Calculator Library / Sigma Metrics
Translate raw counts or a defect rate into sigma language and see it: the live curve shows your defect rate as the shaded tail beyond the spec limit, with the 1.5σ shift as a ghost curve. Results arriving from the Capability or OEE tools load automatically.
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Defects live in the tail
Enter either DPMO or DPU. If both are filled, DPMO takes priority.
Comparison
| Process | DPMO | Yield | Sigma LT | Sigma ST |
|---|
Instructions
Long-term sigma reflects observed process performance. Short-term sigma adds the conventional 1.5σ shift, which is how the familiar “6σ = 3.4 DPMO” table is built.
This calculator turns defect counts, units, and opportunities into the language of Six Sigma. It helps teams move from raw defect data to DPU, DPO, DPMO, DPM, yield, RTY, and long-term versus short-term sigma levels.
That makes it useful for quality dashboards, improvement-project baselines, supplier comparisons, and customer-ready summaries where leaders need more than a scrap count.
| Metric | Formula | Meaning |
|---|---|---|
| DPU | Defects / Units | Average defects per unit produced. |
| DPO | Defects / (Units x Opportunities) | Defect rate at the opportunity level. |
| DPMO | DPO x 1,000,000 | Defects per million opportunities. |
| RTY | exp(-DPU) | Rolled throughput estimate for defect-free flow. |
| Sigma level | Z from defect probability, with or without 1.5 sigma shift | Converts defect performance into a common quality scale. |
If a team produces 1,000 units with 25 defects across 8 opportunities per unit, DPO is 25 / 8,000 = 0.003125 and DPMO is 3,125. That corresponds to roughly 4.2 sigma short-term, a capable, above-average process, but still well short of the 3.4 DPMO six-sigma frontier.
The value is not the number alone. The calculator helps the team compare that result across time, product families, suppliers, or improvement waves on a consistent scale.
DPM is defects per million units. DPMO is defects per million opportunities and adjusts for how many ways a unit can fail.
Because complex products do not have just one way to fail. Opportunity-based normalization makes cross-product quality comparisons more meaningful.
RTY estimates the chance that a unit moves through the process without defects. It is useful when quality affects flow, rework, and total hidden factory cost.
Short-term sigma reflects a tighter, more controlled view. Long-term sigma reflects actual field performance after drift and variation accumulate.
Inflating or inconsistently defining opportunities per unit. If the opportunity count is arbitrary, the DPMO value becomes less trustworthy.
Use the spreadsheet version when you need an offline workbook for capability, variation, DPMO, and sigma review in one file.
Use sigma and DPMO as baseline and control metrics inside structured improvement projects.