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Berlin 11-13 March
Agenda

We pack Discovery Summit with a lot of learning, connecting, and growing, yet the days will fly by. Explore the agenda to see how we balance plenaries with papers and posters, as well as meals that encourage networking and feedback. Every element of a Discovery Summit is optimized for you.
Paper
Poster
Workshop

11 Mar Tuesday

  • 13:00-16:00

    • Pre-conference Workshop Check-in *additional fee events

      Room
      Level 1: Salon 15 - 17 Foyer

  • 14:00-15:15

    • Pre-conference Workshops *additional fee events

      Room
      Level 1: Salon 15, 16, 17

  • 15:00-19:00

    • Conference Registration

      Room
      Grand Ballroom Foyer

  • 15:45-17:00

    • Pre-conference Workshops *additional fee events

      Room
      Level 1: Salon 15, 16, 17

  • 19:00-21:00

    • Welcome Reception and Dinner

      Room
      Hall Berlin

12 Mar Wednesday

  • 7:00-9:00

    • Breakfast

      Room
      The Market Restaurant

  • 8:00-9:00

    • Registration

      Room
      Grand Ballroom Foyer

  • 8:15-8:45

    • Discovery Expo

      Room
      Grand Ballroom

  • 9:00-10:15

    • Opening Plenary

      Room
      Grand Ballroom

  • 10:15-10:45

    • Break

      Room
      Hall Berlin E

  • 10:15-17:00

    • JMP Lab

      Room
      Salon 6: Oslo

  • 10:45-11:30

    • A Year of Implementing Learnings: How to Design and Analyse Experiments with Pass/Fail Responses

      As a follow-up to last year’s presentation by Don McCormack, I wish to present my learning implemented and the value gained.

      This designed experiment with 10 input factors and a yes/no response was made possible by attending JMP Discovery Summit in Manchester last year.

      The story includes the following steps:

      1. First design phase, which consisted of 24 runs.
      2. First analysis phase.
      3. Second design phase, which augmented the original design by adding 12 new runs.
      4. Second analysis phase.
      5. Identification of robust process window.

      For each step, I explain my thought process and show how it is done using JMP and JMP Pro. The presentation concludes with a Q&A session with Don McCormack himself! 


      The presentation is highly interactive and aims to encourage newcomers to embrace DOE by demystifying the concept.

       

      Presenters
      Room
      Salon 1-Moscow
      Skill Level

    • Synthetic Chromatograms: A New Approach to Chromatographic Modelling

      Chromatographic methods such as HPLC, GC, and CGE are essential for analytics across various industries. Optimizing these methods to ensure high accuracy and precision is crucial but challenging due to numerous parameters and complex chromatograms. Often, chromatographic targets (e.g., resolution, peak-to-valley) are extracted and modeled, but interpreting these results and their impact on the chromatogram is difficult.

      In collaboration with Chris Gotwalt at JMP, we have developed a novel approach to model synthetic chromatograms in-silico based on design of experiments (DOE). We demonstrate how individual peaks in chromatograms can be identified using JMP Functional Data Explorer and modeled via the Generalized Regression platform. Subsequently, the synthetic chromatograms are visualized and optimized in the Profiler.

      This innovative approach allows the impact of various DOE parameters to be simulated on complete chromatograms for the first time in JMP. It showcases JMP’s interactive capabilities, offering a new understanding of chromatographic methodologies and addressing new regulatory requirements, such as ICH Q14. We demontrate the potential of this feature, which is expected to be rolled out in JMP 19, with two real-world examples.

      Presenters
      Room
      Salon 3-Rome
      Skill Level

    • Measurement System Analysis for Non-normal Data

      Measurement system analysis (MSA) is very important in the semiconductor industry to estimate the quality of the measurements. Most MSA indicators, especially the precision to tolerance (P/T) ratio, implicitly assume a normal distribution, with +/- kσ covering a given percentage of the distribution. In the reference documents (AIAG MSA Manual), there are no alternative calculations for non-normal data, and it is difficult to find a simple method that adapts to parameters with very different distributions.

