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Interpret the capability plot in Capability Six pack

Discussion in 'SPC - Statistical Process Control' started by essegn, Sep 2, 2016.

  1. essegn

    essegn Member

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    Hi there,

    Could someone help me with an interpretation of the following Capability Six pack result? Please look at the attached file.


    I am aware that there is only 32 observations instead of at least 50, but in this case there are enough to determine that the process needs to be changed.

    Individual and Moving Range Charts should be ok. I used in Minitab the Nelson Rules feature and there are any red points. It could be said that the process is stable.

    Capability Histogram looks not really good, because the measurements do not lie within USL – LSL Range.

    Capability plot looks not good as well. The parameters Cp, Cpk resp. Pp and Ppk are too low (lower than 1.33) and “SPECs Range” is narrower as “Overall” and “Within” Range

    PPM means that more than 10 percent of measurements are off the tolerance.

    Result:

    The process is stable (Control Charts), but not acceptable because of Capability Histogram / Plot results. The process needs to be modified.

    Am I right or there some other indications, which need to be considered ?

    Thank you for your sharing your experience.

    Peter
     

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  2. Miner

    Miner Moderator Staff Member

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    You have summarized it nicely. Stable, but not capable.
     
  3. tony s

    tony s Well-Known Member

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    Since the spread of your process is wider than the specification limits and a bit skewed to the left, you need to analyze causes of having values that tend to go lower than the nominal. I'll suggest that you attempt to address the centering of your process then look for ways to reduce the variability of the process.
     
  4. essegn

    essegn Member

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    Thank you for both replies guys.
    In which way could be the p-value (0,543) by Normal Prob Plot interpreted ?
     
  5. Miner

    Miner Moderator Staff Member

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    Since the p-value is > 0.05, there is insufficient evidence to reject the null hypothesis (sample data are drawn from a normally distributed population).
     
    Bev D likes this.

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