What it measures
% of units requiring rework before release
Rework Rate measures a production failure after it has happened. The associated costs of wasted labor, energy, and line capacity are already sunk before the metric is calculated.
Business impact
- The 'chaos tax' of rework includes unscheduled line cleaning, extra QA testing, and production schedule interruptions that create knock-on delays for other orders.
- Reworked batches create QA-held stock that is physically present but unavailable-to-promise, corrupting the inventory position and causing planners to miss order commitments.
- Consistently high rework rates lead to erratic batch release timings and unpredictable stock availability, triggering formal retailer service improvement plans for unreliable supply.
Formula
(Reworked units / Total units) x 100Grain
One row per production order batch produced
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.
Fact_Production_Order_Batch— 9 mandatory columns — recipe key, shift 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 | 0.01 — performance considered compliant at or below this line |
| Warning | 0.045 — 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 Rework Rate in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.