OK, lets put a little more gasoline on the fire.
First, examine the time ordered sequence to see if the data appears to be random or has a function. (See attached chart) In this case, there does not appear to be a function BUT it could be that the tool wear is so slight that it takes 600 data points to see its affect. So, just because it appears random, it could be sampling error. How often do you have to adjust the process? How often do you have to change the cutter? How do you know it is time to change the cutter?
Now, let's force the data into a normal distribution (see attached). The p value truly does show that it is not a great model of the data. Does it mean it is not normal? No, because even though it is a bad model, it may be the best model...making it normal. Before losing our minds, note that the Ppk is 4.37. That means if the model was even close, it uses up so little of the tolerance that your process has little risk of making out of specification parts. How do we really know? Look at the confidence data.
So, let's take the next, more accurate step (which, actually is usually my first step!), and find the best fitting model and see how it compares to the normal distribution. We find the Johnson Family is a better fit (see attached). Well over that .05 pvalue (although a perfect fit is 1.00) Using a statistically more accurate model for the decision on your process, is it capable? Ppk says yes  1.83. Better yet, the confidence data confirms it.
So, what makes your data nonnormal? There is a slight skew to the high side. How does the device locate the tube to cut? Against at stop? If so, a skew away from the stop is both expected and physically supported. The tube cannot go past the stop (physical limit), but can readily bounce or otherwise not nestle right up against the stop, causing a skewed distribution. In that case, a normal distribution is never expected. Much like the 0 as a physical limit to runout (also, only normal if your process is horrible and have very high runout), your stop is a physical limit that will created an expected skewed distribution. I don't know that this is the case. You have to go back to the true first step of the capability analysis  developing the total variance equation. It will help explain multimodality or skewness, if it is complete enough. You also may have tool wear, but it may be masked by location error.
But, as you see, there are many reasons why you might not expect the process to be "normal", which makes the customer's expectation 100% statistically incorrect. In fact, Ford in its customer specific requirements addresses expected distributions and nonnormal distributions. They are not all correct, but, they get partial credit for at least realizing not everything should be normal!
Unfortunately, not all suppliers have the statistical juice to call their customer's bluff on poor application of statistics, and that makes for a long, long day.....
First, examine the time ordered sequence to see if the data appears to be random or has a function. (See attached chart) In this case, there does not appear to be a function BUT it could be that the tool wear is so slight that it takes 600 data points to see its affect. So, just because it appears random, it could be sampling error. How often do you have to adjust the process? How often do you have to change the cutter? How do you know it is time to change the cutter?
Now, let's force the data into a normal distribution (see attached). The p value truly does show that it is not a great model of the data. Does it mean it is not normal? No, because even though it is a bad model, it may be the best model...making it normal. Before losing our minds, note that the Ppk is 4.37. That means if the model was even close, it uses up so little of the tolerance that your process has little risk of making out of specification parts. How do we really know? Look at the confidence data.
So, let's take the next, more accurate step (which, actually is usually my first step!), and find the best fitting model and see how it compares to the normal distribution. We find the Johnson Family is a better fit (see attached). Well over that .05 pvalue (although a perfect fit is 1.00) Using a statistically more accurate model for the decision on your process, is it capable? Ppk says yes  1.83. Better yet, the confidence data confirms it.
So, what makes your data nonnormal? There is a slight skew to the high side. How does the device locate the tube to cut? Against at stop? If so, a skew away from the stop is both expected and physically supported. The tube cannot go past the stop (physical limit), but can readily bounce or otherwise not nestle right up against the stop, causing a skewed distribution. In that case, a normal distribution is never expected. Much like the 0 as a physical limit to runout (also, only normal if your process is horrible and have very high runout), your stop is a physical limit that will created an expected skewed distribution. I don't know that this is the case. You have to go back to the true first step of the capability analysis  developing the total variance equation. It will help explain multimodality or skewness, if it is complete enough. You also may have tool wear, but it may be masked by location error.
But, as you see, there are many reasons why you might not expect the process to be "normal", which makes the customer's expectation 100% statistically incorrect. In fact, Ford in its customer specific requirements addresses expected distributions and nonnormal distributions. They are not all correct, but, they get partial credit for at least realizing not everything should be normal!
Unfortunately, not all suppliers have the statistical juice to call their customer's bluff on poor application of statistics, and that makes for a long, long day.....
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