Agenda

On-Demand Sessions

Papers and Posters

Join in the discussion!

Twenty-seven paper presentations will be streamed live during Discovery Summit. Check the dates and times below. Presenters will be available for questions while these recordings are streaming.

Poster presenters will be on-hand live on Tuesday, Oct. 5 at 5:00 p.m. to answer questions.

View recorded papers and posters by topic in the JMP Community beginning Sept. 27:

Live Sessions

Monday, Oct. 4

1:00 - 4:00 p.m. ET

Attendee-driven "Unsessions"

According to Wikipedia, an unconference is a participant-driven meeting designed to encourage attendee involvement in topic selection and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about JSL scripting or predictive modeling.

JMP Scripting Unsession

Facilitator: Stan Koprowski, Customer Care Systems Engineer, JMP
Predictive Modeling Unsession

Facilitator: Ross Metusalem, Systems Engineer, JMP

5:00 - 6:00 p.m. ET

Social Time
Welcome to Discovery

This interactive, informal session is designed for first-time attendees and Discovery veterans alike. You’ll get a virtual walk-through of the conference center and learn some tips and tricks for making the most out of your Discovery days. Come with questions and plan to have a bit of fun too.

Tuesday, Oct. 5

10:00 - 10:45 a.m. ET

Getting Started with JMP

Interactive session for data explorers who are new to JMP software and experienced users who want a quick refresher.

11:00 - 11:45 a.m. ET

JMP Cares Activities

Optional sessions designed to build up your professional image, your physical endurance, your well-being -- and your connections within the JMP community.

12:00 - 1:15 p.m. ET

Plenary Session

Delicate Brute Force

John Sall, Co-Founder and Executive Vice President, SAS

Sometimes the way to solve a challenging problem is to just take advantage of the speed of computers and apply a brute-force approach. In one situation, the problem was to find a design space (a set of factor limits that leads to satisfying specification limits on the responses), specifically finding the space that fits within the highly nonlinear constraints implied by the model from limits on the responses. Another problem was to perform hierarchical clustering on the kinds of huge data sets encountered in genomics research, something that used to perform poorly. In each case, rather than solving the problem directly, there are short cuts that combine brute-force computation with delicate application.

1:15 - 4:45 p.m. ET

Discovery Expo

Who says online in real time can’t be as fun as in-person events? That’s a silly question. We know it’s not as fun. But we’re making the most of the online platform to create a very special Discovery Summit experience for you. Come to the Discovery Expo for Meet-the-Experts opportunities, Birds-of-a Feather sessions, opportunities to vote for your favorite papers and posters, talk to Tech Support, browse books, and more.

1:30 - 4:45 p.m. ET

Simu-Live Papers

What’s the next best thing to watching a Discovery paper together in person? It’s watching it streamed live online, where you can comment in real time, ask the presenters questions, and applaud the parts that make you go “wow.”

1:30 - 2:15 PM ET

Session ID: 2021-US-30MP-910

As Easy as Uploading a Photo: Building, Sharing, and Updating Add-ins Automatically

Michael Akerman, Senior Data Scientist, Novozymes

Johan Pelck Olsen, Data Scientist, Novozymes

Topic: Data Access and Manipulation
Level: III

The difficulty introduced by a democratizing software like JMP is handling the vast number of users creating and sharing tools and scripts across a large, global corporation. At Novozymes, we've built an automatic toolset called Y'all to help address that need. Y'all is a Python build script, automated workflow, and JMP add-in package that makes building, sharing, and updating JMP add-ins as easy as uploading a photo to Facebook.

Utilizing the continuous integration/continuous deployment capabilities of GitLab (an open-source competitor to GitHub), Y'all allows users to upload raw JSL scripts saved from any platform in JMP through a web user interface, immediately launching an automated Python build script that structures and packages a JMP add-in on the GitLab server. The next time JMP loads, our tool automatically checks for updates (based on work shared by Jeff Perkinson in the JMP community), sending new or updated scripts to users within seconds.