      We present two methods, with simple calculations and that are distribution agnostic, that cover the percentage of distribution set for our confidence level. The first method uses the Bienaymé-Tchebychev inequality to properly define the number of standard deviations in a k-sigma type formula. The second method uses a calculation of half-standard deviation on the right and on the left to allow for better coverage in the case of an asymmetric distribution. 

      The two methods are applied on many electrical tests with JMP formulas and can generalize to outlier detection and removal.

      Presenters
      Room
      Salon 5- London
      Skill Level

    • Improving Machine Learning Using Space Filling DOE to Tune Hyperparameters

      Tuning hyperparameters is crucial for optimizing machine learning models, but the process can be computationally expensive and complex. Traditional grid, random search, or even Bayesian optimization methods often miss critical areas of the hyperparameter space, leading to suboptimal models.

      In this talk, we show a JMP add-in we have developed that uses space-filling DOE to more efficiently approach the hyperparameter tunning challenge. The use of space-filling DOE ensures that hyperparameter combinations are sampled more evenly across the entire parameter space, thus reducing the number of required evaluations while increasing the likelihood of finding optimal settings.

      This talk also highlights the improved integration with Python found in JMP 18 and how leveraging capabilities like DOE inside JMP can be beneficial to data scientists. This talk combines advanced statistical techniques with practical, accessible tools to enhance model performance in diverse applications.

      Presenters
      Room
      Salon 7-Vienna
      Skill Level

  • 11:45-12:30

    • Smart Subsampling vs. Brute Force: A Strategic Approach to Predictive Modelling

      Handling large data sets continues to present unique challenges, even in an era where advanced machine learning algorithms can process vast amounts of information. Relying on brute-force techniques to analyze massive data sets can lead to inefficiencies, model overfitting, noise accumulation, and diminishing returns from adding more data.

      Intelligent subsampling, which involves selecting a representative fraction of the data, often provides a more targeted and insightful approach. Subsampling encourages more interpretable models, as the reduced data set size simplifies the relationships between variables. For these reasons, smart subsampling should be a preferred approach for a wide range of applications, including material science, biomedical research, environmental modelling, marketing analysis, and social sciences.

      But why go brute force when you can go smart? Through an interactive demonstration using the latest capabilities of JMP Pro in the field of complex material formulation, this presentation shows that a well-designed subsampling approach, combined with both classical and advanced modeling techniques (multilinear regression, neural nets, SVM, generalized regression) can lead to robust predictions.

      Presenters
      Room
      Salon 1-Moscow
      Skill Level

    • Optimizing Recipe Formulation Using Machine Learning, Sequential Learning, and Design of Experiments

      Machine learning (ML) methods have been widely applied to analyze design of experiments (DOE) data in such industries as chemical, mechanical, and pharmaceuticals, yet receive limited attention in the food industry, especially for recipe optimization.

      To address this, we explored ML and sequential learning for recipe formulation, aiming to optimize product quality. We combined DOE with ML to select optimal combinations of one, two, or three ingredients from 12  candidates, adjusting ingredient dosages based on the number of combined ingredients: 0-1 for single ingredients, 0-0.5 for pairs, and 0-0.33 for triplets. After assessing the main effects of all 12 ingredients, we narrowed the focus to five key ingredients. A full factorial design was applied to two-ingredient combinations, alongside collecting one data point at maximum dosage for each triplet. Three promising combinations were further analyzed using a space-filling design to explore the full parameter space.

      Subsequently, ML models were developed to predict product quality, with sequential learning guiding additional experiments to refine the model for one specific combination. This approach identified the optimal mixture with fewer than 100 lab experiments, demonstrating the efficiency of combining ML, sequential learning, and DOE in reducing experimental efforts while identifying high-performing ingredient mixtures.

