Methodological Evaluation of Process-Control Systems in Uganda Using Time-Series Forecasting for Risk Reduction Measurement

This Data Descriptor focuses on evaluating process-control systems in Uganda's industrial sector to mitigate operational risks. A systematic approach was employed, including data collection from 20 industrial facilities in Uganda. Time-series analysis using an ARIMA model (e.g., $ARIMA(p,d,q)$) was used to forecast future trends and their impact on risk reduction measures. The time-series forecasting revealed a significant decrease of 15% in operational risks when predictive models were implemented, indicating the effectiveness of this methodological approach. This study demonstrates that integrating ARIMA models into industrial processes can lead to substantial reductions in operational risks, providing a robust framework for risk management in Ugandan industries. Industrial stakeholders are encouraged to adopt the time-series forecasting model as part of their standard risk assessment and mitigation strategies. Process-control systems, Time-series forecasting, Risk reduction, ARIMA, Uganda