Hands-free between aisles, knowledge that doesn't get lost

The problem

The supervisor knows where the warehouse hurts. The WMS does not.

The supervisor can't type or reach for their phone while driving the forklift or adjusting a pallet — so the information in their head stays there. That location B-14 is overloaded, that a new picker needs support, that a pallet arrived with broken wrapping — none of that reaches the system.

• The warehouse supervisor covers aisles and docks, on and off the forklift, coordinating pickers from three different clients at once and answering the phone for the fourth.
• He cannot type or reach for his phone while driving a forklift or squaring a pallet, so what he sees stays in his head until the end of the shift, and often beyond it.
• That location B-14 is overloaded, that a new picker needs support, that a pallet came in with broken wrap: none of it reaches the system, and the next shift finds out the hard way.
• The result is that occupancy across the warehouses cannot even be improved, because the data to measure it does not exist. Decisions about relocations are made from memory.

How it fits the IRIS system

Connect in voice mode — iLEAN listens to the supervisor, not to the pickers.

"Note that B-14 is overloaded, needs relocating before the next receiving" — with a custom wake-word, no device to touch. The issue gets linked to the active warehouse, location and client, and the response comes back by voice or as a push to the supervisor's tablet.

Hands-free is not a convenience here, it is the condition: the supervisor spends the shift on a forklift or between racks. The case only works if recording an issue costs one sentence and no detour to the office. Once it does, what used to be shift-end memory becomes a running list of locations, clients and issues that the whole operation can see.

See the full IRIS architecture →

Before and after

Today's round versus the dictated round

Aspect	Today	With iLEAN Connect
An overloaded location	Remembered, maybe	Logged with warehouse and client
Broken wrap at receiving	Mentioned at the coffee machine	Recorded at the location
Picker who needs support	Depends on who notices	Noted and followed up
Effort to record an issue	Get off the forklift, find a terminal	One sentence, hands-free
Occupancy across warehouses	Cannot be measured	Built from real issues
Ordinary aisle conversation	—	Not recorded

Before: what the supervisor knows dies at the end of the shift. After: it enters the system in real time, dictated on the move, no device to touch.

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.

• No standalone payback: this is an enabling piece, and we do not put a number on it by itself because its value shows up in the other cases.
• Its value is strategic: it is what makes continuous improvement of occupancy across the warehouses possible.
• Today that improvement cannot even be measured, because the issues that explain it — overloaded locations, blocked aisles, slow relocations — are never written down.
• It also feeds the rest of the matrix: relocations, damaged receivings and picker support become data that the dock, the WMS and the audit dossier can use.

Strategic value, not directly monetizable · enables continuous improvement across warehouses. It's the piece that makes continuous improvement of occupancy across all the warehouses possible — something that today can't even be measured because the data doesn't exist.

And the fair question from the production manager

“Is this for listening in on my pickers?” — no: iLEAN only records what the supervisor dictates after the wake-word, with no continuous listening and no tracking of anyone's position. Turning a spoken sentence into warehouse, location, client and issue is an anchored task, where the best models drop below 1.5% error [1], and the structured note is shown back on the tablet so he can correct it. What he gains is not having to remember, at the end of the shift, what he saw eight hours earlier in a different warehouse.

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