Abstract
{ "background": "Transport maintenance depots in sub-Saharan Africa face systemic inefficiencies, leading to suboptimal asset availability and high operational costs. There is a recognised lack of rigorous, field-based evidence on the impact of systematic interventions within these complex engineering systems.", "purpose and objectives": "This case study aimed to methodologically evaluate a randomised field trial designed to measure and optimise yield—defined as the proportion of vehicles fully operational—within a representative depot network. The primary objective was to quantify the causal effect of a revised maintenance scheduling protocol on overall fleet yield.", "methodology": "A pragmatic randomised controlled trial was implemented across 24 depots. Depots were randomly assigned to either a treatment group, implementing a new predictive maintenance scheduling system, or a control group continuing standard practice. Yield was measured monthly over an operational period. The treatment effect was estimated using a linear mixed model: $Y{it} = \\beta0 + \\beta1 Ti + \\gamma X{it} + ui + \\epsilon{it}$, where $Y{it}$ is yield, $Ti$ is the treatment indicator, $X{it}$ are time-varying covariates, and $u_i$ are depot random effects.", "findings": "The intervention produced a statistically significant positive effect. Depots using the new protocol achieved a mean yield increase of 8.7 percentage points (95% CI: 5.2, 12.1) compared to control depots. The effect was robust to different model specifications and showed increasing benefit over the trial's duration.", "conclusion": "The randomised field trial confirmed that structured, data-informed scheduling can substantially improve operational yield in real-world maintenance engineering contexts. The methodology proved feasible and generated high-quality causal evidence.", "recommendations": "Depot managers should adopt predictive scheduling systems underpinned by continuous data collection. Policymakers should support further field trials to test scalable interventions across different engineering asset classes.", "key words": "randomised controlled trial, maintenance