From DPMO to Sigma Level
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 This topic has 16 replies, 1 voice, and was last updated 6 months, 4 weeks ago by Erik 2018.

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February 7, 2018 at 11:47 am #55936
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.I have a question regarding DPMO. If you calculate DPMO, why would you want to look up the corresponding sigma level? And does it have any meeting as the DPMO can consist of multiple items with (often) nonnormal distribution of some of these items.
0February 7, 2018 at 7:59 pm #202254
StrayerParticipant@Straydog Include @Straydog in your post and this person will
be notified via email.It’s easier to compare small numbers. More importantly, the sigma level is statistically more meaningful – Look at a bell curve. The differences get finer as you approach the tails and sigma level accounts for that while DPMO doesn’t. For instance the difference between 2 and 3 sigma is 42,851 DPMO. The difference between 3 and 4 is just 2,636. For your second question, if you aren’t looking at a single specification it’s mixing apples and oranges. But it doesn’t really matter as long as you’re accurately counting defects and opportunities and you’re clear about what’s included. The distribution is irrelevant.
0February 8, 2018 at 6:13 am #202256
Chris SeiderParticipant@cseider Include @cseider in your post and this person will
be notified via email.If the opportunities are truly the value added ones, then DPMO is a fantastic tool to measure across services, product lines, etc. And since sigma level is well known, it’s great.
ONLY problem is the question has to be asked–did they shift the number or do straight read from Z table. And….some tables are shifted already “being helpful”.
0February 9, 2018 at 11:11 am #202260
Gabriel GómezParticipant@GomezMGab Include @GomezMGab in your post and this person will
be notified via email.The Sigma level with DPMO is a approximation to normal distribution from binomial distribution; according with central limit theory, its due to the DPMO measure the number of trials and success (%) in each sample or success (%) in the Critical characteristic of the part. Remember that a part could have many opportunities to evaluate (CTQ’s).
Example : Suppose that we have a process with this characteristic:
Example:
Units/shift = 30,000
Defective parts = 300
Defects observed = 350
Opportunities = 15
Dpu = 350/30,000 = .011
DPMO = (.011/15) x 1,000,000 = 777
Yield : 1(0.011/15) = 99.9922%
ZBench : 3.164 *From normal distribution tables
Sigma Level: 4.664
CPk Process: 1.550February 14, 2018 at 5:02 am #202272
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.@straydog Thanks. Never thought about that. This implies that it is more and more difficult to achieve higher sigma scores, the higher your current score is. Am i right?
@GomezMGab Thanks. But this implies that the transformation from binominal to a normal distribution can only be done when p is not that high, isn’t?0February 14, 2018 at 6:06 pm #202273
StrayerParticipant@Straydog Include @Straydog in your post and this person will
be notified via email.@Erik2018 That’s sort of right. Keep in mind that the closer you get to zero defects the smaller the change necessary to raise sigma level. Improving a few DPMO won’t make much difference if your sigma level is low. But it will the closer you get to 6 sigma or even better where those small DPMO improvements get more and more difficult.
0March 15, 2018 at 8:11 pm #202368
Gabriel GómezParticipant@GomezMGab Include @GomezMGab in your post and this person will
be notified via email.you do not need P to perform the calculation, the approximation assumes that if the size of the sample is large enough, it will approach normal …
0March 26, 2018 at 12:38 am #202389
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.Thanks! By the way, why is it based on a binominal distribution? Isn’t it about defectives (a part could have many ctq’s/defectives) which corresponds with a Poisson distribution?
0April 1, 2018 at 12:10 pm #202426
Mike CarnellParticipant@MikeCarnell Include @MikeCarnell in your post and this person will
be notified via email.@Erik2018 You need to remember that there are a lot of metrics that get used and they can all represent the same number. The opportunity count is what is important (as much as I hate to say that because it opens the door for people to play with jacking up opportunity counts rather than fixing things). The Op count reflects complexity. If I was Jack Welch in 1996 and I had GE lighting and GE Aircraft Engines and I am measuring first pass yield then I have that whole apples to oranges issue. If I use DPMO now it is normalized and I can make the comparison. converting it to a sigma value doesn’t change anything it represents but it confuses a lot of people. I only use it with management and only when I have to. When you are dealing with operations people I use DPU. It is a number that makes sense in the context of their jobs.
I wouldn’t waste a lot of time on this. It doesn’t move you any closer to fixing anything.
Just my opinion.
0April 1, 2018 at 4:02 pm #202428
StrayerParticipant@Straydog Include @Straydog in your post and this person will
be notified via email.Well said, @MikeCarnell Pretty much the only thing DPMO or Sigma Level give us is a metric that can be theoretically used for objective comparison between unlike products/processes. And that’s likely to be misleading since we can always miscount or “game” the counts.
0April 3, 2018 at 8:01 am #202433
Mike CarnellParticipant@MikeCarnell Include @MikeCarnell in your post and this person will
be notified via email.@straydog That has always been the issue with DPMO. If you have defects/opportunities there are 2 ways to play the game 1. reduce defects which is what we are supposed to do 2. Increase opportunities which is the game a lot play particularly management. The whole opportunity counting game goes all the way back into Motorola. Unless a person is completely stupid and you see rework everywhere and they line is supposed to be at 4 sigma or greater then they are playing the denominator game.
0November 13, 2019 at 11:03 am #243478
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.One question regarding your example:
Example : Suppose that we have a process with this characteristic:
Example:
Units/shift = 30,000
Defective parts = 300
Defects observed = 350
Opportunities = 15
Dpu = 350/30,000 = .011
DPMO = (.011/15) x 1,000,000 = 777
Yield : 1(0.011/15) = 99.9922%
ZBench : 3.164 *From normal distribution tables
Sigma Level: 4.664
CPk Process: 1.55If you calculate zscore based on current yield, why do you add 1.5 tot get short term sigma? Isn’t the zscore (based on current defects) the short term sigma and the long term sigma 3.1641.5 = 1.6164?
0November 15, 2019 at 12:24 am #243510
StrayerParticipant@Straydog Include @Straydog in your post and this person will
be notified via email.@Erik2018 The 1.5 shift is controversial. You’ll find much discussion here and elsewhere about it, including whether or not 1.5 is the right value to account for long term drift. Most of us just accept it.
1March 23, 2020 at 11:23 am #246785
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.Looking back at these answers I am still interested why it is not poisson instead of binominal?
0March 23, 2020 at 11:25 am #246786
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.@Strayer. Okay but is DPMO always long term? Does not it depend on your sample size? Otherwise both DPMO and sigma level are short term and no 1.5 conversion have to be carried out
0March 24, 2020 at 3:20 am #246795
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.@Straydog: sorry posted a wrong name.
0March 24, 2020 at 3:20 am #246796
Erik 2018Participant@Erik2018 Include @Erik2018 in your post and this person will
be notified via email.@MikeCarnell: sorry forgot your name
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