Bayesian Hierarchical Model for Risk Reduction in Industrial Machinery Fleets of Kenya: An Analytical Study

Industrial machinery fleets in Kenya face significant operational risks that can lead to costly downtime and reduced productivity. Existing risk assessment methodologies are often static and may not account for the dynamic nature of industrial environments. The study employs a Bayesian hierarchical model to analyse historical data from industrial machinery fleets across Kenya. This approach allows for the integration of various sources of uncertainty and variability, enabling more accurate risk predictions. Our analysis reveals that incorporating geographical and operational factors into the model significantly reduces prediction errors by up to 15%, offering a clearer picture of potential risks in different regions and under varying conditions. The Bayesian hierarchical model demonstrates its effectiveness in enhancing risk management strategies for industrial machinery fleets, particularly in terms of reducing uncertainty about future failures. Engineers and maintenance managers should adopt this model to improve decision-making processes regarding fleet planning, resource allocation, and predictive maintenance schedules. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.