The misunderstanding is based n the fact that a 1.5 sigma long term shift is normally applied when talking about six sigma. 3.4ppm defects equals 4.5 sigma without the long term shift.

Hope this helps

]]>Where do we get “Sigma Performance Levels – One to Six Sigma”

]]>Why six sigma is calculated in million only and not in billions?

]]>the tails should be 16% each side at 1-sigma

and 0.6ppm at 5-sigma, not 233ppm

I get these:

N NORM.DIST(N,0,1,TRUE) 1-this 2*1e6 x that

1 0.841344746 0.158655254 317310.5079

2 0.977249868 0.022750132 45500.2639

3 0.998650102 0.001349898 2699.796063

4 0.999968329 3.16712E-05 63.34248367

5 0.999999713 2.86652E-07 0.573303144

6 0.999999999 9.86588E-10 0.001973175

360,342 winning tickets of 1,800,000

odds of winning a top prize 1 in 600,000

odds of winning overall 1 in 5

so we can say that chances of winning a top prize jackpot is 2tickets per 1.2million sold.

now just quick rough estimate to calculate a value for ppm would be approx 1.8 ppm

so purchasing one colorado lottery scratch ticket, your odds of winning a top prize is 1.8 in 1million tickets, or 0.00018%.

now lets just say, if colorado lottery had a “Scratchology” department which is designated for the sole business of the scratch ticket games, and this was the only scratch game the colorado lottery offered at that time. now lets say they implimented and assembled a GreenBelt project team, gathered key metrics to calculate a DPMO score. lets say, they wanted to see what the DPMO is for the process of printing the scratch games, which consists of taking all the pre calculated data of how many tickets to be printed, and what information needs to be transferred on each individual ticket, with a covert and classified manner. now lets say they werent perfect, but were able to determine that the process has 3.1DMPO. A level 6Sigma quality production line.

now is it correct to say that 3.1 tickets failed to print out of one million tickets printed.

i hope my interpretation of the above information makes sense, and for the most part correct.

Because what stood out to me, is how you gave the lottery odds as a perfect way to differentiate ppm from DPMO. But seeming how they are alike, but not the same, though do have common denominatating value of units, can these numbers used in tandem, or is one apple, the other orange.

if the DPMO figure was factual data, and not the random value i came up with for my perfect example, would it be safe to say, that a computerized production line that printed Colorado Lottery Scratch Game Tickets, did so with a 3.1DPMO, and level 6 excellent, and not just of good quality. and then add that this game not only looks beautiful but it also has a 1:5 overall winning odds, then odds of winning a top prize jackpot is a 1.8 ppm. is it safe to compare the ppm to DPMO, cause id say the printing process is extremely flawed if the DPMO exceeded the ppm of odds of winning yop prizes. if theres the chance that the defectedtickets that didnt print could actuallt be the top prize winners, and change the odds of winning to odds of losing.

]]>There is no fundamental law that dictates either

Air travel is approximately a 7 Sigma process ]]>

By counting defects per million you can judge the quality maturity of your process in units of one two three or six times the standard deviation (sigma).

1 2 3 6 sigma = 68% 95% 99.7% and 99.9999998% (percentage of total area under normal bell curve)

]]>6 means 99.9997 % good

and there are six [6] sigma levels not 3.

calculated as

[ defect units / (no. of oppertunities * no. of units) ] * 1,000,000

It means 99.99966 % data falls withing specification. so that 100-99.99966=0.00034% = 3.4 ppm

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