AI System for Checking Food Ingredient Receipt Documents

The problem

In day-to-day operations, the correct product may arrive with a missing or outdated document. When the purchase order, delivery note, lot, analysis document, date and supplier record are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

This fragmented data delays preparation of the ingredient receipt variance report and forces teams to search for supporting evidence again by hand. Before making a decision, the food engineer and incoming quality manager must complete missing records and verify each conflict individually.

A common exception is that an approved partial delivery may be valid, or a current document may arrive through a separate channel. Without this context check, incomplete or incorrect automation could accept a non-conforming ingredient or reject a suitable delivery, so no physical or operational action can be applied directly.

What we set out to improve

• Number of verified discrepancies in receipt documents
• Proportion of outputs corrected or rejected by the food engineer and incoming quality 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 incoming quality manager roles
2. 02 Retrieve inputs: purchase order, delivery note, lot, analysis document, date and supplier record
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match the purchase order and delivery
5. 05 Check the lot, document version and dates
6. 06 Check for an exception: an approved partial delivery may be valid, or a current document may arrive through a separate channel
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 incoming quality manager approve, correct, defer or reject the output

Methods we used

• Document-schema and lot reconciliation
• Source, format, timing and required-field checks for the purchase order, delivery note, lot, analysis document, date and supplier record
• 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, ingredient receipt variance report and human decision

Where people stay involved

Data and security

Access to the purchase order, delivery note, lot, analysis document, date and supplier record 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 incoming quality 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