Zero shipping errors, end to end

The problem

A shipping error is almost never a single mistake.

With a food or medical client, an incident triggers a formal complaint and, in the worst case, loses the contract to a competitor. The ERP/WMS order, the photographed picking sheet, the scale-confirmed weight, the dock's Edge camera, and the evidence-pack dossier — all cross-checked against each other.

• Zero shipping errors means no wrong pallet, no wrong lot or expiry date and no goods delivered to the wrong client, on every truck that leaves the dock.
• With a food or medical client, one incident triggers a formal complaint and, in the worst case, the contract goes to a competitor at the next tender.
• Each check exists today in some form, but separately: the order in the ERP/WMS, the picking sheet, the dock scale, the dock team's eye, the paperwork in a binder.
• Each one alone looks fine, and the error passes between them. A correct weight on the wrong order, a sound pallet with the wrong lot: it is caught when the checks are crossed, not one by one.

How it fits the IRIS system

Six rings that cross-check — the previous eleven cases, orchestrated by Agents.

JIDOKA AI blocks the load right at the dock, not later, once it's already a client complaint. That way, coordinating all the rings doesn't cost loading productivity.

No single ring solves the problem: each already exists today in some form and the wrong pallet still leaves. The strength is in the cross-check, and in JIDOKA AI stopping the load at the dock instead of in the client's complaint. A pallet that agrees across order, pick, weight and camera loads as it does today; only the one that disagrees waits.

See the full IRIS architecture →

Before and after

The six rings, and what each one stops at the dock

Ring	Cases it draws on	What it stops
1 · Order	Client email (3) + stock spreadsheet (6)	Shipping an order the client already changed
2 · Receiving	Delivery note and lot labels (7)	A wrong lot or expiry born at the inbound dock
3 · Picking	Picking sheet (1) + dock validation (5)	Wrong reference, location or client
4 · Weight	Dock scale (2)	A short or over-weight shipment
5 · Pallet	Damaged pallet (9) + ADR segregation (10)	A damaged or badly segregated load
6 · Evidence	Evidence pack (11) + fleet GPS (8)	A delivery nobody can prove

Before: a shipping error is discovered in the client's complaint, risking the account. After: the verification rings stop it right at the dock.

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 6-12 months, measured against the first serious incident avoided.
• Protection against anything from a one-off contractual penalty to the complete loss of a major account.
• The error is caught at the dock, not in the client's complaint, when correcting it still costs minutes and not a redelivery.
• And coordinating all the rings does not cost loading productivity, because each check runs where the work already happens and only a real discrepancy holds a pallet.

Payback 6-12 months · zero shipping errors against the first serious incident avoided. It protects against anything from a one-off contractual penalty to the complete loss of a major account, catching it right at the dock instead of in the client's complaint.

And the fair question from the production manager

“Do we have to deploy all twelve cases before we see anything?” — no, and framing it that way would be the way never to start. The rings are built in layers and each case pays on its own. And JIDOKA AI stops on a verifiable discrepancy between sources, such as a weight that does not match the order, not on model uncertainty: the captures behind it are anchored tasks, where the best models drop below 1.5% error [1]. When the doubt is the model's rather than a discrepancy, the case is escalated to a person instead of blocking the truck.

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