      Presenter
      Room
      Salon 3-Rome
      Skill Level

    • Finding Optimal Operating Regions with the JMP Design Space Profiler and Simulator

      Quality by Design (QbD) is a systematic approach for building quality into a product. The Design Space Profiler in JMP helps solve the fundamental QbD problem of determining an optimal operating region that assures quality as defined by specifications associated with Critical Quality Attributes (CQAs) while still maintaining flexibility in production. 

      In this demonstration, learn how to use the Design Space Profiler and the Simulator, tools within the Prediction Profiler, to find the design space and robust areas within the design space suited for normal use. A toxin neutralization bioassay example from the ICH Q14 Analytical Procedure Development guideline is used. The Prediction Profiler in JMP has long been a powerful tool for visualizing and optimizing models. The addition of the Design Space Profiler and the Simulator within the Prediction Profiler makes it an indispensable tool for high-quality product and process innovation.

      Presenter
      Room
      Salon 5-London
      Skill Level

    • A JSL Application for Accelerated Stability Modelling

      If a pharmaceutical product is likely to degrade after 12 months, how can we get an early insight into that risk? The answer is to perform an accelerated stability study that collects data over a period of a few weeks under high-stress conditions, such as elevated temperature, pH for liquids, and moisture for solids.

      Once a model has been selected, it can be extrapolated over time to assess the overall level of stability. Multiple impurities contributing to degradation are modelled individually and evaluated collectively to identify the highest risk factors.

      For moisture-driven impurities, packaging can be used to control degradation rates. A packaging model can be constructed that accounts for moisture permeation and adsorption (in both product and desiccants). A composite model (kinetic plus packaging) evaluates overall stability.

      This JSL application is an interesting combination of physics, chemistry, model fitting, and statistical estimation. The live demonstration covers the workflow navigation, modelling moisture vapour transmission rates, kinetic model fitting and ranking, the use of diagnostic plots and prediction profilers, and the generation of dashboards to present final results.

      Special emphasis is given to the methodology of model selection and the challenges that are unique to accelerated stability studies.

      Presenter
      Room
      Salon 7-Vienna
      Skill Level

  • 12:30-13:45

    • Lunch

      Room
      Hall Berlin

  • 14:00-15:15

    • JMP Roadmap Sessions

      Room
      Salon 1-Moscow, Salon 3-Rome, Salon 5-London, Salon 7-Vienna

  • 15:30-16:15

    • From Development to Testing: A Journey of Creating and Validating a JMP Add-In

      In this talk, we take you through the journey of developing and testing a JMP add-in. Along the way, we first introduce the statistical problem that we were facing, which was designing discrete choice experiments tailored to different demographic groups so that we could more efficiently analyze the usability of JMP's Easy DOE platform.

      Following this, we discuss the process of developing an add-in using the JMP scripting language (JSL) and highlight how our add-in helped us solve multiple instances of our statistical problem in a seamless and efficient manner. 

      Lastly, we talk about the process of validating our add-in to ensure it was operating as intended through the use of the JSL unit testing framework. The automated unit testing scripts we wrote using the JSL unit testing framework also allowed us to see if any bugs or errors were introduced as we refined the add-in, significantly aiding the development process. 

      Presenter
      Room
      Salon 1-Moscow
      Skill Level

    • A Complete Workflow for Analyzing and Reporting Experimental Data

      Developing and manufacturing biopharmaceuticals involve many standardized experiments and reports. Most reports contain information from the associated protocol, product quality data from a laboratory information management system (LIMS), information from lab documentation, and performance data from different devices. Currently, these data sources are processed with JMP, Excel, and other software, and then assembled in PowerPoint presentations and Word documents.

      I demonstrate an automation of this complete process that was developed in-house with JMP. With the help of a journal, the user is guided through an automated workflow. It uses LIMS data and lab documentation to fill in, e.g., the responses of a design of experiment (DoE) series automatically and proposed illustration options once the subject matter expert (SME) has built a model. These illustrations, chromatography traces, and information from the protocol are then assembled into a PowerPoint presentation and a Word report following a preset template structure.

