So 233 PPM corresponds to about 3.5 sigma. Now, we input this probability into the inverse standard normal distribution and find our z-value (sigma), like so: So we divide by a million and get 0.000233. First, we need to get this from PPM to a probability. (No, 2.5 sigma isn’t very good.) From PPM to SigmaĪlright, let’s say we wanted to go from 233 PPM to sigma.
So, 0.006210 is a probability. To get this into PPM, we would multiply by a million. We could certainly work with that it’s just not as convenient. If we entered =NORMSDIST(2.5), we would get 1-0.006210 as our answer. That means they start from the left and work their way forward. Excel and most other calculators use the cumulative normal distribution to calculate values. If you’re wondering about that negative sign, let me explain. (I’m giving away the secrets of my spreadsheet here.) In Excel, you could find this using the formula =NORMSDIST(-2.5). (I will never share your email address with anybody.) Or you can go ahead and read on to understand how this conversion works, and set it up yourself. To get the calculator, and to sign up for more updates like this one, go ahead and sign up using the form below.
You can use the calculator to convert between PPM and sigma in both traditional Statistical Process Control and Six Sigma (and yes, the results are different). I went ahead and put together a “calculator” for you to accomplish this (okay, it’s an Excel spreadsheet). It should go without saying that being able to convert back and forth between PPM and sigma can be very handy. The terminology DPMO (defects per million opportunities) is also sometimes used in place of PPM, but it means essentially the same thing. (More on that later, considering that it technically corresponds to 4.5 sigma.)ĭefects are often measured in PPM (parts per million), but statistical processes are usually understood in terms of standard deviations (sigma). Probably the most widely known example is Six Sigma, which aims to keep the number of defects below 3.4 per million. One of the primary goals of statistical process control is to reduce the probability of a “defect,” however you define it, to acceptable levels.
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