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Cp Cpk Calculation Formula

Cp and Cpk Formulas:

\[ Cp = \frac{USL - LSL}{6\sigma} \] \[ Cpk = \min\left(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma}\right) \]

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1. What Are Cp and Cpk?

Cp and Cpk are statistical measures used in process capability analysis to determine how well a process meets specification limits. Cp measures potential capability while Cpk measures actual performance considering process centering.

2. How Do the Formulas Work?

The calculator uses the following formulas:

\[ Cp = \frac{USL - LSL}{6\sigma} \] \[ Cpk = \min\left(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma}\right) \]

Where:

Explanation: Cp compares the process spread to the specification range, while Cpk considers both spread and centering of the process relative to specifications.

3. Importance of Process Capability Indices

Details: These indices are crucial for quality control, Six Sigma projects, and manufacturing processes to ensure products meet design specifications and minimize defects.

4. Using the Calculator

Tips: Enter all values in consistent units. Standard deviation must be greater than zero. The calculator provides both Cp and Cpk values as dimensionless indices.

5. Frequently Asked Questions (FAQ)

Q1: What is the difference between Cp and Cpk?
A: Cp measures potential capability assuming the process is centered, while Cpk measures actual capability considering both spread and centering.

Q2: What are acceptable values for Cp and Cpk?
A: Generally, values ≥1.33 indicate capable processes. For Six Sigma quality, values ≥2.0 are typically required.

Q3: When should I use Cp vs Cpk?
A: Use Cp when the process is centered. Use Cpk when the process may not be centered or when you want to assess actual performance.

Q4: What if Cpk is much lower than Cp?
A: This indicates the process is not centered within the specification limits and needs adjustment to improve centering.

Q5: Are there limitations to these indices?
A: They assume normal distribution and stable process. They may not be appropriate for non-normal distributions or unstable processes.

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