What it measures
Overtime hours as % of total hours worked
the overtime has already been worked and the cost incurred. It's a historical record of a planning or capacity failure that occurred in the previous period.
Business impact
- A rising 'chaos tax' from premium pay rates, higher rates of human error leading to scrap, and increased recruitment costs to backfill employees who burn out and leave.
- Degraded labor capacity and flexibility as burnout increases voluntary turnover and absenteeism, leaving fewer experienced operators to run complex lines or train new hires.
- A major safety incident resulting from chronic worker fatigue can trigger an immediate suspension of supply by any major retailer, leading to a full ethical and safety audit that puts the entire account at risk.
Formula
(Overtime hours / Total hours worked) x 100Grain
One row per employee per month — employee × cost centre × site × plant × period
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_Employee_Snapshot.
Fact_Employee_Snapshot— 7 mandatory columns — site key, overtime hours, ...Dim_Cost_Center— key + nameDim_Employee— key + nameDim_Period— key + nameDim_Plant— 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 | 0.05 — performance considered compliant at or below this line |
| Warning | 0.1 — 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 Overtime Rate in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.