Abstract
{ "background": "Maintenance systems for transport depots are critical infrastructure assets, yet robust frameworks for diagnosing their cost-effectiveness are lacking. Current evaluations often rely on descriptive analytics, which fail to establish causal links between interventions and outcomes, leading to inefficient resource allocation.", "purpose and objectives": "This paper develops and presents a novel quasi-experimental framework specifically designed to diagnose the cost-effectiveness of maintenance systems in transport depots. The objective is to provide a methodological tool that isolates the causal effect of maintenance strategies on cost and performance metrics.", "methodology": "A difference-in-differences (DiD) design is proposed, comparing depot performance before and after a systematic intervention against a control group of similar, untreated depots. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ is the causal parameter of interest. Robust standard errors are clustered at the depot level to account for serial correlation.", "findings": "The application of the framework to a pilot study demonstrated its diagnostic capability, revealing that a targeted preventive maintenance programme yielded a statistically significant reduction in unscheduled downtime. The estimated treatment effect indicated a 22% decrease in downtime hours, with a 95% confidence interval of [15%, 29%].", "conclusion": "The proposed quasi-experimental framework provides a rigorous, evidence-based methodology for evaluating maintenance system efficiency, moving beyond associative analysis to causal inference.", "recommendations": "It is recommended that depot operators and infrastructure managers adopt this diagnostic framework for periodic system audits and to validate the return on investment of new maintenance technologies or procedural changes.", "key words": "quasi-experimental design, maintenance management, cost-effectiveness, transport infrastructure, difference-in-differences, causal inference", "contribution statement": "This paper