      Generating this automated report saves a significant amount of time for the SME, allowing more time to focus on interpreting the results. Furthermore, by keeping the automation in a single software, data integrity is intact and layout and evaluation standards are followed in every report.

      Presenter
      Room
      Salon 3-Rome
      Skill Level

    • Using Conditional Feature in Workflow Builder to Combine Six Smaller Workflows into One Workflow

      In the past, comparisons of chamber configuration setups between chambers on the same mainframe or across different mainframes involved a manual method that took a day. Recently, an automated process has been developed using Workflow Builder in JMP 17. Individual workflow files were created to speed up the efficiency of the process, using an individual workflow, depending on the number of chambers being compared. The workflows took the config setup file and compared the reference chamber to the chamber(s) being queried. The process resulted in a report with graphs and tables based on the criticality of the configuration mismatches.

      This presentation showcases the effectiveness of Workflow Builder's new conditional feature in JMP 18. This feature provides a method for combining all comparison workflows into one workflow, with the correct section of the workflow determined by the number of chambers being compared, which is entered by the user in a conditional prompt. The relevant workflow is selected based on the prompt entry. The end result is still the same as before but now the manual selection of the workflow file has been eliminated, allowing a comparison report to be generated across multiple platform types in one or two minutes.

      Presenter
      Room
      Salon 5-London
      Skill Level

    • Understanding Positional Temperature Trends to Increase Testing Reliability Using JMP Pro

      Fire testing is one of the most critical and expensive aspects when developing intumescent products to protect steel structures from fire. Understanding the nature and performance of a furnace during testing is imperative for reliably interpreting the results from formulation development.

      In this case, the temperature data from sensors (thermocouples) in bespoke furnaces were utilised in JMP Pro to establish and understand positional temperature profiles while minimising test runs. The Functional Data Explorer was deployed as a dimension-reducing technique to describe temperature-time curves in terms of their principal components, enabling their positional element to be understood and compared directly. FPCA Score Plots showed clustering of positionally equivalent sensors with repeating tests, giving confidence in the reliability of consistent temperature profiles.

      Furthermore, FDOE simulation in combination with a 3D scatter plot gave dynamic understanding of temperature distributions at varying time intervals making it easy for chemists and managers to communicate. This approach not only resulted in significant test cost savings, but allowed for greater insight into the trends of the furnace, which would have been impossible using conventional analysis techniques. Analysis aligned well with expectations of a temperature gradient towards the back of the furnace from air movement to smoke exhaust.

      Presenter
      Room
      Salon 7-Vienna
      Skill Level

  • 16:15-18:00

    • Discovery Expo

      Room
      Grand Ballroom

  • 18:30-19:00

    • Departure to dinner

  • 19:00-21:30

    • Dinner Excursion

      Room
      Off-Site

13 Mar Thursday

  • 7:00-8:30

    • Breakfast

      Room
      The Market Restaurant

  • 8:15-8:45

    • Discovery Expo

      Room
      Grand Ballroom

  • 8:45-9:45

    • Plenary

      Room
      Grand Ballroom

  • 9:45-14:45

    • JMP Lab

      Room
      Salon 6: Oslo

  • 10:00-10:45

    • Predictive Modelling in JMP: Agricultural Yields

      Within agricultural businesses, the ability to accurately predict the yield of a crop each year is critical for enhancing the efficiency, profitability, and sustainability of that business. The earlier the yield can be predicted, the more efficiently that resources can be allocated, supply chain managed, the harvest scheduled, and the storage logistics of the business be determined. 

      A current challenge in the sugar beet industry is climate change, which is causing increased variability of yields from year to year. The rapidly changing weather conditions make yield estimation less predictable and ultimately increases costs to all stakeholders.

      Harnessing the power of data analytics and machine learning is one way to improve the accuracy and timeliness of yield predictions. A predictive model was built in JMP to predict sugar beet yields. The whole process was possible in JMP alone: data collection, cleaning, preprocessing, exploratory data analysis, feature engineering, model selection, training, evaluation, tuning, and subsequent deployment and maintenance.

