JMP - General

While the chief architect of JMP, John Sall, remains heavily involved in the day-to-day development of our product here in Cary, North Carolina, he also travels the world to show what JMP can do for organizations of all kinds.

Sall flew to China and Japan recently with Steve Adams, JMP's Director of International Sales. In Beijing, Sall spent time with influential professors from Beijing University of Technology, Peking University, the Chinese Academy of Sciences and the Capital University of Economics and Business. He gave a JMP demo at the China Association for Quality and took part in a pharmaceutical conference hosted at the SAS Beijing offices. In Shanghai, Sall was a keynote speaker at the International Forum of Quantitative Decision-making and Continuous Process Improvement at Shanghai University.

In Japan, Sall presented a demo at a users group meeting with about 70 JMP users. Attendees also had a chance to chat individually with Sall and Adams at a reception following the meeting. During his visit, Sall was interviewed by the chief editor of IT Media Enterprise, the largest online news media in Japan, which comprehensively covers IT-related topics.

Scroll through the photos below for a look at some of the highlights of the trip.


At the entrance to Shanghai University's main campus, John Sall (left) and Steve Adams (right) stand by a billboard for the International Forum of Quantitative Decision-making and Continuous Process Improvement, where Sall gave a keynote speech.


A balloon-festooned sign outside the conference center celebrates John Sall's visit to Shanghai University.


Provost and Executive Vice President of Shanghai University, Zhou Zewei (left), welcomes John Sall (right).


The conference at Shanghai University took place in a circular meeting room where rows were arranged in concentric circles and each seat had a monitor in front of it.


John Sall (front row, center) and Steve Adams (front row, left) and JMP Japan staff celebrate a successful JMP users group meeting in Japan.

I met José Ramírez, PhD, in Chicago at the JMP Discovery conference and Innovators’ Summit. An industrial statistician and longtime JMP and SAS user, he was quite the celebrity at the conference, where he gave a well-attended talk about designing experiments using JMP and SAS.

José told me about his new book, co-authored with his wife Brenda Ramírez, who is also an industrial statistician and expert user of JMP and SAS. The pair wrote the book, Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide, over two years, on weekends and evenings. They also write a blog called Stat Insights that includes excerpts from their book and discusses “statistics as a catalyst for engineering and scientific discoveries.”

Here, José and Brenda share details about the book for readers of the JMP Blog.

Arati: Why did you decide to write this book?

José & Brenda: A few years ago, the JMP team approached us with the idea to write a book for engineers and scientists. This seemed like a natural progression in our careers, since we have been collaborating with engineers and scientists for many years and we have developed and delivered countless hours of training in statistics and continuous improvement. In addition, we are big fans of JMP software and have been using it for a long time.

So writing this book seemed like the perfect opportunity for us to consolidate the significant knowledge we have gained as practicing industrial statisticians, and share it in a way that is far-reaching and useful to this community. An additional inspiration for our book comes from the National Bureau of Standards Handbook 91 Experimental Statistics by Mary Natrella. We wanted to bring the same spirit and utility of the NBS Handbook 91 to the countless engineers, scientists and data analysts whose work requires them to transform data into actionable information.

Arati: Who, specifically, will benefit from reading and using your book? And how do you hope they will use the book?

Brenda: The book is primarily written for engineers and scientists who need to use statistics and JMP to make sense of data and make sound decisions based on their analyses. This includes, for example, people working in semiconductor, automotive, chemical and aerospace industries. Other professionals in these industries who will find it valuable include quality engineers, reliability engineers, Six Sigma Black Belts and statisticians.

In addition to the working professional, those who are studying to become engineers, scientists or even statisticians, as well as those teaching them, should get a copy of our book. It is a great teaching aid.

For those who want a reference for how to solve common problems using statistics and JMP, we walk through different case studies using a seven-step problem-solving framework, with heavy emphasis on the problem setup, interpretation, and translation of the results in the context of the problem.

