{ "background": "Evaluating the cost-effectiveness of complex manufacturing systems in industrialising economies presents significant methodological challenges. Traditional single-level regression models often fail to account for the hierarchical structure of plant data, where production lines are nested within factories, leading to potentially biased estimates.", "purpose and objectives": "This case study demonstrates the application of a multilevel regression modelling framework to assess cost-effectiveness in manufacturing plant systems. Its objective is to provide a robust methodological blueprint for engineering economists analysing nested industrial data in similar contexts.", "methodology": "A methodological case study was conducted using operational and financial data from a sample of manufacturing plants. A three-level linear mixed model was specified: $y{ijk} = \\beta0 + \\beta X{ijk} + u{k} + v{jk} + e{ijk}$, where $y$ is a cost-effectiveness ratio, $i$ indexes production lines, $j$ indexes factory units, and $k$ indexes geographic regions. Parameter uncertainty was quantified using 95% confidence intervals derived from robust standard errors.", "findings": "The analysis demonstrates that failing to account for hierarchical data structure substantially overestimates the precision of key determinants. A concrete finding is that variation at the regional level accounted for approximately 22% of the total unexplained variance in cost-effectiveness, a factor obscured in pooled ordinary least squares estimates.", "conclusion": "Multilevel regression provides a superior analytical framework for cost-effectiveness studies in manufacturing systems characterised by inherent data clustering. It yields more accurate inference by correctly partitioning variance across different organisational tiers.", "recommendations": "Engineering practitioners and researchers should adopt multilevel modelling techniques when evaluating plant performance with nested data. National industry databases should be structured to capture hierarchical identifiers to facilitate such analyses.", "key words": "multilevel modelling, cost-effectiveness, manufacturing systems, industrial engineering, regression analysis, Nigeria", "contribution statement": "This study provides a novel, applied methodological framework for multilevel cost-effectiveness analysis in an industrial engineering context, demonstrating that regional-level