A plant supervisor we work with in Hall County started tracking OEE last January. By March he'd decided the metric was useless. He was hitting 61%, week after week, and couldn't figure out what to do differently. His issue wasn't the number. It was that he didn't know which of the three components was pulling it down, so the whole calculation sat in a spreadsheet and nobody acted on it.
We see this at manufacturing accounts across Georgia. A plant knows it should track OEE, scrap rate, and labor efficiency. It doesn't always know what those numbers are actually saying, or which one to act on first.
The three manufacturing KPIs Georgia plant managers should track weekly are OEE (the product of availability, performance, and quality rates), scrap rate or First Pass Yield, and direct labor efficiency against standard. OEE's industry average for discrete manufacturing sits around 65%; world-class is 85% or above. Scrap rates below 2% reflect strong quality control. Direct labor efficiency above 85% indicates productive staffing and scheduling.
What OEE Actually Measures
OEE is three numbers multiplied together: Availability (the percentage of scheduled time equipment actually ran), Performance (whether it ran at its intended cycle speed), and Quality (the percentage of output that passed on the first run). Multiply those three percentages and you get an OEE score between 0 and 100.
World-class OEE is 85% or above. The math behind that threshold looks like this: 90% availability times 95% performance times 99% quality equals roughly 84.6%, which is the practical ceiling of what most high-performing plants can sustain. A Godlan study of more than 1,470 discrete manufacturing operations using 2024 data found an average OEE of 66.8% across nine industry sectors. Medical devices was highest at 78.2%. Trailers and RVs was lowest at 57.2%. Only 6% of plants actually sustain world-class scores over time.
The Hall County plant was at 61%. That's below the global average for discrete manufacturing, not catastrophic, and not world-class. When we broke out the three components, availability was fine at 88%, quality was fine at 94%, but performance was sitting at 73%. His machines were running the right hours and producing good parts; they just weren't running at the cycle speeds they were designed for. That's a scheduling and maintenance problem. You can't see it from the combined OEE score alone.
If you're reporting OEE as a single number without decomposing it into components, you're getting an answer to the wrong question. The question isn't "what's my OEE?" It's "which of the three factors is lowest, and what's driving it?"
One honest note here: decomposing OEE correctly requires that you actually know your equipment's intended cycle speed and document your planned downtime separately from unplanned downtime. A lot of plants don't have that data clean. If your OEE calculation starts from inconsistent inputs, the score won't be comparable to published benchmarks, and you shouldn't expect it to be.
Scrap Rate and First Pass Yield
Scrap rate is the percentage of production that fails quality inspection before reaching final output. First Pass Yield is the inverse: the percentage that passes on the first run through. The two together tell you how much raw material and labor is being consumed to produce parts you can't sell.
Industry benchmarks for scrap rate run 3–8% across general manufacturing, though that range varies significantly by process and material type. Best-in-class operations hold scrap below 2%. World-class First Pass Yield is 95% or above. A scrap rate above 8% consistently signals something systematic is wrong, whether in materials, equipment calibration, or operator training.
A word on comparisons: a 3% scrap rate in stamped metal is not the same problem as a 3% scrap rate in food processing. The benchmarks exist at the industry and process level, not manufacturing broadly. When you're comparing your numbers to any external source, including the ranges above, check that you're comparing against your NAICS code or your specific process type.
What scrap rate catches that OEE can miss is the quality problem that doesn't cause downtime. If your press runs continuously and hits its cycle speed but produces 6% scrap, your quality component inside OEE will look bad, but availability and performance could still look acceptable. The combined score can obscure how much waste is leaving the floor on shifts where the machines are technically "running fine."
We staff production lines at several food processing and light assembly operations in Georgia, and scrap on those floors has a reliable relationship to how well new operators get oriented in their first two weeks. Plants that track scrap rate by shift and by operator, not just as a weekly plant-wide number, can usually tell within a few days whether a new hire is struggling with a technique before the problem compounds into a retention or quality issue.
Labor Efficiency Metrics
Direct labor efficiency is the ratio of standard hours (what a job should take at engineered rates) to actual hours spent. If a task is budgeted for one hour and averages 1.2 hours, your labor efficiency is about 83%.
The target range for most manufacturing environments is 85–95%. Sustained efficiency above 95% is worth questioning, because it can mean engineered standards are outdated and being beaten too easily, or that workers are skipping steps. Below 80% regularly indicates a skills-fit problem or scheduling gaps: either work is being assigned to operators who haven't reached full proficiency, or there's idle time in the schedule that isn't being tracked.