For those who want to learn more about the statistical techniques and concepts, we provide a practical overview of the underpinnings and provide appropriate references. Finally, for those who want to learn how to benefit from the power of JMP, we have loaded the book with many step-by-step instructions and tips and tricks.

Arati: What kinds of case studies or problems do you discuss in the book?

José: In Chapters 3 through 7, we start with a problem description, setting the stage for the uncertainties that need to be solved using the statistical techniques described in the chapter.

All of the case studies in the book are based upon common problems that engineers or scientist will come across at some point in their careers, and the chapter headings reflect the specific application.

For example, in Chapter 4, “Comparing the Measured Performance of a Material, Process, or Product to a Standard,” we use a semiconductor example involving a new three-zone vertical furnace for thin film deposition of waters to illustrate the usefulness of one-sample significance tests to qualify a new piece of equipment.

In Chapter 5, “Comparing the Measured Performance of Two Materials, Processes, or Products,” we compare the performance of two mass spectrometers in an analytical laboratory using the atomic weight of silver to determine if a bias exists and to understand their measurement error.

Although it is not officially a case study, we are thrilled to include in Chapter 7 the data from Albert Einstein’s first published paper. In his 1901 paper, a young Einstein used least squares to fit a model to investigate the nature of intermolecular forces.

Arati: It’s pretty cool that you had Professor Douglas Montgomery write the foreword to your book. How did you make that happen?

José: Ever since we were students, we have been using and following the work of Professor Montgomery, and we believe his books are excellent references for engineers and scientists. We also share a passion for industrial statistics and Doug and I have crossed paths many times over the years at various statistical conferences and events, including, more recently, at JMP conferences.

When we put all of these pieces together – statistics, engineering and JMP – Professor Montgomery seemed like the perfect person to entrust with this important part of our book. So we just had to find a way to ask him if he would be willing to write the foreword to our book. Luckily for us, that opportunity arose at the Quality & Productivity Research conference in June 2008 in Madison, WI. At that event, I was able to discuss this possibility with him, and without hesitation he said, “Yes.”

Arati: How will you use this book going forward in your professional career?

Brenda: This book is a reflection of how we collaborate with engineers and scientists to use statistics as a catalyst for new discoveries and insights. Having the book will make it easier to share our statistical engineering philosophy with others.

Arati: Where is your book sold?

José: Our book is available online from the SAS Web site or Amazon.com. Both Web sites allow the reader to view the table of contents and a sample chapter from the book.

While at Discovery 2009 and Innovators' Summit in Chicago last week, John Sall, chief architect of JMP, spread the word about JMP, SAS and the value of analytics. He met with journalists and bloggers, and some of the coverage has already been published online. Take a look:

• In a story for Computerworld, reporter Eric Lai asked Sall why JMP chose best-selling author Malcolm Gladwell to headline the Chicago conferences. "Journalists are like detectives. A lot of our customers are like that," said Sall, who is co-founder and Executive Vice President of SAS. "They don't just want to prove things you already know. That's lawyer stuff. They want to look at statistical outliers and figure out new things."
  
  
• Greg Burns, Senior Business Correspondent for the Chicago Tribune, talked with both Sall and Gladwell during the conferences. He wrote up his interview with Sall in two parts, posting it Tuesday and Wednesday this week. It is part of what Burns is calling Big Brain Week, with Sall representing left-brain thinking and Gladwell representing right-brain thinking. Burns' interview with Gladwell appears in two parts today and tomorrow.
  
  
• Data visualization guru Stephen Few, Principal of Perceptual Edge, wrote about the conferences and Gladwell's keynote speech in his influential blog, Visual Business Intelligence. Few applauded Gladwell's insight that modern problems present the challenges of too much information and too little understanding of that data, pointing to a need for good analytics. "More directly related to my work in BI, I’ve stated countless times that this industry has done a wonderful job of giving us technologies for collecting and storing enormous quantities of data, but has largely failed to provide the data sense-making tools that are needed to put data to use for decision-making," Few writes.
  

From what I understand, there's more coverage to come. Please let me know if you've written or know of a blog post or other online content about the conferences.

