Computer vision workflow / Industrial operations / Concept prototype

Moving an under-fill alert to where someone can still act

Check pallets in the staging lane before they enter an export container, and alert the shift lead while the load can still change.

Role: Product Manager, self-initiated
Period: September 2026

Simulated decision moment. Shipment totals are real; pallet heights, sequence and outcome are simulated, and the prototype says so on every screen.

83 export shipments analysed

64 / 83 near the payload ceiling

19 loads prioritised for measurement

2 interactive prototypes

Team: Solo, with peer and AI review
Product stage: Concept prototype
Customer: Shift leads and operations leaders
My ownership: Discovery, analysis, prototype, pilot plan
Key constraint: Real totals, no pallet-level data

The customer asked for an alert nobody could act on.

This is a deliberate product study, built from an industrial computer-vision brief. The customer, an automotive parts exporter, believed its containers left the plant 30% empty. The VP of Operations wanted a real-time alert when a container was going out under-filled.

The logistics manager pushed back: the doors are sealed and the driver is moving about four minutes after the last pallet goes in. In my first submission I agreed with him and set the alert aside, focusing instead on the shipping export.

The data could not answer the question it was asked.

The brief's three-week shipping export covered 83 containers. Weights were recorded. The declared volume was not measured: it was pallet count multiplied by one fixed figure per product family, and the brief confirmed nobody had ever measured actual cube.

36.6% Nominal volume gap across the fleet. Pallet-master volume, not measured void.

64 / 83 Containers at 90% or more of payload. Weight likely explains much visible headroom.

19 / 83 Light, high-cube loads where measured space might be recoverable.

Fleet overview separating nominal volume from recorded weight across 83 export containers — Fleet baseline calculated from the real shipping export, alongside door status. Only the D09 load activity is simulated.

Move the check to where a decision still exists.

The site's existing safety cameras already faced the staging lane, not the container interior. The brief also noted that the shipping system builds a load plan the day before, and the shift lead overrides it by pulling the next thing that fits. So the product works with that habit rather than against it.

Measure in stagingReplay load planAdvise earlyHold at the doorShift lead decides

After every pallet, the prototype replays the remaining load plan under a simple stacking rule and tries pulling each reachable pallet forward. It shows a known fix as an advisory straight away, and only blocks a pallet at the door when waiting one more pallet would lose the fix.

Designing for trust before accuracy.

A recommendation from a camera is only useful if the shift lead trusts it. The prototype treats uncertainty as part of the product, not an edge case.

Abstain on weak readings A height reading below the confidence bar is withheld; the staging measurement stands.

No recommendation without identity A pallet whose label was not read is excluded until it is scanned.

Overrides are signals The shift lead can override with a reason. Repeated overrides mean the rule needs review.

Every claim is tagged Evidence is marked real, specification, inferred or simulated, on every screen.

Decision moment showing a held pallet in the scan zone, previews inside the container, and simulated comparison cards — The decision moment. The held pallet is still outside the container; the comparison and outcome are explicitly simulated.

Open the prototype

Sizing it honestly.

Sequencing alone is small. As an illustrative scenario, if every volume-bound export rolled one pallet that sequencing could have saved, that would be about nine containers and €8–10k a year. No paid container has been shown saved.

The case for building it is different: a screen the shift lead uses on every load leaves measured pallet heights behind, which is data the customer has never had. Container-count decisions also depend on floor positions, stacking rules and rates, so measured cube is one input, not the answer.

What a pilot has to prove

Camera height error against tape, well inside the decision margins.
Access to the load plan and a link between shipments and physical containers.
Packaging approval for stacking the product families involved.
How often the load plan actually loses positions or leaves pallets behind.

What I learned, and what I would change.

Interrogate the request, then return to it

I was right that an end-of-load alert had no action behind it, and wrong to stop there. The customer's request was pointing at a real need; the timing was the problem.

Label simulation as boldly as results

Early reviewers understood the idea but remembered the simulated 38-of-38 outcome as if it had happened. Banners, labels on every result and a persistent counterfactual note fixed what small disclaimers could not.

Next experiment

Measure a week of staged pallets against tape and the real load plan. That single dataset would show whether the camera is accurate enough and whether the sequencing problem occurs often enough to matter.

Next case study

Designing a digital BAU operating model

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