Abstract
{ "background": "The adoption of advanced manufacturing systems in emerging industrial economies is a critical driver of productivity, yet rigorous evaluation of adoption rates and causal impacts remains methodologically challenging. Existing studies often rely on descriptive or self-reported data, lacking robust counterfactual analysis.", "purpose and objectives": "This case study presents and evaluates a quasi-experimental design to measure the causal adoption rate of computer-aided design and manufacturing (CAD/CAM) systems within an industrial context. It aims to demonstrate a methodological framework for isolating the effect of system implementation from other confounding factors.", "methodology": "A difference-in-differences (DiD) design was implemented, comparing ten treatment plants that received a targeted CAD/CAM intervention with a matched control group of ten plants that did not. Adoption was measured via direct system audits over multiple periods. The core statistical model is a two-way fixed effects regression: $Y{it} = \\alpha + \\beta (Treatmenti \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where robust standard errors were clustered at the plant level.", "findings": "The methodological application revealed a significant positive treatment effect. The DiD estimator indicated that the intervention increased the intensive margin of system adoption by approximately 32 percentage points (95% CI: 18 to 46). The framework successfully identified heterogeneous adoption patterns linked to prior workforce technical training.", "conclusion": "The quasi-experimental design proved viable and valuable for generating credible causal estimates of technology adoption in a real-world engineering setting, moving beyond correlational evidence.", "recommendations": "Future evaluations of manufacturing systems in similar contexts should employ quasi-experimental designs with direct outcome measurement. Policymakers and plant managers should consider pre-implementation technical upskilling to mitigate adoption heterogeneity.", "key words": "quasi-experimental design, difference-in-differences, technology adoption, manufacturing systems, causal inference, industrial engineering", "contribution statement": "This study provides a novel, applied methodological