Bayesian Hierarchical Model for Measuring System Reliability in Ugandan Industrial Machinery Fleets

Industrial machinery fleets in Uganda face significant reliability challenges due to varying maintenance practices and environmental conditions. A Bayesian hierarchical regression model was employed to analyse data from multiple Ugandan industrial machinery fleets. The model accounts for both fixed and random effects, allowing for the estimation of mean failure times with associated uncertainty intervals. The analysis revealed that a significant proportion (35%) of components in one fleet had failure rates exceeding industry standards, indicating potential maintenance deficiencies. The Bayesian hierarchical model provided robust estimates of system reliability, facilitating targeted interventions to enhance fleet performance and safety. Implementing preventive maintenance strategies based on the identified critical component failures can improve overall machinery reliability in Ugandan industrial settings. Bayesian Hierarchical Model, System Reliability, Industrial Machinery, Uganda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.