Bayesian Hierarchical Model for Cost-Effectiveness Analysis in Tanzanian Manufacturing Systems

Manufacturing systems in Tanzania face challenges related to cost-effectiveness, which can be influenced by various factors such as production efficiency, resource allocation, and technological integration. A Bayesian hierarchical model was employed to analyse data from Tanzanian manufacturing plants, accounting for both fixed effects (common factors across all plants) and random effects (plant-specific variations). The analysis revealed that technological investment had a significant positive impact on cost-effectiveness, with an estimated effect size of 15%. This study confirms the effectiveness of Bayesian hierarchical models in assessing manufacturing system cost-effectiveness. The findings suggest that increased technological investment could lead to substantial improvements in cost-efficiency. Manufacturing plants should prioritise investments in technology, particularly in areas like automation and data analytics, to enhance their operational efficiency and competitiveness. Bayesian hierarchical model, manufacturing systems, cost-effectiveness, Tanzania, production efficiency Model estimation used θ̂=argminθ∑ᵢ(yᵢ,f_θ(xᵢ))+λθ₂², with performance evaluated using out-of-sample error.