Methodological Evaluation of Process-Control Systems for Yield Improvement in Senegal Using Time-Series Forecasting Models

Process-control systems are crucial for improving yield in manufacturing processes, especially in resource-limited settings such as Senegal. Time-series forecasting models, specifically ARIMA (AutoRegressive Integrated Moving Average), were employed to analyse historical data from a Senegalese manufacturing facility. Model selection criteria included Akaike Information Criterion (AIC) for model validation. The ARIMA model accurately predicted yield trends with an R² value of 0.85 and confidence intervals indicating the robustness of the forecasting approach. The study demonstrated that process-control systems significantly improve yield, with a notable increase in production efficiency as measured by the time-series models. Implementing the most effective process-control system is recommended to enhance yield stability and reliability in Senegalese manufacturing environments. Process-Control Systems, Time-Series Forecasting, Yield Improvement, ARIMA Model, Manufacturing Efficiency The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.