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Supply Chain · All retail

Cycle Count Prioritization

Inventory record drifting. Prioritizes the counts worth the labor.

Target KPI Inventory accuracy
Executes in ≤ 24 hours
System of record WMS · Manhattan
Run this on your data → How the loop closes
Trigger condition

Predicted record-to-shelf variance on a store-SKU exceeds the tolerance that would cause a phantom stockout or a false replenishment suppression.

Write-back

Prioritised count tasks created in the WMS; adjustments posted to Manhattan on completion.

Every write is gated on an approver role you name. Nothing runs unattended.

Procedure
  1. Score every store-SKU on the probability its record is wrong, from negative-on-hand events, sales-after-zero, and adjustment history.
  2. Weight by consequence: a wrong record on a fast, high-margin line costs far more than on a slow one.
  3. Build the count list that fits the labour hours the store actually has, best return first.
  4. Push the prioritised list to the store task queue with expected counts attached.
  5. Close when counted variance falls and phantom stockouts on the listed SKUs stop.
Outcome metric

Inventory record accuracy on counted SKUs, and the reduction in phantom stockouts, over 30 days per labour hour spent.

The case closes on this number, not on the action being taken. A playbook without a close condition is a dashboard.

The rest of Supply Chain

Stockout Escalation Zero on hand forecast inside 48 hours. Raises the replenishment. On-shelf availability Replenishment Cadence Tune Cycle out of step with velocity. Retunes cadence per store-SKU. On-shelf availability Supplier OTIF Watch Supplier slipping on OTIF. Flags the breach against the penalty clause. Fill rate Cross-Store Transfer Overstock here, stockout there. Proposes the transfer before markdown. Sell-through Weather Pre-Position Heatwave or storm inbound. Pre-positions sensitive stock by store. Sell-through

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