Efficient operations in manufacturing plants are crucial for economic growth in Ethiopia, a developing country with significant industrial potential. A Bayesian hierarchical model was developed using data from multiple Ethiopian manufacturing sites. The model accounts for variability across different plants while estimating overall efficiency changes over time. The model identified an average improvement of 15% in system efficiency across the studied manufacturing plants, with significant variation among individual facilities. The Bayesian hierarchical approach effectively captured both overall and site-specific efficiency trends, providing actionable insights for policymakers and practitioners. Policy recommendations should focus on identifying high-performing sites to replicate best practices and support improvements in less efficient operations through targeted interventions. Bayesian Hierarchical Model, System Efficiency, Ethiopian Manufacturing Plants, Machine Learning 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.