African Industrial Engineering | 02 February 2008
Methodological Evaluation of Process-Control Systems for Yield Improvement in Senegal Using Time-Series Forecasting Models
M, a, m, a, d, o, u, D, i, o, p
Abstract
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 $Y<em>{it}=\beta</em>0+\beta<em>1X</em>{it}+u<em>i+\varepsilon</em>{it}$, with robustness checked using heteroskedasticity-consistent errors.