{ "background": "Process-control systems in industrial settings are critical for operational efficiency and product yield. In many developing economies, systematic evaluation of these systems and predictive modelling for yield optimisation are underdeveloped, leading to suboptimal performance and resource utilisation.", "purpose and objectives": "This study aims to methodologically evaluate existing process-control frameworks and to develop a robust time-series forecasting model specifically for predicting and improving production yield in an industrial context.", "methodology": "A hybrid methodology was employed, integrating a diagnostic evaluation of control system architectures with the development of an Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model, specified as $Yt = \\mu + \\sum{i=1}^{p}\\phii Y{t-i} + \\epsilont + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\sum{j=1}^{r}\\betaj X_{t-j}$. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals.", "findings": "The diagnostic evaluation identified significant latency in sensor-data feedback loops as a primary constraint. The ARIMAX(2,1,1) model, incorporating temperature and flow rate as exogenous variables, demonstrated a statistically significant forecasting capability, reducing mean absolute percentage error (MAPE) in yield prediction by 18.7% compared to a baseline naive model.", "conclusion": "The study confirms that integrating systematic architectural evaluation with advanced statistical forecasting provides a viable pathway for substantial yield improvement in process industries.", "recommendations": "Implementation of the proposed forecasting model within a real-time monitoring dashboard is recommended. Further research should focus on adaptive control algorithms that directly utilise the model's predictions for automated process adjustment.", "key words": "process control, time-series analysis, yield optimisation, ARIMAX, industrial engineering, forecasting", "contribution statement": "This paper presents a novel integrated framework combining control system diagnostics with a tailored ARIMAX forecasting model, validated on