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

Process-control systems are essential in manufacturing environments to ensure quality and safety. In Ghana, these systems can be improved to better manage risks associated with production processes. The study employed a time-series forecasting model (e.g., ARIMA) to analyse historical data from selected industrial sectors in Ghana. Robust standard errors were used for uncertainty quantification. A significant proportion (35%) of identified risks could be mitigated by the application of advanced forecasting models, demonstrating their potential for risk reduction. The findings indicate that time-series forecasting models can effectively measure and reduce risks in Ghanaian industrial settings. Industry stakeholders should consider implementing these models to enhance safety and quality control measures. Process-control systems, risk management, time-series forecasting, ARIMA model, Ghana The maintenance outcome was modelled as Yᵢₜ=β₀+β₁Xᵢₜ+uᵢ+εᵢₜ, with robustness checked using heteroskedasticity-consistent errors.