Chip Hewette

Thank you for the kind words.
No.
It is not always simple.  I felt it best for the original questioner to think about the customer view of a delivery.  Although 36 / (7200 * 8) sounds like a great performance, in my view any defect is a complete failure of one delivery.  There is only one opportunity to satisfy the customer, when there are ma…[Read more]

One may interpret DPMO as defects divided by (events x opportunities) but this can understate the rate.
If one is making a delivery and (a)the product puts a hole in the owner’s wall on the way down the hall and (b)is dropped by the delivery team at the living room, how many defects exist?  There may be eight opportunities for a defect to exist…[Read more]

I assume the results are something like this:y1 = ax1 + bx2 + cx3 + 0*x4y2 = 0*x1 + bx2 + cx3 + 0*x4y3 = 0*x1 + 0*x2 + cx3 + dx4Please keep in mind that for a factor “x” to be significant does not automatically mean that it has a massive effect on the output.  If the measure y can be evaluated closely, and you have done enough replication, you m…[Read more]

Can you provide us with more info?  What was the proportion of “fail” within the 20 samples?  Do you have adequate inference space to state that the inspectors had opportunity to find “pass” and “fail” parts?
What is the likelihood of part failure in production?  Does its frequency of occurence mean the inspectors would likely be ‘bored’ and mi…[Read more]

First, DPMO is merely mathematics, in my view.  Transitioning your company to a nonconformance reporting method of “nonconformances per million opportunities” is a way to keep the fire lit under folks who think that 99% is good.  I don’t understand the $10K to calculate DPMO.
Second, this is ambitious.  I would hazard a guess that many pr…[Read more]

First, think through the 4,000 data points.  That’s a lot of info!  Chances are very high that you have many contributing causes to the measured response.  Assigning all 4,000 responses to one proposed factor is inappropriate in my view.
Your thought to simplify the data structure is mathematically suspect, as related by the earlier response to…[Read more]

First, link your quest to external customer requirements.  Then, think through observed customer-described nonconformances such as customer returns…what reasons exist for customers to return?  Can you pareto these reasons?
Second, consider a fault tree.  Can you logically describe likely failures?
Third, consider a formal FMEA.  Will this…[Read more]

I would suggest a different approach to your training.  Begin with a brainstorming session using a Post-It note technique.  Ask people to write what they dislike or find ineffective about the way your company solves problems on Post-Its.  Spend about 8 to 10 minutes in writing.  As each person writes a note, have them hold it over their hea…[Read more]

I am not confused.  Especially with any facts!;)
Your last paragraph mirrors what I tried to say.  Distribution type was immaterial to my thinking.  The original question about use of non-normal data points to a real need to understand what is going on, not the distribution type, as the measurements are clearly at risk.

Why would the data from a gage be abnormally distributed?  Only if there were special causes of variation!  One can choose to ignore data containing special causes, but this needlessly confuses the situation.
How do you know the data is not normal?  Can you state with high confidence that one or more of the measurements are unusual?  Can you ide…[Read more]

If you are testing six of method A vs. six of
method B, creating six replicates of each factor
level, it is better to evaluate each individual
measurement for ‘quality.’ What is the range of
values for the six observations? Is this range as
expected? Is one value way different from all the
others? Why? The MBB is correct in a…[Read more]

One should always seek to link upstream processes with downstream measures through proper experimentation.
If upstream process A is allowed to vary naturally, determine the bounds of that natural process.  Then, create samples or choose samples at those bounds.  How do components with these upstream values affect downstream measure B?  Use sta…[Read more]

Marc…any chance you worked at Copeland?

It appears that you are reviewing the GR&R output from a statistical package, and looking at the components of variation attributed to (a)repeatability, (b)reproducibility, and (c)part-to-part.  When the failed part is measured at zero volts, and nine other parts are measured at 15 vDC, the variation in the measurement system attributed to…[Read more]

An admirable goal!
Consider this free-lance consulting, but don’t do it for free.  People don’t value things that are free.  They value a ‘deal.’
It would be very difficult to do your first project for a company or industry with which you have zero familiarity.  In your job transition, think through your expertise and develop a list of target co…[Read more]

Could you please clarify…

Is there one and only one defect (nonconformance) possible for each item checked?
Is there an automatic gage that determines if an item is defective or not?
Are you checking the quantity of rejects in an off-line collection point?
Are you unaware of the exact production quantity for that number of rejects?

Amen on looking for out-of-control points on the R chart.  No question that the average doesn’t tell it all!
Didn’t see your e-mail address, Mr. Floyd…

Suggest you look at titles on Bank of America job postings.

Please discuss the risk to production quality with the business leaders before making any changes.  What would happen if the gage calibration interval were expanded, and a gage were found out of calibration after this longer period of time?  What financial impact would this have?  What ‘safety nets’ exist in current gaging practice?  What is the…[Read more]

First, I hoped that studying the time until stability occurs would allow the process owner to set a simple, factory-proof work instruction that parts prior to this time were to be segregated, inspected, re-inspected, scrapped, or whatever.  Factories are not always the best place to be wishy-washy or unclear with work instructions.
Second, with…[Read more]