AI System for Matching Freight with Transport Documents

The problem

In day-to-day operations, item-level discrepancies may arise between documents and the physical load. When transport orders, delivery notes, packages, pallets, weights, seals and vehicle records are scattered across sensors, documents, field records or team files, issues may not become visible in time.

This fragmented data delays preparation of the freight-document discrepancy report and forces teams to search manually for supporting evidence again. The warehouse and transport operations lead must complete missing records and verify each conflict before making a decision.

A common exception is an approved partial load or transhipment. Incomplete or incorrect automation that fails to check this context could allow an incomplete load to be dispatched or stop a valid dispatch, so no physical or operational action can be carried out directly.

What we set out to improve

• Number of verified freight-document discrepancies
• Proportion of outputs corrected or rejected by the warehouse and transport operations lead
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the role of the authorised warehouse and transport operations lead
2. 02 Retrieve the inputs: transport orders, delivery notes, packages, pallets, weights, seals and vehicle records
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match item and package identifiers
5. 05 Check weight, seal and vehicle details
6. 06 Check for exceptions: an approved partial load or transhipment may apply
7. 07 Place low-confidence or conflicting records in a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the warehouse and transport operations lead approve, correct, defer or reject the output

Methods we used

• Document-schema and freight-identifier reconciliation
• Source, format, timing and required-field checks for transport orders, delivery notes, packages, pallets, weights, seals and vehicle records
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• A separate exception check outside the main rule
• Combined retention of the source, rule result, freight-document discrepancy report and human decision

Where people stay involved

Data and security

Access to transport orders, delivery notes, packages, pallets, weights, seals and vehicle records is restricted to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the dataset is sent to the model. Access, outputs and the warehouse and transport operations lead's decision are logged; production use does not begin until the organisation has approved the retention period and data location.

Who this suits

Frequently asked questions