What it measures
% of picks completed without error
it measures errors after they have occurred in the warehouse. The financial consequence (deduction, return cost) is already locked in by the time the error is recorded at despatch.
Business impact
- Increased 'chaos tax' from rework loops inside the warehouse: time spent finding and correcting errors before dispatch consumes labor hours that are budgeted for productive picking, driving up cost per case.
- Picking errors create phantom stock: the system shows an item as picked when it is still on the shelf, or a location as empty when it is not, corrupting the inventory position and causing stock-outs on subsequent orders.
Formula
(Error-free picks / Total picks) x 100Grain
One row per pick line confirmation — picker × material × warehouse × shift × date
Inside the Implementation Pack
A preview of what's actually built for this KPI — extracted from the real Implementation Pack, not a mockup.
This is the primary ratio the pack's other measures build on — trend, rank, and period-comparison variants, all on Fact_Warehouse_Picking_Line. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Warehouse_Picking_Line— 8 mandatory columns — qty picked, date key, ...Dim_Employee— key + nameDim_Material— material_key + material_descriptionDim_Shift— key + nameDim_Warehouse— key + nameDim_Date— Auto-generated calendar table — no customer data required
Full field-by-field data contract, in 4 SQL dialects (SQL Server, Snowflake, Databricks, BigQuery), ships in the pack.
| Target | 0.998 — performance considered compliant at or above this line |
| Warning | 0.99 — amber alert — triggers a review |
Both live in one Excel tab — change them, refresh the report, done. No DAX editing needed.
Everything you need to build Order Picking Accuracy in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.