Industrial machinery fleets in Rwanda face significant operational risks that can lead to downtime and increased maintenance costs. A Bayesian hierarchical model was developed to analyse data from Rwanda's industrial machinery fleet. The model accounts for both individual machine variability and shared environmental influences. The analysis revealed that temperature fluctuations had the most significant impact on equipment failure rates, with a proportion of 40% attributed to this factor. The Bayesian hierarchical model demonstrated its effectiveness in quantifying risk reduction strategies within industrial machinery fleets in Rwanda. Based on the findings, recommendations are made for implementing targeted maintenance schedules and upgrading infrastructure to mitigate temperature-induced risks. Bayesian Hierarchical Model, Risk Reduction, Industrial Machinery Fleets, Rwanda The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Nziza et al. (Thu,) studied this question.