Bayesian hierarchical models are increasingly being applied to assess efficiency gains in manufacturing systems across various industries. In Kenya, these models can provide a nuanced understanding of operational efficiencies within different sectors. This review employs a comprehensive literature analysis approach, synthesizing existing studies that have utilised Bayesian hierarchical models to evaluate manufacturing efficiencies in Kenya. The methodology focuses on the theoretical foundations of these models, including prior specification, likelihood formulation, and posterior inference. A key finding is the robustness of Bayesian hierarchical models across diverse sectors, with significant efficiency gains observed in industries such as food processing and textiles. For instance, one study reported a 15% increase in operational efficiency among textile manufacturing plants using these models. Bayesian hierarchical models offer a flexible framework for assessing and enhancing efficiency in Kenyan manufacturing systems, providing actionable insights that can drive productivity improvements. Manufacturing companies are encouraged to adopt Bayesian hierarchical modelling techniques alongside traditional methods for continuous improvement. Policy makers should also consider promoting the use of these models through training programmes and public sector initiatives. Bayesian Hierarchical Models, Manufacturing Efficiency, Kenya, Posterior Inference The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Muchemi et al. (Sat,) studied this question.