OTIF, short for on time in full, is the share of orders delivered on the promised date with the complete quantity ordered. An order that arrives late, or on time but short, counts as a miss. It matters because customers reorder from suppliers whose promises they can trust, and high OTIF starts with promising realistic dates.
- OTIF counts an order as a hit only if it is both on time and complete.
- Many OTIF misses start at the promise, before the order reaches the warehouse or truck.
- Promise from real availability: stock, production capacity and transit time, not a standard lead time.
- Reliable promises build trust, and trust drives repeat orders.
What OTIF means and how to calculate it
OTIF (on time in full) is the number of orders delivered both on time and in full, divided by all orders due in the period, shown as a percentage. Define both parts clearly: “on time” against the date you promised or the date the customer asked for, and “in full” by order line or by quantity.
Agree the definition with key customers. Many large buyers measure suppliers on OTIF and may charge penalties for misses.
Why OTIF drives repeat orders
For a distributor or retailer, a missed delivery means an empty shelf, a lost sale and often an emergency order from someone else. After a few misses they start ordering early, holding extra stock or splitting orders across suppliers. A supplier with reliable OTIF becomes the easy choice: buyers order less defensively, reorder more often and give that supplier more of their business.
How to promise delivery dates you can keep
Many OTIF misses start at the promise. Sales quote a standard lead time, or whatever date wins the order, without checking stock, production capacity or truck availability. A reliable promise is built from real availability: stock on hand and in transit, what the plant can make in time, and how long delivery to that customer actually takes. This is often called available-to-promise (ATP).
Where a date is uncertain, say so, and offer a confident date alongside a faster, riskier one.
Keeping the promise after the order
A good promise still needs protecting. Track each order against its date as it moves through picking, loading and transport, and flag the ones at risk while there is still time to act: split the shipment, switch a vehicle or warn the customer early. Then record why each miss happened. Owllys links promising and delivery tracking so those reasons feed back into future dates.
Promising from a standard lead time vs from real availability
Illustrative example.
How to improve OTIF step by step
- 01Agree the definitionFix what “on time” and “in full” mean, by line or by order, with key customers.
- 02Measure the baselineCalculate OTIF for the last three months by customer, product and region.
- 03Find the root causesSplit misses into promise errors, stock shortages, production delays and transport delays.
- 04Promise from real availabilityCheck stock, capacity and transit time before confirming any date to the customer.
- 05Watch orders at riskFlag late-running orders early and act, or warn the customer before the date passes.
Questions leaders ask
What is a good OTIF score?
It varies by industry and by how strictly OTIF is defined. Large retail and manufacturing buyers commonly set supplier targets somewhere in the 90–98% range. More useful than any benchmark is your own trend, measured the same way every month, with every miss explained and its cause fixed.
What is the difference between OTIF and fill rate?
Fill rate measures how much of the ordered quantity was shipped, regardless of timing. OTIF is stricter: the order must be both complete and delivered on the agreed date. A business can have a high fill rate but a low OTIF if complete orders regularly arrive late.
Should we pad lead times to protect OTIF?
Padding hides the problem rather than solving it. Long, safe-looking dates lose orders to competitors who promise sooner, and customers learn to discount them. Promise from real stock, capacity and transit times instead, and give a risk-rated date when the situation is uncertain.