Hello, my name is Laura Lancaster
and I'm here with my colleague, Annie Dudley Zangi,
to talk about recent developments in JMP quality and SPC.
The first thing I want to talk about is some improvements that we've made
to the distribution platform
specifically related to limits of detection.
So limited detection is when we're unable to measure
above or below a certain threshold.
And in JMP Pro 16, some functionality was added for limits of detection.
Specifically in the DOE platform, we added the ability
to account for limits of detection and a Detection Limits column property
was added that's used by the Generalized Regression platform
to specify censoring for responses.
However, what was left unaddressed was a problem with process capability
and limits of detection.
The problem is that when you ignore limits of detection
when analyzing process capability, it can give misleading results.
And there was no way to do process capability with censored data.
But in JMP Pro 17, and I just wanted to note
that this is the only feature that we're going to talk about
that's JMP Pro.
Everything else is regular JMP in this talk.
So in JMP Pro 17, now in the Distribution Platform,
we recognize that Detection Limits column property
and we can adjust the fitters for censored data.
That means that the Process Capability report
that's within those fitters that use the adjusted fit
to account for censored data will give more accurate results.
And the available adjusted distribution fitters
are Normal, Log normal, Gamma, Weibull, Exponential, and Beta.
And before I go to the example,
I just wanted to give a shout out to check out the poster session
Introducing Limits of Detection in the Distribution Platform
that Clay Barker and I worked on if you want to learn more about this.
Let's go ahead and go to JMP.
Here I have some drug impurity data
where I have an issue with being able to detect impurities
below a value of one.
And this data that I've recorded is actually in the second column
and anywhere that I wasn't able to record an impurity
because it was below one, I've simply recorded it as a one.
So this is censored data.
This first column is really the true impurity values
that I'm unable to know, unable to detect with my detection.
So let's go ahead and compare both of these columns
using distribution.
So if I go to Analyze, Distribution, and I look at both of these columns,
you can clearly see there's a pretty big difference
between having true impurity values which I'm unable to know,
and the censored data.
Ultimately, what I want to do is I want to do a log normal fit
and run a process capability analysis on this data.
So I'm going to go ahead and do that for both of these distributions.
So I'm going to do log normal fit for both of them.
You can see that I get...
Obviously the histograms look pretty different
and my fits look pretty different too, which isn't surprising.
Now, I want to do Process Capability on both of these.
I've already added an upper spec limit as a column property,
and you can see that when I have my true data,
which I'm unable to know,
my capability analysis looks pretty different
from having the censored data.
With the true data, my capability looks pretty bad.
There's probably something I need to address.
But because I'm not able to see the true data,
and I only have the censored data that I can analyze in JMP,
the PPK value is a lot better.
It's above one, and I may blissfully move along
thinking that my process is capable
when in actuality, it really isn't so good.
But thankfully, in JMP Pro 17,
I can add a detection limits column property in my data.
So this third column is the same as my second column,
except that I've added a detection limits column property.
So I've added that I have a lower detection limit of one.
And now when I run Distribution platform on this third column
with a detection limit column property,
and I do my logn ormal fit and notice because I have censored data,
I have a limited number of distributions available,
I'm going to do my log normal fit,
and it's telling me it detected that detection limit column property,
and it knows I have a lower detection limit of one.
And when I do Process Capability,
you can see that my capability analysis is more in line with when I had
the true data because my PPK is 0.546,
doesn't look so good.
And I realize that there's probably something
that I need to address with this process.
It's not very capable.
All right, so let's move along to the next topic.
The next thing I want to talk about
is some improvements in Measurement Systems Analysis,
specifically the Type 1 Gauge Analysis platform.
A Type 1 Gauge Analysis platform is a basic measurement study
that analyzes the repeatability and bias of a gauge
to measure one part relative to a reference standard.
It's usually performed before more complex types of MSA studies
such as EMP or Gauge R&R that are already in JMP.
It's required by some standard organizations
such as VDA in Germany,
and this has been requested by our customers
for quite a while, but we believe it's useful for anyone,
whether it's required by a standard organization or not.
It's located in JMP 17
in the Measurement Systems Analysis launch dialog
as an MSA Method type.
