{ "background": "Manufacturing systems in Nigeria face persistent reliability challenges, yet rigorous field-based diagnostic methodologies for these contexts are underdeveloped. Existing reliability models often rely on theoretical assumptions not validated in real-world industrial settings within the region.", "purpose and objectives": "This study aimed to methodologically evaluate a randomised field trial (RFT) framework for conducting reliability diagnostics in active manufacturing plants. The objective was to assess the framework's feasibility, precision, and practical utility for identifying systemic failure modes.", "methodology": "A randomised field trial was implemented across twelve functionally similar production lines in three Nigerian manufacturing plants. The intervention involved a structured diagnostic protocol applied to randomly assigned lines, with others serving as controls. System reliability was modelled using a Weibull proportional hazards model: $h(t|X) = \\frac{\\beta}{\\eta} \\left( \\frac{t}{\\eta} \\right)^{\\beta-1} \\exp(\\theta X)$, where $X$ represents treatment assignment. Inference was based on robust standard errors clustered at the plant level.", "findings": "The RFT methodology proved feasible and yielded high-fidelity operational data. Diagnostic precision improved significantly, with the intervention group identifying 40% more latent subsystem failure modes than controls (95% CI: 22% to 58%). The primary failure theme shifted from perceived operator error to mechanical wear in specific transfer mechanisms.", "conclusion": "The randomised field trial presents a robust methodological approach for reliability diagnostics in manufacturing systems, providing more actionable insights than retrospective analyses. Its structured, in-situ nature enhances the validity of findings for maintenance decision-making.", "recommendations": "Manufacturing engineers should adopt structured field trial methodologies for critical system diagnostics. Further research should integrate this approach with predictive maintenance scheduling and explore its cost-effectiveness across different industrial sectors.", "key words": "reliability engineering, randomised field trial, maintenance diagnostics, manufacturing systems, Weibull analysis, industrial engineering", "contribution statement": "This paper provides a novel