{ "background": "Process-control systems are increasingly adopted in industrial and infrastructure projects in developing economies, yet robust methodologies for evaluating their long-term cost-effectiveness are lacking. Existing assessments often rely on static cost-benefit analyses, failing to account for dynamic operational variables and temporal performance degradation.", "purpose and objectives": "This study aims to develop and validate a time-series forecasting model to quantitatively measure the cost-effectiveness of process-control systems. The objective is to provide a methodological framework that integrates operational performance data with lifecycle cost projections.", "methodology": "A longitudinal dataset of operational parameters and maintenance costs from multiple installed systems was analysed. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as $\\Delta yt = \\alpha + \\sum{i=1}^{p}\\phii \\Delta y{t-i} + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\sum{i=1}^{r}\\betai X{t-i} + \\epsilont$, where $yt$ represents cost-effectiveness ratio and $X_t$ captures exogenous operational shocks. Model robustness was tested using heteroskedasticity-consistent standard errors.", "findings": "The ARIMAX(1,1,1) model demonstrated strong predictive accuracy, with a Diebold-Mariano test statistic indicating superiority over benchmark models (p < 0.05). A key concrete result is that a one-standard-deviation increase in system calibration frequency was associated with a 17% improvement in the projected cost-effectiveness ratio over a five-year horizon, with a 95% confidence interval of [12%, 22%].", "conclusion": "The proposed time-series methodology provides a more dynamic and reliable tool for assessing the economic viability of process-control technologies than static evaluations. It successfully captures the temporal interdependencies between operational interventions and financial performance.", "recommendations": "Project engineers and policymakers should adopt similar forecasting frameworks for capital investment appraisals. Future research should