Stockout rate

Stockout rate measures how often inventory is unavailable when customers want it. It's the most direct measure of whether your inventory is doing its primary job: being there.

Two common formulas, and they answer different questions.

SKU-day vs order-line stockout rate

The two formulas:

SKU-day stockout rate = (SKU-days out of stock) / (Total SKU-days) × 100

Order-line stockout rate = (Order lines unfulfilled) / (Total order lines) × 100

The first measures availability in the catalogue. A 5% SKU-day stockout rate means 5% of all SKU/day combinations had zero stock.

The second measures the customer-facing impact. A 2% order-line stockout rate means 2% of order lines couldn't be filled because the requested SKU was out.

The order-line measure is usually the more important one operationally — a stockout on a slow-mover affects almost no customers; a stockout on a top seller affects many.

Worked example

A brand has 200 SKUs. Over a 30-day month:

• Total SKU-days = 200 × 30 = 6,000
• SKU-days out of stock = 240 (e.g. 8 SKUs out for 30 days each, or various shorter outages totalling 240)
• SKU-day stockout rate = 240 / 6,000 = 4%

Same period, the brand received 5,000 order lines:

• Order lines that hit a stockout = 100
• Order-line stockout rate = 100 / 5,000 = 2%

The two numbers tell different stories. 4% of the catalogue had availability gaps, but only 2% of customer demand was actually disrupted.

Benchmarks

Reasonable industry ranges by category:

• Fast-moving consumer goods: target ≤2% order-line stockout rate
• Fashion/apparel: 5–10% is typical (high SKU count, fast turnover, planned end-of-season run-out is normal)
• Beauty / wellness: target ≤3%
• Speciality / long tail: 5–8% is acceptable on slow-movers if your top SKUs are at <2%

A 10% stockout rate on top sellers is a crisis. A 10% stockout rate on the long tail might just be deliberate range management.

What drives stockouts

The usual root causes, in order of frequency:

• Forecast under-shoot. Demand outpaced the forecast, reorder point hit too late.
• Supplier lateness. PO arrived after the planned date — see lead time variability.
• Insufficient safety stock. The buffer was sized for a calmer demand pattern than what materialised.
• Allocation errors. Total stock was fine; the location with demand was empty.
• Planning system failure. Reorder point not maintained, or the system didn't trigger the order.

Each cause needs a different fix. A stockout post-mortem that doesn't identify the root cause is a stockout that will repeat.

Stockout rate vs fill rate

Closely related but distinct:

• Stockout rate = % of SKUs/orders that hit a stockout
• Fill rate = % of customer demand actually fulfilled (the inverse perspective)

If you have a 2% stockout rate on order lines, you have a ~98% fill rate. The difference is in the framing — stockout rate measures the failures; fill rate measures the successes. Most operators track both because they communicate differently to different audiences.

Why stockouts cost more than the missed sale

The direct cost is the lost sale. The indirect costs are larger:

• Customer acquisition wasted. A customer who landed on the page and bounced because the SKU was out cost you the same to acquire as one who converted.
• Lifetime value impact. A repeat-purchase customer who couldn't get their usual SKU may switch to a competitor permanently. That's a much bigger number than the missed transaction.
• Algorithmic penalties. Retail platforms (Amazon, ASOS, Faire) penalise OOS rates with reduced search visibility. The stockout creates a downstream ranking problem.
• Operational firefighting. Emergency reorders, expedited freight, customer service responses. All cost margin.

The cost of a stockout is roughly 3–5× the value of the missed sale. That's the multiplier most planning processes use when comparing "carry more stock" vs "accept more stockouts."