Journal Design Engineering Masthead
African Civil Engineering Journal | 23 June 2025

Comparative Evaluation of Maintenance Depot Methodologies

A Quasi-Experimental Analysis of System Adoption in Nigeria (2000–2026)
F, a, t, i, m, a, S, u, l, e, i, m, a, n, ,, C, h, i, n, e, l, o, O, k, o, n, k, w, o, ,, A, d, e, b, a, y, o, A, d, e, y, e, m, i
Maintenance EngineeringSystem AdoptionQuasi-ExperimentalTransport Infrastructure
Predictive Maintenance showed highest adoption (68%) in Nigeria's transport depots.
Quasi-experimental design compared three methodologies across 42 depots.
Data-driven Predictive systems proved most effective for local operational environments.
Findings support prioritising data infrastructure for maintenance policy.

Abstract

{ "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: $\\log\\left(\\frac{P(\\text{System}=j)}{P(\\text{System}=\\text{Baseline})}\\right) = \\beta{0j} + \\beta{1j} \\text{Time} + \\beta{2j} \\text{Treatment} + \\beta{3j}(\\text{Time} \\times \\text{Treatment}) + \\epsilon_{ij}$, 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,