Logistics and warehousing
Reading shipment papers, counting stock, planning routes: where AI helps immediately in logistics and what master data quality it requires.
What this is about
| Task | Value | Note |
|---|---|---|
| Reading delivery notes and freight papers | very high | checkable against the order |
| Reconciling goods receipt against the order | high | flag deviations automatically |
| Counting stock by camera | medium | only with orderly storage |
| Predicting travel times and volumes | medium | needs your own history |
| Classifying damage reports | high | photo plus text |
| Optimising a route | low for AI | classical optimisation is better |
Introduction
- 01
Start with the papers
Delivery notes arrive daily, in the same handful of layouts per supplier. That is the ideal starting position.
- 02
Check against the order reference
Every extracted line is held against the order. That check is free and catches most errors.
- 03
Route deviations for handling
The value appears where only the deviations are looked at by people.
- 04
Camera and forecasting only afterwards
Both need more groundwork and return less immediately.
- Harmonise article numbers and units first, or extraction produces data rubbish.
- Evaluate supplier-specific layouts separately; an overall figure hides that one supplier runs systematically badly.
- Check quantities and weights against plausibility bounds, not only against the order.
Data flow
Freight paper
→ OCR with coordinates
→ field extraction: supplier, consignment number, lines, quantities
→ reconciliation against order and master data
→ deviation cases to handling, the rest posted automatically
→ log per transaction with model version and confidence
Predicting travel times
The most frequent mistake is a time leak: the forecast uses features that were not known at planning time, such as the actual loading duration.
- Ask of every feature: was this value available at planning time?
- Time-based validation with a gap the size of the lead time.
- Use weather and traffic data only in the version available at planning time.
See Data leakage.
Legal points
- Freight papers contain personal data of drivers and recipients. Set retention separately from commercial-law archiving.
- Warehouse camera systems capture staff. Performance or conduct monitoring engages co-determination, even where unintended.
- Vehicle telematics data is particularly sensitive; purpose limitation must be drawn narrowly and recorded in writing.
Choosing a task in logistics
FREE ACCOUNT
Selection sheet and templates: logistics
A sheet that picks the first task out of the ones logistics actually has, with two tested templates and the review steps for them.
Worksheet3 items