AGD Intelligence

Deposit deformable sandwich fillings onto bread bases

On Greencore's high-volume sandwich lines (764m food-to-go units in FY25), the core assembly step is placing fillings - sliced cooked meats, cheese slices, salad leaves, tomato slices, egg/mayo bound mixes - evenly across a bread base before lidding. The objects are limp, wet, sticky, fragile and variable in geometry, and they tend to cling together in stacks. The task sits mid-line, downstream of bread de-nesting and upstream of closing/cutting/packaging, and is one of the most labour-intensive manual steps in the operation. It is hard to automate because a single limp slice must be peeled off a stack, carried without folding or doubling, and laid conformally onto a soft base without tearing it - all at line rates measured in tens of units per minute per lane. 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.

Readiness
build now
Demand
promising
Source
researched
Failure tol.
high
Tactile value
high
i

What the task is

RESEARCHED · our reconstruction

On Greencore's high-volume sandwich lines (764m food-to-go units in FY25), the core assembly step is placing fillings - sliced cooked meats, cheese slices, salad leaves, tomato slices, egg/mayo bound mixes - evenly across a bread base before lidding. The objects are limp, wet, sticky, fragile and variable in geometry, and they tend to cling together in stacks. The task sits mid-line, downstream of bread de-nesting and upstream of closing/cutting/packaging, and is one of the most labour-intensive manual steps in the operation. It is hard to automate because a single limp slice must be peeled off a stack, carried without folding or doubling, and laid conformally onto a soft base without tearing it - all at line rates measured in tens of units per minute per lane.

To confirm with the customer

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.