African Nanotechnology Applications (Technology) | 17 May 2005
Bayesian Hierarchical Model for Measuring Cost-Effectiveness of Transport Maintenance Depots in Ethiopia
Z, e, r, i, h, u, n, A, s, s, e, f, a, ,, S, e, l, a, s, s, i, e, G, e, b, r, e, a, b, ,, F, i, k, a, d, u, H, a, i, l, e, m, a, r, i, a, m, ,, M, a, m, o, N, e, g, u, s, s, e
Abstract
This study focuses on evaluating the cost-effectiveness of transport maintenance depots (TMDs) in Ethiopia's logistics system. A Bayesian hierarchical model was employed to analyse data from multiple depots across different regions of Ethiopia. The model accounts for spatial variability in cost-effectiveness and incorporates uncertainty through robust standard errors. The analysis revealed significant variation in the cost-effectiveness of TMDs, with some depots showing substantial cost savings compared to others. Bayesian hierarchical modelling provides a nuanced approach to understanding cost-effectiveness, enabling targeted improvements in depot operations and resource management. Based on findings, recommendations include prioritising the expansion of more cost-effective depots and enhancing maintenance practices for improved efficiency. The maintenance outcome was modelled as $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.