Architecting the Future Factory: Industrial PLC-IoT, Digital Twins and Predictive AI based Maintenance

This paper presents an integrated smart factory automation framework aimed at overcoming the inherent limitations of conventional Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems in predictive maintenance within Industry 4.0 environments. The proposed architecture synergistically combines four emerging technologies, namely Industrial Embedded Personal Computers (IEPCs), Industrial Internet of Things (IIoT) networks, Digital Twins (DT), and Artificial Intelligence (AI)-driven anomaly detection. This combination enables bidirectional data and control flows between physical equipments and their virtual counterparts, scalable sensor integration, real-time monitoring, and AI-based predictive maintenance to significantly reduce unplanned downtime and improve operational resilience. The envisioned implementation plan includes phased upgrading of existing PLC-based laboratory setups to IEPCs, expansion of multi-modal sensor networks, deployment of a robust IIoT infrastructure, DT development, and application of AI models for anomaly detection. A representative use case, involving a multi-conveyor robotic automation, is detailed to illustrate system capabilities in synchronizing physical and virtual operations, supporting dynamic feedback loops and intelligent decision-making. The framework also addresses key aspects such as interoperability, cybersecurity, and data standardization, laying the foundation for scalable, intelligent mass-production automation aligned with the future of digitalized manufacturing.