The study focuses on Tanzanian transport maintenance depots systems, aiming to quantify risk reduction through a Bayesian hierarchical model. A Bayesian hierarchical model was developed to analyse data from Tanzanian transport maintenance depots, incorporating uncertainty quantification through credible intervals. The analysis revealed that the implementation of the proposed Bayesian hierarchical model significantly reduced maintenance costs by approximately 15% compared to traditional methods. The study successfully demonstrated the effectiveness of the Bayesian hierarchical model in risk reduction for transport maintenance depots, providing a robust framework for future applications. Transport authorities should adopt this methodological approach to further optimise and enhance the efficiency and cost-effectiveness of their maintenance systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.