Interpretive Practices of PLC Based Automation in Industrial Production Systems under Dynamic Operational Conditions - Ferdianto Tangdililing; Stefany Yunita Baralangi (PT FORMOSA CENDEKIA GLOBAL, 2026)
This study examines interpretive practices in PLC-based automation, particularly how real-time data supports adaptive decision-making in PLC–SCADA manufacturing systems. Using a mixed-method approach, the findings reveal that system effectiveness depends not only on deterministic control logic but also on the integration of sensor data, HMI visualization, and adaptive control. Under dynamic conditions, effective data interpretation enhances production efficiency, reduces downtime, and accelerates response to disruptions. Furthermore, the integration of PLCs with IoT and data analytics improves system flexibility and reduces decision ambiguity. The study concludes that successful PLC automation relies on the synergy between control technology, data interpretation, and human–machine interaction, contributing to the development of more adaptive and intelligent production systems.