"background": "Process-control systems in industrial settings are critical for operational efficiency and yield optimisation. In many developing economies, including those in West Africa, there is a recognised need to move beyond basic system implementation towards advanced, data-driven methodologies for continuous improvement. However, methodological frameworks for evaluating these systems and forecasting performance gains are often not tailored to local operational constraints and data availability. ", "purpose and objectives": "This study aims to develop and validate a methodological framework for evaluating process-control systems, with the core objective of creating a robust time-series forecasting model to predict and quantify yield improvement. The work seeks to provide a practical tool for engineers to measure the efficacy of system interventions. ", "methodology": "A hybrid modelling approach was employed, integrating system dynamics analysis with statistical forecasting. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as Yt = \ + =1^{p\ Yt-i + \ + =1^q\ -i + =1^r\ X₉, ₓ. Model parameters were estimated using maximum likelihood, and robust standard errors were calculated to account for heteroscedasticity. The methodology was applied to a longitudinal dataset from a Senegalese industrial plant. ", "findings": "The ARIMAX model demonstrated significant predictive capability, with a key process parameter (raw material consistency) showing a positive coefficient of 0. 45 (95% CI: 0. 38, 0. 52). The forecasting analysis indicated that targeted system adjustments could yield a mean improvement in process efficiency of approximately 7. 3% over a standard operational cycle. ", "conclusion": "The proposed methodological framework and forecasting model provide a technically sound and context-adapted approach for quantifying the impact of process-control interventions. This moves yield management from a reactive to a predictive practice. ", "recommendations": "Industrial engineers should adopt structured methodological evaluations
Sarr et al. (Sun,) studied this question.