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Ecommerce inventory discrepancy detection - find the stock number that disagrees
Ecommerce inventory discrepancy detection starts with a daily sheet that compares sellable store stock, warehouse stock, open orders, and the last sync time. A disagreement is a prompt to investigate, not permission for a system to correct inventory.
A dashboard's stock number is not truth because it is displayed confidently. The useful control shows the competing source values and waits for a person.
What should a discrepancy sheet compare?#
| Field | What it proves |
|---|---|
| SKU and location | Which item and warehouse the row refers to |
| Sellable store stock | What customers can buy now |
| Warehouse stock | What the fulfilment source reports on hand |
| Open and unfulfilled orders | Stock already committed but not yet shipped |
| Last sync time | Whether the numbers were read at comparable times |
| Difference | The gap that needs review |
| Evidence and owner | The source link and named investigator |
The last sync time prevents a false comparison between a fresh warehouse export and yesterday's store count. For a scheduled close process across three ledgers, use inventory reconciliation. Detection is the daily watcher. Reconciliation is the deliberate check at a common cutoff.
Which mismatches should be investigated first?#
Start with a SKU that is sellable in the store but unavailable at the warehouse, a high-volume item with a large difference, or an item near a reorder decision. The threshold is business-specific. A low-volume mismatch may be a count timing issue. A sellable item with no fulfilment stock can create an order exception quickly.
Link the flag to order exception management when customers are already affected. Do not make the detector try to solve the fulfilment problem.
How does the discrepancy build actually go?#
We begin with the store, warehouse, and order sources the operator trusts enough to compare. We write the definitions of sellable, committed, and on-hand stock. The client keeps that definition, the source list, and each reviewed discrepancy.
The build reads the agreed values on a schedule and produces rows only where the comparison breaks the team's threshold. It attaches the source values and timestamps. It does not write stock back to any system. A warehouse or ecommerce owner checks the evidence and records the reason for the difference.
We shadow-run the sheet against recent inventory history. The team looks at flags it would have raised and identifies normal timing differences versus real problems. This backtest is where the threshold becomes useful. When a row is confirmed, the team handles the resulting correction in its normal process and keeps the reason for the next reconciliation.
The same source data can support SKU profitability reporting, but stock differences should not be treated as margin data until the inventory count is understood.
What should you compare tomorrow morning?#
Choose ten important SKUs. Put their store stock, warehouse stock, open orders, and source time in one sheet. Do not fix anything yet. First find out which source values actually disagree and whether a customer can still buy an item you cannot fulfil.
Where does this fit with the rest of the operation?#
For Amazon channels, this is the stock-control companion to Amazon seller back office. A detector should also follow the source and output checks in automation that finds problems.