"background": "Maintenance systems for transport depots are critical infrastructure assets, yet robust frameworks for diagnosing their operational efficiency are lacking. Current evaluations often rely on descriptive metrics, failing to isolate the causal impact of specific interventions from confounding operational variables. ", "purpose and objectives": "This article presents a novel quasi-experimental framework designed to rigorously measure efficiency gains within depot maintenance systems. The objective is to provide a methodological tool for engineers and managers to diagnose performance and validate improvement strategies. ", "methodology": "The proposed framework employs a difference-in-differences design, comparing maintenance output metrics between treatment depots (implementing a new intervention) and matched control depots over time. The core statistical model is Y{it = \0 + \1 + \2 + \ (\) + \₈ₓ, where \ captures the causal effect. Inference relies on cluster-robust standard errors to account for depot-level heterogeneity. ", "findings": "As a methodology article, this paper presents analytical findings, not empirical results. The framework's diagnostic power is demonstrated through a simulated case study, where it correctly identifies a 15% efficiency gain attributable to a predictive maintenance intervention, with a 95% confidence interval of 11. 2%, 18. 8%. ", "conclusion": "The developed framework provides a rigorous, transportable methodology for causal efficiency diagnostics in maintenance systems, moving beyond associative metrics. ", "recommendations": "Practitioners should adopt quasi-experimental designs to evaluate depot interventions. Future research should apply this framework to generate empirical benchmarks across different depot types and regions. ", "key words": "quasi-experimental design, maintenance efficiency, transport infrastructure, difference-in-differences, causal inference, depot management", "contribution statement": "This article provides the first formalised quasi-experimental methodology for causal efficiency analysis
Merwe et al. (2014) studied this question.