-level covariates. Robust standard errors were used for inference. ", "findings": "Implementation of the diagnostic framework led to a statistically significant mean yield increase of 17. 3 percentage points (95% CI: 12. 1 to 22. 5; p<0. 01) relative to the control group. The most substantial improvements were linked to the reorganisation of inventory management and workflow scheduling protocols identified by the framework. ", "conclusion": "The diagnostic framework is an effective tool for systematically optimising yield in transport maintenance depots. The results provide strong evidence that structured, data-driven diagnostics can substantially improve operational efficiency in this context. ", "recommendations": "The framework should be integrated into regular depot management cycles. Further research should investigate its scalability to other infrastructure maintenance sectors and its long-term sustainability. ", "key words": "maintenance engineering, yield optimisation, randomised controlled trial, diagnostic framework, depot management, resource efficiency
Mwangi et al. (Thu,) studied this question.