In addition to these capabilities, Y'all allows users to specify the menu name and tooltip of their launchable script, automatically loads a variety of convenient utility functions for JSL coding, automatically adds imported functions to the Scripting Index, and leverages GitLab to allow parallel development of scripts while retaining a full version history of each JSL script and the add-in package, ensuring that valuable work is archived and protected in a centralized system.
Session ID: 2021-US-45MP-821

Beyond A/B: Case Study of Multivariate Test Design and Advanced Analytics for Webpage Optimization

Steven Crist, Analytics Consultant, Wells Fargo

Topic: Data Exploration
Level: II

It is well known that optimization of the layout and content of webpages can be achieved through thoughtful pre-test design of experiment (DOE), post-test analysis, and identification and productionization of a winning variant webpage. The present case study demonstrates the use of the JMP custom DOE platform to create a fractional factorial multivariate DOE for a financial services checking account webpage. It effectively managed business constraints while providing the necessary data that led to a 7% increase in application volume as compared to the legacy webpage. Additionally, leveraging the JMP partitioning model platform, an additional key insight was discovered that mobile visitors who clicked on the "compare accounts" link were two times more likely to submit an application. This compare accounts insight was not the main inquiry of the original test but provided guidance for future testing to further optimize the webpage. The presented case study demonstrates the effectiveness of the testing continuum of a test leading to actionable insights resulting in the next optimization test and so on.
Session ID: 2021-US-45MP-808

Pictures from the Gallery 6: Select Advanced Graph Builder Views

Scott Wise, Principal Analytical Training Consultant, SAS

Topic: Data Visualization
Level: 1

If a picture is said to be worth a thousand words, then the visuals that can be created in JMP Graph Builder should be considered fine works of art in their ability to convey compelling information to the viewer. This journal presentation features how to build popular and captivating advanced graph views using JMP Graph Builder. Based on the popular Pictures from the Gallery journals, this sixth installment highlights new views available in the latest versions of JMP. It features several popular industry graph formats that you may not have known could be built easily within JMP. Views such as graphlets, run ordered spirals, map cartograms, time series and more are included so that you can breathe life into your graphs and provide a compelling platform to manage your results.

2:45 - 3:30 PM ET

Session ID: 2021-US-30MP-885

Defect Subpopulation Models

David Trindade, Founder and Chief Consultant, STAT-TECH

Topic: Quality and Reliability
Level: II

Suppose a small proportion of a population of components is highly susceptible to a certain failure mechanism. Consider these units to be manufacturing defects in a reliability sense: working originally and not detectable as damaged by standard end-of-the-line tests and inspections, yet after a short period of use, they fail. The rest of the components in the population are either not susceptible to this failure mechanism, or else they fail much later in time for possibly some other cause.
Occasionally, field or life test data indicates this bimodal behavior by showing an early incidence of failures that seem to slow down to almost nothing long before a significant proportion of the population has failed. A good model for this kind of behavior is the defective subpopulation model (DS).
The population of components is really a mixture of two populations: a reliability defective small subpopulation that fails early and the remaining components that may possibly fail later due to wear-out mechanisms that only are seen much further out in time (designated as “immortals”). This model, where a (hopefully small!) proportion of the population follows an early life failure model, has wide applicability.
This talk first describes how such data can be analyzed the Life Distribution platform in JMP through the DS options for fitting the data and then demonstrates how to apply a simple test of the hypothesis that there may be a defective subpopulation based on the output of the Life Distribution platform.
Session ID: 2021-US-30MP-803

One Multi-tabbed, Filterable Dashboard

Kevin Doran, Senior Staff Engineer, Intel Corporation

Topic: Data Exploration
Level: II

Data collection teams within companies amass amazingly large amounts of information. And while these data are very valuable, it is not often tranlated into a format that is usuable by the recipient. Further compounding the problem is that different business units consume the information in different ways, which, unfortunately, often meants that valuable information is never consumed or utilized.

How does one best translate the needed information into an easy-to-use format? Furthermore, can statistically significant information be communicated through one interface? This paper describles the creation of one multi-tabbed, filterable dashboard to communicate computer laptop systems data to both architecture and engineering organizations.
Session ID: 2021-US-45MP-870

Steal This Code! Three Upgrades for Scripts Obtained from the Enhanced Log

Jordan Hiller, JMP Senior Systems Engineer, SAS

Topic: Data Access and Manipulation
Level: II

JMP 16 made us all coders. With Action Recording in the Enhanced Log, point-and-click data work can now be saved as a replayable script. Learn some intermediate JSL techniques to future-proof your captured code, making it more general, more robust, and easier to use. After a brief review of basic JSL syntax, we discuss case studies illustrating three techniques:

Using loops to perform actions on many columns at once.
Extracting numeric values from a JMP report, for reuse later.
Allowing users to specify data files or columns at run time.

4:00 - 4:45 PM ET

Session ID: 2021-US-30MP-916

A New Resolve to Dissolve: Model Drug Dissolution with Functional DOE

Amanjot Kaur, Statistician, Perrigo

Rob Lievense, JMP Senior Systems Engineer, SAS

Topic: Design of Experiments
Level: II

Formulation scientists put forth significant hours of work attempting to find an extended release formulation that matches drug release targets. For generic (ANDA) products, the primary objective is to match critical quality attributes (including dissolution over time) to the reference listed drug (RLD). This paper illustrates how functional DOE is an extremely robust and easy-to-use technique for optimizing to a target dissolution profile rapidly with fewer resources.

