What OEE Factor Causes Manufacturers the Most Loss?
Performance was the largest OEE gap in the production data Godlan analyzed. Across nine published production lines, average performance was 80.5% compared with a 95% world-class target, a 14.5-point gap. Availability averaged 78.8% compared with a 90% target, an 11.2-point gap, while quality averaged 97.8% compared with a 99% target, a 1.2-point gap. Performance was also the weakest OEE factor on six of the nine production lines reviewed.
The average production line we reviewed turns 61.6% of its planned production time into good parts, 23.4 points under the 85% world-class mark. Performance accounts for the largest average factor gap in the production lines we reviewed. Availability takes most of what performance leaves, while quality gives back 1.2 points.
We built this report from primary measurements. Peer-reviewed OEE case studies published between 2022 and 2026 supplied the factor-level rates plants recorded on their own machines. Federal survey data priced what machinery maintenance costs US discrete manufacturers. A global downtime study added the hours behind stopped lines. Each section below prices one factor.
What Are the Three OEE Factors?
Overall Equipment Effectiveness (OEE) measures manufacturing productivity using three factors: availability, performance, and quality. Availability measures whether equipment runs when production is scheduled. Performance measures whether equipment is running at its intended speed. Quality measures how much production results in good parts rather than scrap or rework.
OEE is calculated by multiplying the three factors:
OEE = Availability × Performance × Quality
Key Takeaways
- Performance showed the largest average OEE factor gap in Godlan’s analysis, running 14.5 points below its 95% world-class target.
- Performance ranked weakest on six of the nine lines we reviewed. Quality ranked weakest on none.
- The most-reactive quartile of US discrete manufacturers loses 13.0% of planned production time to downtime. The least-reactive quartile loses 4.0%.
- One CNC cell shed 30.6 OEE points in twelve months while its machines ran above rated speed. Availability took every point.
How Godlan Analyzed OEE Availability, Performance, and Quality
Godlan reviewed factor-level OEE measurements from nine published production lines across multiple manufacturing industries and countries. We compared each line’s availability, performance, and quality rates with commonly cited world-class OEE targets of 90% availability, 95% performance, and 99% quality. We then calculated the average rate for each factor and identified which factor had the largest gap from its benchmark on each production line.
The results reflect the production lines included in these studies and should be used as manufacturing benchmarks rather than predictions for an individual facility.
How the Three Factors Compare Against World-Class Targets
Vorne sets world-class OEE at 85%. Each factor carries its own target underneath that number. We averaged the factor rates from nine published production lines and ranked the gaps in the table below.
| OEE Factor | World-Class Target | Average Across 9 Reviewed Lines | Gap |
|---|---|---|---|
| Availability | 90% | 78.8% | 11.2 points |
| Performance | 95% | 80.5% | 14.5 points |
| Quality | 99% | 97.8% | 1.2 points |
| Combined OEE | 85% | 61.6% | 23.4 points |
Key Insights
- Vorne puts the typical plant near 60%. Evocon measured 55-60% across 3,500+ connected machines. Our 61.6% average lands between them.
- Quality reaches 98.8% of its own target while performance reaches 84.7%, so plants that chase scrap first chase a 1.2-point gap.
Which Factor Drags OEE Down Most on Real Production Lines
Averages hide the collapses. We pulled every factor rate from each study, then flagged the one sitting furthest below its world-class target. A line can hold 97.9% availability and still miss the 85% mark by 16 points. Nine lines across six countries carry their own weakest factor, as our analysis below shows.
