Methodological Evaluation and Yield Optimisation in Nigerian Manufacturing: A Randomised Field Trial

"background": "Manufacturing productivity in many developing economies is constrained by systemic inefficiencies, yet rigorous field evidence on effective engineering interventions remains scarce. This study addresses the gap in empirical, plant-level data for yield optimisation. ", "purpose and objectives": "This case study aimed to methodologically evaluate a structured process-improvement protocol and quantify its causal impact on production yield within a Nigerian manufacturing context. The primary objective was to determine the efficacy of a randomised controlled trial (RCT) design in an industrial engineering setting. ", "methodology": "A randomised field trial was conducted across multiple production lines in a consumer goods plant. Lines were randomly assigned to treatment (implementation of a diagnostic and lean manufacturing protocol) or control groups. Yield was measured as the proportion of output meeting quality standards. The treatment effect was estimated using a linear regression model: Y{it = \0 + \1 Treatmenti + \2 Xit + \₈ₓ, where robust standard errors were clustered at the production-line level. ", "findings": "The intervention generated a statistically significant yield increase of 7. 3 percentage points (95% CI: 4. 1, 10. 5) for treated lines relative to controls. The RCT design proved methodologically robust, though implementation required adaptations to real-time production scheduling constraints. ", "conclusion": "The randomised trial successfully demonstrated a substantial, causal yield improvement from the applied engineering protocol, validating its use in similar industrial environments. The study also confirms the feasibility of RCTs for rigorous performance evaluation in manufacturing systems. ", "recommendations": "Manufacturing plants should adopt structured, data-driven protocols for process optimisation. Researchers are encouraged to utilise randomised designs for field evaluations, ensuring protocols are flexible to operational realities. Further trials should test scalability across different industrial sectors. ", "key words": "randomised controlled trial, manufacturing yield, process optimisation, field experiment, lean manufacturing, industrial engineering", "contribution statement": "This paper provides novel empirical evidence from one of