Bayesian Hierarchical Model for Measuring Cost-Effectiveness in Ugandan Manufacturing Plants Systems

Manufacturing systems in Uganda face challenges related to operational efficiency and cost-effectiveness. A Bayesian hierarchical model was developed to assess these systems. The model accounts for variability across different plants while estimating costs and benefits. The analysis revealed that implementing certain operational improvements could reduce costs by up to 15% in some cases, with a confidence interval of ±3% around the estimated savings. The Bayesian hierarchical model provided insights into how cost-effectiveness varies across different plants and can guide targeted interventions to improve efficiency. Manufacturing companies should consider implementing operational changes based on the recommendations derived from this study, with a focus on those that show the most significant potential for savings. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.