A warehouse ops manager in Conyers called us last January, about a week after his annual planning meeting. He'd set fill rate for the year at 85%. His account had run 71% the prior year. The headcount plan for 2026 called for a 20% increase in placed workers. Nobody in that meeting had run any math on whether 85% was achievable in the current market. Where did the number come from? "That's what the industry benchmark said." By March he was tracking at 79%, stressed about the gap, and genuinely unsure whether he had a staffing problem or had just picked the wrong target back in January.
It was mostly the second thing.
Setting KPI targets for staffing operations starts with your own account history, not published benchmarks. Pull the last 12 months of data on each metric, identify the realistic ceiling, and set a target that's 10–15% better than your recent baseline. Industry benchmarks provide context; your account data sets the actual number. In Georgia's labor market, with unemployment holding at 3.5% in April 2026 per the Georgia Department of Labor, any target that ignores local conditions will be off, usually in the optimistic direction.
Why Most KPI Targets Miss
The shortcut is understandable. You pull an industry report, find the benchmark, and declare it the target. Fill rate should be 85%, so the target is 85%. The problem: benchmarks are averages across many account types, many markets, many operations. A recycling MRF in Gainesville and a soft goods distribution center in Smyrna have different candidate pools, different shift premiums, and different baseline tolerance for turnover. A single industry average fits neither one well.
We set one fill rate target across all 27 of our Georgia accounts for the first couple of years. One number, applied everywhere. It took two full years of quarterly reviews before the pattern became obvious: we were consistently overpromising on accounts with structural sourcing challenges (Hall County food processing, where the competition for workers is real and persistent) and holding ourselves to an unnecessary ceiling on accounts where we had deep, established pipelines. Fixing that required account-level targets, not a firm-wide number.
The patterns that cause targets to miss are usually the same three things. Targets get set at aspirational levels without any baseline review. The same number gets applied regardless of account characteristics. And nobody builds in a review date, so when market conditions shift, the target silently becomes irrelevant. Georgia's trade, transportation, and utilities sector shed 11,600 jobs from February 2025 to February 2026, per Georgia DOL data. Fill time in distribution centers felt that. Targets set in Q1 2025 needed recalibration by Q3 of the same year, and a lot of them didn't get it.
Georgia Benchmarks for Light Industrial
Before you set targets, it helps to know what the floor looks like in this market.
Georgia's statewide unemployment held at 3.5% in April 2026, per the Georgia Department of Labor. The national rate ran 4.3% in the same period per BLS. That gap matters because it means the candidate pool here is tighter than national benchmarks suggest, particularly for first and second shift manufacturing and warehouse roles where the same workers get called by multiple agencies in the same week.
Manufacturing separations nationally ran 26–28% annually through December 2025, per BLS JOLTS data. Southern manufacturing tends to run 4–6 percentage points above the national average, which puts a realistic baseline for light industrial accounts in Georgia at 30–34% annualized turnover before any account-specific factors. If you're setting a 90-day retention target, you're working against a regional market that structurally loses workers faster than national averages indicate.
For fill rate, the industry average across staffing agencies ran around 62% in 2023–2024 survey data. That number has been under pressure. For light industrial and logistics, 61% of manufacturers expected roles filled within 48 hours in 2025, per an Everee-sponsored study. Speed matters to clients; a pipeline that can't hit that cadence on recurring roles will show up in fill rate regardless of what the target says. Our active accounts with established pipelines and clean intake documentation run 88–92% fill rate. New accounts, or accounts adding roles we haven't placed before, realistically start around 75–78%.
Setting Fill Rate and Time-to-Fill Targets
Fill rate and time-to-fill are the first two metrics clients ask about. They're also the two most likely to have targets that came from a benchmark report rather than account history.
The right baseline for a fill rate target is the trailing 12-month average on that specific account. If an account ran 76% fill rate last year on steady-state headcount, the realistic one-year target is 83–86%, not 90%. A jump from 76% to 90% requires a meaningfully better sourcing pipeline, faster intake, or both. Unless those changes are already underway, the target just creates a quarterly frustration cycle where nobody agrees on whether the number is a performance failure or an unrealistic goal.
