{ "background": "The pace of technological adoption in manufacturing is a critical determinant of industrial productivity and competitiveness. In many developing economies, however, robust quantitative methodologies to isolate the causal effect of policy interventions on adoption rates are lacking, hindering evidence-based engineering management.", "purpose and objectives": "This study aims to develop and apply a quasi-experimental econometric model to rigorously evaluate the impact of a major national industrial policy on advanced manufacturing system adoption. The objective is to quantify the policy's causal effect while controlling for confounding temporal and sectoral trends.", "methodology": "A difference-in-differences (DiD) model is constructed using panel data from manufacturing plants. The core specification is $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is a binary adoption indicator. Treated and control groups are defined by eligibility criteria. Inference is based on cluster-robust standard errors at the plant level.", "findings": "The policy intervention had a statistically significant positive effect, increasing the probability of adoption by 15.2 percentage points (pp). The estimated coefficient $\\beta$ was 0.152 with a 95% confidence interval of [0.087, 0.217]. The parallel trends assumption, tested using lead terms, was not violated.", "conclusion": "The applied DiD model provides a rigorous methodological framework for evaluating technology adoption drivers in an industrial engineering context. The results demonstrate a substantial causal effect of the specific policy on accelerating the uptake of advanced manufacturing systems.", "recommendations": "Policymakers should consider the demonstrated efficacy of targeted, eligibility-based interventions. Future research should apply this methodological framework to other sectors and incorporate data on complementary factors like workforce skills and infrastructure quality.", "key words": "difference-in-differences, technology adoption, manufacturing systems, industrial policy, causal inference, Kenya", "contribution statement": "This paper