An e-commerce and retail fulfillment center relied on manual void-fill insertion to protect shipments. Variable box contents made automation difficult, requiring workers to visually assess each box and add cushioning by hand, resulting in labor-intensive work, inconsistent output, and variable packing quality.

CMES Robotics implemented an AI Vision–based Void Fill Automation Solution that recognizes the internal volume of each box in real time and determines the required amount of cushioning material automatically.

The system integrates seamlessly with existing packaging equipment and consists of:

- 3D camera–based box interior analysis
- AI-powered predictive algorithms
- Automated void-fill dispensing module

This combination enables flexible automation capable of handling a wide range of packaging sizes, from small to large boxes, with high accuracy and consistency.

By adopting CMES Robotics’s Void Fill Solution, the fulfillment center significantly reduced cushioning material usage, leading to lower material costs and reduced waste production.
Through tight integration with existing automation lines, the overall packing throughput increased by more than 25%, while eliminating the variability inherent in manual packing.

The result is a more consistent packaging quality, improved efficiency, and enhanced sustainability across the fulfillment process.