Bayesian Hierarchical Model for Cost-Effectiveness Evaluation of Ethiopian Manufacturing Plants Systems

Cost-effectiveness evaluation is crucial for optimising resource utilization in manufacturing systems across various industries. In Ethiopia, where industrialization efforts are underway, understanding and quantifying cost-effectiveness can help policymakers make informed decisions that enhance productivity and sustainability. A Bayesian hierarchical model was employed to account for variability across different manufacturing plants while estimating cost-effectiveness metrics. This approach allows for the integration of expert knowledge through prior distributions and the incorporation of data from multiple sources to ensure robust inference. The BHM revealed significant variations in cost-effectiveness among the sampled manufacturing systems, with some demonstrating up to a 30% improvement potential when optimised according to best practices. This finding underscores the need for targeted interventions to maximise efficiency and resource allocation. This study demonstrates the utility of Bayesian hierarchical models in evaluating cost-effectiveness across diverse industrial settings. The model's ability to disaggregate performance metrics provides valuable insights for enhancing manufacturing system sustainability and productivity. Policymakers should prioritise the implementation of targeted interventions based on the findings from this research, particularly focusing on areas where potential improvements are most significant. Additionally, continuous monitoring and periodic recalibration of cost-effectiveness measures will be essential for sustaining optimal performance. Bayesian Hierarchical Model, Cost-Effectiveness Evaluation, Manufacturing Systems, Ethiopian Industries, Industrial Optimization The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.