Methodological Evaluation and Risk Reduction in Ethiopian Transport Maintenance Depots: A Multilevel Regression Analysis

{ "background": "Transport maintenance depots are critical infrastructure for national economies, yet their operational systems in developing nations are often under-evaluated. In the Ethiopian context, a systematic, data-driven assessment of the methodological frameworks used to manage these depots and quantify associated risks is lacking, hindering targeted engineering interventions.", "purpose and objectives": "This case study aims to methodologically evaluate the existing operational systems within a network of transport maintenance depots. Its core objective is to develop and apply a multilevel regression model to measure the potential for risk reduction in structural and operational failures.", "methodology": "A cross-sectional case study design was employed, collecting hierarchical data on depot characteristics, maintenance protocols, and incident reports. The analysis utilised a two-level random intercept model, formalised as yij = \β0 + \β1Xij + uj + eij, where i denotes depots and j denotes regional clusters. Inference was based on robust standard errors to account for heteroskedasticity.", "findings": "The multilevel analysis identified that depots implementing a standardised digital inventory system exhibited a 37% lower incidence of critical spare part shortages (95% CI: 24% to 49%). The model revealed significant variance at the regional cluster level, indicating that systemic risk is strongly influenced by supra-depot management factors.", "conclusion": "The methodological approach confirms that depot performance and risk are inherently hierarchical. Quantifying the variance components provides a robust evidence base for prioritising interventions at the appropriate organisational level to enhance structural engineering resilience.", "recommendations": "Depot managers should prioritise the adoption of integrated digital inventory management. Policymakers and regional authorities must develop standardised operational frameworks to reduce cluster-level variance in maintenance outcomes.", "key words": "infrastructure management, multilevel modelling, maintenance systems, operational risk, developing contexts", "contribution statement": "This study provides a novel application of hierarchical