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Overcome Manual Depalletizing Challenges Without Sacrificing Productivity

With so many downsides to manual depalletization, automation would seem like an obvious solution. Until recently, however, mixed-SKU pallets have been particularly challenging for robots to handle.

The job doesn’t sound like rocket science. Pallets arrive with a virtually random assortment of items or SKUs, each potentially positioned, oriented and labeled in a different way. All you have to do is pick up each item and put it onto a conveyor. But while humans can easily deal with unstructured and ever-changing tasks like this, robot programming has taken a while to catch up. As a result, most automated depalletizing solutions to date have only been practical for operations that handle consistently sized cases and load configurations. These have typically required specialized expertise or costly third-party solutions to integrate, and often suffered from limited aftermarket support.

All of these limitations, however, have been overcome by major improvements in three key technologies:

  • Significant advances in vision and perception
  • Development of sophisticated machine learning
  • Innovations in gripping technology

This podcast will highlight the significant benefits these solutions have to offer modern DCs and other fulfillment operations. It will also examine the increasingly attractive business case for doing so.

You can also learn more about this topic by downloading a white paper on this subject. 

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