How many parts is enough? What your Cpk really is

A Cpk of 1.33 from 30 pieces has a 95% interval of roughly 0.97 to 1.69. See the uncertainty in your own number, and how many parts would settle it.

Nothing you paste leaves this calculation. It is not stored, not logged and not sent anywhere else — the numbers are computed and returned, and that is all.

Why this matters

A capability index is an estimate from a sample, and it is a noisier estimate than its two decimal places suggest. Thirty pieces — the number most PPAP submissions use — leaves a Cpk of 1.33 with a 95% interval running from roughly 0.97 to 1.69. The same process, sampled again, could easily report 1.1 or 1.6.

That is the honest answer to "is thirty parts enough?": enough to estimate, not enough to prove a requirement of 1.33. To claim 1.33 with confidence you either need a much better process, or many more parts — this calculator gives you both numbers.

How this is calculated

SE(Ĉpk) ≈ √( 1/(9n) + Ĉpk² / (2(n−1)) )
CI      = Ĉpk ± z × SE(Ĉpk)

The standard large-sample interval for Cpk (Bissell), the one behind the AIAG and Montgomery treatments. It assumes the data is roughly normal and — critically — that the process was in control while you sampled it.

The part most people get wrong

An unstable process has no capability. Sampling harder does not fix it: the interval narrows around a number that describes a process that no longer exists. Chart it first — that is what the control chart generator and the rules checker are for — and compute capability only over a stable stretch.

More parts is not the only lever. If the interval will not clear your customer's requirement at any sane sample size, the process needs to be better centred or less variable. No amount of measuring changes that.

Questions people ask about this

Is 30 parts enough for a capability study?

Enough to estimate, not enough to prove. A Cpk of 1.33 measured on 30 pieces has a 95% confidence interval of roughly 0.97 to 1.69 — the same process resampled could easily report 1.1 or 1.6. To demonstrate a requirement of 1.33 you need either a genuinely better process or a much larger sample.

How do you calculate a confidence interval for Cpk?

The standard large-sample approximation: SE(Ĉpk) ≈ √(1/(9n) + Ĉpk²/(2(n−1))), and the interval is Ĉpk ± z·SE. It assumes roughly normal data and, critically, a process that was in control while you sampled it.

How many samples do I need to prove Cpk 1.33?

It depends entirely on how much margin you have. A measured Cpk of 1.8 clears 1.33 at about 32 pieces; a measured 1.4 needs several hundred; a measured 1.33 never does, because the interval is centred on the requirement itself. Enter your own numbers above.

Does a bigger sample fix a low Cpk?

No. More data narrows the interval around the number you have; it does not move the number. If the estimate sits at or below the requirement, the process has to be better centred or less variable — no amount of measuring changes that.

Want this to keep itself up to date?

A calculator answers for the data you pasted. A control chart answers for the data your line produced this morning — limits frozen at a baseline you locked, rules evaluated on every new reading, an alert when one trips.

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More capability tools

Cp / Cpk calculator — Paste measurements — or type a mean and a sigma — with your tolerance, and get Cp, Cpk, Pp, Ppk, the sigma level and the expected parts per million out of spec.
Cpk ↔ PPM ↔ sigma level converter — What PPM is a Cpk of 1.33? What Cpk does 3.4 PPM need? Both conventions shown side by side, because the 1.5 sigma shift is why two sources disagree by a factor of ten.

Everything else

Control limit calculator — Give it subgroups or individual readings and it returns the control limits for the chart, the range or sigma chart beneath it, and every constant it used to get there.
Control chart generator — Paste a column of numbers, or rows of subgroups, and get a real control chart: limits from the data, Nelson rules 1–4 evaluated, out-of-control points marked.
Nelson & Western Electric rules checker — Every run rule evaluated on your data, each violation named in plain English with what it usually indicates — a shift, a trend, tool wear, two machines mixed.
Control chart constants — The whole table, n = 2 to 25, with the formula each constant belongs to. The same values the charts on this site are computed from.
X̄-R chart calculator — Subgroups in, X̄ and R charts out — limits from A₂, D₃ and D₄, run rules evaluated, and the range chart shown first because it decides whether the averages chart can be trusted.
X̄-s chart calculator — For subgroups big enough that the range wastes them. Limits from A₃, B₃ and B₄, sigma recovered with the c₄ correction, and the s chart read first because it decides whether the averages chart can be trusted.
I-MR chart calculator — For processes that give you one number at a time — a batch, an oven, a destructive test. Limits from the mean moving range, run rules evaluated, and an honest note about what an individuals chart cannot see.
Levey-Jennings chart with Westgard rules — Paste your QC results with the assigned mean and SD from the package insert, and get the chart with every sigma band drawn and all six Westgard rules evaluated — each one named, and labelled random or systematic error.
Bland-Altman plot — Paired readings from two methods, plotted as difference against average — with the bias, the limits of agreement, their confidence intervals, and a test for whether the disagreement depends on the magnitude.
Pareto chart generator — Categories and counts in, ranked bars and the cumulative line out — with the vital few named, an "Other" bucket that always sorts last, and an honest warning when the distribution is flat and there is no dominant cause to attack.
Box and whisker plot generator — One column per group, boxes side by side. Quartiles, the 1.5×IQR fences, whiskers that stop at real readings and outliers drawn individually — with the quartile method stated, because that is why your plot and Excel's disagree.
Histogram and normality test — A histogram with the fitted normal curve, both standard bin rules with a reasoned recommendation, and an Anderson-Darling test that refuses to tell you your data is normal — because no test can.
Gage R&R calculator (ANOVA) — Paste a crossed study — part, operator, reading — and get the full ANOVA: repeatability and reproducibility separated, the part-by-operator interaction tested rather than assumed away, %GRR, %Tolerance and ndc against AIAG's bands.
p, np, u and c chart calculator — Counted data rather than measured: proportion defective, number defective, defects per unit. Limits that step with the sample size instead of pretending it never changed, and an honest warning when the counts are too low for three-sigma limits to mean anything.
Grubbs outlier test — The G statistic against its critical value, the p-value, and the point implicated — plus a straight answer about what to do next, which is almost never to delete the reading.
Scatter diagram and correlation — Paired readings plotted, with Pearson r, Spearman rho, r², the least-squares line and its p-value — and the two reported together, because when they disagree the disagreement is the finding.
Standard deviation calculator — The usual summary statistics, and the thing a spreadsheet will not tell you: the within-subgroup sigma a control chart uses, beside the overall sigma a textbook means. The gap between them is why Cpk and Ppk disagree.