What it means
A customer places an order and the kitchen begins a workflow. A ticket records the items and timing, and ticket time tells the team how long that workflow has been running under its configured clock.
A kitchen display system may start timing when the order is sent: Toast's documentation describes its ticket timer starting at the send event and running until configured fulfilment, and the end point can include more than one preparation or expediter step. Define what the business calls "ready." One restaurant may stop the clock when the cook finishes, another when an expediter checks the complete order, and for delivery, handoff to a courier can be another separate time.
Do not confuse ticket time with a held course's active preparation time, because a multi-course meal may be sent to the kitchen before each course is fired and Toast distinguishes overall ticket time from fire time in such workflows. Average ticket time divides total measured minutes by the number of completed tickets under the same rule, so 300 orders taking a combined 3,600 minutes average 12 minutes.
An average alone can hide poor experiences, because most orders may be fast while a few take much longer. Look at a median or a high-percentile time alongside the average when the distribution is uneven.
Separate order types and times of day. A coffee, a multi-course dinner and a large delivery order need different preparation work, and comparing them under one target can reward the team for avoiding complex orders.
Friday lunch may have a different volume and staffing pattern from a quiet afternoon, so compare similar periods to find a bottleneck rather than blaming a shift from one aggregate figure. A low ticket time is not automatically a success, since rushing can cause wrong orders, undercooked food or poor presentation.
Track remakes, complaints and food-safety practice as well. A high time may reflect a problem before the kitchen itself, such as orders arriving in a burst because of a point-of-sale or delivery-platform workflow.
Use item or station timing to locate delay, because a bottleneck at one grill or packaging station differs from slow preparation across the whole line. Ticket timestamps should be trustworthy, so sample the records against actual service when a metric changes sharply.
For delivery, measure handoff separately so the team knows where the guest's wait occurs, and pair ticket time with order accuracy, service timing and feedback.
In practice
Real-world examples.
Example
A cafe completes 300 kitchen tickets with 3,600 total measured minutes. Average ticket time under its definition is 12 minutes. The manager notes that the clock starts when the order is sent to the kitchen display and stops when the expediter marks it complete.
Example
A dinner order is sent early while the main course is held. The overall ticket timer and course fire timer show different durations, so the kitchen reports fire time when judging the line's speed. Reading the overall timer alone would wrongly suggest the line was slow.
Example
A delivery kitchen finds that food is ready quickly but courier collection is slow. It separates kitchen and handoff metrics, and it adjusts the quoted preparation time given to couriers so that meals stop waiting on the pass.
Formula
Calculation
Average ticket time = sum of elapsed times for completed tickets / number of those tickets. At 3,600 minutes / 300 tickets, the average is 12 minutes. State the start and stop events.
A percentile adds the missing detail. If the same 300 tickets are sorted from shortest to longest, the 90th percentile is the 270th ticket (300 x 90% = 270). Suppose that ticket took 21 minutes: then nine in ten orders were completed within 21 minutes even though the average was 12, and the slowest 30 tickets deserve a closer look.Case study
Seen in the real world.
This entirely fictional case follows Elm Wraps, an invented fast-casual outlet. Its managers saw a long lunch-hour tail in ticket times, despite a reasonable daily average. They checked the preparation station and tested a different order flow while monitoring accuracy. No real improvement is claimed.
The manager split the data by order type and found that large group orders, which made up a small share of tickets, caused most of the slow ones. She trialled firing those orders to a separate station and compared the 90th percentile before and after. The illustrative lesson was that the average looked healthy throughout, and only the tail showed where the delay sat.
Watch out
Common mistakes.
- Treating kitchen ticket time as the customer's entire wait.
- Comparing outlets whose clocks stop at different fulfilment steps.
- Rushing to lower the average while increasing remakes or safety risks.
Questions
People also ask.
When does ticket time start?
Often when the order reaches the kitchen, but the system's configured event governs.
Is ticket time the same as fire time?
No. A held course may fire later than the overall ticket was sent.
How should it be reviewed?
By comparable order type and period, alongside accuracy and customer experience.
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