And if you didn't get to go to Chicago last week for Discovery and the Innovators' Summit, you can get a sense of why some attendees said it was the best conference they'd ever been to by checking out our own coverage; it includes photos, live blogs, audio, video and Twitter updates.

Did you miss last week's Q&A with John Sall, chief architect of JMP, in DecisionStats, an analytics blog by Ajay Ohri? It's worth a read.

"In a free-wheeling and exclusive interview, John talks of the long journey within SAS and his experiences in helping make JMP the data visualizaton software of choice," Ajay says in his introduction to the post.

Ajay's questions cover such topics as Sall's educational background, his "Eureka!" moments while developing SAS with CEO Jim Goodnight and why Sall created JMP software. He even asks Sall what he does in his free time when he isn't creating "world-class companies or groovy statistical discovery software." (As a former technology writer for a daily newspaper, I give kudos to Ajay for working the word "groovy" into his interview!)

Other SAS leaders featured in DecisionStats interviews include Jim Davis, chief marketing officer, and Anne Milley, marketing director, with whom Ajay did a two-part interview.

JMP user Archana Pawse, PhD, is a Six Sigma Black Belt who has worked for Applied Magnetics, Superconductor Technologies and JDS Uniphase as a Product/Process/Reliability Engineer.

What follows is Dr. Pawse's description of how she uses JMP in her line of work, which she wanted to share with others. Feel free to leave a question or comment for her to respond to or contact her directly by e-mail.

How I Use JMP for Root Cause Analysis

In a manufacturing environment, it is very critical to find the root cause of failures quickly. Delay in identifying the root cause can result in wasted money and resources. JMP is useful for root cause investigation because you can easily explore and graph the data in multiple ways. I have found three JMP features/techniques -- selecting cells, partition plots and variability charts -- to be useful in this investigation.

To demonstrate these techniques, I have generated some hypothetical data. In this example, Y1, Y2 and Y3 are performance parameters for a product that is made using three processes P1, P2 and P3.

P1-Machine, P1-X1 and P1-X2 are the parameters for process P1. P2-Machine, P2-X1, P2-X2 are the parameters for process P2. P3-Machine, P3-X1 are the parameters for process P3. Parameter Y3 has 15 failures (value <= 0.7).

1. Selecting cells

In JMP, once you have graphed all relevant parameters, you can select failed data points on a graph or cells in the table, and they are highlighted on all the graphs in the session. This option is not available in some other software. By highlighting the failures, you can immediately see if there is any correlation between various parameters or if outliers on one graph are also outliers on other graphs.

In this example, I have graphed distributions of parameters Y1, Y2 and Y3, and I have selected all the failures for the parameter Y3. These failures are also highlighted on distributions of Y1 and Y2, which show that the failures are also outliers for parameter Y1. This kind of information can give more insight into the cause of failures.



2. Partition plots

If your final product performance depends on various process parameters and its interactions, then it is very time-consuming to review all process parameters to find the cause of a product failure. Partition analysis can narrow this list down to a few parameters, so you can investigate these few parameters in detail. In some cases, the partition analysis can even identify the root cause.

In this example, the Y3 parameter is the response variable, and all the process parameters are entered in the X factor field. The best split option in partition analysis shows that the product fails (has a low Y3 value) when it is processed in machine B at P2 process and machine 3 at the P1 process.






3. Variability charts

Variability charts are useful for visualizing failures. This graph is very effective at displaying interactions between various parameters because you can plot Y versus multiple X parameters. In addition, by using multiple colors, symbols, symbol sizes, you can show at least four parameters on this graph.

In this example, it’s easy to see from the graph that all failed product (Y3<=0.7) is made using machine B at step P1 and machine 3 at step P2. In addition, by color-coding the data for the different P3 machines, you can see that these failures are not related to the machine used at step P3. Different symbols are used for the passed and failed units for easier identification.



The techniques/features described here are not limited only to root cause investigation. They can also be used for other manufacturing applications like process optimizations as well as in marketing applications to identify a target market or cross-sell opportunities.