"background": "Process-control systems in industrial settings are critical for operational efficiency, yet there is a paucity of longitudinal, quantitative analyses for evaluating their performance gains in emerging economies. Existing assessments often lack a robust forecasting framework to guide strategic investment and optimisation. ", "purpose and objectives": "This data descriptor presents a novel methodological framework and a curated dataset designed to evaluate process-control system efficiency and forecast future performance. The primary objective is to provide a replicable model for quantifying efficiency gains and predicting trends to inform engineering and maintenance decisions. ", "methodology": "A time-series dataset was constructed from multiple industrial sites, capturing key performance indicators including energy consumption, throughput, and downtime. The core forecasting model employs an autoregressive integrated moving average (ARIMA) framework, specified as yt = c + \1 y{t-1 + \1 -1 + \, where parameters were estimated using maximum likelihood. Model robustness was assessed via rolling-origin evaluation and heteroskedasticity-robust standard errors. ", "findings": "The analysis indicates a consistent positive trend in system efficiency, with forecasted gains suggesting a 12–18% aggregate improvement over the forecast horizon. The model demonstrates strong predictive capability, with a 95% confidence interval for the one-step-ahead forecast error of ±2. 7 percentage points. ", "conclusion": "The developed methodological framework and accompanying dataset provide a validated, quantitative tool for assessing and forecasting the efficiency of process-control systems. This enables evidence-based planning for system upgrades and resource allocation. ", "recommendations": "Future work should integrate real-time sensor data streams into the forecasting model and expand the dataset to include a wider variety of industrial sectors and control system architectures. ", "key words": "process control, efficiency metrics, time-series analysis, ARIMA modelling, industrial engineering, predictive maintenance", "contribution statement": "This paper introduces a novel, publicly available longitudinal dataset and a validated ARIMA-based forecasting methodology specifically
Okonkwo et al. (Wed,) studied this question.
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