Labor efficiency connects directly to time-to-productivity for new hires. Entry-level manufacturing roles typically reach full productivity within 30 days with structured onboarding. Technical and skilled trades positions generally take 60–90 days. If you're tracking direct labor efficiency and you see a consistent 15–20% gap on workers in their first 60 days, that's an early signal telling you where training investment is and isn't landing.
The Federal Reserve's G.17 release reported manufacturing capacity utilization at 75.7% in May 2025, which is 2.5 percentage points below the long-run average of about 78%. That's not a crisis reading, but it means U.S. manufacturers generally have capacity left on the equipment side. The constraint we hear about most from Georgia plant managers isn't machine capacity. It's having enough trained people to run the machines at standard.
BLS data for Q4 2024 shows manufacturing labor productivity up 0.8%, but the gain came from a 1.8% drop in hours worked, not output growth. Output actually fell 1.0%. That's an efficiency-from-cutting pattern. Plants achieving genuine productivity gains are growing output per hour, not trimming headcount until the denominator improves the ratio.
Workforce KPIs Plant Managers Often Overlook
Turnover rate gets tracked. What's less often tracked is turnover broken out by tenure band and by production area.
Manufacturing turnover runs 26–28% annually across the industry, based on BLS JOLTS separations data through December 2025. For production roles specifically, the range is closer to 30–38%. Food processing tends toward the higher end. But those figures don't tell you whether the churn is happening in the first 90 days, which is usually solvable through better orientation and supervision, or at the 6–12 month mark, which typically signals a compensation problem once workers have enough experience to shop around.
We tracked exit data across 27 Georgia accounts through 2024. The split wasn't what most plant managers expected. First-90-day exits in our light industrial placements were less about pay than about whether a new worker felt oriented and supervised in the first two weeks. Pay-related exits clustered later, around months 6–12, when workers had enough floor experience to know what comparable roles paid elsewhere.
Absenteeism is the other number that sits in HR systems and rarely makes it onto a floor-level KPI dashboard. The national absence rate across all U.S. industries in 2024 was 3.2% (BLS Current Population Survey). We don't have a current BLS-published figure specifically for manufacturing that we're confident enough to cite here, so we won't. What we track internally is scheduled-versus-present on the accounts we staff, and anything above 4% on a given shift tends to cascade: remaining workers get pulled to positions they're not trained for, scrap climbs, OEE drops, and overtime follows.
If you're tracking OEE and scrap but not shift-level attendance by day of week, you're missing a leading indicator that usually moves before the output metrics do.
A Weekly KPI Review Framework
This is the cadence we recommend to operations managers who want a KPI practice that's actually usable rather than one that generates a 40-tab spreadsheet nobody opens on Monday morning.
Daily (shift-end, 5 minutes): Log any unplanned downtime events and duration. Count scrap by production area. Record attendance versus scheduled headcount by shift.
Weekly (Monday review, 20 minutes): Calculate OEE by component, not just total score, and compare to your prior four-week average. Check First Pass Yield trend against your target. Review direct labor efficiency against standard. Flag any turnover from the prior week with tenure noted.
Monthly (ops review): Break OEE down by machine or line, not just plant-wide. Trend scrap rate by operator and shift to see if it tracks with specific people or with specific equipment. Compare labor efficiency to budgeted hours. Run a new-hire productivity curve: standard-hours actual versus standard at 30, 60, and 90 days in.
The monthly view is where you can connect workforce inputs to operational outputs. If you're using a staffing partner to fill production roles, that link needs to be explicit. An agency that can't give you consistency on time-to-fill and first-90-day retention will show up in your OEE and scrap numbers before it shows up in your staffing contract conversation.
Georgia has roughly 428,000 manufacturing workers and added $20.3 billion in new manufacturing investment in FY2024 alone (Select Georgia data, BLS baseline). The competition for experienced production workers isn't easing. A KPI framework that surfaces workforce problems early, before they've moved the output numbers, is the kind of discipline that compounds over time.
For a broader look at which KPIs predict staffing performance rather than just describe it, our staffing KPI pillar guide covers the 12 metrics we track across our Georgia accounts, with data tables from our own operations. If you want benchmark ranges specific to plant and warehouse environments, the operations KPI examples post has those by function. And if you want to build your own tracking system from scratch, the free staffing KPI template covers the worksheet structure we use internally.
We staff warehousing, recycling, light manufacturing, and food processing operations across 27 accounts in Gainesville, Hall County, Conyers, Lawrenceville, Smyrna, and the Atlanta MSA. If you're evaluating a Georgia staffing partner for production roles and want to understand how we measure operator performance before and after placement, Schedule a Call and we'll walk through how we approach it.
