This study aims to evaluate clinical outcomes in Senegalese manufacturing plants by applying a Bayesian hierarchical model. A Bayesian hierarchical model was employed with clinical outcomes data from Senegalese manufacturing plants. This approach allows for capturing both individual variation within plants and across different regions, providing a nuanced understanding of the factors influencing efficiency. The analysis revealed significant regional disparities in plant performance, with certain districts showing up to 20% higher productivity gains compared to others when implementing specific interventions. This study provides evidence that Bayesian hierarchical models offer a robust framework for evaluating and optimising manufacturing systems across diverse regions. The findings highlight the necessity of tailored interventions based on regional characteristics. Manufacturing managers should consider regional-specific strategies to maximise efficiency, which could include localized training programmes or resource allocation. Bayesian Hierarchical Model, Clinical Outcomes, Manufacturing Systems, Senegal Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.