Postal and e-commerce disruptions caused by non-machinable items solved with the aid of emerging technologies

Prime Vision, global leader in computer vision integration and robotics for logistics and fulfilment, is well aware of the problems that non-machinable or non-conveyable items cause postal or e-commerce sorting operation.

Whether large, heavy, fragile, unstable or oddly shaped, these goods can’t be automatically sorted and instead require inefficient, manual processing. However, with new automation technologies like computer vision and robots, businesses have an opportunity to change this. In the near future, every item could be machinable.

Lars Pruijn, Innovation Director, and Mart Ruijs, Product Manager at Prime Vision explore how computer vision and robotics could make non-machinable items a thing of the past.

“Non-machinable items, often called ‘uglies’ because of the difficulties they pose, can take many different forms – from big TVs, brooms and car tyres to large pots of paint. What they all have in common is that, due to their characteristics, they can’t be accommodated in a standard, automated sorting system. As postal services and e-commerce companies invest heavily in fixed infrastructure that is optimised for smaller items, non-machinables present a sizeable problem. They must be identified, separated, and accommodated in a different process.

“Traditionally, this has been accomplished manually. Warehouse staff identify non-machinables by eye, then move them by hand to a separate chute or destination. This is time consuming and costly, placing unnecessary strain on workers. Furthermore, a manual approach isn’t fact based, raising the possibility that a non-machinable item might slip through.”

Improving the identification and transport of these goods has long been an uphill battle, but computer vision and robotics can provide a solution.

Computer vision technology offers the capability to quickly and accurately identify non-machinable items at the beginning of the sorting process. This eliminates any manual intervention, greatly improving efficiency.

Correct identification of a wide range of items is possible thanks to machine learning. By training artificial intelligence (AI) models using examples from a business’s real-world operations, a computer vision system can not only recognise objects based on dimensions and weight, but other characteristics such as shape, stability, packaging type and more.

The system can assist in automatically pre-sorting them to an appropriate chute, conveyor, area or robot – so no unsuitable packages slip through. Furthermore, computer vision seamlessly integrates within the brownfield environment of existing processes too, relying on camera hardware and standalone software.

Ultimately, computer vision speeds up the processing of non-machinable items. Effective pre-sorting means manual intervention is not required, with items that were initially unsuitable for automation now accounted for in a fast, cost-effective process.

To discover how Prime Vision plan how to efficiently move to non-machinable items to a desired location, visit: