AI-Driven Warehouse Optimization with Queue-Based Picking

Description

This session presents a real-world case study on using system-driven orchestration, operational data, and AI-assisted optimization to improve warehouse efficiency in SAP EWM. The transformation moved warehouse execution from a Warehouse Order (WO)-based picking model to a queue-based, work center-driven approach, addressing manual processes and coordination challenges in high-volume operations.

The legacy process depended on printed VAS instruction sheets, pick tickets, and HU labels that were manually sorted and managed through Kanban boards. The redesigned solution introduced queue-based task allocation aligned with work centers, AI-assisted workload balancing, optimized pick paths, and improved synchronization between VAS order creation and picking execution.

The solution also removed constraints such as one consolidation group per WO and one HU per consolidation group. Mobile printers generated labels at Warehouse Task confirmation, eliminating dependency on pre-printed documents. Multiple pickers could work simultaneously on the same warehouse order, enabling better coordination, real-time operational decision-making, and simultaneous staging for production orders with multiple WOs.

Quantity-based sorting and FIFO enforcement using SRCI further supported intelligent inventory flow, storage utilization, and inventory rotation.

The transformation resulted in increased picks per hour, improved VAS start-to-completion times, greater production-order processing capacity, millions of dollars in savings, increased outbound shipments, and higher stock transfer volumes.

This practical case study demonstrates how operational data, AI-assisted insights, and system-driven warehouse execution can be combined to improve efficiency, scalability, and decision-making in complex warehouse environments.