Methodological Evaluation and Time-Series Forecasting for Cost-Effectiveness of Process-Control Systems in Uganda

"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 \ yt = \ + =1^{p\ \ yt-i + =1^q\ -i + =1^r\ Xt-i + \, where yt represents cost-effectiveness ratio and Xₜ 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