| Production Line | Availability | Performance | Quality | OEE | Widest Gap |
|---|---|---|---|---|---|
| Plastic injection moulding, Spain, Dec 2024 (JMMP, 2026) | 87% | 85% | 99.6% | 73.5% | Performance |
| Beverage bottling, Indonesia (OPSI, 2025) | 97.9% | 70.9% | 99.9% | 69.35% | Performance |
| Aluminum billet casting, Türkiye, 7-month mean (IJFMR, 2026) | 86.5% | 74.1% | 96.0% | 62.4% | Performance |
| CNC machining cell, Brazil, 2025/1 (Machines, 2026) | 54.3% | 100.6% | 96.4% | 54.1% | Availability |
| Galvanized steel coating, Vietnam, Jan 2025 (JST-UD, 2025) | 84.1% | 87.0% | 96.3% | 73.2% | Performance |
| Six-machine plastics plant, Jordan (MSPE, 2024) | 87% | 55% | 99% | 47.3% | Performance |
| Metal sheet printing, Indonesia (JTM, 2023) | 47.49% | 84.91% | 98.68% | 39.76% | Availability |
| Foundry moulding, Indonesia (OPSI, 2022) | 85.61% | 68.29% | 99.78% | 61.94% | Performance |
| CNC cutting, Indonesia (JTI, 2022) | 78.94% | 98.4% | 94.3% | 73.24% | Availability |
Key Insights
- The Brazilian CNC cell shows what availability does on its own. Availability fell from 84.7% to 54.3% while the machines ran above rated speed, and OEE dropped 30.6 points.
- Quality never finished last on any line we reviewed. The 94.3% low in this set still landed within five points of the world-class target.
Which OEE Factor Should Manufacturers Improve First?
Manufacturers should start with the OEE factor that has the largest measurable gap from its target, rather than assuming downtime or quality is the primary constraint.
In Godlan’s analysis, performance was the weakest factor on six of nine production lines, but availability had the largest gap on the other three. Quality was not the weakest factor on any of the reviewed lines.
The priority will vary by facility. A plant experiencing frequent equipment failures may recover the most production capacity by improving availability, while a line with strong uptime but slow cycle times may gain more from addressing performance losses. The first step is to measure availability, performance, and quality separately and identify the factor creating the largest production gap.
Rank Your Three Factors Before the Next Capital Request
Godlan's OEE Calculator scores one shift of production data against each factor. It names the factor that owns your gap before you spend a dollar closing it. Godlan has advised discrete manufacturers on production performance for 40 years.
Availability: What Stopped Machines Cost
Availability losses can carry a high cost per event because the line stops outright. Nothing ships during the interruption, while labor and overhead continue to accumulate. NIST measured the planned production time US discrete manufacturers hand over. Siemens priced those hours. Both sets of figures sit in the table below.
| Availability Metric | Figure | Source |
|---|---|---|
| Unplanned downtime, share of planned production time | 7.8% | NIST, 2020 |
| Downtime share, most-reactive quartile | 13.0% | NIST, 2020 |
| Downtime share, least-reactive quartile | 4.0% | NIST, 2020 |
| Preventable maintenance losses, US discrete manufacturing | $119.1 billion | NIST, 2020 |
| Downtime incidents per plant per month | 25, down from 42 in 2019 | Siemens, 2024 |
| Hours a large plant loses per month | 27, down from 39 in 2019 | Siemens, 2024 |
| Minutes to restart production after a stop | 81, up from 49 | Siemens, 2024 |
| Setup share of availability losses, can-printing line | 36% against 14% for breakdowns | JTM, 2023 |
Key Insights
- Plants cut downtime to 25 incidents a month from 42 in 2019, yet each stop now runs 81 minutes, up from 49. Fewer stops last longer, so every remaining event bills higher than the last.
- Setup work outweighed breakdowns 36% to 14% on the can-printing line. Buy SMED discipline before you buy another spare part.
Performance: The Loss That Never Stops the Line
Performance losses hide because the machines keep turning. Cycle time drifts below the rated rate. Operators clear minor stops in under two minutes, and the shift report shows a running line. Performance outranks the other two factors on most production floors, as our data indicates.
| Production Line | Performance Rate | What the Study Measured | Source |
|---|---|---|---|
| Six-machine plastics plant, Jordan | 55% | Speed losses took 58.1% of all losses. Quality losses took 0.62% | MSPE, 2024 |
| Beverage bottling, Indonesia | 70.9% | Reduced speed consumed 28.48% of loading time | OPSI, 2025 |
| Foundry moulding, Indonesia | 68.29% | Reduced speed took 42.38% of losses, minor stops 31.73% | OPSI, 2022 |
| Plastic injection moulding, Spain, worst month | 71.4% | Cycles ran 53-60 seconds against a 45-second target | JMMP, 2026 |
| Aluminum billet casting, Türkiye, February | 66.3% | OEE fell to 57.5% while quality held at 96.1% | IJFMR, 2026 |
Key Insights
- At the Jordanian plastics plant, speed losses beat quality losses 94 to 1. No study we read splits wider.
