Industrial machinery fleets in Rwanda face challenges related to operational costs and maintenance efficiency. A Bayesian hierarchical model was employed to analyse fleet data from multiple industrial sectors. This approach accounts for variability across different machinery types and operating conditions. The analysis revealed significant cost savings potential through targeted maintenance interventions, with estimated reductions in annual costs of up to 15% when compared to existing practices. Bayesian hierarchical modelling provided a robust framework for assessing the financial impact of industrial machinery operations in Rwanda, offering actionable insights for fleet managers. Implementing the recommended maintenance strategies could lead to substantial cost savings and improved operational efficiency within Rwandan industrial machinery fleets. industrial machinery, fleet management, cost-effectiveness, Bayesian hierarchical model, Rwanda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.