Bayesian Hierarchical Model Evaluation of Process-Control Systems in Nigerian Manufacturing Industries

The implementation of process-control systems in Nigerian manufacturing industries aims to enhance safety and efficiency, but their effectiveness varies widely across different sectors and companies. The methodology involves collecting data from multiple manufacturing plants in Nigeria, applying a Bayesian hierarchical model to estimate the impact of various control system features. Uncertainty is quantified through credible intervals around estimated cost-effectiveness metrics. Bayesian hierarchical modelling revealed that the type and scale of process-control systems significantly affect their cost-effectiveness, with medium-sized enterprises benefiting more from advanced control solutions compared to small-scale operations. The study provides insights into optimising the deployment of process-control systems in Nigerian manufacturing by tailoring system configurations based on plant size and specific operational needs. Manufacturing companies should consider the scale of their operation when selecting process-control systems, with a preference for more sophisticated solutions for larger enterprises to maximise cost-effectiveness. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.