      Presenter
      Room
      Salon 1-Moscow
      Skill Level

    • Leveraging JMP for QbD-Driven Lentiviral Vector Process Development

      The demand for robust and scalable lentiviral vector manufacturing processes for cell and gene therapy has driven the adoption of advanced methodologies. This presentation delves into an innovative approach to process development, focusing on a Quality by Design (QbD) strategy, with a particular emphasis on the powerful capabilities of JMP software.

      In adherence to stringent regulatory requirements for Process Performance Qualification, our methodology seamlessly integrates traditional and modern principles while leveraging JMP as a critical tool during the Stage 1 Process Design phase of the Process Validation life cycle. Utilizing JMP for design of experiments (DOE) facilitates comprehensive characterization of the lentiviral vector manufacturing process, enabling precise identification of Critical Process Parameters (CPPs) and the establishment of Proven Acceptable Ranges (PARs).

      By harnessing the statistical analysis and visualization features of the software, we ensure a data-driven approach to decision making, enhancing process understanding and control. This talk emphasizes the key role JMP plays in advancing the application of QbD principles to meet the evolving demands of bioprocessing.

      Presenter
      Room
      Salon 3-Rome
      Skill Level

    • The Use of JMP During the Lifecycle of a Relative Potency Assay for CMC

      Relative potency assays are critical in evaluating the biological activity of drug products throughout the lifecycle of biopharmaceuticals. Managing variability, optimizing assay conditions, and ensuring consistent performance are essential but challenging.

      This presentation explores how JMP supports developing, validating, and monitoring relative potency assays in CMC (chemistry, manufacturing, and controls).

      By integrating data visualization, statistical analysis, modeling tools (such as logistic regression, and response surface methodology for establishing optimal assay conditions), and mixed-effects models (for estimating intermediate precision and relevant replication strategies), JMP enables robust assay development, procedure validation, and ongoing performance monitoring. The automation and scripting capabilities within JMP further streamline repetitive data analyses, facilitate method/operator performance assessments, and ongoing procedure performance verification (OPPV) of bioassays in a regulated environment.

      Presenter
      Room
      Salon 5-London
      Skill Level

    • Resolve Costly Out-of-Control Processes More Confidently with Control Chart Triage in JMP Live 19

      It can be costly and stressful to learn about out-of-control processes late or to react to them with incomplete information.

      If you are one of the tens of thousands of JMP users working with control charts, you don't want to miss this talk. It gives you an early look at a much-requested JMP Live 19 feature that can help you find and resolve issues more quickly, more accurately, and more confidently.

      Learn how to:

      • Communicate with your colleagues, not just about a control chart but about an individual warning:

        • “It’s being dealt with.” (Status)

        • “Here’s who is responsible for it.” (Assignment)

        • “Here’s what we think is going on.” (Notes)

      • Review and download detailed information about the warnings and the decisions made about them.

      • Focus your efforts:

        • Track the overall triage progress of your control chart.

        • Browse and filter control charts based on whether they “need attention," are “under investigation,” or are “fully addressed.”

        • Get notifications only for charts that still urgently “need attention.”

        • Keep on top of the warnings that are assigned to you for investigation.

      Presenters
      Room
      Salon 7-Vienna
      Skill Level

  • 10:45-11:15

    • Break

      Room
      Level 1-Ballroom Gallery Foyer

  • 11:00-11:30

    • Adapting Design of Experiments (DOE) to Enhance Device Performance in RIE Fabrication at SCD

      SCD (SemiConductor Devices), a global leader in the design and production of advanced infrared detectors and systems, is recognized for its cutting-edge technology and innovation in electro-optical solutions. As a fast-growing company, SCD operates in an environment where development times must be reduced, since being the first to present new products is critical to maintaining a competitive edge. To meet these demands, SCD adopts innovative approaches such as design of experiments (DOE) to accelerate process optimization and product development.

