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 \Δ yt = \α + \∑i=1ᵖ\ \Δ yt-i + \∑i=1q\ \εt-i + \∑i=1ʳ\ 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