{ "background": "Process-control systems are critical for infrastructure and industrial operations, yet their reliability in developing contexts is understudied. There is a lack of robust methodological frameworks for quantitatively evaluating the impact of system upgrades or interventions on operational performance in these settings.", "purpose and objectives": "This working paper proposes and details a methodological framework for the rigorous evaluation of process-control system reliability. Its objective is to provide a replicable model for assessing the causal effect of technological interventions on system uptime and failure rates.", "methodology": "We employ a quasi-experimental difference-in-differences (DiD) design. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the reliability metric for system $i$ at time $t$. The coefficient $\\delta$ captures the causal effect. Inference is based on cluster-robust standard errors to account for serial correlation.", "findings": "As a methodological paper, it presents no empirical results. The findings section illustrates the framework's application, demonstrating how a hypothetical system upgrade showed a modelled positive effect, with a preliminary indicative point estimate of a 15-percentage-point improvement in mean time between failures. The analysis details the required parallel trends diagnostic and robustness checks.", "conclusion": "The proposed DiD framework provides a viable and statistically sound method for evaluating control system reliability in contexts where randomised controlled trials are impractical. It shifts evaluation from descriptive before-after comparisons to causal inference.", "recommendations": "Researchers and engineers should adopt quasi-experimental designs for infrastructure performance evaluation. Future work must prioritise the collection of high-frequency, longitudinal operational data to facilitate such analyses.", "key words": "process control, reliability engineering, difference-in-differences, causal inference, infrastructure evaluation, quasi-exper