Manufacturing plants in Nigeria face significant yield variability due to various operational inefficiencies. A Bayesian hierarchical model was developed to account for the hierarchical structure of data across multiple plants, incorporating process variables and random effects. Uncertainty in predictions is quantified using robust standard errors. The model identified specific processes contributing 20% to overall yield variability, allowing targeted interventions. Bayesian hierarchical modelling provides a nuanced approach to understanding and improving manufacturing yields in Nigeria. Implement process optimization based on the findings of this study for sustainable yield improvement. manufacturing systems, Bayesian hierarchical model, yield improvement, Nigerian plants The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Felix Nnamdi (Sat,) studied this question.