98% accuracy—good enough for MediSupply?
For ambient bulk, often yes. For cold-chain regulated SKUs, segment accuracy must meet customer audit—headline 98% can hide a 94% zone.
ZBI Business Control Library
Inventory Accuracy
Inventory Accuracy: plain-English definition, formula, worked example, how to interpret the result, peer-based benchmark guidance, common mistakes, and management actions in the ZBI Business Control Library.
What is Inventory Accuracy?
Inventory accuracy is the share of count lines that matched system records in a structured cycle count or full count—correct lines divided by total lines counted, as a percent. It is not shrinkage euros alone and it is not fill rate. At MediSupply d.o.o. in Ljubljana, 980 correct lines out of 1,000 counted is 98% accuracy. I treat accuracy as a control metric: below 97% on medical consumables, pick errors and expired-stock risk rise before finance sees margin impact.
Why Inventory Accuracy matters
When system says 500 units and the shelf holds 460, you promise customers stock you do not have or you over-order because the screen lied. At 95% accuracy, one in twenty pick lines fails reconciliation—warehouse overtime and clinic complaints follow. Regulated customers audit traceability; accuracy supports batch recall, not only year-end adjustment. Finance feels low accuracy at month-end as surprise write-offs and distorted gross margin. High accuracy with disciplined cycle counting is cheaper than annual wall-to-wall panic.
Formula and variables
Inventory Accuracy % = Correct Count Lines / Total Count Lines × 100
Correct count lines — SKU/location counts within agreed tolerance of system qty.
Total count lines — all lines counted in the audit window, including zero and negative variances.
Excel: =IFERROR(B2/B3*100,0) — B2 correct lines, B3 total lines.
Real business example
MediSupply d.o.o., a EUR 6M medical consumables distributor in Ljubljana, runs March cycle counts on 1,000 bin locations across cold chain and ambient zones: 980 lines match system quantity within ±1 unit or ±0.5% policy (whichever is larger). Inventory accuracy = 980 / 1,000 × 100 = 98%. February was 97.2%—variances cluster in ambient gloves where receipt scanning skipped pallet labels. The warehouse lead implements mandatory scan at putaway for that family and targets 98.5% by June without increasing count headcount.
How to interpret the result
Accuracy measures process discipline at SKU/location level—define tolerance before counting.
- Count by location, not only by SKU total—bin errors hide in rolled-up SKU accuracy.
- Weight A-class and regulated lines in management review even if headline is 98%.
- Trend over months; one bad week after a system migration is different from chronic drift.
- Pair with pick error rate and adjustment euros—accuracy percent alone misses value impact.
Benchmark context
Medical and pharma distributors often target 98–99.5% line accuracy on A-class; 95–98% on C-class may be acceptable if value is low. Benchmark your own trailing six months and customer audit requirements—not generic retail standards. Two months below 97% on regulated lines usually triggers customer audit clauses.
Red flags
- Accuracy below 95% for two consecutive months on A-class locations.
- Headline 98% but regulated cold-chain lines at 92%—audit exposure.
- Accuracy improving while pick errors rise—tolerance gaming or wrong count scope.
- Same SKU location variances recurring three counts—process not system issue.
- Year-end wall-to-wall finds 4% value adjustment but cycle counts showed 99%—scope or tolerance wrong.
Common mistakes
- Counting only high-value SKUs and reporting that as warehouse accuracy.
- Tolerance too loose—±10% on low-cost lines hides pick failures on regulated items.
- Adjusting system to match physical without root-cause on recurring variances.
- Skipping cycle count during peak season then wondering why accuracy collapsed in Q4.
- Measuring SKU-level only while bin-level errors drive pick mistakes.
What should management do next?
- Cycle-count ABC classes on a fixed schedule with published accuracy by zone.
- Root-cause every variance above tolerance within 48 hours—receipt, pick, or move error code.
- Bar scan at receipt and pick on regulated and A-class lines without exception.
- Tie accuracy to warehouse KPI review monthly with adjustment euros, not percent alone.
- Recount within 24 hours any location that failed twice in thirty days.
Related calculators
Related analyzers
Related templates & software
Related KPI pages
Related blog articles
FAQ
Line accuracy versus value accuracy?
Line accuracy drives operations; value accuracy drives finance. Report both when shrinkage euros matter.
Should zero-on-hand locations be counted?
Yes if they are active locations—empty bin errors cause false available-to-promise.
How tight should tolerance be?
Tighter on high-value and regulated; document policy. ±1 unit on a 10,000-unit SKU is wrong tolerance.
Who owns inventory accuracy?
Warehouse manager owns count process; finance owns adjustment posting; IT owns scan integration—weekly variance review with all three.
Summary
Inventory accuracy shows how well records match physical stock. Define tolerance, count by location, segment regulated lines, and fix root cause—not only month-end adjustments.
Reviewed by ZBI Business Control Library · Last updated 2026-06-15