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Inventory Count Shortfall IQR Outlier Flags
x402
Inventory Count Shortfall IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-21).
Detects IQR-based statistical outliers in retail inventory count shortfall data across up to 200 records
Price / call$0.01
Success—
StatusUnknown
How this service works
Inventory count shortfall - IQR outlier flags: IQR outlier flags for Inventory count shortfall. Supplied retail figures only, up to 200 records. No platform access, live rates or order execution. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.
Output
A bounded IQR outlier flags report identifying which rows in the input have inventory count shortfalls that fall outside the interquartile range, flagging statistically anomalous discrepancies between expected and counted units. Up to 200 records processed per call.
Example use cases
Warehouse cycle count audit
“I just finished a warehouse cycle count and have up to 200 rows of expected vs counted units for our SKUs. Can you flag which ones have statistically unusual shortfalls using IQR outlier detection so I know where to investigate?”
Retail shrinkage anomaly detection
“We track inventory shortfalls across store locations — here are the expected and counted unit numbers. Use IQR outlier detection to tell me which locations have abnormal shrinkage compared to the rest.”
Supplier delivery discrepancy analysis
“I have a batch of received shipments with expected_units and counted_units for each delivery. Can you run IQR outlier flagging to identify which deliveries have unusually large count discrepancies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-retail-count-shortfall-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cost-effective ($0.01 USDC) statistical outlier analysis specifically for retail inventory count shortfalls using the IQR method. It is ideal for batch analysis of up to 200 inventory rows where you want to flag anomalous discrepancies between expected and actual unit counts, without needing live inventory system access or order execution capabilities.
Side effects
- Fewer than 2 rows provided — minimum input requirement not met
- expected_units or counted_units missing from any row — required fields must be present
- Values exceed maximum of 1,000,000,000,000 — out of range error
- More than 200 rows submitted — maxItems limit exceeded
- Non-numeric values in numeric fields — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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