On-Time In-Full (OTIF)

Measures adherence to the delivery window specified by the customer, not the carrier. This is a binary metric—a delivery is either on time or it is not.

• Hard windows: Delivery must occur within a precise time range (e.g., 9:00 AM–11:00 AM)
• Soft windows: Penalties accrue for early or late arrivals but the delivery is still accepted
• Carrier vs. customer time: The clock is set by the recipient's requested date, not the carrier's estimated arrival

A shipment arriving 5 minutes late to a 2-hour window counts as a failure, even if the goods are perfect.

Measures whether the exact quantity ordered was delivered in a single shipment. Partial shipments, split orders, and substitutions all count as failures.

• Order completeness: Every line item must be delivered at the ordered quantity
• No partial credit: Delivering 99 of 100 units is a failure for that order line
• Substitution impact: Replacing an out-of-stock item with an alternative breaks the In-Full condition unless pre-approved

This dimension directly reflects inventory accuracy and warehouse picking precision.

OTIF is the product of the On-Time rate and the In-Full rate, not an average. This multiplicative structure penalizes poor performance in either dimension.

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OTIF% = (On-Time Deliveries / Total Deliveries) × (In-Full Deliveries / Total Deliveries)

Example: A carrier with 90% On-Time and 90% In-Full achieves only 81% OTIF. The gap between individual metrics and the composite score reveals hidden failure points that averaging would mask.

Major retailers like Walmart and Target enforce OTIF as a contractual requirement with financial penalties for suppliers who fall below threshold.

• Walmart: Requires 98% OTIF for full-line suppliers; charges 3% of cost of goods sold for non-compliance
• Target: Uses a similar framework with chargebacks for late or incomplete deliveries
• Amazon: Enforces strict purchase order accuracy through its Chargeback Program

These mandates transformed OTIF from an internal KPI into a cost of doing business with enterprise retailers.

Failures typically originate in one of three domains, each requiring different corrective action:

• Demand-side: Inaccurate forecasting leads to stockouts at the distribution center, breaking In-Full
• Supply-side: Supplier lead time variability causes inventory to arrive after the customer's required ship date
• Execution-side: Carrier capacity constraints, weather disruptions, or warehouse labor shortages delay last-mile delivery

Diagnosing the dominant failure mode is essential before investing in corrective technology.

Traditional fill rate measures only whether inventory was available to ship. OTIF adds the temporal dimension, making it a far stricter standard.

A warehouse can have a 99% fill rate but only 85% OTIF if shipments consistently arrive late. OTIF reveals execution gaps that fill rate alone obscures.