"background": "Maintenance systems in transport depots are critical for operational safety and efficiency, yet they are often managed using ad-hoc, reactive approaches. In Nigeria, the lack of structured, evidence-based methodologies for evaluating and improving these systems contributes to persistent operational risks and infrastructure failures. ", "purpose and objectives": "This article presents a novel methodological framework for conducting randomised field trials (RFTs) specifically designed to evaluate engineering interventions within transport depot maintenance systems. The primary objective is to provide a rigorous protocol for measuring causal impacts on risk reduction metrics. ", "methodology": "The proposed RFT methodology employs a cluster-randomised, stepped-wedge design where depots are randomised to receive a systematised preventive maintenance intervention at different sequential steps. The core impact is estimated using a generalised linear mixed model: \ (P (Y{it=1) ) = \0 + \ Tit + \ + ui +, where Yit is a binary failure event for depot i at time t, Tit is the treatment indicator, \ are time-fixed effects, and ui are depot random effects. Inference is based on cluster-robust standard errors. ", "findings": "As a methodology article, it presents no empirical results from a completed trial. However, the framework's simulation studies, based on parameter estimates from pilot data, indicate that the design can detect a minimum 30% reduction in critical failure rates with 80% power. A key thematic finding from the protocol development is the critical importance of embedding lead engineers as co-researchers to ensure intervention fidelity. ", "conclusion": "The developed RFT protocol provides a viable and rigorous experimental framework for generating high-quality evidence on engineering interventions in complex, real-world depot environments. It addresses specific challenges such as contamination between units and sequential rollout constraints. ", "recommendations": "Researchers and depot engineers should adopt this structured RFT approach to move
Sani et al. (Sat,) studied this question.
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