{ "background": "The adoption of automated process-control systems in industrial and infrastructure projects is increasing across sub-Saharan Africa. However, rigorous empirical evidence quantifying their operational efficiency gains within local contexts remains scarce.", "purpose and objectives": "This study aims to methodologically evaluate the impact of implementing modern process-control systems on project efficiency. The primary objective is to estimate the causal effect on key performance metrics, isolating the technology's contribution from other confounding factors.", "methodology": "A quasi-experimental difference-in-differences (DiD) design was employed. Data were collected from a panel of 42 construction and manufacturing sites. The DiD model, $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, was estimated using ordinary least squares with robust standard errors clustered at the site level.", "findings": "The intervention group showed a statistically significant increase in material utilisation efficiency of 18.7 percentage points (95% CI: 12.4 to 25.0) relative to the control group. The parallel trends assumption was validated, and results were robust to several sensitivity checks.", "conclusion": "The implementation of process-control systems confers substantial and measurable efficiency improvements in the studied context. The DiD approach provides a robust methodological framework for evaluating such technological interventions in engineering projects.", "recommendations": "Project planners and policymakers should consider process-control systems as a viable investment for efficiency gains. Future evaluations of engineering technologies should adopt causal inference designs to strengthen evidence-based decision-making.", "key words": "process control, efficiency, difference-in-differences, causal inference, engineering management, Tanzania", "contribution statement": "This paper provides novel empirical evidence, using a causal inference framework, to quantify the efficiency gains from process-control systems in a Tanzanian context, demonstrating the utility of the Di