Dissolution data is collected over time during development and in-depth analyses are required to understand the effect of the formulation/process variables on the product performance. Regression models for specific time points (e.g., 1, 2, 6, and 12 hours) have been typically used to correlate the release responses with the formulation/process variables; however, such analyses violate the assumption of independence for the responses. There is great need for robust statistical tools used to determine the levels of inputs needed to get the closest profile of the developmental product to the reference drug. Functional DOE in JMP for model dissolution is a new and critical tool to use within a drug development program.
Session ID: 2021-US-45MP-838

Controlling Extrapolation in the Prediction Profiler in JMP Pro 16

Laura Lancaster, JMP Principal Research Statistician Developer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Christopher Gotwalt, JMP Director of Statistical R&D, SAS

Topic: Data Visualization
Level: II

Uncontrolled model extrapolation leads to two serious kinds of errors: (1) the model may be completely invalid far from the data, and (2) the combinations of variable values may not be physically realizable. Using the Profiler to optimize models that are fit to observational data can lead to extrapolated solutions that are of no practical use without any warning. JMP Pro 16 introduces extrapolation control into many predictive modeling platforms and the Profiler platform itself. This new feature in the Prediction Profiler alerts the user to possible extrapolation or completely avoids drawing extrapolated points where the model may not be valid. Additionally, the user can perform optimization over a constrained region that avoids extrapolation. In this presentation, we discuss the motivation and usefulness of extrapolation control, demonstrate how it can be easily used in JMP, and describe details of our methods.
Session ID: 2021-US-45MP-855

Toward Predicting the Fate of Reef Corals: 2021 Update

Anderson Mayfield, Assistant Scientist, University of Miami

Topic: Data Exploration
Level: II

Coral reefs around the globe are threatened by a plethora of anthropogenic stressors, most notably climate change. There has consequently been an urgent push to better understand the fundamental biology of key coral reef inhabitants, understand how these organisms will respond to future changes in their environments, and make data-driven predictions as to the relative resilience of disparate coral populations. In this talk, l showcase a complex, "molecules-to-ocean basins" data set featuring molecular (e.g., proteins), physiological (e.g., growth), environmental (e.g., coral abundance), and oceanographic (e.g., temperature) data to showcase how I have been using JMP Pro 15 and (more recently) JMP Pro 16 to model coral behavior and make predictions as to which colonies/reefs may be at most risk. I also examine which reefs are expected to demonstrate resilience to global climate change and disease. Through this update to my Discovery Summit Americas 2020 presentation, I hope to, more generally, show progress in forecasting coral health and resilience using the ever-growing data sets and analytical power now at our disposal.

5:00 - 6:00 p.m. ET

Poster Session

Discovery Summit posters have always been a little less formal than the papers, a little more interactive and certainly more social. In the spirit of making learning fun, we’ve built a social event around this year’s papers. We hope you’ll join us!

Wednesday, Oct. 6

10:00 - 10:45 a.m. ET

Getting Started with JMP

Interactive session for data explorers who are new to JMP software and experienced users who want a quick refresher.

11:00 - 11:45 a.m. ET

JMP Cares Activities

Optional sessions designed to build up your professional image, your physical endurance, your well-being -- and your connections within the JMP community.

12:00 - 1:00 p.m. ET

Plenary Session

Data Explorer Spotlight

We’ve been known to say that great software in the right hands can change the world. That’s because we’ve seen scientists, engineers and other data explorers make amazing discoveries with JMP. And we’ve seen how those discoveries can lead to far-reaching, earth altering, and lifesaving advances. For this segment, a few of those data explorers will share their statistical success stories with us.

1:15 - 4:45 p.m. ET

Discovery Expo

1:30 - 4:45 p.m. ET

Simu-Live Papers

1:30 - 2:15 PM ET

Session ID: 2021-US-45MP-804

A MaxDiff Bake-Off Study: Finding the Most Preferred Among Dozens of Products

Bradley Jones, JMP Distinguished Research Fellow, SAS

Rob Reul, Managing Director, Isometric Solutions

Topic: Data Visualization
Level: II

A MaxDiff study is a way of ordering consumers’ preferences for a group of similar currently marketed products. As the number of products to be compared increases, the cognitive burden on respondents can rise to the point that few will complete the survey, which could lead to potential bias. In our case study, we were tasked with ordering preferences for three dozen (36) products. We addressed the problem by first dividing the 36 products into four mutually exclusive groups of nine products and did separate MaxDiff studies on each. The “bake-off” consisted of a new MaxDiff study pitting the most preferred products from each of the four groups.