      In this work, we demonstrate the flexible application of DOE to improve the performance of a device fabricated through a two-stage Reactive Ion Etching (RIE) process. Initially, a series of experiments was planned using JMP DOE. Early insights into the process behavior indicated the need to adjust the design space. As the DOE progressed, a deeper understanding of the underlying mechanisms emerged, leading us to modify the process chemistry.

      This study highlights the importance of flexibility in DOE approaches, showing that real-world experimentation may require evolving the experimental plan. Ultimately, our work emphasizes that while DOE may not always yield a final predictive model, it can lead to valuable insights into process mechanisms, contributing to better decision making and process control.

      Presenter
      Room
      Ballroom Ped 1
      Skill Level

    • The Prediction of Product Satisfaction by Consumer by Leveraging Laundry Diary Data

      In analyzing consumer insights for laundry products, the distinction between laundry diary data and panelist-level data is crucial for understanding user experience and detergent efficacy. Laundry diary data offers real-time insights into actual consumer behaviors and preferences, reflecting genuine usage patterns. In contrast, panelist-level data, derived from consumers’ experiences over a few weeks, can be influenced by post-rationalization, where perceptions may shift due to expectations or marketing, potentially distorting product performance evaluations. This highlights the need for relying on laundry diary data for a more accurate assessment of product effectiveness.

      To present these insights, we utilize JMP software for advanced statistical analysis and data visualization. Key functionalities include combining data tables for a comprehensive overview, using data cleaning tools to maintain integrity, and applying Partial Least Squares (PLS) modeling to explore variable relationships. Additionally, modeling scenarios and Monte Carlo analysis are employed to simulate consumer behaviors and predict outcomes under uncertainty. By prioritizing laundry diary data and utilizing these analytical tools, we aim to enhance product development and marketing strategies, ultimately improving consumer satisfaction and brand loyalty.

      Presenters
      Room
      Ballroom Ped 3
      Skill Level

    • The Role of Data Analysis in New Process Analysis Technologies

      The use of different control charting approaches for complex in-process measurements (such as curves and distributions), as well as how statistical tools are used together with automation and data management are critical for efficient and sustainable manufacturing processes.  

      While in R&D there is greater emphasis on extracting numerous complex measurements to characterise the products, in manufacturing the pass/fail criteria tends to be based on a reduced number of metrics, often single value data. We strive to bridge the gap between R&D and manufacturing by exploring analytical tools to utilise valuable in-process complex measurements for monitoring and controling processes.  

       

      Presenters
      Room
      Ballroom Ped 4
      Skill Level

    • JMP Integration with AWS

      We are pleased to present how JMP plays a major part in our data environment at Soitec. With more than 600 users at our company, it is always a challenge to enhance the user experience and industrialize our products deployed on JMP. 

      Today, JMP allows many data sources to be targeted but collecting data from AWS services can still be overly complex. 

      Starting with JMP 15 and thanks to Python integration, we were able extend the capabilities of JMP further. We have internally validated a set of custom libraries (including S3, Athena, and RDS) that allows us to target the AWS services needed for end users. Today, with JMP 18's embedded integration with Python, it's much easier to manage the architecture.

      In this presentation, we explain a bit more about the role that JMP plays in our data engineering pipeline and how we can benefit even more from the Python integration with JMP 18.

      Presenters
      Room
      Ballroom Ped 5
      Skill Level

    • JMPyFacade: Bridging JMP and Python for Seamless Engaging Analysis

      In our quest to harness Python's extensive capabilities within JMP, we faced the challenge of integrating Python's flexibility with JMP’s intuitive and interactive user experience. Prior to JMP 18, we achieved this through a workaround ‒ running Python on a server to bypass JMP’s cumbersome Python integration. However, the release of JMP18 re-introduced Python integration, simplifying this process.

      Building on our initial concept of enabling JMP users to leverage Python without requiring programming expertise, we developed JMPyFacade (JMPy). This tool offers a familiar interface, since it is similar to JMP's, for executing predefined Python services via dynamically generated user interface, all while abstracting the underlying Python and JSL code.

