Intelligent Warehouse Automation Using PLC, IoT and Artificial Intelligence: A Novel Framework for Smart Material Handling Systems

The experimental results demonstrate that the proposed Artificial Intelligence (AI)-Based Smart Warehouse Automation System significantly outperforms conventional PLC-based warehouse automation across all evaluated performance parameters. The integration of Artificial Intelligence, Programmable Logic Controllers (PLCs), the Industrial Internet of Things (IIoT), Edge Computing, Digital Twin technology, the Warehouse Management System (WMS), Automated Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs), and predictive analytics enables intelligent real-time decision-making, adaptive resource allocation, and continuous operational optimization. The AI-assisted conveyor optimization algorithm improved conveyor efficiency by approximately 10%, while reducing transportation delays and warehouse congestion. The Smart Energy Optimization Model achieved an 18.5% reduction in total energy consumption, demonstrating that AI-based dynamic equipment control can substantially improve warehouse sustainability. Likewise, the predictive maintenance framework increased fault prediction accuracy to more than 98%, thereby reducing unexpected equipment downtime and maintenance costs. Inventory management also exhibited remarkable improvement through the integration of RFID technology, barcode systems, AI-based inventory optimization, and Digital Twin synchronization, achieving an inventory accuracy of 99.3%, minimizing stock discrepancies, and enhancing warehouse visibility. Furthermore, the proposed Edge Computing architecture reduced communication latency to 29 ms, enabling near real-time synchronization among PLC controllers, IIoT devices, and warehouse equipment. The Digital Twin platform-maintained synchronization accuracy exceeding 99%, providing reliable simulation and validation of warehouse operations before physical implementation. Comparative analysis further confirmed that the proposed framework consistently achieved superior performance compared with existing PLC-based and conventional smart warehouse systems across all evaluated metrics. Overall, the proposed intelligent warehouse automation framework demonstrates excellent scalability, robustness, reliability, and operational efficiency. The seamless integration of Artificial Intelligence with PLC-based industrial automation establishes a comprehensive cyber-physical warehouse platform capable of supporting Industry 5.0 manufacturing, autonomous logistics, predictive maintenance, intelligent resource allocation, sustainable warehouse operations, and future smart factory applications

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