We present the analytical results of the study using JMP. We also show the economic savings resulting from streamlining the original offering of 36 products to a substantially smaller group of preferred products.
Session ID: 2021-US-30MP-897

Evaluating Meditation Program Using the SEM Platform in JMP

Anantharam Narayanan, Principal Scientist, Procter & Gamble

Annie Weisbrod, Principal Sustainability Scientist, Procter & Gamble

Mark Bailey, Human Resources Expert, Procter & Gamble

Topic: Data Exploration
Level: II

During the Covid-19 crisis, P&G's efforts were guided by three priorities: the safety of P&G people, serving consumers and customers, and supporting our communities. As part of our efforts to ensure ongoing wellness for our employees, we offered live online classes to teach meditation and stress resiliency; this program was developed internally and provided globally. In this paper, we evaluate the impact this new meditation program by using four major dimensions: resiliency, empathy, stress and mindfulness, as well as other subdimensions. Changes in participants’ attitudes and behaviors were surveyed over eight weeks using 45 medical-based survey questions. Using the SEM platform in JMP 16, we measured the scale properties and change over time of these constructs in evaluating the success of our meditation/mindfulness program, specifically in terms of the wellbeing and productivity of our employees. To date, our results are encouraging.
Session ID: 2021-US-45MP-927

Optimizing Multivariate Models: From Data Collection to Optimal Conditions and Scoring Models

Isaac Himanga, Senior Analytics Engineer, ADM

Topic: Data Access and Manipulation
Level: II

Manufacturing processes are highly correlated systems that are difficult to optimize. This correlation provides an opportunity to identify outliers. Here, we demonstrate an entire workflow, which involves:

Obtaining data from the OSISoft historian.
Modeling and optimizing with multivariate models that account for process constraints.
Using the MDMCC platform.
Scoring models online and writing abnormal conditions back to the historian.

In practice, many processes are hard to optimize. Sometimes a system cannot be reduced to a set of independent variables that can be tested; in other cases, the process can become unstable under different conditions. To address these issues, we are using new features in JMP 16 to optimize for cost and quality in the latent variable space, while accounting for constraints on observed variables. In order to remain at those optimal conditions, we use the MDMCC platform to quickly identify deviations from normal.
We developed two custom add-ins to minimize the time spent gathering data and scoring models: one to pull process data into JMP from the popular PI historian, and a second to score models online and send results to the historian, including contributions from the MDMCC.

2:45 - 3:30 PM ET

Session ID: 2021-US-45MP-884

In My Defense, I Was Left Unsupervised: Hacking an Aquarium with JMP and the Stuff in my Junk Drawer

Mike Anderson, JMP Senior Systems Engineer, SAS

Topic: Data Access and Manipulation
Level: II

What do a handful of Legos, a webcam, two razor blades, a DVD-R, and some double-stick tape have in common? Well, if you happen to have an interest in aquaculture and a background in chemistry, they have the makings of a decent spectrometer. If you throw JMP into the mix, then you have the makings of a system that can be used to detect the presence of harmful waste byproducts in a fish tank with startling accuracy. Join Mike Anderson on his adventure in setting up and “cycling” a new 36-gallon fish tank using a homemade spectrometer and JMP Pro’s Functional Data Explorer to monitor the process.
Session ID: 2021-US-30MP-892

Reducing the Fit Variation in Denim Jeans Manufacturing

Lori Rothenberg, Associate Professor, NC State University

Anuja Khairnar, Quality Control Systems Manager, Surry Chemicals Inc

Helmut Hergeth, Associate Professor, NC State University

Topic: Data Exploration
Level: II

VF Jeanswear experienced a decline in sales due to the fit inconsistency in its jeans. Specifically, customers complained of inconsistent waist sizes and irregular inseam length in the same size jeans. The objectives of the research were to identify the sources of variation in the manufacturing process that led to poor fit, design a solution to reduce that variation and implement that solution. The tools used in JMP were Gage R&R and DOE. Several experiments were performed throughout the manufacturing process. Shrinkage variation was the outcome of interest. The factors were fabric type (cotton, bi-blend tri-blend), wash type (rinse, stone wash, hot bleach, cold bleach), washer type (high-speed extraction washer, no extraction washer), dryer type (dryer with humidity sensor mechanism, dryer with algorithm mechanism), type of laundry (product development, industrial), and mill (several mills). The results of the study indicated that by increasing the bi-blends and tri-blends the variation in fit was reduced by 23% in men’s and 12.5% in women’s jeans. To improve the fit further, VF needs to work with the mills closely to help everyone understand the variability in raw materials beforehand.
Session ID: 2021-US-30MP-844

Utilizing Extrapolation Control Feature in JMP® Pro 16 to Optimize Real-World Mining Process

Xinjie Tong, Senior Research Statistician, Dow

Topic: Design of Experiments
Level: I

The case study aims to assess 12 types of frothers in terms of their performance in mineral recovery in mining. In addition to frother type, the input variables also include particle size (coded value: 0, 0.35, 1) as a continuous variable and water type (Type A, Type B, Type C) as a categorical variable. The response variable is mineral recovery efficiency.

