QUANTITATIVE ASSESSMENT OF AUTOMATION AND CONTROL STRATEGIES FOR PERFORMANCE OPTIMIZATION IN U.S. INDUSTRIAL PLANTS

Abstract

Industrial automation continues to redefine modern production systems through advanced control strategies, intelligent monitoring, and data-driven optimization. This study presents a comprehensive quantitative assessment of automation and control systems implemented across 126 U.S. industrial plants, encompassing manufacturing, petrochemicals, power generation, and materials processing. The research explores how automation sophistication measured through integration level, predictive maintenance capability, control responsiveness, real-time monitoring, and system adaptability translates into measurable improvements in operational efficiency, reliability, and sustainability. Findings demonstrate clear and statistically significant relationships between automation variables and industrial performance outcomes. Correlation coefficients revealed strong positive associations between automation integration and OEE (r = .71), energy efficiency (r = .57), and MTBF (r = .53), alongside strong negative correlations with downtime ratio (r = –.49). Predictive maintenance was particularly influential, showing strong correlations with MTBF (r = .68) and downtime reduction (r = –.64), underscoring the value of data-driven asset reliability frameworks. Regression models further quantified these effects. The OEE model produced an Adjusted R² of .724 (F(5,120) = 48.37, p < .001), with automation level (β = .42, p < .001) and predictive maintenance (β = .31, p < .01) emerging as the strongest predictors. Energy efficiency was similarly explained by automation integration, predictive maintenance, and system adaptability (Adjusted R² = .61; F(4,121) = 35.19, p < .001). Downtime ratio showed a strong inverse relationship to predictive maintenance (β = –.41, p < .001) and control responsiveness (β = –.22, p < .01), indicating that responsive and proactive systems are highly effective in preventing production disruptions. ANOVA confirmed significant performance differences across automation maturity levels (p < .001). Fully adaptive plants achieved the highest outcomes (mean OEE = 89.8%, downtime = 4.7%, energy efficiency = 0.82), significantly outperforming manual and semi-automated facilities. Robust diagnostic testing—Cronbach’s α (.87–.94), VIF (1.28–2.43), Durbin–Watson (2.03), and 5,000-bootstrap validation—verified reliability and model stability. Collectively, results demonstrate that U.S. industrial performance is strongly driven by a triad of automation integration, predictive intelligence, and control responsiveness.