Organizational Performance Part 27: Understanding Out of Control Conditions | Operational Excellence Quick Hits

Video Transcript:

Speaker 1: (00:00)
In this session, we’re going to talk about stability and specifically we want to understand system stability. So if the system is stable and in control, what does that mean? So we’re going to start by looking at it from a process perspective. If we look at it from a process perspective, the same rules apply to a process as they do the system. And so the first thing we want to understand is the empirical rule. So the empirical rule tells us for a normal distribution, what percentage of the data should fall within different standard deviations. So empirical rule 68-95-99.7 says that all data from the process, 68% of the data should fall within one standard deviation from the average. Also, 95% should fall within two standard deviations, and 99.7 within three standard deviations.

Speaker 1: (01:03)
For the system to be stable and in control, what we want is the average of the data to be consistent and the range of the data to be consistent. So it doesn’t matter what the process is or what the system is, if we can measure the output of that process or output of the system, we can understand if the process is in control. Of course, we need to understand what out of control is so we can understand how the system is performing.

Speaker 1: (01:33)
So in this diagram here, we have the distribution. So we’re taking samples from the process at various times throughout the process being performed. And we want the subgroup of data being collected to be consistent in terms of the average and consistent in terms of the range. So you can see we’ve got these normal distributions where the average is right on the average of the process, and we have consistent width of our distribution. So this is a stable process and it’s performing consistently.

Speaker 1: (02:13)
We can also have a situation where the average is not consistent, but the range of the data is consistent. So in this situation, you need to understand in your process, what factors control the average of the process and what factors control the variation or range. So next session, we’re going to talk about process characterization and how to identify what variables in the process affect what conditions in our data, whether it’s the range or the average.

Speaker 1: (02:51)
So we don’t want a condition like this, where we have the average all over the place, but the range is consistent. Also, we can have a situation where like this, where the average is consistent, but the range is varying all over the place. Again, this is not a good situation to be in. And the worst situation to be in and this is where we have this condition where the average is all over the place, and the range is all over the place. This process is really out of control.

Speaker 1: (03:20)
What we want to do is we want to understand when we look at the data, how do we understand if the process or system is stable? That means the process data or system data is consistent in terms of the average and consistent relative to the range of the data. If we see data that’s varying either with the average or the range, then we can identify out of control conditions. Now there are specific rules that we can put in place to understand how that process is performing over time, and we don’t wait for process nonconformance before we take action.

Speaker 1: (04:01)
Also, when we look at those rules, if we understand how to interpret the data and say, “Is there any violations?” Then we can understand out of control conditions before the process becomes nonconforming. This is where we want to be. We don’t want to wait for a non-conformance or something to be out of spec before we stop the process and take action, we want to be able to be proactive and take action before the process, both out of control, and we have a nonconformance in the process. Understanding these basic principles will help you understand where to focus your efforts, to reduce variability and where not to focus your efforts to reduce variability. And again, our operational excellence is all about the right focus.