Manufacturing systems in Tanzania are crucial for economic development but often face inefficiencies that hinder cost-effectiveness. This study aims to evaluate these systems using panel data methods. This research employs a fixed effects model to analyse the performance metrics of manufacturing plants over time. The dataset includes production costs, labour inputs, and output volumes from 20 manufacturing enterprises in Tanzania. Panel data analysis revealed that controlling for unobserved heterogeneity significantly improved cost-effectiveness estimates compared to pooled cross-sectional models, with a reduction in residual variance by approximately 15%. The methodological approach demonstrated the potential of panel data estimation for evaluating manufacturing systems and highlighted its importance in resource allocation strategies. Manufacturing companies should consider implementing these methods to improve cost-effectiveness and support sustainable development goals. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.