"background": "Manufacturing systems in developing economies often operate below optimal yield due to a lack of empirical, context-specific methodologies for process evaluation and improvement. Existing frameworks are frequently derived from industrialised contexts and lack rigorous field validation in settings with distinct infrastructural and operational constraints. ", "purpose and objectives": "This study aimed to develop and test a novel methodological framework for evaluating and optimising production yield in manufacturing plants. The primary objective was to quantify the efficacy of a structured intervention package through a randomised field trial, comparing yield outcomes against standard practice. ", "methodology": "A comparative, randomised controlled trial was conducted across multiple manufacturing plants. Participating sites were randomly assigned to an intervention group, receiving a systematic diagnostic and optimisation protocol, or a control group continuing with existing practices. The primary outcome was percentage yield improvement over a defined production period. Analysis employed a mixed-effects model: Y{it = \0 + \1 Ti + \2 Xit + ui +, where Yit is yield for plant i at time t, Ti is the treatment indicator, Xit are covariates, and ui is the plant-level random effect. Robust standard errors were clustered at the plant level. ", "findings": "Plants receiving the intervention demonstrated a mean yield increase of 17. 3% (95% CI: 12. 1% to 22. 5%), a statistically significant improvement (p < 0. 01) compared to control plants, which showed a non-significant mean change of 2. 1%. The greatest gains were observed in plants with initial yields below 65%, where process variability was markedly reduced. ", "conclusion": "The trial confirms that a structured, data-driven methodological framework can substantially enhance manufacturing yield in the studied context. The results provide strong evidence for the transferability and effectiveness of systematic engineering interventions when adapted to local operational environments. ", "recommendations
Aisha Mwinyi (Fri,) studied this question.