A Data-Driven Approach to AI-Powered Supply Chain Optimization in Industry 4.0

The inclusion of intelligent AI-driven technologies makes supply chain management more effective during this era of Industry 4.0. It propounds new data-driven methodology for optimizing demand forecast and inventory management alongside logistics, dependent on state-of-the-art models in machine learning. We propose the use of Transformer models to accurately predict demand patterns, Reinforcement Learning (RL) for dynamic inventory control, and Graph Neural Networks (GNNs) to enhance logistics network efficiency. This has led to challenges in demand fluctuation, resource allocation, and adaptation of real-time data in traditional supply chain methods. Our solution addresses the challenges of providing scalable real-time solutions with improved accuracy for forecasting, optimizing inventories, and routes. Through the use of AI, we expect to improve decisions, cut operational costs, and overall efficiency of supply chains. Preliminary results show that proposed models outperform traditional techniques and, thus have the potential to be a promising tool for the future generation of supply chain management. This paper adds itself to the growing literature regarding the application of AI towards the optimization of the supply chain with the possibility to change the operational practice for industries dealing with dynamic, complex challenges.