Group filled, foil-sealed yogurt cups into trays/multipacks (secondary packaging)
After dosing and lid sealing, filled yogurt cups are collated into multi-cup blocks, variety trays or cartons before shrink-wrapping and palletizing. The objects are now filled and heavier but still deformable: side-wall pressure can dent the cup or pop/puncture the thin foil lid, causing leaks and product loss, and mixed-flavor variety packs require placing several distinct SKUs into a defined pattern. The task is high speed and repetitive and is exactly the kind of line-loading Danone is targeting with cobots, but it straddles the dexterity boundary — much of it is robust pick-and-place, with the contact-rich element being grip-force modulation to avoid denting/puncturing. A dropped or dented block is recoverable scrap rather than a catastrophe. There is no public Danone signal specific to robotizing this collation step. We identified this through our own research; we have not confirmed the specifics with the customer directly. This page is our researched read — a starting point for that conversation.
What the task is
RESEARCHED · our reconstructionAfter dosing and lid sealing, filled yogurt cups are collated into multi-cup blocks, variety trays or cartons before shrink-wrapping and palletizing. The objects are now filled and heavier but still deformable: side-wall pressure can dent the cup or pop/puncture the thin foil lid, causing leaks and product loss, and mixed-flavor variety packs require placing several distinct SKUs into a defined pattern. The task is high speed and repetitive and is exactly the kind of line-loading Danone is targeting with cobots, but it straddles the dexterity boundary — much of it is robust pick-and-place, with the contact-rich element being grip-force modulation to avoid denting/puncturing. A dropped or dented block is recoverable scrap rather than a catastrophe. There is no public Danone signal specific to robotizing this collation step.
Is this the actual task and sequence? What are the real tolerances, cycle rate, and reject criteria, and which steps are today's manual bottleneck? Answering these is what turns this from a researched signal into a validated use case.