Comparative Evaluation of Maintenance Depot Methodologies: A Quasi-Experimental Analysis of System Adoption in Nigeria (2000–2026)

{ "background": "The persistent underperformance of transport infrastructure in Nigeria is partly attributed to inefficient maintenance regimes. While various depot management systems have been introduced, there is a paucity of rigorous, comparative evidence on their real-world adoption and efficacy within the national context.", "purpose and objectives": "This study aims to comparatively evaluate the adoption rates and operational impacts of three distinct maintenance depot methodologies—Preventive, Predictive, and Reliability-Centred Maintenance (RCM)—implemented across the country's transport sector.", "methodology": "A quasi-experimental, difference-in-differences design was employed, analysing longitudinal operational data from 42 depots. Adoption rates were modelled using a multinomial logistic regression: $\log\((=j)/P(=)\) = \β{0j} + \β{1j} + \β{2j} + \β{3j}( \× ) + \εᵢⱼ$, with robust standard errors clustered at the depot level.", "findings": "The Predictive Maintenance system demonstrated significantly higher full adoption (68%, 95% CI [62, 74]) compared to Preventive (41%) and RCM (52%) systems. The treatment effect for Predictive Maintenance on mean time between failures was positive and statistically significant (p < 0.01).", "conclusion": "Methodological choice substantially influences the successful implementation of depot systems. Predictive Maintenance, leveraging data-driven diagnostics, proved most readily adoptable and effective within the studied operational environments.", "recommendations": "Policy and investment should prioritise data infrastructure and skills development to enable Predictive Maintenance adoption. A phased integration of Predictive principles into existing Preventive frameworks is advised for legacy depots.", "key words": "infrastructure management, maintenance engineering, quasi-experiment, adoption rate, transport depots,