What inventory accuracy measures
Inventory accuracy compares what your system records as on-hand for a SKU against what a physical count of that SKU actually finds. It is usually calculated per SKU and then averaged (often weighted by value or by count frequency) into a single catalog-wide figure that gets tracked over time.
It matters because almost every other inventory calculation — reorder points, safety stock, available-to-promise quantities on a sales order, even the inventory value on a balance sheet — assumes the recorded on-hand number is correct. When it is not, those downstream calculations quietly produce wrong answers without any obvious error message.
The formula
Inventory accuracy (%) = (1 − |counted quantity − system quantity| ÷ system quantity) × 100, per SKU. A SKU the system says has 100 units, where a physical count finds 96, is 96% accurate for that count. Roll individual SKU accuracy up (commonly weighted by inventory value, so a high-value SKU error counts more than a low-value one) to get a single number for the whole catalog.
Where inaccuracy actually comes from
Outright theft or fraud gets the headlines, but most inventory inaccuracy in practice comes from ordinary operational gaps: a manual cycle count that overwrites a scanned record with a miscounted number; a customer return placed back on the shelf without being logged back into stock; a picker who grabs a substitute SKU to fill an order without updating what actually shipped; or a receiving clerk who logs a received quantity from memory instead of a scan.
Every one of those gaps has the same root cause: a point where stock physically changed but the system was updated manually, or not at all, instead of through a scan that forces the record to match reality at the moment it happens.
How to improve it
Scanning at every point stock changes hands — receiving, put-away, picking, transfers between locations, and returns — closes most of the gap, because it removes the manual re-typing step where transcription errors and forgotten updates happen.
Frequent cycle counts (small, rolling counts of a subset of SKUs) catch drift much earlier than a single annual full count, and let you fix the process that caused an error while it is still fresh enough to diagnose, rather than months later when the cause is impossible to trace.
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