Bayesian hierarchical models have been increasingly applied in various fields to analyse complex systems, including process-control systems in engineering applications. This study employs a Bayesian hierarchical model, which allows for the integration of data from multiple sources and levels of aggregation, thereby enhancing the accuracy and robustness of risk assessment. The analysis reveals that the Bayesian hierarchical model can effectively reduce uncertainty by incorporating prior knowledge and local data into a global model, with an average reduction in prediction error of approximately 15% compared to traditional methods. The findings suggest that Bayesian hierarchical models provide a promising method for assessing risk in process-control systems, particularly when dealing with limited or high-variance data. Future studies should explore the scalability and applicability of this model across different types of engineering processes and regions. Additionally, further research is needed to validate these results through controlled experiments. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.