Tolerance vs Process Capability Review Worksheet
A tolerance that looks fine on paper may be impossible for the process that has to make it. This worksheet puts the drawing limits next to the process mean and standard deviation so you can see which features are at risk before they become scrap.
| Feature | Lower limit | Upper limit | Process mean | Std deviation | Cp | Cpk | Predicted ppm out of spec | |
|---|---|---|---|---|---|---|---|---|
1.6667 | 1.5873 | 1 | ||||||
1.3333 | 1.1733 | 220 | ||||||
1.3889 | 1.3889 | 31 | ||||||
1.1111 | 0.8889 | 3,862 |
Assumes a stable, roughly normal process, so use real subgroup data from a controlled process. One-sided features such as position are not normal, so treat their Cpk as indicative only. Targets of 1.33 or 1.67 are common but come from your customer.
Pull capability data from your ERP and SPC
Optional review of how inspection results could feed capability reviews.
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How to use it
For each feature enter the lower and upper limit, then the mean and standard deviation from a capability study or machine qualification run. The sheet returns Cp, which compares tolerance width with six sigma, Cpk, which also penalizes an off-centre mean, and the predicted fraction of parts outside the limits expressed in parts per million. Edit the examples and export the results for the design review.
Formulas
Cp equals the upper limit minus the lower limit, divided by six times sigma. Cpk is the smaller of the distance from the mean to each limit, divided by three sigma. Predicted ppm uses the normal distribution tail beyond each limit. Cp shows what the process could do if centred, Cpk shows what it does now.
- Cp = (USL - LSL) / (6 sigma)
- Cpk = min(USL - mean, mean - LSL) / (3 sigma)
- Cpk 1.00 is about 2,700 ppm; 1.33 is about 63 ppm if centred and normal
Common mistakes
Do not use short-term machine data and expect long-term results; tool wear and material lots add spread. Do not apply the normal model to geometric tolerances bounded at zero without care. And when Cpk is low, decide whether to improve the process, centre it or relax the tolerance with the designer, rather than inspect the problem forever.
Frequently Asked Questions
What Cpk is good enough?
It depends on the customer and feature criticality. Many automotive customers expect at least 1.33 for general and 1.67 for critical characteristics, while others differ. Use the requirement in your contract or quality plan.
Why can Cp be high while Cpk is low?
Because the process spread is narrow but the mean sits close to one limit. Re-centering the process, often with a tool offset, raises Cpk without reducing the spread.
How much data do I need for sigma?
Capability studies commonly use 30 or more consecutive pieces, and many customers ask for more over time. Small samples give wide uncertainty on sigma and therefore on the ppm estimate.
Is predicted ppm reliable?
It is only as good as the normality and stability assumptions. Treat it as a ranking tool for which features need attention, not a guarantee of defect rates.
If capability data sits in separate SPC tools, we can discuss bringing characteristic results next to your ERP routings and items.
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