AI System for Checking Loading Sequences and Bay Conflicts

The problem

The same resource may be scheduled for overlapping times in day-to-day operations. When vehicle appointment, loading bay schedule, load readiness, team and delay records are scattered across sensor, document, site or team records, the issue may not be identified in time.

This fragmented data delays preparation of the loading bay conflict list and forces teams to search for supporting evidence again by hand. Before making a decision, the warehouse shift supervisor must complete missing records and verify each inconsistency individually.

A common exception is that a delayed vehicle or urgent dispatch may change the sequence. Incomplete or incorrect automation that does not check this context could cause yard congestion or an incorrect loading sequence, so no physical or operational action can be taken directly.

What we set out to improve

• Number of conflicts confirmed by the shift supervisor
• Proportion of outputs corrected or rejected by the warehouse shift supervisor
• 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 shift supervisor
2. 02 Retrieve the inputs: vehicle appointment, loading bay schedule, load readiness, team and delay records
3. 03 Check the source identifier, date, calibration or version, and data recency
4. 04 Combine vehicle and loading bay times
5. 05 Check load and team readiness
6. 06 Check the exception: a delayed vehicle or urgent dispatch may change the sequence
7. 07 Place low-confidence or inconsistent records in a separate specialist queue
8. 08 Write the source identifier, rule, output and time to the audit trail
9. 09 Have the warehouse shift supervisor approve, correct, defer or reject the output

Methods we used

• Resource schedule and dependency checks
• Source, format, time and required-field checks for vehicle appointment, loading bay schedule, load readiness, team and delay records
• A missing-data warning instead of an estimate when inputs are incomplete or inconsistent
• A separate exception check outside the main rule
• Joint retention of the source, rule result, loading bay conflict list and human decision

Where people stay involved

Data and security

Vehicle appointment, loading bay schedule, load readiness, team and delay records are accessed with the narrowest permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the data is sent to the model rather than the complete raw dataset. Access, outputs and the warehouse shift supervisor's decision are recorded; production does not begin until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions