A Multilevel Regression Analysis of Maintenance Depot Systems for Yield Improvement in Ethiopian Transport Networks

{ "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 Yij = \β0j + \β1X1ij + ... + \εij, where \β0j = \γ00 + \γ01Zj + 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