"background": "Industrial process control in emerging economies faces unique challenges, including infrastructure variability and skill gaps. While advanced methodologies are well-documented in global literature, their comparative efficacy and practical implementation in specific regional contexts, such as Central Africa, require rigorous, context-sensitive evaluation. ", "purpose and objectives": "This study aims to empirically compare the impact of three distinct process-control methodologies—statistical process control (SPC), model predictive control (MPC), and a hybrid adaptive control system—on yield optimisation within Rwandan manufacturing. The primary objective is to identify the most effective approach for sustainable implementation given local operational constraints. ", "methodology": "A quasi-experimental design was employed, assigning matched industrial sites to each control methodology intervention. Longitudinal yield data were collected under controlled conditions. The core treatment effect was estimated using a linear mixed-effects model: Y{it = \0 + \1 Tit + \ Xit + ui +, where ui denotes site-specific random effects. Inference was based on cluster-robust standard errors to account for intra-site correlation. ", "findings": "The hybrid adaptive system demonstrated superior performance, yielding a mean improvement of 17. 3% (95% CI: 14. 1, 20. 5) over baseline, significantly outperforming both SPC and MPC. The MPC approach showed high performance volatility correlated with raw material input variability, a key contextual challenge. ", "conclusion": "The hybrid adaptive control methodology offers the most robust and significant yield gains for the studied industrial environment, effectively compensating for systemic input fluctuations where conventional methods falter. ", "recommendations": "Industrial policy and engineering practice should prioritise investment in flexible, adaptive control systems over rigidly optimised ones. Implementation should be coupled with targeted training programmes focusing on system calibration and maintenance. ", "key words": "process control, yield optimisation, quasi-experiment, industrial engineering, adaptive systems, manufacturing", "contribution statement
Nsengiyumva et al. (Fri,) studied this question.