Cycle Count Inventory Accuracy Calculator

What accuracy level do I actually need?

For a manual pick operation, 95% is workable but generates noticeable pick exceptions. For automated fulfillment, wave planning, or any system that auto-allocates inventory to orders without human review, you need 98-99%+ or the system will promise inventory it doesn't have, generating cancellations and chargebacks from marketplace partners.

What tolerance should count as 'matching'?

For high-value or serialized items, use zero tolerance — any variance is a miss. For bulk, low-value C-items, a small tolerance (for example ±2% of quantity) is common because rounding and unitization differences create noise that isn't a real accuracy problem. Set the tolerance in your count program before you start counting, not after you see the results.

Why does ABC velocity drive count frequency?

A-items represent maybe 20% of SKUs but 80% of transaction volume, so they drift out of accuracy fastest and cost the most when wrong. Counting them monthly catches errors before they compound into stockouts. C-items move rarely enough that annual counting is sufficient and counting them more often just burns labor without improving service.

How do I fix accuracy once I know it's low?

Root-cause the discrepancies first — most fall into a small number of buckets: uncounted damages, location mix-ups, unscanned put-backs, or system lag on in-transit inventory. Fixing the top two root causes typically recovers more accuracy than simply counting more often.