AI System for Preparing Delivery Route Exceptions for Review

The problem

In day-to-day operations, the reason for a route deviation may not be visible on the operations screen. When planned and actual routes, traffic, stops, driving and break records, and delivery 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 route exception file and forces teams to search manually for supporting evidence again. The transport operations lead must complete missing records and verify each conflict before making a decision.

A common exception is that a road closure, safe break or customer request may justify a deviation. Incomplete or incorrect automation that fails to check this context could flag a driver unfairly or miss a genuine issue, so no physical or operational action can be carried out directly.

What we set out to improve

• Proportion of events confirmed by operations as genuine exceptions
• Proportion of outputs corrected or rejected by the 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 transport operations lead
2. 02 Retrieve the inputs: planned and actual routes, traffic, stops, driving and break records, and delivery records
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match the plan to the GPS trace
5. 05 Add traffic, break and delivery context
6. 06 Check for exceptions: a road closure, safe break or customer request may justify a deviation
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 transport operations lead approve, correct, defer or reject the output

Methods we used

• Geographical route-difference analysis and event correlation
• Source, format, timing and required-field checks for planned and actual routes, traffic, stops, driving and break records, and delivery 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, route exception file and human decision

Where people stay involved

Data and security

Access to planned and actual routes, traffic, stops, driving and break records, and delivery 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 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