{ "background": "The persistent gap in manufacturing productivity within developing economies necessitates robust, causal evaluation methods. Current assessments of industrial systems often rely on descriptive or correlational analyses, which fail to isolate the true effect of systemic interventions from confounding temporal trends.", "purpose and objectives": "This study aims to develop and apply a rigorous quasi-experimental framework to evaluate the cost-effectiveness of modernised manufacturing systems. The primary objective is to quantify the causal impact of system upgrades on production costs, controlling for external economic factors.", "methodology": "A difference-in-differences (DiD) model was employed, leveraging panel data from a sample of plants that implemented upgraded systems and a control group that did not. The core econometric specification is $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the log of unit production cost. Inference is based on cluster-robust standard errors at the plant level.", "findings": "The intervention led to a statistically significant reduction in average unit production costs of 17.4% (95% CI: 12.1% to 22.7%). The parallel trends assumption, tested via event-study analysis, held for the pre-intervention period. The cost-benefit ratio of the system upgrades was estimated at 1:3.2 over the study period.", "conclusion": "The application of the DiD model provides credible causal evidence that targeted modernisation of manufacturing systems is a potent driver for substantive cost reduction. The methodological approach demonstrates high utility for engineering management decisions in resource-constrained settings.", "recommendations": "Manufacturing plant managers should prioritise investments in integrated process control and automation systems. Policymakers are advised to incorporate quasi-experimental evaluation designs into industrial support programme assessments to ensure funding efficacy.", "key words": "causal inference, industrial engineering, productivity, quasi-experiment, developing economy", "contribution statement