Time
9 hours 53 minutes
Difficulty
Intermediate
CEU/CPE
10

Video Transcription

00:00
Hi, guys. Welcome back. I'm Katherine MacGyver, and this is your lean six sigma green belt in this module. We're gonna pick up where we left off in our last module about control charts. So last time we talked about control charts and very broad general terms. This time we're actually going to get into the actual control chart.
00:18
So we're going to go over your ex bar are a chart which is gonna be your bread and butter. This is like your normal distribution. This is the majority of your work. If you use an expert are
00:28
run chart. You're never going to be wrong there. The next one we're gonna go over has a little bit of variation, and I want to talk about it for very specific reasons. But in this module, you're gonna understand how to read a next bar. Are you're gonna understand the data required for an expert are
00:45
so with that expert ours monitor your mean and variation, so process, stability, thes air, the things that we're looking for it to report to, you can use it for individual measures or subgroups. So if you um so you have your process running and you create
01:03
Ah 100 widgets a week. You decide to sample 10 of those
01:07
to see how you're performing, Then that's going to be a subgroup. Or if you make 10 widgets a week and you decide you're going to measure one for each day, that could be an individual measure. So it does work for both. You do need to have continuous data, so your quality characteristic
01:25
where you're why axes
01:26
does need to be continuous data. Um, not discreet data. It needs to be able to be measured on a spectrum. So with that bread and butter mean and variation, this is what your expert archer, it looks like
01:42
so first
01:45
it is called an X bar are because the thimble for a mean you will notice over here on your right is an ex with a bar on top of it. You're our indicates it's a range. So what you are looking for here is how do your data points perform
02:05
compared to your average,
02:07
And then what is your range or your measure of variation too? Remember, mean and variation. These were going to be very important to you. So if you look at this process this process is very badly out of control. And that's because you look at your upper and lower control limits. You see that you've got several data points above and below
02:27
your upper and lower control limits. This is bad news.
02:29
You do see your average cruising along in the middle at your X bar or five and change. And then if you look at your lower graph, what you are going to be looking for is your sample range. So this is a sample eso you took 10 data points of your 100 widgets.
02:46
Andi, it's going to tell you how close or far apart from each other
02:51
your data points are, so you'll notice the our bar in here is your average range. So as you continue to add on samples, it will, of course, refine. But your range for sample number one was to between your data points.
03:09
And then if you see as you came over starting and sample number four,
03:14
it's down to one. So this is good. This tells us that even though our process is out of control from a mean perspective, we are decreasing our variation. So we do have minimal variation so our data points are close to our average.
03:30
Our average is rocking and rolling and all over the place.
03:34
So that's what you're going to be looking for in your X bar are a chart.
03:38
We are not going to go over building these in Excel, because if you remember when I was like, we're going to do everything and excel Except, you know, there are a few things that are a little bit more challenging. The control charts are the main reason
03:54
to invest in some form of statistical software. So either the Cadillacs that I talked about, where there are some Adan's for Excel that does this.
04:04
You can do this by hand
04:09
by creating stable numbers for your upper and lower control limits. So you're going to be creating a line graph where your ex axes, of course, are your measures of time and your Y axes. You're going to end up with three of them, one that's constant with your upper control limit, one that's constant with your lower control limit, as in your average
04:29
in between
04:30
so it can be done. It's just a little bit more challenging to do it from a line graph Ping me if you have questions. So with that today we went over our conditions for expert are you know that this measures mean and variation, You know that you need tohave
04:45
discreet that or excuse me continuous, not discrete data. And you know that this is going to be your bread and butter control chart with your normal distribution,
04:53
you know how to read it. You know that your upper graph is your measures of your average or your mean your lower graph is your measures of your range or your variation and that you want to see you want to balance the performance in both.
05:09
So, like the example we saw, we actually were doing pretty good with our range, but we were rocking and rolling with our mean.
05:15
So with that, we're going to switch over to X bar M r for your control for your next control chart. So I will see you guys there

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Lean Six Sigma Green Belt

This Six Sigma Green Belt course teaches students how and where to apply the Six Sigma process improvement methodologies. Upon completing the course, students will have the skills and knowledge to pass the Six Sigma Green Belt certification exam.

Instructed By

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Kathryn McIver
Lead Instructor at Evidence-Based Management Association
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