Ten Really Good Reasons to Predict Process Capability using Tolcap

Ten rather excellent reasons to use Tolcap - a unique, data and standards driven, process capable tolerancing tool:

1. Don’t wait until production starts to find out if your parts will be out of tolerance.
   Predict the manufacturing process capability (and hence defect rate) of the parts from the start of your design.
2. Protect your profits! Out of tolerance parts cost money.
   They add significant quality costs that eat away at the profitability of your product - and your engineering budget.
3. Protect your weekend at home! Out of tolerance parts make the next design late.
   They swallow scarce engineering resources as you struggle to put things right rather than getting on with the next project.
4. Understand the factors that affect process capability.
   A prediction built up from the manufacturing process, material and geometry of the part pin-points the issues eating away at process capability.
5. Identify Special Characteristics.
   Special Characteristics are exactly those marginal cases where you cannot just assume adequate process capability. You need to identify these and then have a rational engineering discussion with your supplier…….
6. Talk constructively with your Supplier.
   Predicting process capability enables you to ask the right question when reviewing your design with a supplier.
   The wrong question is: ‘Can you make this part (capably)?’
   A better question is: ‘Analysis shows that the predicted process capability for this dimension is (say) 0.95*, and we need 1.33**… so what evidence do you have, or what tests can you do, to demonstrate the process capability you will really achieve?’
   * Process capability (called C_pk) = 0.95 means 3000 parts per million out of tolerance.
   **C_pk = 1.33 means 30 parts per million out of tolerance.
7. Get the design right before it goes wrong!
   Identify where you need to make changes in order to release a design to suppliers which they can meet capably.
8. If you’re going to use statistical tolerancing for tolerance stack-ups, don’t fool yourself!
   Remember the assumptions of Statistical Tolerancing. Combining tolerances by taking the root sum square isn’t valid unless the tolerances are all the same multiple of the ‘sigmas’ of the parts. You need to know process capability to set equally capable tolerances.
9. Enable Design for Six Sigma
   ‘DFSS’ has been linked to Quality Function Deployment, Taguchi methods, Design of Experiments and other really useful tools, and it’s still a good concept. But what it says on the label is ‘Design for Six Sigma’: so all the ‘Y’s (Outputs) must be six sigma at six sigma environmental conditions - and all the ‘X’s (including all those dimensions) must be six sigma - or C_pk = 1.5. You really need to predict process capability to Design for Six Sigma!
10. Be the professional designer who gets it right first time.
    ……rather than the ‘knight in shining armour’ who comes up with the ‘fix’ when it’s all gone wrong!

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