"background": "The operational efficiency of transport maintenance depots is critical for national infrastructure, yet systematic, data-driven evaluations of their performance in developing contexts are scarce. Existing studies often lack the methodological rigour to account for hierarchical data structures inherent in networked systems. ", "purpose and objectives": "This study aims to develop and apply a multilevel modelling framework to evaluate the performance of maintenance depot systems, with the objective of identifying key operational factors that significantly influence yield improvement within a large transport network. ", "methodology": "A multilevel regression model was specified to analyse depot-level performance data nested within regional administrative clusters. The core model is expressed as Y{ij = \0j + \1X1ij +. . . +, where \0j = \00 + \01Zj + u0j. Robust standard errors were used for inference. Data were collected from a census of depots across the national network. ", "findings": "The analysis revealed that depot yield is significantly predicted by inventory turnover rate (p < 0. 01) and technician-to-vehicle ratio (p < 0. 05). A one-standard-deviation increase in inventory turnover was associated with a 17. 3% improvement in yield. Random effects indicated substantial unexplained variance (31%) at the regional cluster level. ", "conclusion": "The multilevel approach successfully quantified the hierarchical determinants of depot yield, demonstrating that both depot-specific practices and broader regional logistical factors are consequential. The model provides a robust analytical foundation for performance benchmarking. ", "recommendations": "Network managers should prioritise policies to optimise inventory management and workforce allocation at the depot level, while also developing region-specific strategies to address cluster-level inefficiencies identified by the model. ", "key words": "multilevel modelling, infrastructure maintenance, depot performance, regression analysis, transport engineering, yield improvement", "contribution statement": "This paper introduces a novel
Abebe et al. (Tue,) studied this question.