What it measures
% of stock positions (SKU x location) out of stock at DC level
It is a lagging indicator of a planning or production failure that has already occurred, but a leading indicator of imminent customer service failures (shelf gaps, order cuts) which will materialize in the next 24-72 hours.
Business impact
- Increased 'chaos tax': last-minute production schedule changes, premium freight to rush stock from other sites, and customer service overtime managing allocation cuts and retailer complaints.
- Directly triggers order cuts and short shipments, generating retailer penalty invoices for service level failures and eroding working capital through unfulfilled demand.
Formula
(OOS stock positions / Total stock positions) x 100Grain
One row per material per plant per warehouse per storage location per day
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_Inventory_Snapshot. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Inventory_Snapshot— 7 mandatory columns — sku count, warehouse key, ...Dim_Material— material_key + material_descriptionDim_Plant— key + nameDim_Storage_Location— 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 | 0.015 — performance considered compliant at or below this line |
| Warning | 0.03 — 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 OOS Rate at Distribution Center in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.