Process-control systems are critical for industrial efficiency and safety, yet their methodological evaluation in developing economies remains understudied. In Nigeria, a lack of robust empirical frameworks has hindered the systematic assessment of these systems' operational reliability. This study aims to develop and apply a rigorous methodological framework for evaluating the reliability of process-control systems. The primary objective is to quantify the causal impact of system upgrades on reliability metrics. A quasi-experimental difference-in-differences (DiD) model was employed. Data were collected from 42 industrial sites. The core econometric specification is $Y_{it} = \alpha + \beta (Treat_i \times Post_t) + \gamma_i + \delta_t + \epsilon_{it}$, where robust standard errors were clustered at the site level. System reliability was measured via mean time between failures (MTBF). The intervention group, following system upgrades, exhibited a statistically significant 18.7% increase in MTBF compared to the control group (p < 0.01, 95% CI: 12.3% to 25.1%). This improvement was robust to multiple model specifications. The applied DiD model provides a valid and powerful methodological framework for reliability assessment. The results confirm that targeted upgrades substantially enhance the operational reliability of process-control systems in the studied context. Industry practitioners should adopt quasi-experimental evaluation methods for capital investment decisions. Regulatory bodies should consider incorporating such methodological standards into technical audit guidelines. process control, reliability engineering, difference-in-differences, quasi-experimental design, industrial systems, Nigeria This paper provides a novel application of the difference-in-differences econometric model to the field of engineering system reliability, establishing a causal methodology absent from prior regional literature.