What it measures
% of outbound picks following first-expiry-first-out rule
it measures pick compliance after the event has occurred. The consequence (a short-dated product shipped, a potential write-off created) is already locked in before the score is calculated.
Business impact
- Increased 'chaos tax' from emergency stock counts, manual inventory sweeps to find aging batches, and expedited shipments to replace customer-rejected pallets.
- Systematic non-compliance creates layers of older, hidden stock, turning physical inventory into a balance sheet liability that culminates in a total product and disposal cost write-off.
Formula
(FEFO-compliant picks / Total picks) x 100Grain
One row per pick line confirmation — warehouse × material × batch × employee × 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— 9 mandatory columns — date key, warehouse key, ...Dim_Batch— batch_key + batch_nbrDim_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 | 0.99 — performance considered compliant at or above this line |
| Warning | 0.96 — 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 Stock Rotation Compliance (FEFO) in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.