A Sensible Cobot Use Case: Fixing the Journey Time Drawback in Warehouses and Distribution Facilities
I like to make use of actual world examples for example the expertise about which I write, and particularly for expertise round which swirl clouds of hype and fantasy. On the subject of 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
Key Excerpt:
“Collaborative Robots Present Rising Affect
Cobots (collaborative robots) accounted for a rising share of the market with 3,085 models ordered within the first half of 2025, valued at $114 million. In Q2 alone, cobots made up 23.7% of all models and 14.7% of income. These techniques are more and more favored for his or her skill to work safely alongside people and deal with 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 developments 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 unravel. Merely put, how can journey time be dramatically lowered for human order selectors, thereby growing their productiveness.
For the reason that daybreak of distribution, order choice for the objects saved in a warehouse, and later, in distribution facilities has included a certain quantity 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 right down to what number of objects a human may 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, lowered the hourly productiveness.
Journey time is the enemy of productiveness.
Efforts to make people extra productive have concerned applied sciences reminiscent of RF (radio-frequency) scanning and voice-directed workflows, usually 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 major motive for this has been older warehouses and distribution facilities will not be optimized to scale 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) might be designed to optimize order choice, there are nonetheless loads of older warehouses and distribution facilities in use as we speak.
A standard resolution 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 deal with beginning the subsequent task with out the journey time delay. In fact, whereas order choice productiveness elevated, so did the price of general order achievement as a result of 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 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, 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 previous 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 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, usually a singular barcoded label is affixed to the pallet. The barcode identifies the pallet as belonging to a range task for a selected buyer, and the place of the pallet if there are a couple of for the task (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 task 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 by 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 cell robotic) working system. This method may decide which AMR was closest to the drop that would make the pickup. The AMR would transfer to that location, learn the situation 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 must be moved to a selected packing or staging location that was preassigned to deal with and put together the client’s order, to which the pallet label referred. Whereas the AMR was shifting the complete pallet, the augmented human would have already acquired and began his or her subsequent choosing task 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 Creator
Tim Lindner develops multimodal expertise options (voice / augmented actuality / RF scanning) that concentrate on assembly or exceeding logistics and provide chain clients’ productiveness enchancment goals. He might be reached at linkedin.com/in/timlindner.
Wish to tweet about this text? Use hashtags #Cobots #WarehouseAutomation #SupplyChainTech #LogisticsInnovation #AMR #DistributionCenters #ProcessOptimization #RFScanning #BrownfieldSolutions #HumanRobotCollaboration #IoT #AI
