{ "background": "The adoption of advanced manufacturing systems in industrialising economies is critical for productivity gains, yet reliable measurement of adoption rates remains methodologically challenging. Prior studies in sub-Saharan Africa have often relied on self-reported survey data, which can be subject to significant bias.", "purpose and objectives": "This short report presents a methodological evaluation of a novel field trial approach designed to generate robust, empirically grounded estimates of technology adoption rates within manufacturing plants.", "methodology": "A randomised field trial was conducted with a cohort of manufacturing plants. The core intervention was a structured technical support package for implementing a specified production management system. Adoption was measured via direct, blinded technical audit rather than managerial report. The primary analysis estimated the causal effect using a probit model: $P(Adoptioni=1) = \\Phi(\\beta0 + \\beta1 Treatmenti + \\mathbf{X}_i'\\gamma)$, where $\\mathbf{X}$ is a vector of plant-level covariates.", "findings": "The intervention significantly increased verified adoption. The estimated average treatment effect was 0.32 (95% CI: 0.18, 0.46), indicating that 32% more plants in the treatment group achieved audited adoption compared to the control group. This effect was robust to alternative model specifications using heteroskedasticity-consistent standard errors.", "conclusion": "The randomised trial methodology, coupled with audit-based verification, provides a more rigorous framework for measuring real-world technology adoption, revealing that reported rates may substantially overstate actual implementation.", "recommendations": "Future research and policy evaluations concerning industrial technology uptake should incorporate direct verification mechanisms. The methodological framework is recommended for replication in similar contexts to build a comparative evidence base.", "key words": "technology adoption, randomised controlled trial, manufacturing systems, industrial engineering, impact evaluation, Sub-Saharan Africa", "contribution statement": "This study provides a novel methodological demonstration of an audit-based randomised trial for measuring engineering technology adoption, generating a uniquely robust