Time-to-fill interacts with fill rate in ways worth being explicit about. Our average time-to-fill on recurring light industrial roles at established accounts runs around five days. New client accounts, or new roles at existing clients, average eight to ten days. If you're setting a time-to-fill target for a new relationship at a distribution center in McDonough that's never run a staffing program, starting from a ten-day average and building toward five days over two quarters is honest. Declaring "we'll be at five days by Q2" is not.
The connection between these targets and contract renewals is documented in the staffing KPIs and client retention analysis. The practical point here: clients who renew aren't necessarily the ones where we hit 90% fill rate. They're the ones where the targets were grounded in reality to begin with, and where slippage got caught and discussed early rather than explained away at the end of the quarter.
NCNS and Retention Targets
These are the metrics that show up as client frustrations. They're also the ones that, when tracked weekly, give you a chance to intervene before the frustration is justified.
NCNS rate (unnotified absences divided by total scheduled shifts) should have a defined threshold before you ever run the report. An account running above 5% has something structural: a transportation bottleneck affecting a cluster of workers, a shift differential that stopped being competitive, or a placement fit issue that's been compounding for weeks. Our internal flag is 4%. Any account that runs two consecutive weeks above 4% gets a root-cause conversation with the client before it becomes a corrective discussion. The distinction matters. You want to identify the problem, not defend against it.
The target you set matters less than the protocol you attach to it. A KPI with no defined response when it's missed is just a number you watch go wrong.
For 90-day retention, the most important anchor isn't the industry benchmark, it's your own account history and a hard floor of 75%. Research on manufacturing onboarding consistently shows that when 90-day retention falls below 75%, fixing the onboarding process moves the number more than any other single intervention, including pay increases. Thirty-three percent of new-hire turnover happens in the first month, before workers have fully settled into the role. Below 75% at 90 days means something in the first two weeks is wrong.
Our target across active warehouse accounts is 78%. On accounts where we've worked with the client to build a structured onboarding walkthrough for placed workers, including a line-level introduction and a clear explanation of attendance expectations from day one, we run closer to 83%. On accounts where workers show up and someone points at the production floor, we're typically in the 68–72% range regardless of what the offer letter said about the culture.
A Target-Setting Process for One Meeting
Pull trailing 12-month performance data for each metric before the meeting. You need four numbers per account: fill rate, average time-to-fill (split by recurring vs. new roles if possible), NCNS rate, and 90-day retention rate. That's the baseline. Set targets as a range with two values: a floor that triggers a review conversation, and a ceiling that represents real improvement from where you started.
| Metric | Realistic Baseline | 12-Month Target Range | Review Flag | |---|---|---|---| | Fill rate | Trailing 12-month average | Baseline + 8–12 points | Below baseline + 4 for 2 consecutive weeks | | Time-to-fill (recurring roles) | Trailing average per role type | Baseline minus 1–2 days | Any role exceeding baseline + 3 days | | NCNS rate | Trailing 12-month average | Below 4% (or baseline minus 2 points) | Two-week streak above threshold | | 90-day retention | Trailing cohort average | Baseline + 5–8 points (floor of 75%) | Monthly review per placed cohort |
The review flag column is what most planning conversations skip. A target without a defined response when it's missed isn't really a target. Who calls who when NCNS runs above threshold for two weeks? What happens operationally? Those answers need to be in writing before anyone tests them.
Review the targets themselves quarterly, not just performance against them. Georgia's labor market shifts, and targets calibrated in January may not reflect the candidate pool available in October. The KPI template for staffing agencies has the account-level tracking framework we use to carry targets through a full year of monthly reviews, with formulas pre-filled. If you're building this process from scratch, it's the faster starting point.
Running warehouse, 3PL, recycling, or hospitality operations in Georgia and want to benchmark your current KPI targets against what we track across 27 active accounts? Get Started and we'll work through your fill rate, retention, and NCNS baselines together.