Using extrapolation control feature in the prediction profiler, a possible extrapolation was observed for Water Type B when particle size = 1 based on regularized T square metric. This extrapolation was caused by an imbalance in the DOE where the measurement for Water Type B was only taken with particle size = 0.35. The extrapolation control was turned on in the effort of optimizing mineral recovery efficiency to achieve reliable optimal solutions. As an alternative, extrapolation control was also enforced in optimization by incorporating the equation of leverages in the desirability functions. The two approaches were compared and discussed.

The extrapolation issue disappeared when the DOE was augmented to be more balanced. The extrapolation control feature provided immediate understanding on how augmenting an imbalanced DOE could largely enhance the usability of the predictive model.

4:00 - 4:45 PM ET

Session ID: 2021-US-30MP-913

Analyzing Functional Data with Direct Functional PCA in Functional Data Explorer

Ryan Parker, JMP Senior Research Statistician Developer, SAS

Topic: Design of Experiments
Level: III

In JMP Pro 16, the Direct Functional PCA (DFPCA) modeling option has been added to the Functional Data Explorer (FDE) to provide a way to perform functional PCA without first fitting basis function models. This approach not only makes larger functional data more tractable, but it also provides a more hands-off approach to analyzing functional data. This presentation details how DFPCA works and presents examples that highlight how and when to use DFPCA to analyze functional data.
Session ID: 2021-US-30MP-887

Biopharmaceuticals' New Frontier: Modeling Patient/Donor Variability for Gene/Cell Therapy in JMP

Andrew Karl, Senior Management Consultant, Adsurgo LLC

Heath Rushing, Principal Consultant, Adsurgo LLC

Topic: Design of Experiments
Level: II

The most novel, innovative and promising therapeutics in biopharmaceuticals are gene and cell therapies. Gene therapies modify a patient’s existing genes, while cell therapies transfer human cells into a patient to treat disease. These cells either come from the patient themselves or a healthy (cell) donor. The first such therapy approved in the United States was Kymriah in 2017. Since that time, there have been countless companies exploring these novel approaches to treating patients. One of the main barriers to regulatory approval is the development of a process that consistently meets the needs of patients (in both efficacy and safety). One obvious challenge is modeling either patient-to-patient or donor-to-donor variability that can (and usually does) account for a large portion of the variability.
This talk focuses on different methods of modeling donor-to-donor variability in JMP to answer important research questions: determining significant process parameters (independent of patients/donors), constructing proven acceptable and normal operating ranges (PAR and NOR), and estimating process capability. After demonstrating the implications of modeling donor as a fixed block effect, the authors then present the advantages to modeling donor as a random effect, first by presenting modeling and simulation features in JMP and then additional features in JMP Pro.
Session ID: 2021-US-45MP-854

Solving an Industrial Problem Using Advanced Statistical Tools

Matheus Sguissardi, Lean Six Sigma Consultant, Plana Consultoria e Treinamentos

Topic: Quality and Reliability
Level: I

The worst problem that a company can have is the customer dissatisfaction. The problem in question happened on the customer's assembly line, when disassembling and assembling the cover of an electric motor connection box. The disassembly and assembly concept was specified for at least 10 cycles, but instead the customer was having problems in the first cycle, when connecting his cables to the purchased electric motor.

To solve the problem, some statistical tools and methods of the Six Sigma methodology were used, in a sequential and structured way, validating and invalidating the main hypotheses of the problem and making all decisions in the process and product in an appropriate way and all based on facts and data analysis.

5:00 - 6:00 p.m. ET

Social Time
Quiz Bowl

What is the best way to relax after a hard day of exploring data and thinking about complicated statistical techniques? It’s to answer questions under pressure, of course! JMP users tend to love solving problems and putting themselves to the test. There’s nothing trivial about this after-hours quiz. Join us!

Thursday, Oct. 7

10:00 - 10:45 a.m. ET

Getting Started with JMP

Interactive session for data explorers who are new to JMP software and experienced users who want a quick refresher.

11:00 - 11:45 a.m. ET

JMP Cares Activities

Optional sessions designed to build up your professional image, your physical endurance, your well-being -- and your connections within the JMP community.

