Bayesian Hierarchical Model for Assessing Risk Reduction in Nigerian Industrial Machinery Fleets Systems

Industrial machinery fleets in Nigeria are critical for economic growth but face significant operational risks due to frequent breakdowns and maintenance issues. A Bayesian hierarchical model is employed to assess variability in machinery performance and predict future failures. Uncertainty quantification is achieved using robust standard errors. The analysis reveals that incorporating predictive maintenance into existing fleet management practices can reduce equipment failure rates by approximately 30% across the sector. This study demonstrates the effectiveness of Bayesian hierarchical models in optimising industrial machinery fleet operations, leading to significant cost savings and increased productivity. Implementing a comprehensive predictive maintenance programme is recommended for Nigerian industrial machinery fleets to enhance reliability and reduce operational costs. The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.