Intelligent event-driven architecture for real-time supply chain optimization in legacy warehouse systems

The current supply chains require systems that can react to real-time operations, but the old warehousing management systems, with their monolithic batch-processing systems, could not meet this requirement. This paper proposes a smart event-driven architecture (EDA) that enables optimization of the supply chain in real-time and preserves existing legacy systems. The system opens an event streaming layer that gathers operational events containing inventory adjustment, order placement, and shipment disruption, and handles these events with clever analytics elements. The system employs machine-learning-capable frameworks to enable predictive decision-making, including demand prediction, smart inventory management, and reconfigurable logistics routing. The hybrid integration approach will enable effective communication between the old systems and the new event-driven systems, without disrupting the operational functions of either system. According to the experimental evaluation, the system achieves better performance in terms of increased latency, operational efficiency, and decision accuracy compared with traditional batch-based systems. The suggested architecture offers a scalable solution that enables organizations to transform their warehousing processes, as it can accommodate a supply chain environment that is constantly evolving and generating new data.