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KPI · Logistics

Picking accuracy: formula and example.

Picking accuracy is the percentage of order lines picked without an error: the right item in the right quantity. A picking error almost always leads to extra cost, such as a return, a reshipment or a complaint. That makes it a core KPI for warehouse quality.

Picking accuracy formula

(Order lines picked − lines with an error) ÷ order lines picked × 100%

Worked example

In May 2026 Acme Groothandel's warehouse picked 48,000 order lines. 144 errors were found. Picking accuracy is (48,000 − 144) ÷ 48,000 × 100% = 99.7%.

Fictitious figures for Acme Groothandel, a sample wholesaler.

How to read this KPI

Higher picking accuracy means fewer returns, reshipments and complaints. Because the percentages tend to sit close together, errors per 1,000 lines is sometimes easier to read. Compare by zone, product group and picking method with your own earlier periods, and look at which type of error is most common.

Pitfalls with picking accuracy

  • Counting only errors found at the packing check and not feeding back customer complaints.
  • Mixing errors per order with errors per line.
  • Comparing pickers without allowing for harder zones or products.

SQL & DAX

Calculating picking accuracy in SQL and DAX

Two examples on a fictitious data warehouse for Acme Groothandel: a PostgreSQL query and a measure for a Power BI model. Adjust table and column names to your own model.

SQL · PostgreSQL
SELECT date_trunc('month', picked_at) AS month,
       SUM(lines) AS lines_picked,
       SUM(errors) AS pick_errors,
       ROUND(100.0 * (SUM(lines) - SUM(errors)) / NULLIF(SUM(lines), 0), 2) AS picking_accuracy_pct
FROM picks
GROUP BY 1
ORDER BY 1;
DAX · Power BI
Lines Picked = SUM(Picks[Lines])
Picking Accuracy = DIVIDE([Lines Picked] - SUM(Picks[Errors]), [Lines Picked])

In ENABLE

Tracking this KPI in ENABLE

Plain-language questions

Ask for picking accuracy per month, customer or product in the SQL Explorer with AI. The AI writes the SQL, you see the query and run it yourself; the AI only receives metadata.

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In your Power BI dashboard

Define the KPI as a measure in your Power BI model and show it in the portal, with "Last refreshed" next to it. Access profiles and optional row-level security decide who sees which figures.

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Custom dashboards (pilot)

Add the KPI as a tile to a custom dashboard, compared with last year. The Dashboard Builder is a pilot and can be switched on per client.

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Monthly targets

With the built-in Targets app you set a target for the KPI per month, so you see actuals and target side by side.

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FAQ

Frequently asked questions

How do you calculate picking accuracy?

Subtract the number of order lines with an error from the total lines picked and divide by that total. An error is a wrong item, a wrong quantity or a damaged item that was packed anyway. Combine errors from your own checks with those reported by customers.

What types of picking errors are there?

The most common are a wrong item, a wrong quantity, a missing item and the wrong variant, such as size or colour. Record the type with every error. That tells you whether to look for the cause in locations, labelling or the picking method.

How do you improve picking accuracy?

Scanning during picking is usually the biggest improvement, because it stops wrong items straight away. Clear locations, keeping look-alike items apart and a check at packing also help. Measure after each change whether errors per 1,000 lines go down.

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