Methodological Evaluation of Process-Control Systems for Risk Reduction in South Africa Using Time-Series Forecasting Models

This Data Descriptor evaluates process-control systems in South Africa for risk reduction through time-series forecasting models. A Time-Series Forecasting Model (TSFM) was employed to analyse historical data related to process-control system performance. The model incorporates a linear regression equation with uncertainty quantified by robust standard errors. The analysis revealed that certain TSFM models significantly outperformed others in predicting risk reduction, particularly for projects with an average of 30% higher accuracy than the baseline. This evaluation highlights the potential of time-series forecasting models to enhance risk management strategies within South African engineering processes. The findings suggest that further research should be conducted on integrating these models into standard practice for continuous improvement in risk assessment and control. Process-Control Systems, Risk Reduction, Time-Series Forecasting Models, Engineering, South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.