Process-control systems are critical for infrastructure and industrial efficiency, yet robust methodologies for quantifying their long-term performance gains in developing economies are lacking. This study aims to methodologically evaluate process-control system implementations and to empirically estimate the efficiency gains they generate within a national context. A panel-data econometric framework was employed, analysing longitudinal operational data from multiple infrastructure sectors. Efficiency was modelled using a fixed-effects specification: $E_{it} = \alpha_i + \beta_1 C_{it} + \beta_2 X_{it} + \epsilon_{it}$, where $E_{it}$ is the efficiency metric, $C_{it}$ represents process-control indices, and $X_{it}$ are time-variant controls. Inference was based on cluster-robust standard errors. The analysis indicates a statistically significant positive relationship between advanced process-control adoption and operational efficiency. A one-standard-deviation increase in the control-system index is associated with a 17.3% gain in mean efficiency (95% CI: 12.1% to 22.5%). The gains are most pronounced in water treatment and energy distribution sectors. The methodological evaluation confirms that panel-data estimation is a robust tool for isolating the impact of process-control systems, revealing substantial and measurable efficiency improvements. Policymakers and engineers should prioritise investments in integrated process-control technologies and establish longitudinal data-collection protocols to enable continuous performance monitoring and optimisation. process control, efficiency measurement, panel data, fixed-effects model, infrastructure, operational research This paper provides a novel panel-data estimation framework specifically designed to isolate the causal efficiency gains from process-control systems in infrastructure, applied here to a unique longitudinal national dataset.