Revolutionizing Depalletization: The Power of AI in Mixed SKU and “Rainbow” Pallets

Learn how AI combined with a human in the loop tackle the challenge of the mixed SKU pallet and help increase efficiency, scalability, accuracy, and safety in your warehouse.

Anyone working in a warehouse knows that depalletization is a critical first step in the process of moving incoming goods from a pallet to a conveyor or staging area. Unless boxes and packages are unloaded quickly, accurately, and safely, throughput suffers as operation costs skyrocket.

Several approaches to the depalletizing challenge exist. The most common solution has long been reliance on human labor. However, given the current difficulty in finding workers, warehouse managers are increasingly turning to automation as the most efficient and cost-effective way to unload pallets and get products where they need to go.

While robots are good at the repetitive labor that humans would rather avoid, they’re often limited in terms of task complexity and decision-making capabilities. In semi-automated environments where workers are on hand to assist a "stuck" robot, this isn't usually a problem. But, for companies attempting to fully automate to keep up with high demands, manual intervention causing downtime is not an option.  

This is why Plus One developed its human-in-the-loop approach to automation. Combining Plus One’s PickOne AI software with a human-in-the-loop dramatically improves a robot's accuracy and decision-making capabilities and allows one remote supervisor to manage many robots simultaneously, providing real-time support if handling exceptions occur.

These capabilities have allowed many warehouses to do far more with fewer people—in some cases running around the clock with minimal supervision—yet a huge hurdle remained: mixed SKU pallets.

Not anymore.

Go Ahead and Mix it Up

Building on the capabilities of its human-in-the-loop and PickOne software, Plus One has developed the PickOne depalletization solution, which can quickly and accurately unpack goods. This AI-powered solution is designed to meet the growing demand for automation systems able to cope with the complex task of unloading, sorting, and separating products from mixed pallets. Users find the solution can achieve higher rates than manual labor, helping to reduce the costs associated with these operations. Whether the pallet features multiple package types to be picked one piece at a time or homogenous layers (rainbow pallets) of a single product type with known dimensions, it works.

Key features include layer-by-layer and offset picking, item classification, empty pallet, slip sheet and case height detection, multiple pick positions, and more. The fully integrated system also boasts deployment times measured in weeks, not months, with best-in-class speed and accuracy.

Designed as a modular solution approach that enables quick identification of mixed goods, it works out of the box; businesses can start using the system almost immediately, with minimal time spent on training and setup. It also provides repeatable performance, even in the most randomly packed pallets.

Although there are many considerations in adapting and optimizing any robotic implementation, the PickOne mixed depal solution accommodates boxes, overwrapped trays, cartons, and bags.

All of this makes PickOne's mixed depalletizing solution an excellent option for brownfield installations as well as new facilities. Additionally, it is highly versatile, and capable of replacing practically any manual station with a robot cell, either all at once or in stages.

Given the industry's rising demands, ongoing labor shortages, and the need to drive down costs, it's clear that the future of warehouse automation lies in AI-powered technology to tackle warehouse variability. By offering superior efficiency, accuracy, safety, and scalability, Plus One’s mixed depalletization solution is set to revolutionize warehouse operations, making it a crucial investment for businesses looking to stay ahead in the increasingly competitive market.