What it measures
% of order lines with no errors across all dimensions
it measures the outcome of an order cycle after it is complete. The financial consequences, such as retailer deductions for failures, are already locked in by the time the KPI is calculated.
Business impact
- Increased 'chaos tax' from last-minute fixes: premium freight to meet a closing delivery window, unplanned production runs to cover shortages, and customer service overtime to manage the fallout of broken promises.
- Sustained underperformance triggers a formal retailer service improvement plan — the structured final warning before range removal and delisting.
Formula
(Perfect order lines / Total order lines) x 100Grain
One row per customer sales order line — customer × product × site × 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_Customer_Order_Line. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Customer_Order_Line— 9 mandatory columns — qty delivered, site key, ...Dim_Customer— key + nameDim_Product— key + nameDim_Site— 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 | 0.945 — 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 Perfect Order Rate in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.