Case Study: Körber AG cuts annotation time and improves pallet handling with SuperAnnotate

Körber supply chain enhances pallet handling with SuperAnnotate

Körber AG, a global leader in supply chain technology, faced challenges with its automated pallet handling systems. Their equipment relied on error-prone manual operator settings and non-vision sensors, which often led to product damage and costly line stoppages. To overcome this, the company sought to develop a computer vision system and turned to SuperAnnotate for its data annotation needs to build the required large training datasets efficiently.

By implementing SuperAnnotate's AI-powered tools, including Magic Select for rapid annotation, Körber AG reduced image annotation time by over 66%, cutting the process from 15 minutes to 5 minutes per image. The platform's seamless integration with AWS allowed for a streamlined workflow from data storage to model training in SageMaker. These improvements enabled the development of accurate object detection models that automate machine settings, significantly reducing operator errors and enhancing operational efficiency for their Layer Picker Solutions.

Körber AG

Emil Blixt Hansen

PhD and IoT Digital Developer