Abstract
{ "background": "Manufacturing productivity in Nigeria is constrained by inconsistent process yields, yet rigorous quantitative analysis of the efficacy of installed process-control systems is lacking. Existing evaluations often fail to account for the hierarchical structure of factory data.", "purpose and objectives": "This study aims to methodologically evaluate the impact of automated process-control systems on production yield within the Nigerian manufacturing sector, employing a multilevel modelling framework to account for plant- and production-line-level variations.", "methodology": "A multilevel linear regression model was fitted to a novel dataset comprising yield observations from multiple production lines nested within 47 manufacturing plants. The core model is specified as $Y{ij} = \\beta{0} + \\beta{1}X{ij} + u{j} + e{ij}$, where $Y{ij}$ is the yield for line i in plant j, $X{ij}$ denotes control system status, $u{j}$ is the plant-level random effect, and $e{ij}$ is the residual error. Robust standard errors were calculated.", "findings": "Implementation of automated process-control systems was associated with a statistically significant mean yield increase of 17.3% (95% CI: 14.1% to 20.5%). Plant-level random effects accounted for 31% of the total variance in yield, underscoring the importance of the hierarchical analysis.", "conclusion": "The application of multilevel regression provides a robust methodological framework for evaluating industrial systems in contexts with nested data structures. The results confirm that process-control systems are a significant determinant of manufacturing yield.", "recommendations": "Manufacturing managers should prioritise investment in automated control systems. Researchers should adopt multilevel modelling techniques for similar factory-level studies to avoid biased inferences.", "key words": "multilevel modelling, process control, manufacturing yield, regression analysis, industrial engineering", "contribution statement": "This paper provides the first application of multilevel regression to analyse process-control system efficacy in Nigerian manufacturing,