By Matt Wampler, CEO of ClearCOGS
Quick Answer: Almost no restaurant knows how long its prep actually takes. Back-of-house hours are scheduled and reported as a single block and measured with a single ratio, usually labor as a percentage of sales, which is far too coarse to show where kitchen time goes. Prep hours and line hours are driven by different things: prep is a function of what tomorrow’s forecast requires, and line hours are a function of what walks in the door today. Separating them, and attaching a rough time standard to each prep task, is the first step toward managing kitchen labor rather than just reporting it.
The question nobody can answer
Ask a multi-unit operator what their labor percentage is and you will get an answer immediately, often to one decimal place.
Ask how many minutes it takes to break down a case of chicken, and the conversation stops.
That gap is not a failure of attention. It is a measurement gap, and it exists in brands that are otherwise rigorous. Plenty of groups maintain recipes obsessively, track actual against ideal food cost, and run tight financial reporting, and still have no idea whether the person prepping on a Tuesday morning takes twenty minutes or fifty to do the same task.
It persists because the one number everybody does have conceals it.
What does a labor percentage actually tell you?
It tells you what you spent relative to what you sold. That is a useful financial control and a poor operating diagnostic.
The research is direct about the limitation. Writing about multi-unit foodservice productivity, researchers noted that the methods used to benchmark and assess productivity have been limited to overly simplistic approaches, with the result that they offer limited utility, and that traditional partial-factor statistics such as meals per labor hour do not adequately reflect the many factors influencing the metric. They also cite the broader observation that no generally accepted means of productivity measurement exists in the field (Reynolds and Biel, International Journal of Hospitality Management, 2007).
That was written about an industry-wide problem, and the kitchen version is specific. Two locations can post identical labor percentages while one prepares efficiently and carries extra bodies on the line, and the other does the reverse. The percentage is the same. The problem is not, and neither is the fix.
Two different jobs in one number
The deeper issue is that back-of-house hours contain two activities with almost nothing in common.
| Prep hours | Line hours | |
|---|---|---|
| What drives the quantity | Tomorrow’s forecast demand and batch sizes | Today’s traffic, hour by hour |
| When it is decided | In advance, usually the morning or night before | During service |
| How it flexes | Batch size and frequency, within shelf-life limits | Sending someone home, calling someone in |
| What a standard looks like | Minutes per batch, yield per case | Covers per hour, tickets per station |
| What goes wrong | Prepping too often, in batches too small | Over-staffing a slow period |
Prep hours and line hours respond to different inputs and fail in different ways.
Scheduling those as one undifferentiated block makes both harder to manage. You cannot tell a kitchen manager that prep should take three and a half hours of an eight-hour shift if nobody has ever established what three and a half hours of prep looks like. So the shift gets filled, and whatever is left over gets absorbed somewhere.
A useful framing some operators are moving toward: stop scheduling forty back-of-house hours and start scheduling twenty hours of prep and twenty hours of line coverage, with each driven by its own input.
Why prep standards have stayed out of reach
There is a legitimate reason this has not been solved, and it is worth stating because the usual answer is expensive.
The traditional way to establish prep standards is a time and motion study. Someone with a stopwatch spends weeks in a kitchen timing every task. It works, it produces real numbers, and it has two problems. It costs money, and it covers one or two locations. Whatever it finds is an average from a handful of people in one building, applied across an estate that may run to dozens.
So the standard, if it exists at all, is an outsider’s snapshot rather than a living measurement of how your own teams actually work.
What has changed is that the data can now come from the work itself rather than from an observer. If a prep task is started and stopped in a system the kitchen already uses, and the output quantity is recorded, you get two things at once: how long it took, and what it yielded. Across every location and every person, continuously, rather than once from a consultant.
What measurement unlocks
Three things, roughly in order of value.
Yields get accurate. When a case comes in at one weight and leaves prep at another, the difference is your real yield. Most recipe databases carry a yield assumption that was entered once and never checked. Measuring the actual output of a prep task corrects the recipe, which corrects the theoretical cost, which corrects everything built on top of it.
Training targets become obvious. If one cook consistently takes twice as long on the same task as their peers, that is a coaching conversation with evidence attached rather than an impression. Equally, if one person is consistently faster, that is a method worth copying and a performance worth recognizing.
The schedule gets a real input. Once you know a given day’s prep list takes roughly three hours at your own measured pace, the kitchen schedule stops being a habit and becomes a calculation. That is also the point at which a genuine hours reduction becomes possible, because you are removing time nobody needed rather than cutting blindly and hoping service holds.
How to start without a consultant
Four steps, none requiring new hardware.
- Pick ten prep items. The ones with the most labor in them and the highest volume. Not the whole list.
- Record start and stop for two weeks. A clipboard and a pen is enough to begin. Who did it, when they started, when they finished, how much came out.
- Look at the spread, not the average. The useful finding is not the mean time. It is the gap between your fastest and slowest execution of the same task, because that gap is the opportunity.
- Write one standard and test it. Pick a single item, publish an expected time, and see whether it holds across locations. If it does not, you have learned something about either the method or the equipment.
Two weeks of this on ten items usually tells a brand more about its kitchen labor than a year of labor percentage reporting.
Frequently Asked Questions
How long should restaurant prep take?
There is no industry benchmark worth using, because prep time depends on your recipes, equipment, batch sizes, and layout. The number that matters is your own measured time for your own tasks, and the spread between your best and worst execution of each.
Why doesn’t labor percentage show kitchen inefficiency?
Because it aggregates everything into one ratio against sales. Two locations with identical percentages can have very different prep and line splits underneath, so the metric cannot tell you which lever to pull.
Do I need a time and motion study?
Not to start. A study produces precise numbers from a small sample at real cost. Recording start and stop times on a handful of high-labor items across all your locations gives you a less precise but broader and continuously updating picture.
Should prep and line hours be scheduled separately?
For scratch kitchens with meaningful prep, yes. They respond to different inputs, so combining them into one target means neither gets managed on its own terms.
What does measuring prep time have to do with forecasting?
Prep quantity comes from the forecast, and prep hours come from quantity multiplied by time per unit. Without the time standard, a production forecast tells you what to make but not how long it will take or how many people you need to make it.
Kitchen labor is the second-largest controllable line in the business and the least precisely measured. Food gets recipes, counts, and variance reports. Prep labor gets a percentage and a shrug.
The gap is not that operators do not care. It is that the data has never been collected anywhere the work actually happens. That is now a solvable problem, and solving it turns back-of-house hours from a number you report into a number you can manage.
If you cannot say how long your prep list should take today, that is the place to start looking.
Sources
- Reynolds, Dennis, and Biel, David. Incorporating Satisfaction Measures into a Restaurant Productivity Index. International Journal of Hospitality Management, 26(2), 352–361. June 2007. doi.org