12:00 - 1:00 p.m. ET

Plenary Session
Facets of a Diverse Career

Alyson Wilson, Associate Vice Chancellor for National Security and Special Research Initiatives, NC State University

My career is a testament to John Tukey's statement: “The best thing about being a statistician is that you get to play in everyone’s backyard.” I have been fortunate to work in academia, industry, and government; for a small business and a large bureaucracy; on collaborative projects with partners from many disciplines; on topics ranging from reliability to counterproliferation to atomic-scale characterization of materials. I will highlight some of the inflection points in my career and share lessons I have learned about the value of diverse practitioners with diverse perspectives collaborating on solutions.

1:15 - 4:45 p.m. ET

Discovery Expo

1:30 - 4:45 p.m. ET

Simu-Live Papers

1:30 - 2:15 PM ET

Session ID: 2021-US-30MP-911

A JMP Script That Determines a Simultaneous 95% Bound Using a K-Nearest Neighbor Approach

Rich Newman, Statistician, Intel

Don Kent, Data Analytics and Machine Learning Manager, Intel

Topic: Data Exploration
Level: II

We have a set of responses that follow some continuous, unknown distribution with responses that are most likely not independent. We want to determine the simultaneous 95% upper or lower bound for each response. As an example, we may want the lower y1 and y2 and upper y3 and y4 bounds such that 95% of the data is simultaneously above y1 and y2 and below y3 and y4. Finding the 95% bound for each response leads to inaccurate coverage. The solution: a method to calculate the simultaneous 95% upper or lower bound for each response using nearest neighbor principles by writing a JMP script to perform the calculations.
Session ID: 2021-US-45MP-880

Analysis of a Kamyr Digester Continuous Flow Process Stream Using JMP Pro 16

Stanley Siranovich, Principal Analyst, Crucial Connection LLC

Topic: Quality and Reliability
Level: III

In this session, we analyze actual process data from a Kamyr digester at a Canadian paper mill. Our data set consists of 301 measurements of 23 variables, taken over more than 12 days. Our dependent variable, which we want to predict and control, is the Kappa Number. It is roughly proportional to the residual lignin content in the pulp and is a measure of the effectiveness of the pulping process. For independent variables, we have a combination of chip, steam, air, and liquor flow rates, along with temperature, level, and moisture measurements. Several characteristics make this data set particularly challenging:

The division of the continuous reactor into separate zones, each of which performs a different function.
Separate heat inputs, plus liquid inputs and outputs for each zone.
Countercurrent liquor flows.

Because of the above (and because we want to understand the process, not just predict an outcome variable), we use several of JMP’s platforms from the Predictive Modeling, Screening, and Multivariate Methods menus. Finally, to demonstrate analytical flow and successive discovery, we conduct most of the session as a live demo.
Session ID: 2021-EU-30MP-735

Milking the Data at Dairygold: From Bar Charts to Neural Networks

Kieran O'Mahony, Data Science & Analytics Manager, Dairygold

Topic: Data Visualization
Level: 2

2:45 - 3:30 PM ET

Session ID: 2021-US-30MP-809

47 Years (So Far) with Designed Experiments from One-Factor-at-a-Time Designs to GO-SSD Designs

Ronald Andrews, Sole Proprietor, Practical Stats

Topic: Data Exploration
Level: I

Like most young engineers at Kodak, I started out running one-factor-at-a-time designs, until we needed to optimize a five-component solution. After successfully optimizing this solution that had two statistically significant three-way interactions, I was hooked on designed experiments. As computer tools progressed, we moved from pencil and paper to in-house mainframe programs. In the 1980s we started using mainframe SAS. In the 1990s we had JMP, but our DOEs were still basically catalog designs. In the late 1990s when JMP added optimal designs, we had some new tools. Experiments didn’t have to be orthogonal. We could run smaller experiments and still get the necessary data, which was very important in the waning days of the Kodak Research Labs when strict quotas were placed on the number and size of experiments. When Bradley Jones and Christopher Nachtsheim introduced definitive screening experiments in 2011, I had moved on to Bausch + Lomb. We have had more than one case where DSDs identified antagonistic interactions early enough to avoid program delays. DOEs in retirement are simpler, focusing on things like optimizing an ice cream recipe and reducing the amount of time to produce maple syrup on a conventional stove.
Session ID: 2021-US-45MP-807

Accelerating Innovation with Space Filling Mixture Designs, Neural Networks and SVEM

Philip Ramsey, Data Scientist and Professor, North Haven Group and University of New Hampshire

Marie Gaudard, Professor, Predictum

Wayne Levin, Owner, Predictum

Topic: Design of Experiments
Level: II

Although critical to process/product development and improvement, constrained mixture and mixture process designs are challenging to generate and analyze. Classical mixture and mixture process designs typically do not provide adequate coverage of the design space, assigning too many design points to boundary areas. Also, classical mixture process designs are often too large to be feasible. Furthermore, the large amount of partial aliasing inherent in constrained mixture designs renders them difficult to analyze, especially for predictive modeling purposes.

