This study evaluates the effectiveness of transport maintenance depots in Tanzania by applying Bayesian hierarchical models to assess risk reduction strategies. Bayesian hierarchical models were employed to analyse data from multiple depots, allowing for the incorporation of varying levels of uncertainty and providing insights into system performance variability across different locations and conditions. The analysis revealed significant differences in failure rates among depots, with some showing a 20% reduction in maintenance costs compared to baseline estimates. Bayesian hierarchical models effectively highlight variations in depot performance, enabling targeted interventions to improve overall efficiency and safety of Tanzanian transport systems. Based on the findings, recommendations include prioritising depots with lower failure rates for investment and training programmes to enhance maintenance capabilities. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Sserunkuma et al. (Tue,) studied this question.