The GS1-128 label fills itself in

The problem

The last step before the truck is also the most manual one.

Typing 20+ fields per shipment is slow and risky if one gets mistyped.

• The label generator already exists and already produces a compliant GS1-128 label. The problem is upstream of it.
• Twenty or more fields — batch, species, net weight, counts, dates, destination — are typed in by hand before every shipment, by somebody under time pressure at the dock.
• A mistyped batch on a logistics label is the cleanest way to lose traceability at the very last step, after everything upstream was done properly.
• And when a customer scans the label and it does not match the delivery note, the argument starts at their goods-in, not at yours. Sorting it out costs a phone call, a credit note and a small amount of standing, every single time.

How it fits the IRIS system

Connect stitches the active batch to the label generator through its API.

Connect stitches the ERP's active batch/shipment to the label generator via API.

The label generator is not replaced and the label format is not touched, which matters because that format is already agreed with your customers. What changes is where the data comes from: the active batch and shipment in the ERP feed the fields, and the person at the dock validates instead of typing. It is the shortest case of the twelve to deploy, because everything it needs already exists on both sides.

See the full IRIS architecture →

Before and after

The label today versus the label fed from the batch

Aspect	Today	With iLEAN Connect
Fields typed per shipment	Twenty or more	Zero
Where the data comes from	A person reading a screen	The active batch in the ERP
Time per label	Minutes at the dock	One validation tap
A mistyped batch number	Found by the customer	Impossible: it is not typed
Label versus delivery note	Compared by eye	Same source for both
The label format	—	Unchanged, same generator

20+ typed fields → zero, just one validation tap.

Impact estimate

Impact estimate — to be validated with your numbers.

The block below is an estimate to be validated against your plant's actual data. We put it forward so the committee has an order of magnitude; we refine it during the assessment.

• Estimated payback 3-6 months, the fastest of the whole matrix because the work it removes is measurable per shipment.
• From twenty-plus typed fields to one tap, which at the dock is minutes back on every load.
• Labels stop being a place where traceability can break, since nothing about them is retyped from another screen.
• And a scan at the customer's goods-in matches the delivery note because both were built from the same batch.

Fast payback, 3-6 months. *Estimate to be validated*.

And the fair question from the production manager

“Our label generator is old — will it have an API?” — more often than not it does, and it is the first thing checked before anything is proposed. Where there is none, the batch data is delivered in the file format the generator already imports, which is a well-trodden path. And where a field still has to come from a document rather than from the system, reading it is an anchored task with a fixed layout, where the best models drop below 1.5% error [1]. Either way the label is not redrawn: what is replaced is the typing in front of it, and the operator still confirms the shipment before it prints.

^[1] OpenAI paper "Why Language Models Hallucinate", 2025 — on the reliability of AI in anchored tasks.