A Quasi-Experimental Evaluation of Maintenance Regimes and Yield Optimisation in Nigerian Transport Depots

{ "background": "Transport maintenance depots in Nigeria face systemic inefficiencies, leading to suboptimal asset availability and yield. Existing evaluations often lack rigorous comparative frameworks to isolate the effects of specific maintenance interventions within operational environments.", "purpose and objectives": "This case study aims to methodologically evaluate the impact of two distinct maintenance regimes—preventive and predictive—on yield optimisation within a major transport depot. The primary objective is to quantify yield improvement attributable to each regime using a quasi-experimental design.", "methodology": "A quasi-experimental, non-equivalent groups design was implemented across two comparable depot sites. Yield, defined as operational vehicle-hours per available vehicle-day, was the primary outcome. The treatment site adopted an enhanced predictive maintenance regime using condition monitoring, while the control site continued with scheduled preventive maintenance. Data were analysed using a difference-in-differences model: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, with robust standard errors clustered at the depot level.", "findings": "The predictive maintenance regime demonstrated a statistically significant positive effect. The estimated coefficient $\\beta_3$ was 0.18 (95% CI: 0.12 to 0.24), indicating an 18% relative improvement in yield compared to the control group. This translates to a substantial increase in average daily operational vehicle-hours.", "conclusion": "The structured quasi-experimental approach provides robust evidence that a predictive maintenance strategy, when systematically applied, can significantly enhance yield in transport depots beyond the capabilities of traditional scheduled preventive regimes.", "recommendations": "Depot managers should prioritise investment in condition monitoring technologies and staff training for predictive maintenance. Policymakers should develop frameworks to support the adoption of such data-driven regimes across the transport sector.", "key words": "quasi