FMEA Occurrence Rating Guide
Occurrence 1–10 scale at a glance
The full AIAG-VDA Occurrence scale anchored to process capability and historical data. Use this as the team reference during rating workshops.
Occurrence (O) — 1 to 10
Likelihood the cause produces the failure mode
Proven, capable, stable process (Cpk > 1.67)
Capable process with rare failures (Cpk > 1.33)
Known variability, occasional failures (Cpk ≥ 1.00)
Marginal capability, recurring failures
No effective prevention, failure expected
What is Occurrence in FMEA?
Occurrence estimates the probability that a specific failure cause will occur. It does not measure customer impact and it does not measure how likely the failure is to be detected.
How likely is this failure cause to happen?
Teams frequently confuse Occurrence with Severity. A cosmetic defect may occur every day but still have low Severity. A safety-critical failure may occur extremely rarely but still have Severity 10.
AIAG-VDA Occurrence Scale Overview
| Occurrence | Likelihood | Typical Interpretation |
|---|---|---|
| 10 | Extremely High | Failure expected very frequently |
| 9 | Very High | Repeated failures observed |
| 8 | High | Frequent process instability |
| 7 | Moderately High | Known recurring issue |
| 6 | Moderate | Occasional failures |
| 5 | Low-Moderate | Controlled but visible risk |
| 4 | Low | Rare but realistic |
| 3 | Very Low | Uncommon occurrence |
| 2 | Remote | Extremely unlikely |
| 1 | Almost Impossible | No known occurrence history |
Occurrence Rating Examples
| Failure Cause | Scenario | Typical Occurrence |
|---|---|---|
| Tool wear | No tool life monitoring | 8 |
| Wrong component loaded | Manual loading process | 7 |
| Operator assembly error | Strong poka-yoke present | 2 |
| Material defect | Certified supplier with stable history | 3 |
| Machine parameter drift | Poor preventive maintenance | 7 |
| Barcode mismatch | Automated verification system | 1 |
How to Select the Right Occurrence Rating
The best Occurrence ratings are based on evidence, not opinion.
- Historical scrap data
- Customer complaints
- Warranty information
- Supplier performance records
- Process capability studies
- Engineering judgement where data is unavailable
Teams should always use the same logic across projects to maintain consistency.
Occurrence vs Detection
Occurrence measures how often the cause happens. Detection measures how likely the current controls will find it.
| Situation | Occurrence | Detection |
|---|---|---|
| Frequent defect with strong automated inspection | High | Low |
| Rare defect with weak visual inspection | Low | High |
| Frequent defect and weak controls | High | High |
Many weak FMEAs artificially lower Occurrence because strong inspection exists. That is incorrect.
Can Occurrence Be Reduced?
Unlike Severity, Occurrence can be reduced.
Examples:
- Tool life monitoring
- Poka-yoke devices
- Supplier qualification programs
- Process automation
- Preventive maintenance
- Improved work instructions
Common Occurrence Rating Mistakes
- Using Detection performance to justify a lower Occurrence rating.
- Ignoring historical quality data.
- Using optimistic assumptions.
- Rating by intuition instead of evidence.
- Changing scales between projects.
- Copying ratings from old FMEAs without validation.
Industry Examples
Automotive Manufacturing
- Incorrect torque application – Occurrence 6–8
- Automated vision inspection escape – Occurrence 2–4
CNC Machining
- Tool wear causing dimension drift – Occurrence 7
- Fixture misalignment – Occurrence 5
Electronics Manufacturing
- Solder bridge formation – Occurrence 6
- Component polarity error with barcode validation – Occurrence 2
Relationship Between Severity, Occurrence and Detection
Occurrence is only one part of the AIAG-VDA risk evaluation process.
Occurrence in the Qhubio knowledge graph
How Occurrence connects to other FMEA concepts, standards, examples and software.
Frequently asked questions
Learning path
Three tiers, automatically derived from the Qhubio knowledge graph. The tier containing this guide is highlighted.
Further reading
Curated next steps — methodology guides, worked examples, and the relevant tool.
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