Am I getting too many false positive alerts?

The system calibrates against your historical data. During the first 2–4 weeks it establishes a normal behaviour baseline and reduces false positives. Typical warehouses report 2–5 meaningful alerts per month.

Can AI detect collusion between an employee and a customer?

The system tracks patterns — for example a specific warehouse worker with a specific customer consistently showing short stock. This is almost impossible to spot without a system; AI surfaces it after 2–3 occurrences.

Does the system record who performed a stock movement and when?

Yes — every warehouse movement carries a timestamp, user identity, and device. The audit log is immutable and usable for internal investigations and legal purposes.

AI warehouse anomaly detection: see what you have been missing

A stocktake discrepancy of 2–3 % can look like a “normal variance”. At a warehouse turnover of 500,000 €/year, that translates to 10,000 € – 15,000 € per year — every year, with no identified cause.

What causes unexpected discrepancies

Human errors:

Wrong pick (incorrect item issued)
Incorrectly posted receipt
Returns not properly recorded

Theft and dishonest practices:

Employee removes goods without posting
Customer receives more than the order
“Samples” issued without documentation

System errors:

Duplicate receipt posting
Incorrect quantity entered

Without AI these discrepancies only surface at a physical stocktake — once every six months or a year.

How AI detects anomalies in real time

Statistical baseline

The system learns the normal behaviour of your warehouse:

Typical daily consumption of each item (min/max/average)
Normal issue patterns (who, to whom, when, what)
Seasonal variations (December vs. July)

Anomaly detection

AI alerts when:

Stock anomaly:

Daily issue of item X is 3× higher than the typical average with no corresponding order
Stock of item Y has gone negative (logically impossible)
A goods receipt has not been confirmed by a delivery note

Movement anomaly:

Goods movement outside working hours
Repeated issue by the same person without manager authorisation
Greater quantity issued to a customer than appears on the order

Pattern anomaly:

A specific employee consistently shows a higher stocktake discrepancy than colleagues
Goods leave the warehouse via “reversals” (cancellation, error correction) at an unusually high rate

Immediate alert

When AI detects an anomaly:

Instant notification to the warehouse manager (email + push)
Detailed report: what, when, who, which numbers do not match
Recommendation: physical check of the specific warehouse area

Real-world example

Distribution company, 300 SKUs, 5 warehouse staff:

AI flagged: warehouse worker A consistently shows 18 % higher issues in the “electronics” category compared to colleagues for the same orders. Alert sent to the manager.

After a physical check: undocumented samples found being systematically issued to one specific customer. Value: 2,400 € over 4 months.

Without AI: discovered at the annual stocktake — or not at all.

AI detection of unusual warehouse movements — catch theft and errors early