What it measures
% of volume from sustainably certified suppliers
This KPI measures the outcome of sourcing decisions already made. A low score indicates a commercial or compliance risk that has already been incurred, with consequences like a retailer supply agreement review appearing weeks or months after the purchasing decision.
Business impact
- Increased 'chaos tax' from last-minute scrambles to find compliant material when a non-certified supplier is flagged during a customer audit, often requiring premium spot-market purchases.
- Bloated inventory position when forced to switch to a new certified supplier with a high Minimum Order Quantity (MOQ), tying up cash in slow-moving stock to close a compliance gap.
- Sustained underperformance moves a supplier into the delisting zone. For key commodities like palm or cocoa, this is not a negotiation; it is a hard market access failure.
Formula
(Certified volume / Total volume) x 100Grain
One row per purchase order line — supplier × material × commodity × plant × goods receipt 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_Purchase_Order_Line.
Fact_Purchase_Order_Line— 7 mandatory columns — actual goods receipt date key, qty ordered, ...Dim_Commodity— key + nameDim_Material— material_key + material_descriptionDim_Plant— key + nameDim_Supplier— 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 | 0.95 — performance considered compliant at or above this line |
| Warning | 0.7 — 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 Certified Supplier Rate in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.