Restaurant KOT Timing Report Template: Send to Serve Without Guessing
A timestamp and percentile-based report that shows where kitchen work waits instead of reducing every delay to one average.
Last verified: 2026-08-31
A restaurant KOT timing report should measure distinct events: sent to station, acknowledged or started, ready, handed off and served or picked up. One total prep-time number cannot show whether delay happened before cooking, at a slow station, at expo or after the food was ready.
Rosuii's KDS uses visible aging and station workflows. Use event data to find bottlenecks, but first test whether staff consistently change statuses at the real event. Bad timestamps create precise-looking but false conclusions.
Event dictionary
Publish the definitions beside the report. Different branches cannot compare if one taps ready before plating and another after handoff.
Keep system time synchronised and record offline/manual periods that may distort events.
| Event | Definition | Derived interval |
|---|---|---|
| Sent | Order reaches station queue | Queue starts |
| Start | Station begins work | Queue wait = start - sent |
| Ready | Station declares item complete | Work time = ready - start |
| Expo complete | All required stations complete | Assembly delay |
| Handoff | Food leaves kitchen/pickup counter | Ready wait |
| Served/picked up | Front or rider receives | Final service delay |
| Cancel/remake | Exception event | Exclude or segment under rule |
Report grain and fields
Store order ID, KOT ID, station, item, quantity, order type, branch, daypart, each timestamp, target group, cancellation/remake flag and reason. Remove unnecessary customer data from analysis.
For multi-station orders, retain both station-level events and order-level expo completion. Averaging them together hides dependency waits.
Use percentiles, not only averages
A few forgotten status changes can destroy the average. Show sample size and inspect extreme records before interpreting them.
| Metric | Question |
|---|---|
| Median | What does a typical ticket experience? |
| 75th percentile | How does a busy but common case perform? |
| 90th percentile | Where do long delays become visible? |
| Maximum | Was there an incident or abandoned status? |
| Target attainment | What share met the local target? |
| Volume | Was the sample large enough? |
Segment before comparing
Compare like with like: breakfast versus dinner, dine-in versus delivery, single item versus large order, normal versus remake, and each station. Use item families when individual items have low volume.
Do not rank employees from raw KOT time without accounting for assignment, batch cooking, queue, equipment and status behaviour.
Exception reason table
Reason codes should be added close to the event and reviewed, not guessed by an analyst at month end.
| Code | Reason | Process owner |
|---|---|---|
| QUE | Queue/capacity | Scheduling |
| AVL | Ingredient/component unavailable | Inventory |
| EQP | Equipment fault | Maintenance |
| MOD | Complex/unclear modifier | Menu/front |
| REM | Remake/quality | Kitchen |
| EXP | Expo/assembly wait | Expo |
| RDR | Rider/handoff wait | Delivery |
| STA | Status timestamp missed | Training/system |
Daily dashboard
- Ticket volume by station
- Median and 90th-percentile queue wait
- Median and 90th-percentile work time
- Expo and ready-wait intervals
- Target attainment
- Old open tickets
- Top verified exception reasons
Investigate a bottleneck
Sample the underlying tickets, observe the station and compare queue arrival to capacity. Check menu routing, prep batch, equipment, layout, modifiers and handoff before changing the target.
Run one controlled change and compare the same segment afterward. Record volume and daypart so seasonal demand does not masquerade as improvement.
Data-quality checks
Report data-quality rate beside performance. Pause branch comparisons when event completeness is poor.
| Check | Warning |
|---|---|
| Start before sent | Clock or event error |
| Ready equals start repeatedly | Batch status clicking |
| Very old open KOT | Missed close |
| One branch no start events | Workflow not adopted |
| Remakes in normal set | Segmentation error |
| Offline interval | Delayed sync may distort timestamp |
Copyable summary layout
Use date range, branch, station, item group, order type, volume, median queue, P90 queue, median work, P90 work, expo delay, ready wait, target attainment, remake rate, timestamp completeness and top reason.
Team review questions
- Which interval changed?
- Which segment drives it?
- Is data complete?
- What evidence supports the reason?
- Who owns the control?
- What will we test?
- When will we remeasure?
How we calculated and verified this
This is an operator measurement template grounded in Rosuii's implemented KDS event and aging workflow. It does not claim industry-standard target minutes.
Each restaurant should set targets by item, station, daypart and service model, validate timestamp quality and use timing as a process signal rather than an automatic staff score.
KOT timing becomes useful when every interval has a definition and operational owner. Measure queue, work, assembly and handoff separately, then improve the verified bottleneck.
Rosuii KDS provides event and aging signals; local targets and fair interpretation remain management decisions.
Related guides
See this workflow in Rosuii: Use Rosuii KDS aging and order reports to see kitchen flow
Sources checked
Last updated:
Frequently asked questions
Which KOT times should a restaurant record?
Why use the 90th percentile?
What is queue wait?
Can KOT time rank chefs?
What if statuses are clicked late?
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