What it measures
% of delivery lines arriving within agreed time window
OTD measures service failures after the delivery attempt; the consequence (retailer penalty, production line stop) is already locked in before the score is calculated.
Business impact
- Increased 'chaos tax' from last-minute recovery actions: premium freight spend to re-book rejected loads, customer service overtime managing complaints, and warehouse labour costs for double-handling returned stock.
Formula
(On-time delivery lines / Total delivery lines) x 100Grain
One row per outbound delivery line — customer × product × carrier × route × 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_Outbound_Delivery_Line. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Outbound_Delivery_Line— 10 mandatory columns — qty delivered, warehouse key, ...Dim_Carrier— key + nameDim_Customer— key + nameDim_Plant— key + nameDim_Product— key + nameDim_Route— key + nameDim_Warehouse— 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 | 0.94 — 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 On-Time Delivery Rate in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.