The reliability of process-control systems is a critical yet under-researched factor in the operational efficiency of industrial and infrastructure projects. In many developing economies, systematic methodologies for forecasting and enhancing this reliability are lacking, leading to unplanned downtime and resource wastage. This study aims to develop and validate a novel time-series forecasting model to measure and predict the reliability of process-control systems. The objectives are to evaluate existing methodological approaches and to generate accurate, forward-looking reliability metrics. A methodological evaluation of historical system performance data was conducted. A seasonal autoregressive integrated moving average (SARIMA) model, specified as $\text{SARIMA}(1,1,1)(1,1,1)_{12}$, was developed for forecasting. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% confidence intervals. The SARIMA model demonstrated strong predictive capability, with a mean absolute percentage error of 8.7% on test data. Forecasts indicate a sustained positive trend in system reliability over the forecast horizon, with a projected increase of approximately 15% in the mean time between failures. The confidence intervals for these projections remained narrow, indicating robust model estimates. The proposed forecasting model provides a statistically robust tool for anticipating system reliability, enabling proactive maintenance planning. The methodological evaluation underscores the superiority of time-series analysis over retrospective, descriptive assessments for this context. Industry practitioners should adopt similar forecasting methodologies for infrastructure asset management. Further research should integrate real-time sensor data into the model framework to enhance its predictive accuracy and responsiveness. system reliability, forecasting, time-series analysis, process control, maintenance engineering, SARIMA This paper presents a novel application of the SARIMA model for forecasting process-control system reliability, providing a new, evidence-based tool for infrastructure management.