Calculate Overall Equipment Effectiveness from your production data
Integrate OEE metrics into your MES, ERP, or custom dashboards with our production-ready API.
Get the Six Sigma API Download OEE TemplateOEE (Overall Equipment Effectiveness) is the gold standard metric for measuring manufacturing productivity. It identifies the percentage of planned production time that is truly productive. An OEE score of 100% means you are manufacturing only good parts, as fast as possible, with no downtime.
OEE is calculated as the product of three factors:
OEE = Availability × Performance × Quality
Each factor captures a different category of productivity loss, giving manufacturers a clear framework to identify and address the root causes of inefficiency.
Follow these three steps to calculate OEE from your production data:
Final OEE: Multiply all three factors together. For the example above: OEE = 0.90 × 0.8102 × 0.99 = 72.19%.
Use the table below to understand where your process stands relative to industry benchmarks:
| OEE Score | Rating | Description |
|---|---|---|
| < 40% | Needs Improvement | Significant losses present. Root cause analysis is urgently needed to address major downtime, speed, or quality issues. |
| 40% – 60% | Typical | Common for manufacturers just beginning to track OEE. Improvement opportunities are abundant and achievable. |
| 60% – 85% | Good | Solid performance with room for incremental improvement. Many well-managed operations fall in this range. |
| > 85% | World Class | Top-tier performance. Only achievable through sustained continuous improvement and disciplined manufacturing practices. |
OEE is designed to highlight the "Six Big Losses" that reduce equipment effectiveness. Understanding which losses affect which OEE component helps you prioritize improvement efforts:
OEE provides a single, unified metric that captures the full picture of manufacturing efficiency. By tracking OEE over time, manufacturers can identify trends, measure the impact of improvement initiatives, and benchmark performance across lines, shifts, and plants. It is a cornerstone of Total Productive Maintenance (TPM) and lean manufacturing programs, enabling data-driven decisions that reduce waste, increase throughput, and improve product quality without requiring additional capital investment.