<![CDATA[Despite the extensive studies on industrial automation and energy monitoring, most existing works focus on either PLC–SCADA automation or IoT-based monitoring separately. Only limited studies have experimentally integrated IoT, PLC, and SCADA into a unified architecture specifically for real-time energy optimization in material processing systems. The novelty of this research lies in three main contributions. First, this study proposes an integrated IoT–PLC–SCADA architecture specifically designed for adaptive energy management in material processing systems. Second, the proposed system implements real-time energy monitoring combined with adaptive PLC control logic to dynamically adjust process operation based on real-time sensor data. The results showed that the system succeeded in reducing average energy consumption by 12.2%, increasing process time efficiency by 9%, and recording a system uptime of 97.3%. The statistical test yielded a p-value of 0.0012, indicating that the energy reduction was statistically significant. In addition, the system proved to be accurate and reliable with sensor measurement deviations below 5%. Third, the proposed framework is experimentally validated through a quantitative pretest–posttest approach combined with statistical hypothesis testing to verify the significance of energy efficiency improvements. Therefore, this study contributes both technically and experimentally by providing a validated implementation model of intelligent industrial energy optimization based on IoT-integrated PLC–SCADA architecture.]]>
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