AI System for Preparing Warehouse Stock Count Variances for Review

The problem

In day-to-day operations, a count variance can lead directly to a record adjustment. When system inventory, physical counts, locations, lots, movements and open transfers are scattered across sensors, documents, field records or team files, issues may not become visible in time.

This fragmented data delays preparation of the stock count variance review file and forces teams to search manually for supporting evidence again. The stock control lead must complete missing records and verify each conflict before making a decision.

A common exception is that an open transfer or delayed movement record may create the variance. Incomplete or incorrect automation that fails to check this context could adjust accurate stock incorrectly or conceal a loss, so no physical or operational action can be carried out directly.

What we set out to improve

• Number of genuine variances verified by the stock control lead
• Proportion of outputs corrected or rejected by the stock control 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 stock control lead
2. 02 Retrieve the inputs: system inventory, physical counts, locations, lots, movements and open transfers
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Reconcile physical counts with system balances
5. 05 Check open transfers and the latest movements
6. 06 Check for exceptions: an open transfer or delayed movement record may create the variance
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 stock control lead approve, correct, defer or reject the output

Methods we used

• Location-lot reconciliation and event-trail checks
• Source, format, timing and required-field checks for system inventory, physical counts, locations, lots, movements and open transfers
• 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, stock count variance review file and human decision

Where people stay involved

Data and security

Access to system inventory, physical counts, locations, lots, movements and open transfers 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 stock control 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