What it measures
% of batches released without rework or rejection
RFTQ measures the outcome of a completed production run. The costs of failure (rework, scrap, line time) are already sunk by the time the KPI is calculated.
Business impact
- A rising 'chaos tax' from rework loops that steal scheduled line capacity, forcing last-minute production plan changes and driving up labor costs through unplanned overtime.
- Bloated inventory position with unsaleable 'QA-held' stock. This phantom inventory exists on the balance sheet but cannot be shipped, creating service failures and eroding shelf-life while it waits for disposition.
Formula
(Batches released first pass / Total batches) x 100Grain
One row per production order batch — plant × product × production line × shift × recipe × batch × QA release 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_Production_Order_Batch. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Production_Order_Batch— 9 mandatory columns — batch count, batch key, ...Dim_Batch— batch_key + batch_nbrDim_Plant— key + nameDim_Product— key + nameDim_Production_Line— key + nameDim_Recipe— key + nameDim_Shift— 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 | 0.97 — performance considered compliant at or above this line |
| Warning | 0.93 — 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 Right First Time Quality (RFTQ) in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.