A Sensible Cobot Use Case: Fixing the Journey Time Downside in Warehouses and Distribution Facilities
I like to make use of actual world examples for example the know-how about which I write, and particularly for know-how round which swirl clouds of hype and fantasy. With regards to cobots there’s no lack of those. It was due to this fact refreshing to learn a report issued by The Affiliation for Advancing Automation, which offered a brand new benchmark for the adoption of cobots:
New A3 Report Indicators 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 capability to work safely alongside people and tackle automation wants in space- or labor-constrained environments. A3 started monitoring cobots as a definite class in 1Q 2025 and plans to increase future reporting to incorporate development traits by sector.”
My space of experience being course of optimization in warehouses and distribution facilities, I can affirm 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 cutting-edge 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 decreased 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 appropriately 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, decreased the hourly productiveness.
Journey time is the enemy of productiveness.
Efforts to make people extra productive have concerned applied sciences equivalent to RF (radio-frequency) scanning and voice-directed workflows, sometimes used collectively in a multimodal resolution. Whereas these applied sciences have improved the location-to-location productiveness of people, they haven’t definitively solved for journey time.
A big purpose for this has been older warehouses and distribution facilities will not be optimized to cut back journey time, particularly from the choosing areas to the drop-off staging areas. Altering the infrastructure inside these buildings is dear. Whereas new warehouses and distribution facilities (inexperienced fields) will be designed to optimize order choice, there are nonetheless a whole lot of older warehouses and distribution facilities in use immediately.
A standard resolution to this downside 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 subsequent project with out the journey time delay. After all, whereas order choice productiveness elevated, so did the price of general order achievement because of the further price of the movers.
The cobot use case I helped develop took benefit of some fundamental elements: The prices of robots (on this case autonomous cellular robots – AMRs) was dropping and changing into favorable to human labor prices, and the price of the bodily modification of the distribution middle was financially and operationally prohibitive.
The cobot resolution was, at a excessive degree, simple: 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 previous saying: “God (or the Satan) is within the particulars.”
Effectively, on this case it was the Satan. A number of the particulars that needed to be thought of included the notably essential 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 singular barcoded label is affixed to the pallet. The barcode identifies the pallet as belonging to a range project for a selected buyer, and the place of the pallet if there are a couple of 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 along 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 during which the final merchandise on the pallet was picked). This location additionally has a barcode.
The cobot resolution built-in the systemic drop notification on the strategic location from the augmented human’s multimodal resolution with the AMR’s (autonomous cellular robotic) working system. This technique might decide which AMR was closest to the drop that might 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 selected 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 shifting the total pallet, the augmented human would have already acquired 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 sort of cobot use case will see growing adoption because the rise of cobots continues.
Concerning the Creator
Tim Lindner develops multimodal know-how options (voice / augmented actuality / RF scanning) that target assembly or exceeding logistics and provide chain prospects’ productiveness enchancment targets. He will be reached at linkedin.com/in/timlindner.
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