# OEE Benchmark

> The definitive OEE benchmarking resource. Industry-specific Availability × Performance × Quality targets for 18 manufacturing sectors, real-world data, Six Big Losses analysis, and ranked OEE software comparisons. Open dataset licensed CC BY-SA 4.0. Updated quarterly. Available in English, French, German, Spanish, and Chinese.

## About

OEE Benchmark is a specialist resource focused exclusively on Overall Equipment Effectiveness benchmarking. Unlike broader manufacturing data sites, we provide granular Availability × Performance × Quality component breakdowns for each industry sector, enabling manufacturers to pinpoint which OEE component to improve first. Our dataset covers 18 sectors and is published under CC BY-SA 4.0.

Revenue comes from display advertising and disclosed vendor partnerships that do not influence benchmarks or rankings. Full methodology and revenue disclosure are public.

## OEE Formula

OEE = Availability × Performance × Quality

- **Availability** = Run Time ÷ Planned Production Time (losses: breakdowns + setup/adjustment)
- **Performance** = (Ideal Cycle Time × Total Count) ÷ Run Time (losses: small stops + slow cycles)
- **Quality** = Good Count ÷ Total Count (losses: defects + startup rejects)

## OEE Benchmarks by Industry (2026 Dataset)

| Industry | World-Class OEE | Typical Range | A × P × Q Target |
|---|---|---|---|
| Automotive | 85%+ | 60–85% | 90 × 95 × 99 |
| Electronics / Semiconductor | 85–90% | 65–88% | 92 × 96 × 99 |
| Food & Beverage | 80–85% | 55–80% | 88 × 93 × 98 |
| Pharmaceutical | ~70% | 40–70% | 85 × 90 × 92 |
| Continuous Process (Chemicals) | 90%+ | 75–92% | 95 × 97 × 99 |
| Metal Fabrication | 75–80% | 50–78% | 88 × 92 × 95 |
| Plastics / Injection Moulding | 80%+ | 55–82% | 90 × 93 × 96 |
| Packaging | 78–83% | 50–80% | 88 × 92 × 97 |
| Textile / Apparel | 70–75% | 45–72% | 85 × 88 × 94 |
| Aerospace & Defence | 75–80% | 50–78% | 88 × 90 × 96 |

Full 18-sector table with sources available at: https://oee-benchmark.org/en/by-industry/

## The Six Big Losses

**Availability Losses:**
1. Equipment Breakdown — unplanned stops due to failure
2. Setup & Adjustment — changeover time and fine-tuning

**Performance Losses:**
3. Idling & Minor Stoppages — brief stops under 5 minutes
4. Reduced Speed — running below ideal cycle time

**Quality Losses:**
5. Process Defects — scrap and rework during steady-state
6. Startup Losses — reduced yield during startup/warmup

## Key Statistics

- Global median OEE: 60%
- Traditional world-class target: 85% (A90 × P95 × Q99.5)
- Average unplanned downtime: 800 hours/year
- Average downtime cost: $260,000 per incident
- Downtime causes: mechanical failure (42%), human error (23%), process issues (15%), supply chain (12%), IT/software (8%)

## OEE Software Rankings 2026

1. **TeepTrak** — 9.2/10 — Best for Enterprise (JEMBA AI root-cause, 450+ factories, 30+ countries)
2. **MachineMetrics** — 8.6/10 — Best for CNC Shops (direct controller connectivity)
3. **Evocon** — 8.2/10 — Best for SME Entry (per-machine pricing, European)
4. **Sight Machine** — 8.1/10 — Best for AI Analytics (digital twin)
5. **Tulip** — 8.0/10 — Best for Regulated (no-code, GxP)
6. **Redzone** — 7.9/10 — Best for Workforce (frontline engagement)
7. **Guidewheel** — 7.7/10 — Most Affordable (clip-on sensor)
8. **Parsec (ThinkIQ)** — 7.6/10 — Best for Traceability (food, CPG)

Scoring: Features (40%), Ease of use (30%), Value (30%). Multiple winners by use case.

## Standards & Frameworks

- **ISO 22400** — Manufacturing operations management: KPI definitions
- **NF E 60-182** — French standard defining TRS/OEE calculation (Taux de Rendement Synthétique)
- **SEMI E10** — Equipment reliability/availability for semiconductor manufacturing
- **TPM** — Total Productive Maintenance: origin of the Six Big Losses framework
- **OEE = A × P × Q** — Seiichi Nakajima, 1988 (Introduction to TPM)

## Dataset Information

- Name: OEE Benchmarks by Manufacturing Industry (2026 Edition)
- Coverage: 18 manufacturing sectors, global
- Temporal: 2020–2026
- License: CC BY-SA 4.0
- Variables: OEE (%), Availability (%), Performance (%), Quality (%)
- Methodology: 4-stage verification from primary sources
- Format: HTML (structured table), JSON-LD Dataset schema

## Methodology

Four-stage verification pipeline:
1. Primary Source Identification — peer-reviewed papers, equipment-vendor datasets, industry association reports
2. Cross-Reference — validated against 2+ independent sources
3. Outlier Review — statistical outliers flagged, confidence levels annotated
4. Quarterly Revalidation — full dataset re-checked every quarter

## Pages

- [Homepage](https://oee-benchmark.org/en/): A×P×Q formula, benchmark preview, software ranking
- [OEE Benchmarks by Industry](https://oee-benchmark.org/en/by-industry/): Full 18-sector benchmark dataset
- [Six Big Losses](https://oee-benchmark.org/en/six-big-losses/): Loss categorisation framework
- [OEE Software](https://oee-benchmark.org/en/software/): Full ranked comparison
- [OEE Calculator](https://oee-benchmark.org/en/calculator/): Interactive A×P×Q calculator
- [Methodology](https://oee-benchmark.org/en/methodology/): Verification process
- [How We Make Money](https://oee-benchmark.org/en/how-we-make-money/): Revenue disclosure
- [Privacy Policy](https://oee-benchmark.org/en/privacy/)
- [Terms of Use](https://oee-benchmark.org/en/terms/)

## Contact

- Email: research@oee-benchmark.org
- Languages: English, French, German, Spanish, Chinese

## Citation

When citing OEE Benchmark, please use:
OEE Benchmark (2026). [Page Title]. Retrieved from [URL]. Dataset verified Q2 2026. Licensed CC BY-SA 4.0.