What Does a Frontline Quit Really Cost Your Plant?

Key takeaways:

• Estimates on the cost of a replacement range from $1,200 to $10,000+ because they rely on different portions of the cost. That’s why it’s key to calculate the cost for your own plant.
• There are four line items that add up to a total cost per quit. Once you have that total, you can multiply it by your quit rate to get the annual figure.
• Divide the cost of the retention initiative you’ve been deferring by your cost per quit. The result is the number of quits it has to prevent to pay for itself.

The median cost of filling a nonexecutive role, according to SHRM’s 2025 benchmarking data is $1,200, but the average is $5,475. Meanwhile, UKG’s 2025 manufacturing survey found that 84% of manufacturers spend at least $10,000 to replace a single skilled frontline worker.

These turnover estimates disagree because they’re counting differently. 

SHRM’s cost-per-hire metric totals talent acquisition expenses, such as agency fees, job ads, referral bonuses, recruiter pay, and software, and divides them by total hires. This calculation ends at the offer letter, so it excludes training, lost output, and vacancy overtime. Additionally, its early-2025 survey of 2,371 SHRM members spans all industries, blending hourly food plant roles with professional office positions.

UKG’s estimate is closer to the plant floor, but it provides a general threshold of turnover costs rather than a detailed breakdown. It’s helpful for gauging industry sentiment, but not for supporting a business case.

And while the Bureau of Labor Statistics (BLS) tracks how often people quit through JOLTS, its Job Openings and Labor Turnover Survey, it doesn’t track the price of a quit. JOLTS also stops at nondurable goods manufacturing, the category that includes food.

So if you’re trying to secure funding for retention initiatives, these data sources won’t be helpful. And the finance department is likely to ask for a figure that applies specifically to your plant. The good news is you can calculate one using just four numbers you should already have on hand.

Four of your data sources add up to a reliable cost per quit

Pull each of the following figures for one production role at one plant. Use last year’s actuals where you have them and conservative estimates where you don’t, and label which is which. Finance is more likely to trust an estimate it can trace than a general benchmark.

• Recruiting and onboarding spend per hire: This includes job ads, agency or staffing fees, referral bonuses, background checks, drug screens, HR hours, and paid orientation. 
• Paid training hours before a new operator can run the line unsupervised: Count the trainee’s hours and the trainer’s hours, then multiply by a loaded labor rate. For reference, BLS reports average hourly earnings for production and nonsupervisory employees in food manufacturing of roughly $24.60 to $24.80 between June and September 2026, before benefits and payroll taxes.
• The scrap, rework, and output differential in the first 90 days: Compare shifts staffed with new hires against the same lines run by experienced crews. Price the extra scrap, the rework labor, and the cases you didn’t make.
• Overtime your remaining crew accrues during the vacancy: Count only the premium, since you’d pay straight time to the new hire anyway. According to SHRM’s 2025 data, median time-to-fill across roles is about a month and a half. Hourly roles tend to fill faster, though overtime often continues until the new hire can run the line alone.

Here’s an example of what this calculation might look like:

In this example, recruiting only accounts for 14% of the cost, while early-tenure scrap and vacancy overtime together make up half.

Multiply by your quit rate to see what turnover costs each year

Using your HR data, divide voluntary quits over the last 12 months by average frontline headcount. Then compare it to BLS data. In 2025, an average of 1.6% of nondurable goods manufacturing workers quit each month. Annually, that’s about 19% of the workforce, or roughly one in five. About four in 10 manufacturers in UKG’s survey said their frontline turnover was 20% or more.

The quit rate is rising, too. The seasonally adjusted quit rate in nondurable goods manufacturing increased to 2.0% from 1.5% in July. Note that this reading is preliminary and could be revised at some point, but it’s worth keeping in mind as you continue tracking turnover costs.

Putting it all together, a plant with 200 frontline workers and 19% turnover loses 38 people a year. At $10,740 each, that’s about $408,000 annually, accounting for recruiting, training, scrap, and overtime budgets.

Turnover at that rate also puts a cap on growth. Before the plant can staff a new shift, take on a co-manufacturing contract, or open a line, it has to hire 38 people just to maintain its current headcount. Every replacement hire uses recruiting time and trainer hours that expansion would otherwise get.

Divide the deferred fix by your cost per quit to find its break-even

Most plants have a retention solution waiting for approval. It might be more predictable shift schedules, a pay step at 90 days, a dedicated trainer, or a structured buddy program for the first month. (Check out this resource if you need ideas for reducing turnover.) 

But if you can’t calculate what the project could return, it will be put on hold. So divide its annual cost by your cost per quit. The result is how many quits the initiative has to prevent each year to pay for itself.

Going back to our example, a $60,000 onboarding overhaul breaks even if it prevents six of the 38 quits, a 16% reduction. Finance can debate whether six is realistic. But that’s a far more productive conversation than debating which benchmark to believe. 

Look at early tenure first. In UKG’s survey, 32% of frontline departures came in a new hire’s first 30 days. Those exits carry the full recruiting cost and most of the training cost, with very little production in return.

Use AI to pull the line item most plants can’t produce by hand

The first-90-days differential is the most challenging number to build and, in our example, one of the largest. It means combining data from separate systems. Hire dates are found in the human resources information system (HRIS); crew assignments in scheduling; and scrap, rework, and line speed in the manufacturing execution system (MES) or production logs. Matching them by employee and shift is work that rarely has an owner.

This is a sensible first job for an AI-assisted analysis tool, because it’s tedious matching rather than judgment:

1. Export 12 months of hire dates, shift rosters, and line-level scrap and output.
2. Have the tool match each shift’s output and scrap to the tenure mix of the crew that ran it.
3. Compare shifts that included a new hire in their first 90 days against shifts that didn’t. Price the difference at your cost per case. 
4. Run the same match on time-clock data to separate vacancy-driven overtime from volume-driven overtime.

Expect messy inputs. Free-text downtime notes, missed badge scans, and operators who float between lines will blur the result. Have the line supervisor review the output before it goes to finance. A rough number with a visible method holds up better in a budget meeting than a borrowed one.

Build it for one line while the 2027 budget is still open

Pick one role on one line. Build the cost per quit from the four inputs, show your work, and set the deferred retention solution next to its break-even. If the model works well on the first line, extend it to the rest of the plant.