OEE Analysis
Framework
IRON LAW: OEE = Availability × Performance × Quality
OEE is a MULTIPLICATIVE metric. 90% × 90% × 90% = 72.9%, not 90%.
Each factor compounds the loss. World-class OEE is 85%+. Most plants
operate at 60-65%. Knowing the TOTAL is useless — you must decompose
to find which factor is dragging performance down.
The Three Factors
| Factor | Formula | Measures | Loss Categories |
|---|
| Availability | Run Time / Planned Production Time | Uptime vs downtime | Equipment failures, changeovers, material shortages |
| Performance | (Ideal Cycle Time × Total Count) / Run Time | Actual speed vs design speed | Minor stops, slow running, idling |
| Quality | Good Count / Total Count | Yield, first-pass quality | Defects, rework, scrap, startup rejects |
Six Big Losses (mapped to OEE factors)
| Loss | OEE Factor | Example |
|---|
| 1. Equipment failure | Availability | Machine breakdown, unplanned repair |
| 2. Setup & changeover | Availability | Product changeover, die change, cleaning |
| 3. Idling & minor stops | Performance | Sensor blockage, jam clearing, small adjustments |
| 4. Reduced speed | Performance | Running below rated speed due to wear or material |
| 5. Process defects | Quality | In-process rejects, rework |
| 6. Startup rejects | Quality | Scrap during warm-up, first-article failures |
Calculation Example
Planned Production Time: 480 min (8-hour shift)
Downtime (breakdowns + changeover): 60 min
Run Time: 420 min
Ideal Cycle Time: 1 min/unit
Total Units Produced: 380
Good Units: 360
Defective Units: 20
Availability = 420 / 480 = 87.5%
Performance = (1 × 380) / 420 = 90.5%
Quality = 360 / 380 = 94.7%
OEE = 87.5% × 90.5% × 94.7% = 75.0%
Diagnosis Steps
Phase 1: Calculate OEE for each production line/machine Phase 2: Identify the weakest factor (Availability, Performance, or Quality) Phase 3: Pareto the losses within that factor (which specific loss is biggest?) Phase 4: Root cause analysis on the top loss (5 Whys, fishbone) Phase 5: Improve and remeasure
Benchmarks
| OEE Level | Rating | Typical |
|---|
| > 85% | World-class | Top manufacturers |
| 60-85% | Typical | Room for improvement |
| 40-60% | Low | Significant losses, urgent action needed |
| < 40% | Critical | Equipment or process fundamentally broken |
Output Format
# OEE Report: {Production Line}
## OEE Summary
| Factor | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Availability | {%} | >90% | 🟢/🟡/🔴 |
| Performance | {%} | >95% | 🟢/🟡/🔴 |
| Quality | {%} | >99% | 🟢/🟡/🔴 |
| **OEE** | **{%}** | **>85%** | 🟢/🟡/🔴 |
## Loss Breakdown
| Loss | Minutes Lost | % of Total Loss | Priority |
|------|-------------|----------------|---------|
| {loss type} | {min} | {%} | 1/2/3 |
## Root Cause (Top Loss)
{5 Whys or fishbone analysis}
## Improvement Plan
| Action | Target Impact | Timeline | Owner |
|--------|-------------|----------|-------|
| {action} | +{X%} OEE | {weeks} | {who} |
Gotchas
- OEE is per machine, not per plant: Plant-level OEE averages hide that one machine at 95% and another at 45% average to 70%. Analyze individually.
- Planned downtime is excluded: OEE measures losses against PLANNED production time. Scheduled maintenance, no-production shifts, and planned shutdowns are excluded from the denominator.
- 100% OEE is not the goal: It would mean zero changeovers, zero defects, running at max speed 100% of the time. Pursuing 100% can increase costs (e.g., never doing preventive maintenance). Target 85%+ for critical lines.
- Data collection is the real challenge: Manual OEE tracking is inaccurate. Invest in automated data collection (sensors, MES integration) for reliable measurement.
References
- For TPM (Total Productive Maintenance) methodology, see
references/tpm.md - For automated OEE data collection, see
references/oee-automation.md