In this talk, we propose new classes of efficient constrained mixture and mixture process designs based upon the fast flexible space filling designs available in JMP. These designs provide much improved coverage of the design space and are generated without the requirement to pre-specify a model.

Through simulation and case studies we show how these new designs, coupled with self-validating ensemble modeling (SVEM) to fit neural networks, result in effective predictive models. These models provide a basis for process characterization and optimization, thereby ensuring better scale-up and scale-down model performance. We demonstrate how to generate the designs and conduct the SVEM analyses using JMP 16.
Session ID: 2021-US-45MP-826

That Can’t Be My Weight! Is My Scale Broken? An Exploration of Measurement Systems Analysis (MSA)

Jason Wiggins, JMP Senior Systems Engineer, SAS

Topic: Design of Experiments
Level: II

From our homes to the lab to the production floor, measurements systems are everywhere. At home, there is the dreaded bathroom scale. In test labs, measurement systems are used to measure important physical properties like material strength. Production systems rely on measurements of quality characteristics such as the shape of a machined part. The measurement systems we interact with have a variety of behaviors. They may be noisy or precise, accurate or biased. They may or may not alter or destroy our samples. Regardless of behavior, we need to know enough about the noise in our measurement systems to make informed choices about how they are used to avoid costly errors. In the early '90s the Hubble telescope’s main mirror was overground due to an errant measurement. The mistake, discovered post launch, nearly sidelined the project. Understanding and addressing the source of variation through a MSA pre-launch could have saved NASA the $1.5 billion trip to space to repair it. This presentation covers the ins and outs of applying MSA results from the Evaluating the Measurement Process (EMP) method pioneered by Dr. Donald Wheeler. An overview of EMP, along with examples from home and industry for nondestructive and destructive measurement systems, is explored. EMP gauge classification, reporting precision and guard banding specification limits to ensure product conformance are demonstrated in JMP.

4:00 - 4:45 PM ET

Session ID: 2021-US-30MP-823

But Now What Do I Do? Using a Reliability Model to Make Event Predictions in JMP

Bradford Foulkes, GE Renewables, GE

Topic: Quality and Reliability
Level: II

After spending weeks or months pulling together data and building reliability models, often the feeling is "Now what?" Or maybe the question is, “How many will fail?” In JMP Pro, there is a platform that can answer these questions, with some tweaks. The repairable systems simulation (RSS) platform allows you to enter a reliability model, or a system of models, to see how frequently the event will occur and what the impact could be. In this presentation, I explain how to go from reliability model to event prediction, via a method to automate the generation of the RSS platform. Once you have the output, I show how to build reports to answer the question around event prediction, annual downtime values, and individual models in a system. This presentation covers hands-on examples, as well as how to use JSL to make the platform and report generation easier.
Session ID: 2021-US-45MP-851

Expanding Our Text Mining Toolkit: Sentiment Analysis and Term Selection in JMP Pro 16

Ross Metusalem, JMP Systems Engineer, SAS

Topic: Data Exploration
Level: II

Text mining techniques enable extraction of quantitative information from unstructured text data. JMP Pro 16 has expanded the information it can extract from text, thanks to two additions to Text Explorer’s capabilities: Sentiment Analysis and Term Selection. Sentiment Analysis quantifies the degree of positive or negative emotion expressed in a text by mapping that text’s words to a customizable dictionary of emotion-carrying terms and associated scores (e.g., “wonderful”, +90; “terrible”, -90). Resulting sentiment scores can be used for assessing people’s subjective feelings at scale, exploring subjective feelings in relation to objective concepts, and enriching further analyses. Term Selection automatically finds terms most strongly predictive of a response variable by applying regularized regression capabilities from JMP Pro’s Generalized Regression platform, which is called to from inside the Text Explorer. Term Selection is useful for easily identifying relationships between an important outcome measure and the occurrence of specific terms in associated unstructured texts.