It requires a reference standard value
to compare your measurements against, and a tolerance range
where you want your measurements to be within 20% of your tolerance range.
Produces a run chart, metrics such as Cg, Cgk,
which are comparable to capability statistics, bias analysis,
and a histogram for analyzing normality.
Let's go ahead and look at this new platform in JMP.
Here is my Type 1 Gauge data.
It's simply measurements of one part with one gauge.
So to get to the platform,
I go to Analyze, Quality and Process, Measurement Systems Analysis.
And the first thing I want to do is change the method
from EMP to Type 1 Gauge.
I'm going to move my measurements as the response
I'm going to leave everything else at default, and I'm going to click OK.
But before I can proceed to get my report, I have to enter that metadata
that I mentioned earlier,
the reference value and the tolerance range.
So I'm going to go ahead and enter that information.
I'm going to enter it as a tolerance range
and my reference value.
I'm going to skip resolution because that's optional.
Click OK.
And this is the default report that I get.
I get a run chart on my measurements graphed against my reference line.
And I also get the 20% tolerance range lines.
One's 10% tolerance range above reference and one is 10% below.
So you get some default capability statistics.
Now notice that my measurements are well within the 20%
of my tolerance range, which is really good.
I could also do a Bias Test to see if my measurements are biased.
It looks okay.
And I could also turn on a histogram to test for normality.
And before we move on,
I wanted to find out one more thing, and that's that in this top outline menu,
if I click on that, there's an option to save that metadata
that I had to enter to be able to get this report.
Remember, I had to enter the reference value
and the tolerance range.
So I could either save this metadata as a column property and we've introduced
a new column property called MSA, or I could save it to a table.
I'm going to go ahead and save it as a column property
so I can show you the new MSA column properties.
If I go back to the data table, this is the new MSA column property.
You can see it's storing my tolerance range
and my reference value,
and it also can hold other metadata for other types of MSA analysis.
Let's move along to the next topic.
I also want to talk about some improvements
to existing MSA platforms, the EMP MSA platform.
EMP stands for Evaluating the Measurement Process,
and this is the platform based on Don Wheeler's approach
and a variability chart platform.
So in both of these platforms,
we've improved the usability when analyzing multiple measurements
at one time.
We have better handling of the metadata,
such as [inaudible 00:10:16] or tolerance values
or process Sigma.
So this has been improved in variability charts
and it's added to the EMP MSA platform.
We've also reorganized the reports
so that they work better with data filters.
In addition, we've filled in the gaps
between the EMP MSA platform and the Variability Chart.
We've done this by adding some reports
to the EMP MSA platform.
We've added the Misclassification P robability Report,
the AIAG Gauge R&R report, and a Linearity and Bias report.
In addition, we've modernized the Linearity report
and the Variability Chart to match the new Linearity report
in the EMP MSA platform.
So let's go ahead and look at some of these changes.
So here I have some measurement systems analysis data for some tablets
where I've measured two different attributes
with multiple operators.
And I want to analyze this using the EMP platform.
So I'm going to go to Analyze, Quality and Process
Measurement Systems Analysis.
First thing I want to do is change the method back to EMP,
take my measurements as response, Tablet as Part, Operator as Grouping.
Notice there's now this standard role,
if I were doing a linearity and bias study,
I would use that, but I'm not in this example.
Also some new options down here in the dialogue,
but the one I want to point out is the Show EMP Metadata Entry Dialogue.
I want to set that to Yes so I can enter tolerance values
and a historical Sigma for the AIAG Gauge R&R report.
So I'm going to click OK and this dialogue pops up.
I don't have to enter this data during the launch,
but I'm going to because I think it's easier.
So I'm going to go ahead and enter the data,
and when I click OK,
my report looks similar to how it's always looked
when I've had multiple measurements.
But I also have an additional outline at the top,
and we'll look at that in a minute.
But the first thing I want to do is I want to turn on
the Misclassification Probabilities report for both of these analyses.
So I'm going to choose Misclassification Probabilities,
and you can see, I get a new misclassification probability report
for both of these and it's available without a prompt
because I've already entered my lower and upper tolerance values.
Now, if I had not already entered that information,
I would have been prompted.
Or I could use the new option, Edit MSA Metadata,
to either enter or edit any of that information,
which would automatically update any of the corresponding reports.
