A home-shopping retail logistics center handled a continuous flow of diverse inbound boxes, requiring manual pallet unloading due to wide variations in size and packaging. Fluctuating inbound and outbound volumes, especially at night and on weekends, made labor planning difficult, leading to staffing shortages, rising labor costs, and inconsistent workforce ROI.

CMES Robotics implemented an AI 3D Vision–based Depalletizing Solution optimized for fast-changing retail logistics environments.
The system detects box size, position, and orientation in real time, enabling automated unloading of mixed-SKU pallets without manual intervention.

Because the system can autonomously respond to diverse product types and varying pallet patterns, it remains highly adaptable during seasonal peaks, promotional surges, and sudden fluctuations in inbound volume.

The system processes 600 boxes per hour, improving throughput and integrating seamlessly with existing equipment. It accurately handles irregular stacks and achieved a payback period of just over one year. By reducing repetitive manual handling, the solution lowered injury risk and improved workforce safety and stability.