- The Indonesian bottling line held 97.9% availability and finished at 69.35% OEE. Performance took every missing point.
Four Recovered Performance Points Before You Sign
Recover four performance points on one work center and the throughput math changes for the whole plant. Godlan's ERP ROI framework converts recovered runtime into payback months your CFO can defend. Infor named Godlan its 2025 Global Partner of the Year. Godlan has closed 40 years without a failed implementation.
Quality: The Small Percentage That Swings Widest
Quality rates look healthy until you trace them back to the machines cutting the parts. NIST tied defect rates to maintenance strategy across US discrete manufacturers. Quality separates plants that fix machines early from plants that wait, as our analysis below shows.
| Quality Metric | Figure | Source |
|---|---|---|
| World-class quality target | 99% | Vorne |
| Average defect rate, US discrete manufacturers | 1.2% | NIST, 2020 |
| Defect rate, least-reactive quartile | 0.2% | NIST, 2020 |
| Defect rate, most-reactive quartile | 3.3% | NIST, 2020 |
| Quality share of total losses, six-machine plastics plant | 0.62% | MSPE, 2024 |
| Rework share of total losses, foundry moulding | 0.11% | OPSI, 2022 |
| Quality rate, manual cutting machine against CNC on the same floor | 76.85% against 94.30% | JTI, 2022 |
Key Insights
- The most-reactive quartile ran 16.0 times the defect rate of the least-reactive quartile, so quality losses trace back to the machines generating availability losses.
- Swapping a manual cutter for a CNC moved the quality rate 17.45 points at one plant. The manual machine finished at 55.32% OEE against 73.24% for the CNC.
How ERP and Shop-Floor Data Help Manufacturers Improve OEE
Improving OEE starts with understanding which factor is creating the largest production loss. Manufacturers need visibility into downtime, machine cycles, production speed, good-part quantities, scrap, and nonconformance data to separate availability, performance, and quality losses.
Godlan helps discrete manufacturers connect shop-floor production data with Infor CloudSuite Industrial (SyteLine) and Prophecy IoT®. Cycle-level machine data and manufacturing ERP information can provide real-time visibility into OEE performance and the production events behind each factor.
Manufacturers can use this visibility to identify:
- Availability losses: equipment failures, downtime, setup, and changeovers
- Performance losses: reduced speed, slow cycles, idling, and minor stops
- Quality losses: defects, scrap, rework, and startup rejects
Instead of seeing only an overall OEE percentage, operations teams can identify which OEE factor is reducing throughput and which underlying production loss is driving it.
Make Your Next System Prove It Holds 99% Quality
NIST priced direct defect costs at $0.8 billion, and the sales those defects and delays cost manufacturers at $100.2 billion. Godlan tracks nonconformance inside every Infor CloudSuite Industrial deployment. The largest CloudSuite Industrial services team in the world runs the work.
Score vendors against the ERP Selection Criteria Checklist before your first demo.
Further Reading
- OEE Benchmarks by Manufacturing Industry Vertical: 2025 Data
- 10 Manufacturing KPIs and How to Track Them Accurately
- How to Reduce Changeover Time in Manufacturing
- Predictive Maintenance for Manufacturers: A Practical Guide
- How Infor SyteLine’s Quality Control Module Reduces Defects and Rework
Hand These Numbers to Your Operations Team
We cite a named source for every figure above. Operations leaders use this page to settle which factor earns the next investment. Request a PDF copy of this report.