Let's go ahead and turn on the AIAG Gauge R&R report
for both of these as well.
And you can see I get an AI AG Gauge R&R report
that looks a lot like what's in the Variability C hart platform,
and it includes that percent tolerance column
because I entered tolerance values and percent process,
because I entered historical Sigma.
I could also turn on the discrimination ratio if I desired.
And before we move on, I just want to point out
at this top outline menu, once again, we have an option to save the metadata.
I can save the metadata,
which includes not only the MSA metadata, but also I can save out measurement Sigma,
which is a result of my MSA analysis,
which can be consumed by the Process Screening platform.
So it's going to be considered process screening metadata,
and there's actually a new process screening
column property for that.
But I'm going to save this as a table just so we can look at it.
I can see I have my MSA metadata, plus I've saved out the measurement Sigma
once I've computed those variance components.
So let's go on to the next topic,
my final topic before I hand this over to Annie,
the last thing I wanted to talk about
was some improvements to the Manage Spec Limits utility.
In fact, the name has been changed
to the Manage Limits utility because now it handles more than just spec limits.
It still handles spec limits
and anything related to process capability.
But now it also can handle Process Screening metadata,
which includes centerline, specified Sigma,
and measurement Sigma,
MSA metadata, and Detection Limits.
So now I'm going to hand this over to Annie.
Hi, everyone. I am Annie Dudley Zengi,
and I am the developer responsible for control charts in JMP.
I'm here to talk with you about some of the new features
that I added for Control Chart Builder in version 17.
So I added a Label Role in addition to the Y,
the subgroup, and the phase role, there's now a label role.
I've added a button so that you can switch an XBar and R chart to an IMR chart.
I added a row legend,
a Connect Thru Missing Command, and I've done some Dialog U pdates.
I'll start with this data table diameter,
which you can find in the sample data.
And let's start with the label role.
So I'm going to alternate between using the interface
and using the dialogs so that everybody can get a feel for both.
If I start with the interface, and I drag D iameter in to the graph,
we immediately see we get an Individual and Moving Range chart.
Now, one thing that you'll notice that's new here
is this new role in the lower left- hand corner of the chart
for the label.
Now I can drag Day in.
Now I want to take a look at Day here in the data table.
So you might notice that there are six different rows
that are associated with May 1st, 1998.
There are six rows associated with every date
in this particular data table.
And we know that if we were to drag that to the Subgroup role,
then Control Chart Builder will automatically aggregate.
But sometimes we don't want that.
So for this example, I'm going to drag this to the Label role.
You notice we still have an Individual and Moving Range chart.
It did not switch and it did not aggregate the data.
We can see that it's a regular axis.
We currently have an increment of 24.
We can change the increment to six.
We can see every date on the x- axis and we can still see that
we have an Individual and Moving Range chart of Diameter.
So there's the Label role.
Now the next option is the switch to the IMR chart.
This option was made available because there's now a Label role.
To switch to an IMR chart, we first have to have an XBar on our chart.
So I will create an XBar on our chart through the dialog.
You can choose Control Chart and then XBar Control Chart.
Again I'll move Diameter to the Y.
And this time I'm going to put Day in as a Subgroup.
You can see here it's aggregated the data
because we have Day as actually the subgroup.
But if I show the control panel and I scroll down,
you'll notice there's a new button here
underneath the old button of the Three Way Control Chart.
And when I click that button,
it moves the variable from the Subgroup role
into the Label role. So you see we now have
an Individual and Moving Range Chart of Diameter.
Now, the next option is a Row Legend.
Row Legend is new for Control Chart Builder.
And I have a little note here.
The Row Legend option is only going to appear
when there's only one row per subgroup.
So if you right- click like you do in a lot of other graphs in many other platforms
in JMP, you'll now see a Row Legend here, but only if you have one row per subgroup.
And the Row Legend acts like a row legend does anywhere else.
I can choose, say, for example, Operator, and it will color by Operator by default.
And now you have your points colored accordingly.
The next option— I'm going to close this— is Connect Thru Missing.
Now, Connect Thru Missing is going to involve some missing data.
So let's open up Coding, which happens to have the Weight
that you might normally be measuring,
but it also has Weight 2 that has missing data.
If I go through the interface and create two control charts,
you notice we have a good- looking control chart here.
Everything is connected and so forth.
But if we scroll down to the second one, we see some gaps.
And sometimes management doesn't want to see the gaps,
so we need to connect those.
So there's a new option under the red triangle menu
called Connect Thru Missing.
You can see the little caption there.
It says, "This item is new as of version 17."
This was in the old Legacy platform.
And so I've been bringing more options into Control Chart Builder
that were available in the old Legacy platform.
So there's your Connect Thru missing.
Now, the next option— I'm going to switch back to my slides here for a moment—
so the next option is the Laney and P prime control charts.
This is a bigger option.
So let's think about Control C harts for a moment.
The purpose of Control Charts is to show the stability of your process.
If your process is not stable, then you cannot reliably make
the same sized part, which is going to be a problem
for all of your customers.
And so there's lots of tests involved in making sure that you are stable,
that you're reliably able to make the same part.
Now, if you're looking at attribute control charts, those are based on either
the Binomial or the Poisson distribution, and those assume a constant variance.
Now, what happens if the variance changes over time?
Maybe there's humidity or there's temperature problems
or there's wear and tear on a gear.
This is what statisticians refer to as over dispersion,
or in rare instances, under dispersion.
And one parameter distribution cannot model this.
So Laney proposes that we normalize the data
in order to account for the variant and account for varying subgroup sizes.
And David Laney wrote a paper in 2002, Improved Control Charts for Quality.
So let's take a look at the Laney charts.
Here I have some data also found in the sample data.
This is not a terribly large lot size,
but here we have a column for teaching purposes
that has a varying lot size.
So let's explore how this works.
If we were to look at, say,
a P chart of the number of defective out of this varying lot size.
I'm going to use the menus, the dialogs again,
I'm going to create a P chart to start with
and let's see how that performs.
Okay, we're going to look at our number defective,
and then we have the lot as our Subgroup identifier.
Now, I'm going to use Lot Size 2 because that's the varying lot size.
And click OK.
All right, so on first glance, yes, we expected the non- constant limits
because we have the varying subgroup sizes.
But we also notice immediately that our chart,
this process is out of control because we have these points
that are beyond the limits and we can turn on
the Test Beyond Limits and they're flagged.
And so this process would probably raise
all kinds of alarms and people would be trying to retool things.
Now, if I show the control panel when I have the statistics set
to proportion, because Laney only, in his paper,
gave formulas for the P and the NP chart, or the P and the U chart.
So currently it's only implemented for a proportion.
But when you have your statistics set
to proportion, you have four choices now instead of just two on your Sigma.
So we could switch to the Laney P prime chart
and see what that difference is going to be.
And suddenly you see your process is not nearly as problematic.
It's not out of control at all.
It looks like this process is actually stable, which is great news.
Now, is this is this really okay, you might ask,
or is this cheating?
Let's take a look at the formulas and help us figure this out.
So Laney suggested that we compute a moving range,
Sigma on the standardized values.
So these Z's, those are our standardized values.
We compute an average moving range on that.
And we have a Sigma sub z ,
which is the average moving range divided by 1.128.
And then we take that Sigma sub z
and we insert it into the exact same formula that we saw for our P limits.
And so what you can see from this is if you actually have
a constant variance, this Sigma sub z is going to approach one.
Many argue, including Laney, that it is generally safe to use this
instead of the P chart since it's going to approach one
and it's going to be the same
anytime you actually do have constant limits.
So there's the Laney P prime chart.
I wanted to show you also, there's a few dialog updates.
Let me show you some of those right here.
So I hinted a little bit at it.
You can see the Laney P prime and U prime.
Those are two new dialogs that you can see there.
The IMR chart now has a label role on the dialog.
The XBar and our Control Chart now has a Constant Subgroup Size option
in case you don't have a subgroup that you want to specify.
There's a little more work that was done on the Three Way Control Charts.
So that now, not only can you specify the constant subgroup size
if you don't have a subgroup already identified,
you can also choose your Grouping Method,
your Between and Within Sigmas for your control chart.
So there's different options that are added
on the Three W ay Control Chart dialog.
And I want to thank you very much for your time.
If you have any questions, please feel free to ask.
Thank you.
