OTD: on-time delivery, formula and example
OTD (On-Time Delivery) is the share of shipments/deliveries completed within the agreed time (by the deadline or within the agreed window). Analyze ship on-time and delivery on-time separately, as well as the line/order/shipment levels.
What the Metric Measures
Timeliness in meeting commitments, taking into account the promised/confirmed base date, the control event (ship or delivery), the SLA format (deadline or time window), and exceptions (rescheduling by the customer).
Calculation
- Delivery OTD, % = Delivered on time / All deliveries × 100
- Ship OTD, % = Shipped on time / All scheduled shipments × 100
- Order-level OTD, % = Orders with all lines "on time" / All orders × 100
- Additionally: average/median delay, P90/P95 delay, On-time to Promise
Agree on time zones, holidays, and cut-off, rules for partial shipments, and the difference between ship and delivery.
Calculation example
1,200 orders were delivered in a month, 1,104 of them no later than promised. OTD = 1,104 / 1,200 × 100 = 92%. If 60 of the on-time orders were incomplete, OTIF (on time and in full) = (1,104 − 60) / 1,200 × 100 = 87%.
Data Sources
WMS/TMS (events, POD), ERP/OMS (promised/requested), carriers/3PL (ETA/ATA, EDI), route planners, calendar reference data, CRM (customer reschedules).
Interpretation
- OTD↓ — inaccurate slot planning, weak ETAs, warehouse bottlenecks, carrier problems, address/contact errors
- OTD↑ with OTIF↓ — on time, but not in full: availability/picking issues
- Assess the OTD ↔ cost balance and the impact on NPS
Useful Breakdowns
- Channel, region/city, carrier/service
- Service type: standard / express / slot
- Warehouse / shipping wave; day of week / hour
- SKU categories / dimensions; season / weather
Dashboard Visualizations
- OTD trend overall and by channel
- Histogram of late/early arrivals
- Heatmap by region/carrier
- Funnel: pick → pack → ship → delivery
- Pareto of delay causes; route map with ETA risks
How to Set Targets
Set thresholds by channel (e.g., urban e-com 95–98%), lock in the promised base and window rules, define cut-off and freeze for waves, SLAs with carriers, and early ETA warnings.
Common Mistakes
- Using requested instead of promised
- Mixing ship and delivery under DDP/delivery SLAs
- Ignoring time zones, holidays, and time windows
- Incorrect accounting for partial shipments and POD
- Counting customer reschedules as "overdue"
Questions for Discussion
- Which top 3 causes create 80% of delays?
- What effect will dynamic ETAs and wave optimization have?
- Where is the profitability limit of express mode?
- Are cut-offs and windows set up correctly for peaks?