In-stock rate
The percentage of SKUs currently available to sell — the snapshot view of catalogue availability that retail platforms rank brands by.
By Oana Bradulet
In-stock rate measures the percentage of SKUs currently available to sell. It's the snapshot view of catalogue availability — different from stockout rate, which measures how often SKUs are out, and from fill rate, which measures how much demand was fulfilled.
Where stockout rate looks across a period, in-stock rate looks at a moment in time:
In-Stock Rate = SKUs in stock / Total active SKUs × 100
A catalogue of 200 active SKUs with 190 available has an in-stock rate of 95%.
Why retailers and marketplaces watch this
In-stock rate is the metric that retail platforms publish on vendor scorecards:
- Amazon: in-stock rate (sometimes called "in-stock percentage") feeds Buy Box eligibility and search ranking
- Walmart: tracked at the supplier scorecard level with chargeback risk
- Target: weekly in-stock visibility for vendor compliance
- Wholesale platforms (Faire, NuOrder, etc.): displayed publicly to potential buyers
Below ~95% on a marketplace usually triggers ranking and discoverability penalties before any direct revenue impact shows up. The downstream traffic loss compounds the direct missed sales.
Weighted vs unweighted in-stock rate
The basic formula treats every SKU equally. A more useful variant weights by demand:
Weighted In-Stock Rate = Σ (Daily demand × In-stock binary) / Σ (Daily demand) × 100
This says: of the demand we expected today, how much could we have served? An out-of-stock long-tail SKU contributes much less to the weighted measure than an out-of-stock top seller.
Most planning processes report both. Unweighted = catalogue health. Weighted = customer-facing health.
Worked example
A brand has 200 active SKUs. At today's snapshot:
- 190 SKUs in stock → unweighted in-stock rate = 190 / 200 = 95%
Apply demand weights. The 10 out-of-stock SKUs include 2 of the top 20 sellers (which together represent 18% of expected demand) and 8 long-tail SKUs (representing 1% of expected demand).
- Weighted in-stock rate = 100% − 19% = 81%
Same physical situation. Very different number once you weight for what customers actually want.
In-stock rate vs related metrics
Three related measures, each answering a different question:
| Metric | Question | Period |
|---|---|---|
| In-stock rate | Of SKUs currently in the catalogue, what % are available right now? | Snapshot |
| Stockout rate | Of SKUs over the period, what % were out at some point? | Period |
| Fill rate | Of customer demand, what % was actually fulfilled? | Period |
A SKU that was out for 2 days during a 30-day month appears in stockout rate (it had a stockout) but might appear as in-stock today in the snapshot. A short stockout barely dents fill rate but ruins the in-stock rate during those 2 days.
What drives in-stock rate
Same root causes as stockouts:
- Forecast misses on top sellers (biggest impact on weighted in-stock rate)
- Long-tail SKUs that aren't economically restocked (drag on unweighted rate)
- Allocation gaps between locations (one warehouse OOS while another has stock)
- Slow reorder cycles
- Supplier delivery variability
The fixes are familiar: better demand sensing, tighter reorder triggers, faster supplier replenishment, smarter allocation.
Targets by channel
Practical in-stock rate targets:
- D2C site (weighted): ≥97%
- D2C site (unweighted): ≥95%
- Amazon (in-stock %): ≥98% to maintain Buy Box and search ranking
- Wholesale platforms: ≥95% to maintain reorder velocity
- Long-tail SKUs: 85–90% can be acceptable if the unit economics don't justify the buffer
The temptation is to chase 100%. The cost rises non-linearly towards the asymptote — going from 95% to 99% often doubles safety stock. Most operations land at 96–98% as the sweet spot.
When low in-stock rate is fine
Some situations where lower is right:
- Pre-launch ramp where SKUs are intentionally unlisted
- End-of-season fashion run-out
- Discontinued lines being deliberately exhausted
- Out-of-season SKUs (snowboards in July) where availability isn't operationally relevant
The metric should reflect active, in-season, intentionally-available SKUs — not your entire historical catalogue.
Formula
- SKUs in stock
- = Number of active SKUs with available, sellable stock at the snapshot moment
- Total active SKUs
- = All SKUs currently listed and intended to be available — excludes pre-launch, discontinued, out-of-season
Worked example
200 active SKUs, 190 in stock → unweighted in-stock rate = 95%. Weighted version: the 10 OOS SKUs include 2 top-20 sellers representing 18% of expected demand. Weighted in-stock rate = 100% − 19% = 81%.
Common mistakes
- →Using the unweighted measure to track customer-facing availability. Top sellers OOS hurt customers far more than long-tail OOS.
- →Including pre-launch, discontinued, or out-of-season SKUs in the denominator. Inflates the count and dilutes the signal.
- →Chasing 100% in-stock rate. The cost rises non-linearly; 97% is usually the right target for D2C, 98% for marketplaces.
- →Reporting in-stock rate without channel context. Same SKU can be in stock at the warehouse but OOS at Amazon FBA.
How Lumina handles in-stock rate for scaling brands
Lumina tracks your in-stock rate at whatever level you need — a product across its size variants, a channel as a whole, a single store — so you can see where availability is slipping and make decisions off the back of it.
Frequently asked questions
What is in-stock rate?
What's a good in-stock rate?
What's the difference between weighted and unweighted in-stock rate?
Why do retailers and marketplaces care so much about in-stock rate?
Should I include discontinued SKUs in the calculation?
Related terms
Stockout rate
The percentage of SKUs (or SKU-days) that are out of stock — a direct measure of how often you're disappointing demand.
Fill rate
The percentage of customer demand actually fulfilled from available stock — measured by units, lines, or orders depending on the question being asked.
Days of cover
How many days of forward demand the current stock-on-hand will satisfy at the current sell-through rate — the operator-friendly view of inventory health.
ABC analysis
A method of classifying SKUs by their relative importance — usually revenue or margin — into A (vital few), B (middle), and C (long tail) buckets, so each gets the right level of planning attention.
Safety stock
Extra stock held above expected demand to absorb forecast error and lead-time variability without stocking out — expressed either as units or as time (days/weeks of cover). Same buffer, two units.