A Sensible Cobot Use Case: Fixing the Journey Time Downside in Warehouses and Distribution Facilities
I like to make use of actual world examples as an instance the know-how about which I write, and particularly for know-how round which swirl clouds of hype and fantasy. In relation to cobots there’s no lack of those. It was subsequently refreshing to learn a report issued by The Affiliation for Advancing Automation, which supplied a brand new benchmark for the adoption of cobots:
New A3 Report Alerts Regular Automation Funding in First Half of 2025
“Collaborative Robots Present Rising Affect
Cobots (collaborative robots) accounted for a rising share of the market with 3,085 items ordered within the first half of 2025, valued at $114 million. In Q2 alone, cobots made up 23.7% of all items and 14.7% of income. These programs are more and more favored for his or her potential to work safely alongside people and handle automation wants in space- or labor-constrained environments. A3 started monitoring cobots as a definite class in 1Q 2025 and plans to broaden future reporting to incorporate development traits by sector.”
My space of experience being course of optimization in warehouses and distribution facilities, I can verify each area and labor constraints apply to DCs.
The use case offered right here is one I’ve personally helped develop and, given the place the state-of-the-art presently exists for human and robotic interactions, is sensible.
Let’s first perceive the issue this cobot use case was developed to resolve. Merely put, how can journey time be dramatically lowered for human order selectors, thereby growing their productiveness.
Because the daybreak of distribution, order choice for the objects saved in a warehouse, and later, in distribution facilities has included a specific amount of journey time {that a} human wanted to do, and the extra journey time was wanted, the much less productive the human was.
How is productiveness measured so as choice?
Historically, order choice productiveness got here all the way down to what number of objects a human might accurately choose from storage areas in an hour. The longer the human needed to journey from location to location within the choose path, then to drop off a pallet or cart at a packing space, get a brand new pallet or cart, and return to a place to begin within the storage space, lowered the hourly productiveness.
Journey time is the enemy of productiveness.
Efforts to make people extra productive have concerned applied sciences similar to RF (radio-frequency) scanning and voice-directed workflows, sometimes used collectively in a multimodal answer. Whereas these applied sciences have improved the location-to-location productiveness of people, they haven’t definitively solved for journey time.
A major motive for this has been older warehouses and distribution facilities usually are not optimized to cut back journey time, particularly from the choosing areas to the drop-off staging areas. Altering the infrastructure inside these buildings is expensive. Whereas new warehouses and distribution facilities (inexperienced fields) could be designed to optimize order choice, there are nonetheless numerous older warehouses and distribution facilities in use in the present day.
A conventional answer to this drawback has been to deploy further people as movers of pallets and carts from the order choice areas to packing and staging, permitting order selectors (pickers) to remain of their areas to give attention to beginning the following project with out the journey time delay. In fact, whereas order choice productiveness elevated, so did the price of general order achievement because of the further value of the movers.
The cobot use case I helped develop took benefit of some fundamental elements: The prices of robots (on this case autonomous cell robots – AMRs) was dropping and turning into favorable to human labor prices, and the price of the bodily modification of the distribution middle was financially and operationally prohibitive.
The cobot answer was, at a excessive stage, easy: Use AMRs to maneuver pallets from the order choice areas to packing and replenish empty pallets at strategic areas throughout the order choice space.
Do not forget that outdated saying: “God (or the Satan) is within the particulars.”
Nicely, on this case it was the Satan. A number of the particulars that needed to be thought of included the significantly vital step of alerting an AMR {that a} picker had dropped a full pallet at a strategic pickup location.
When choosing objects to a pallet, sometimes a novel barcoded label is affixed to the pallet. The barcode identifies the pallet as belonging to a variety project for a specific buyer, and the place of the pallet if there are multiple for the project (e.g., 2 of three).
The human order selector (picker), augmented with multimodal applied sciences (voice-direction plus RF barcode ring scanner), associates the pallet label together with his or her project earlier than choosing the primary merchandise to the pallet. When choosing to that pallet is accomplished, it’s dropped at a strategic location (normally on the finish of the aisle wherein the final merchandise on the pallet was picked). This location additionally has a barcode.
The cobot answer built-in the systemic drop notification on the strategic location from the augmented human’s multimodal answer with the AMR’s (autonomous cell robotic) working system. This method might decide which AMR was closest to the drop that would make the pickup. The AMR would transfer to that location, learn the placement label to confirm it was on the proper place, and in that case, would then learn the pallet label.
The AMR system would know the pallet ought to be moved to a specific packing or staging location that was preassigned to deal with and put together the shopper’s order, to which the pallet label referred. Whereas the AMR was transferring the complete pallet, the augmented human would have already obtained and began his or her subsequent choosing project with out leaving the choosing areas. Journey time was eradicated and order choice productiveness dramatically improved.
This type of cobot use case will see growing adoption because the rise of cobots continues.
In regards to the Writer
Tim Lindner develops multimodal know-how options (voice / augmented actuality / RF scanning) that concentrate on assembly or exceeding logistics and provide chain clients’ productiveness enchancment targets. He could be reached at linkedin.com/in/timlindner.
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