Cpk (Process Capability Index) measures how well a process fits within its specification limits, accounting for centering. Cpk = min((USL - mean) / (3 * sigma_within), (mean - LSL) / (3 * sigma_within)). A Cpk of 1.33 or higher is generally considered capable.

What Cpk value is considered good?

Cpk >= 1.33 is the minimum for a capable process (4-sigma). Cpk >= 1.67 is preferred for critical characteristics. Cpk >= 2.0 represents Six Sigma performance. Cpk < 1.0 means the process is not capable and is producing defects outside specification.

Cpk Calculator

Calculate process capability indices (Cp, Cpk, Pp, Ppk) from your measurements. Paste your data, set specification limits, get instant results.

Input Data

Measurements (paste comma, space, tab, or newline separated values)

Accepts any delimiter. Paste directly from Excel, CSV, or a column of numbers.

Lower Spec Limit (LSL)

Upper Spec Limit (USL)

Target (optional)

Subgroup Size (for within-group sigma)

What Is Process Capability (Cpk)?

Process capability analysis answers a fundamental question in manufacturing: can my process consistently produce parts within specification? The Cpk index quantifies this by comparing the spread of your process to the width of the specification limits, while penalizing any shift away from center.

Cp vs. Cpk — Potential vs. Actual Capability

Cp measures potential: it compares the spec width to the process spread (6σ), ignoring centering. Cpk measures actual capability by also accounting for how far the process mean drifts from the spec midpoint. A high Cp with a low Cpk tells you the spread is fine but the process is off-center — often the cheapest problem to fix.

Short-Term vs. Long-Term: Cpk vs. Ppk

Cpk uses the within-subgroup standard deviation (estimated from the average range or average standard deviation of rational subgroups), representing short-term or inherent process variation. Ppk uses the overall standard deviation of all data points, capturing both within-group and between-group variation. When Cpk and Ppk diverge significantly, the process is likely drifting or shifting over time — a signal to investigate with control charts.

Industry Benchmarks

Common Cpk requirements vary by industry and criticality:

Cpk ≥ 1.00 (3σ) — Minimum for a marginally capable process (~2,700 PPM defective)
Cpk ≥ 1.33 (4σ) — Standard requirement for most manufacturing (~63 PPM)
Cpk ≥ 1.67 (5σ) — Required for safety-critical and automotive (IATF 16949, PPAP)
Cpk ≥ 2.00 (6σ) — World-class, required for aerospace and medical devices

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Frequently Asked Questions

What is Cpk?

Cpk (Process Capability Index) measures how well a process fits within its specification limits, accounting for how centered the process mean is. Cpk = min((USL - mean) / (3 * sigma_within), (mean - LSL) / (3 * sigma_within)). A higher Cpk means fewer defects. A Cpk of 1.33 or higher is generally considered "capable."

What is the difference between Cp and Cpk?

Cp measures potential capability — it only considers how much of the spec width your process spread uses: Cp = (USL - LSL) / (6 * sigma). Cpk measures actual capability — it also penalizes off-center processes. Cpk is always ≤ Cp. If Cp is high but Cpk is low, your process spread is fine but it's not centered.

What is the difference between Cpk and Ppk?

Cpk uses within-subgroup standard deviation (estimated from R-bar or S-bar), representing short-term capability. Ppk uses the overall standard deviation of all data, representing long-term performance. If your process is in statistical control, Cpk ≈ Ppk. A big gap between them suggests the process has shifted or drifted over time.

What Cpk value do I need?

Common benchmarks: Cpk ≥ 1.00 = minimum (3-sigma, ~2,700 PPM). Cpk ≥ 1.33 = capable (4-sigma, ~63 PPM). Cpk ≥ 1.67 = good (5-sigma). Cpk ≥ 2.00 = excellent (6-sigma, ~3.4 PPM). Many industries require 1.33 minimum; automotive and aerospace often require 1.67+.

How many data points do I need?

At minimum 25-30 measurements to get a reasonable estimate. For production capability studies, AIAG recommends at least 125 measurements (25 subgroups of 5). More data gives more confidence in the results.