AI System for Opening Cold-Chain Variances for Review

The problem

In day-to-day operations, a single sensor alert may not explain the product's actual exposure. When temperature records, sensor identity, door events, batch, time and maintenance records are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

This fragmented data delays preparation of the batch-level variance file and forces teams to search for supporting evidence again by hand. Before making a decision, the food engineer and cold-chain manager must complete missing records and verify each conflict individually.

A common exception is that calibration, loading or a briefly opened door can cause a temporary variance. Without this context check, incomplete or incorrect automation could dispose of safe products or release products that require further review, so no physical or operational action can be applied directly.

What we set out to improve

• Proportion of events the specialist confirms as genuine variances
• Proportion of outputs corrected or rejected by the food engineer and cold-chain manager
• Time between the source event and the inspection output

How the system works

1. 01 Define the review scope and the authorised food engineer and cold-chain manager roles
2. 02 Retrieve inputs: temperature records, sensor identity, door events, batch, time and maintenance records
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Check sensor recency and calibration status
5. 05 Match the variance with batch movements and door events
6. 06 Check for an exception: calibration, loading or a briefly opened door can cause a temporary variance
7. 07 Route low-confidence or conflicting records to a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the food engineer and cold-chain manager approve, correct, defer or reject the output

Methods we used

• Time-series threshold checks and event correlation
• Source, format, timing and required-field checks for temperature records, sensor identity, door events, batch, time and maintenance records
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• Separate evaluation of exceptions and the main rule
• Combined retention of the source, rule result, batch-level variance file and human decision

Where people stay involved

Data and security

Access to temperature records, sensor identity, door events, batch, time and maintenance records is limited to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary subset of the raw dataset is passed to the model. Access, outputs and the food engineer's and cold-chain manager's decisions are logged; the system is not released to production until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions