Today we're going to cover the JMP Analytic Workflow,

so that you can see firsthand

how all these capabilities come together.

Anybody who's doing data analysis has a shared objective.

They're trying to take raw information, data,

and we're trying to turn that into a shareable insight

or an actionable insight.

The only thing that's different is the steps that we'll take

as we move from one end of this process to the other.

Whether you're new to the field of analytics and statistics

and have more simpler needs,

or whether you're a more advanced practitioner

with more sophisticated needs,

JMP Software offers the flexibility to meet those needs

wherever you are in your analytics journey.

The JMP Analytic Workflow

is a quick and easy set of analytical capabilities

to bring you from data to insights.

What we're going to cover is a few workflows

so that you can see how this can be implemented in practice.

I want everybody to just picture right now that we're responsible for this machine,

and this machine produces product for a business.

Recently,

the performance of our product has been outside of our expectations,

and we believe the answer for what is going on

can be determined by analyzing some of the data

that's available on this machine.

So what we're going to do is

we're going to build an analytic workflow to see if we can figure out

what's happening with this issue.

Building the workflow involves these three steps.

The first step is having an understanding of the data.

The data that we'll be using in this case

are machine logs that are saved on the machine as Excel files.

The second step relates to the analytical capability.

In order for us to know which analytical capability we need to use in the workflow,

we have to understand the question that we're trying to answer,

and the question that we're trying to answer here is a simple one.

What is happening with this machine?

The third element is a shareable insight.

So after we get an answer to this question,

we are going to have to share that insight with others.

In this case, we're going to be sharing our insights with management,

and we're going to be sharing those findings as a Word document.

So more specifically, when we take a look at the workflow,

we're going to be working with Excel files.

We're going to leverage JMP's data access platform

to bring the data into JMP.

Once the data is in JMP,

we're going to perform some data exploration and visualization,

and then lastly,

we'll share and communicate those results as a business document,

which in this case will be a Word document.

So to begin the process, we open our Excel file.

When you open Excel files in JMP,

it opens a special tool called the Excel Import Wizard.

And the Excel Import Wizard allows us to do many things.

We can access different worksheets in the Excel file,

and we can perform some very simple data cleaning steps

before we import the data.

We can also preview the data.

So as I look at the preview,

I can see that I have information from June of last year,

and I can see that I'm correctly capturing

the measurements from our machine.

I can import this data now into JMP, where we have a JMP data table.

Now that we have our data, we can perform a visual exploration.

I will use the Graph Builder tool, which is available under the Graph menu.

And I'll plot our measurements over time for our piece of equipment.

And as I plot our measurements over time,

I can begin to see something that's quite surprising.

The performance of our machine was meeting expectations initially,

but over time you can see that the performance has slowly drifted,

and now we're into a region where we're performing and producing bad material.

This is the first time that we've now

used data and analytics to understand what's happening in our process.

And what we're seeing here is that the machine has actually

been performing for quite some time without a calibration.

So if we can calibrate the machine,

we can get it back to the original performance

for what we need in order to produce stable material.

So this is quite a significant finding.

And now we want to share this finding with our management

so that we can take that additional action,

which is to perform the calibration.

When it comes time to share this insight,

we can just simply export this,

and in this case, we're going to export it as a Word document.

So we're going to capture that Word document,

and we're going to share that now with our management.

So here we have the Word document.

We've captured that visual,

where they can see exactly what we saw in JMP,

and they can see that the performance of the machine

has been drifting over time,

and that a calibration needs to be performed.

This also represents the first time that management is starting to use analytics,

and they're now starting to see the value of data in their organization

and how that can help them improve their business decision making.

And they have a new ask for us.

They want to know what else can be done with their data,

and what else can be done in terms of analytics

to improve their manufacturing processes.

So now the analytics journey has evolved,

and JMP is very much a part of that journey.

It's not just a tool that offers you the ability

to access individual analytical capabilities,

but it's also very much a part of the process

so that you know how and when to implement certain strategies.

So you spend some time reviewing a variety of JMP resources,

and you review white papers to learn about best practices.

You also read through customer success stories to see how others in your industry

are leveraging analytics and how that's improving their business.

You also participate in a complimentary statistics course

where you learn about many things.

You learn about predictive modeling,

and how that can help you root cause production issues.

You learn about reliability analysis and how that can help you understand

how your product is going to perform in the field over time.

But one of the most informative things

that you learn about is the field of quality analytics.

And by taking that learning now,

you apply that to exactly what you're responsible for in your process.

So with this new learning now you have a more advanced analytic workflow.

So here in our more advanced workflow, we have some new iterations.

Now, data is no longer being stored and accessed as Excel files.

All the data is centralized into a database

so that the integrity of the data is never affected,

but also so that everybody can access the data

without having to work with these individual files.

In terms of analytical capabilities, you now have a better understanding of

what statistics can do and what analytics can do.

So your questions are more refined and specific.

The question that you want to answer now is,

is the machine experiencing special cause variation?

Because you've learned about the difference between

common cause variation and special cause variation,

and you know that it's a special cause variation

that ends up being problematic to your processes.

And the last thing is the shareable insights.

Before, when you were sharing your reports as Word documents,

what it was doing was it was creating a lot of additional work for yourself.

As people were consuming these reports,

you're now getting inundated with requests to make modifications to graphs.

You're also getting inundated with requests

to the location of the most recent outputs.

And then so what you want now is a better tool,

one that allows you to centrally store all those reports in one location,

but offers the people who are consuming those reports

additional capabilities,

so that they can perform their own exploration

without having to come back to you for additional requests.

So the analytic workflow that we're preparing now involves these steps.

Our data now begins by being accessed from a database.

We leverage JMP's database utility to get access to the data imported into JMP.

We continue our data exploration and visualization,

but we also incorporate some quality and process engineering elements

that we've recently learned about from JMP resources.

And when we share analyses,

we want to both manage the content

but also share these analyses with the wider audience

in a way that's going to offer them greater capabilities,

which they weren't able to do with their Word documents.

And then so we'll be using the JMP Live platform to do this.

So we begin the process by accessing our data.

We use JMP's built- in query builder tool to access our data connection.

Once we're connected to the database, we can access any of the data tables.

Here, we've selected the data table

that contains the data that we're interested in.

And now, unlike before,

we're able to pull data from everywhere in the factory,

not just on an individual piece of equipment.

We import the data into JMP,

and we can now perform our new analysis,

which leverages some new tools that we've learned about

under the Quality and Process module in the JMP.

In order to get an answer to the question that we have,

it will require us building a control chart.

The control chart allows us to set up a visual

that looks very similar to what we created before,

but the control chart allows us to access some additional capabilities

that we weren't able to do in just a normal graphical visualization.

Built into the control chart are some rules that we can leverage

to determine if we're experiencing some special cause variation.

And that's the question that we're trying to get answered.

So we will enable some warnings,

which are some special customized tests,

to signal to us if we are experiencing special cause variation.

Now that we've turned on that test,

we can see that there are many batches where

we're facing special cause variation.

And had we been monitoring our equipment using this tool,

very early on in the process

we could have detected that there was an issue

and we could have taken the appropriate action.

So this is quite a significant finding,

and this is something that we want to share with a wider audience.

So now when we share this report, we share it with the JMP Live tool.

So we're going to publish this to JMP Live.

So I'm going to connect with my account to JMP Live,

and I'm going to create a new post.

And I'm going to share this post with everybody on my equipment team

who 's interested in these results

and needs to know these new insights that we've just discovered.

I'm going to publish this report to our JMP Live.

And now we can take a look at that report in JMP Live.

So JMP Live is a web- based tool

that allows anybody to access the report from their browser.

So here now we can see that report in JMP Live.

JMP Live also allows for all the reports to be centralized.

So we no longer have to pass and share around

static documents and Word docs ,

where sometimes people can be consuming old results

and not be up to date with the latest findings.

Now because everything is centralized in JMP Live,

there's one version of the truth,

and you'll always have access to the most recent files.

You can also do things tha t you would not be able to do

in static versions of the analyses.

So JMP Live is still very interactive,

and anybody consuming the report can perform their own exploration

and get answers to their own questions without having to come back to you,

the analysis, the person who prepared the report,

for additional modifications.

And as management is consuming this result and they're getting additional value,

a very often thing is occurring,

and their needs are now changing.

And instead of seeing this information once a week in a weekly report,

they want to see this information more rapidly.

They want to see this information daily or even hourly,

and they don't want a chart for just one piece of equipment.

They want a chart like this for every piece of equipment in the factory,

because they recognize how powerful this analysis is,

and they ask us,

"Is there a way that we can do this?"

And JMP tool offers that flexibility to do this,

because a critical part of the JMP Analytic Workflow

is the ability to automate.

As we were building these analyses, in the background,

JMP was actually capturing the JMP scripting language

to automate all of these steps.

So by simply saving the script,

we can stitch together all of these actions.

We can stitch together the action to connect to the database

and import the data.

We can stitch together the action to generate the chart,

and we can stitch together the analysis to upload the analysis to JMP Live.

And in the click of a button,

we can have those analyses automatically created by JMP.

And in our case,

we want these analyses produced every hour

so we can leverage the Windows Task Scheduler

to automatically run the script on our behalf,

so that we don't even have to do it manually.

So very quickly, you've seen a variety of different examples of how

the JMP Analytic Workflow

can be leveraged to solve a variety of different problems,

depending on where you are in your analytic journey.

We can put together the workflow to save both time and effort.

We can easily access data from a variety of different sources

and share your discoveries with other team members.

We can get more from your investment.

We can increase your efficiency without increasing the head count,

and also eliminate the need for multiple tools.

We can remove barriers in complexity.

We can tackle problems of any size, like we've seen today,

by using JMP's extensive suite of analytical platforms

And we can accelerate process improvement by leveraging the automation

to reduce time spent on repetitive tasks

and get to those actionable insights faster.

As we've seen firsthand today,

your analytical needs might start off as being very simple,

but when you're ready to grow, we'll be ready for you.