DPMO ↔ Sigma Level Converter

Counts refer to defect opportunities, not necessarily defective units.

This selection changes review context only. Formulas and example inputs stay unchanged. Check the model assumptions and applicability for the actual process.

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1 Inputs

Minimum: 0

Minimum: 1

Minimum: 1

2 Results

Process sigma level
3.96σ
DPMO
7,000
DPO
0.007
Process yield
99.3%
Defects per unit
0.035
DPO = d/(n × o); DPMO = DPO × 10⁶; Z = Φ⁻¹(1−DPO)+1.5
Counts refer to defect opportunities, not necessarily defective units. The sigma conversion includes a conventional 1.5 shift. Zero or all-defective opportunities have no finite normal quantile; report sample size and DPMO instead.

Worked example

Example values are illustrative. Replace them with your measured data.

InputValue
Number of defects35 count
Number of units1000 count
Opportunities per unit5 count

Process sigma level: 3.96σ

References

Related tools

About this calculator

How it works

DPO = defects ÷ (units × opportunities per unit). DPMO = DPO × 1,000,000 and yield = (1 − DPO) × 100%. The sigma level is the standard normal Z value for the yield (1 − DPO) plus the conventional 1.5σ long-term shift. The tool also shows defects per unit.

Worked example (illustrative inputs)

35 defects found on 1,000 units with 5 opportunities each. DPO = 35 ÷ 5,000 = 0.007. DPMO = 7,000. Yield = 99.3%. Sigma = Z(0.993) + 1.5 ≈ 3.96σ. Defects per unit = 0.035.

How to read the result

Bands: 6σ or more is world-class, 5σ excellent, 4σ good, 3σ industry average, and below 3σ poor. Each sigma step is roughly a tenfold cut in defects, so the tool's reference points are 66,807 DPMO at 3σ, 6,210 at 4σ, 233 at 5σ and 3.4 at 6σ.

When to use it

Use it to compare processes with different numbers of defect opportunities on one scale. Define what counts as an opportunity once and keep that definition fixed, or the comparisons will not hold.

À propos de ce calculateur

Traduction (brouillon) — à relire.

Fonctionnement

DPO = défauts ÷ (unités × opportunités par unité). DPMO = DPO × 1 000 000 et rendement = (1 − DPO) × 100 %. Le niveau sigma est la valeur Z de la loi normale correspondant au rendement (1 − DPO), plus le décalage conventionnel de 1,5σ à long terme. L'outil affiche aussi les défauts par unité.

Exemple chiffré (valeurs d'illustration)

35 défauts relevés sur 1 000 unités, 5 opportunités chacune. DPO = 35 ÷ 5 000 = 0,007. DPMO = 7 000. Rendement = 99,3 %. Sigma = Z(0,993) + 1,5 ≈ 3,96σ. Défauts par unité = 0,035.

Lire le résultat

Seuils : 6σ ou plus classe mondiale, 5σ excellent, 4σ bon, 3σ moyenne industrielle, moins de 3σ faible. Chaque niveau sigma divise environ les défauts par dix ; les repères de l'outil sont 66 807 DPMO à 3σ, 6 210 à 4σ, 233 à 5σ et 3,4 à 6σ.

Quand l'utiliser

À utiliser pour comparer sur une même échelle des procédés dont le nombre d'opportunités de défaut diffère. Définissez une fois ce qu'est une opportunité et ne changez plus cette définition, sinon les comparaisons ne tiennent pas.

حول هذه الحاسبة

ترجمة (مسودة) — للمراجعة.

طريقة الحساب

DPO = العيوب ÷ (الوحدات × فرص العيب لكل وحدة). DPMO = DPO × 1,000,000، والمردود = (1 − DPO) × 100%. مستوى سيغما هو قيمة Z في التوزيع الطبيعي المعياري المقابلة للمردود (1 − DPO) مضافاً إليها الإزاحة المعتادة طويلة الأمد 1.5σ. وتعرض الأداة أيضاً العيوب لكل وحدة.

مثال محسوب (قيم توضيحية)

35 عيباً في 1000 وحدة، لكل منها 5 فرص. DPO = 35 ÷ 5000 = 0.007. DPMO = 7000. المردود = 99.3%. سيغما = Z(0.993) + 1.5 ≈ 3.96σ. العيوب لكل وحدة = 0.035.

قراءة النتيجة

التصنيف: 6σ فأكثر مستوى عالمي، 5σ ممتاز، 4σ جيد، 3σ متوسط الصناعة، وأقل من 3σ ضعيف. كل مستوى سيغما يخفض العيوب نحو عشر مرات، ونقاط المرجع في الأداة هي 66,807 DPMO عند 3σ، و6,210 عند 4σ، و233 عند 5σ، و3.4 عند 6σ.

متى تستخدمه

استخدمها لمقارنة عمليات تختلف في عدد فرص العيب على مقياس واحد. حدّد مرة واحدة ما يُعدّ فرصة عيب وثبّت هذا التعريف، وإلا فلن تصح المقارنات.

In one line

Counts refer to defect opportunities, not necessarily defective units. The sigma conversion includes a conventional 1.5 shift. Zero or all-defective opportunities have no finite normal quantile; report sample size and DPMO instead.

Worked example

EXAMPLE

Illustrative values (the calculator defaults), not data from a real site.

Inputs
Number of defects35 count
Number of units1000 count
Opportunities per unit5 count
Results
Process sigma level3.96σ
DPMO7,000
DPO0.007
Process yield99.3%
Defects per unit0.035

Frequently asked questions

What counts as an opportunity for a defect?

Any characteristic or step on a unit that could independently fail to meet a requirement — a circuit board might have one opportunity per solder joint, an invoice might have one per required field. Define this consistently for your process; more opportunities per unit means the same raw defect count converts to a lower DPMO.

Why is my sigma level lower than I expected?

The 1.5-sigma long-term shift built into this calculation is the usual reason — it deliberately makes the reported sigma level more conservative than a naive normal-distribution calculation, because real processes drift over weeks and months in ways a short capability study does not capture.

What DPMO or sigma level is considered good?

It varies by industry and how safety- or cost-critical the defect is, but as a general reference point 3-sigma (about 66,800 DPMO) is often called industry-average, 4-sigma (about 6,200 DPMO) is considered good, and 6-sigma (3.4 DPMO) is the world-class benchmark most associated with the methodology.

Can I use this if I only know the number of defective units, not total defects?

This calculator is built around counting total defects (a unit can have more than one), not just defective-or-not units. If you only track pass/fail per unit, set opportunities per unit to 1 and defects equal to the number of failing units to get a DPMO based on units rather than individual defect occurrences.

Why does zero defects show sigma level 6 instead of infinity?

Mathematically, zero defects implies infinite sigma, which is not meaningful. The tool caps the displayed value at 6-sigma, the conventional Six Sigma ceiling, as a practical placeholder — in reality your true capability with a small sample and zero defects is uncertain and needs more data to confirm.

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