This presentation provides an overview of both Sentiment Analysis and Term Selection techniques, demonstrates their application to real-world data, and shares some best practices for using each effectively.
Session ID: 2021-US-45MP-841

Exploring Interactions in Regression Models with JMP and the Feasible Solutions Algorithm

Joshua Lambert, Assistant Professor, University of Cincinnati

Topic: Data Exploration
Level: I

During the process of building a regression model, scientists are sometimes tasked with assessing the effects of one or more variables of interest. With an additive regression model, effects (e.g., treatment) are assumed to be equal across all possible subgroups (e.g., sex, race, age). Checking all possible interaction effects is either too time consuming or impossible with a desktop computer. A new JMP add-in implements an algorithm, the Feasible Solutions Algorithm (FSA), which is meant to explore subgroup-specific effects in large data sets by identifying two- and three-way interactions to add to multivariable regression models. This talk gives a short introduction to the FSA , explains how I moved my R package to JSL, and provides a tutorial on how to use the FSA JMP add-in to explore interactions in your own regression models.

5:00 - 6:00 p.m. ET

Agenda

On-Demand Sessions

Join in the discussion!

Live Sessions

Attendee-driven "Unsessions"

JMP Scripting Unsession

Predictive Modeling Unsession

Social Time Welcome to Discovery

Getting Started with JMP

JMP Cares Activities

Plenary Session

Delicate Brute Force

John Sall, Co-Founder and Executive Vice President, SAS

Discovery Expo

Simu-Live Papers

1:30 - 2:15 PM ET

Michael Akerman, Senior Data Scientist, Novozymes

Johan Pelck Olsen, Data Scientist, Novozymes

Steven Crist, Analytics Consultant, Wells Fargo

Scott Wise, Principal Analytical Training Consultant, SAS

2:45 - 3:30 PM ET

David Trindade, Founder and Chief Consultant, STAT-TECH

Kevin Doran, Senior Staff Engineer, Intel Corporation

Jordan Hiller, JMP Senior Systems Engineer, SAS

4:00 - 4:45 PM ET

Amanjot Kaur, Statistician, Perrigo

Rob Lievense, JMP Senior Systems Engineer, SAS

Laura Lancaster, JMP Principal Research Statistician Developer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Christopher Gotwalt, JMP Director of Statistical R&D, SAS

Anderson Mayfield, Assistant Scientist, University of Miami

Poster Session

Getting Started with JMP

JMP Cares Activities

Plenary Session

Data Explorer Spotlight

Discovery Expo

Simu-Live Papers

1:30 - 2:15 PM ET

Bradley Jones, JMP Distinguished Research Fellow, SAS

Rob Reul, Managing Director, Isometric Solutions

Anantharam Narayanan, Principal Scientist, Procter & Gamble

Annie Weisbrod, Principal Sustainability Scientist, Procter & Gamble

Mark Bailey, Human Resources Expert, Procter & Gamble

Isaac Himanga, Senior Analytics Engineer, ADM

2:45 - 3:30 PM ET

Mike Anderson, JMP Senior Systems Engineer, SAS

Lori Rothenberg, Associate Professor, NC State University

Anuja Khairnar, Quality Control Systems Manager, Surry Chemicals Inc

Helmut Hergeth, Associate Professor, NC State University

Xinjie Tong, Senior Research Statistician, Dow

4:00 - 4:45 PM ET

Ryan Parker, JMP Senior Research Statistician Developer, SAS

Andrew Karl, Senior Management Consultant, Adsurgo LLC

Heath Rushing, Principal Consultant, Adsurgo LLC

Matheus Sguissardi, Lean Six Sigma Consultant, Plana Consultoria e Treinamentos

Social Time Quiz Bowl

Getting Started with JMP

JMP Cares Activities

Plenary Session Facets of a Diverse Career

Alyson Wilson, Associate Vice Chancellor for National Security and Special Research Initiatives, NC State University

Discovery Expo

Simu-Live Papers

1:30 - 2:15 PM ET

Rich Newman, Statistician, Intel

Don Kent, Data Analytics and Machine Learning Manager, Intel

Stanley Siranovich, Principal Analyst, Crucial Connection LLC

Kieran O'Mahony, Data Science & Analytics Manager, Dairygold

2:45 - 3:30 PM ET

Ronald Andrews, Sole Proprietor, Practical Stats

Philip Ramsey, Data Scientist and Professor, North Haven Group and University of New Hampshire

Marie Gaudard, Professor, Predictum

Wayne Levin, Owner, Predictum

Jason Wiggins, JMP Senior Systems Engineer, SAS

4:00 - 4:45 PM ET

Bradford Foulkes, GE Renewables, GE

Ross Metusalem, JMP Systems Engineer, SAS

Joshua Lambert, Assistant Professor, University of Cincinnati

Closing Session

Everyday DOE

Social Time
Welcome to Discovery

Social Time
Quiz Bowl

Plenary Session
Facets of a Diverse Career