{ "background": "Process-control systems are critical for infrastructure and industrial operations, yet their methodological evaluation in developing contexts lacks rigorous, comparative frameworks. In Uganda, ad hoc assessments have predominated, creating a gap for robust, quantitative reliability analysis.", "purpose and objectives": "This study aims to methodologically evaluate process-control systems by developing and applying a comparative difference-in-differences (DiD) model to assess system reliability. The objective is to quantify the causal impact of system upgrades on reliability metrics.", "methodology": "A quasi-experimental comparative study was conducted, analysing performance data from treatment and control groups of systems before and after interventions. The core statistical model is a two-way fixed effects DiD specification: $Y{it} = \\alpha + \\beta (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where robust standard errors were clustered at the system level. Reliability was measured via mean time between failures (MTBF).", "findings": "The DiD estimator revealed that upgraded systems exhibited a statistically significant 23.4% increase in MTBF compared to the control group (β = 0.211, 95% CI: 0.187, 0.235). The parallel trends assumption was validated using pre-intervention data.", "conclusion": "The application of a DiD model provides a rigorous methodological framework for evaluating process-control systems, demonstrating a significant causal improvement in reliability following targeted upgrades.", "recommendations": "Adopt the DiD methodology for longitudinal performance evaluation of engineering systems. Prioritise investments in control-system upgrades that mirror the intervention characteristics linked to the highest reliability gains identified in this study.", "key words": "process control, reliability engineering, difference-in-differences, quasi-experimental design, infrastructure management", "contribution statement": "This paper provides a novel application of econometric causal inference methodology, specifically the difference-in-differences model, to the field of engineering system reliability assessment, offering a