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Production monitoring system using SCADA-IIOT for improving efficiency and productivity on stamping machines Maulana, Gun Gun; Mulia, Sandy Bhawana; Purnomo, wahyudi; Supriyanto, Hadi
Jurnal Polimesin Vol 24, No 2 (2026): April
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i2.8713

Abstract

The manufacturing industry plays a crucial role in the global economy, requiring continuous improvements in efficiency and productivity. Digital transformation through Supervisory Control and Data Acquisition (SCADA) systems has enabled better monitoring and control of production processes. However, recent industrial challenges demand more advanced solutions, particularly through the integration of Industrial Internet of Things (IIoT) technologies. This research proposes a web-based SCADA–IIoT production monitoring system for stamping machines that supports real-time machine monitoring, reliable communication, and automated abnormal event notifications. The system architecture integrates sensors for monitoring critical production parameters, SCADA software for visualization and control, and a web-based interface for remote monitoring. System security and communication reliability are ensured through appropriate protocol implementation. A case study conducted in a manufacturing environment evaluates the performance of the proposed system. Experimental results show that the system achieves a data integrity level of 98.99% during monitoring operations. The system is also capable of storing machine condition history and operational activities with a storage capacity of 604,800 data points and an average storage delay of 45.50 ms. Additionally, an error notification feature integrated with the Telegram messaging platform provides automated alerts with an average message latency of 445.99 ms. The proposed system demonstrates the potential of SCADA–IIoT integration to enhance production monitoring, improve decision-making accuracy, and increase operational efficiency in manufacturing environments.