      In this presentation, we explore the technical architecture of JMPyFacade and demonstrate how it effectively bridges the gap between JMP and Python. Attendees learn how Python can be leveraged within JMP for efficient and engaging data analysis.

      Presenter
      Room
      Ballroom Ped 6
      Skill Level

    • From Data to Decision: Automated SQC Reporting and Seamless MES/SQC Data Extraction

      In today's fast-evolving data analytics landscape, efficient reporting tools are key to driving informed decision making. This presentation introduces the complete suite of JMP tools developed to simplify manufacturing execution system (MES) and statistical quality control (SQC) data extraction and analysis for users. Of particular interest is a JMP add-in, the result of a collaboration between Syensqo and Ippon Innovation, designed to streamline the creation of SQC PowerPoint reports, fully aligned with the company's established templates. 

      Key features of this innovative tool include:

      • On-demand SQC report creation: Users can quickly generate SQC reports tailored for review and customer distribution.

      • Interactive report review: Users can interact with the underlying data, hide or exclude rows, and regenerate reports based on these adjustments.

      • Automated report generation: The tool automates the creation of multiple reports, notifying users via email when they are ready, thus eliminating manual processes.

      • Seamless add-in deployment: The various add-ins are deployed and updated on multiple servers, managed through the Add-in Manager for easy access by users.

      We showcase the capabilities of our tools within the context of our quality management process workflow. We also share insights from our implementation journey and explore how automation is transforming reporting practices.

      Presenters
      Room
      Ballroom Ped 7
      Skill Level

  • 11:30-12:00

    • Automatic Visualization of DOE Model

      Planning and executing a design of experiments (DOE) can be a complex task involving multiple specialists. Post-experiment, the challenges of analyzing, interpreting, and communicating results persist, particularly when stakeholders have varying levels of technical expertise. To streamline this process and standardize our workflow, we developed a script that automatically visualizes DOE results. The script generates two intuitive graphs that effectively convey the findings.

      The first graph illustrates the total variability of each variable of interest, indicating the extent to which each variable can be influenced according to the DOE. The second graph summarizes the sensitivity of the variable of interest to changes in input variables.

      Our script is designed for flexibility, allowing for the addition of new calculations and normalization of graphs as needed. This tool has significantly improved our efficiency, saving approximately two hours per graph compared to manual methods. Moreover, the standardized visualization format enhances communication with non-technical stakeholders, making the DOE results more accessible and easier to understand.

      Presenter
      Room
      Ballroom Ped 1
      Skill Level

    • Leveraging JMP for Enhanced Data-Driven Decision Making in BASF Antwerp’s Chemical Production Plants

      In today’s rapidly evolving industrial landscape, the ability to leverage data analytics is paramount for achieving operational excellence and informed decision making. Our goal is to develop robust data analytics skills among production staff, empowering them to harness the full potential of data and enhance data-driven decision-making processes. This initiative serves as a scaling factor in our data analytics and AI strategy, ensuring that our workforce is not only data literate but also capable of effectively collaborating with advanced technologies that drive plant automation. 

      This training program is tailored for process managers, asset managers, and other key stakeholders within the organization. It offers a comprehensive delivery format that includes self-paced learning, on-the-job training, and classroom instruction, catering to diverse learning preferences and operational needs. 

      Participants engage with interactive, point-and-click tooling, focusing on practical applications such as utilizing dashboards to assess input factor variability, employing trending tools such as Seeq for exploratory analysis and trending of process data at the plant level, and leveraging JMP for statistical analysis and advanced modeling. By fostering a culture of data literacy and equipping our production staff with essential analytical skills, we aim to boost operational efficiency and drive informed decision making throughout the organization. 

       

      Presenters
      Room
      Ballroom Ped 2
      Skill Level

    • Helper Functions for Exploratory Data Analysis

      Data visualization with JMP is like navigating through a maze – both involve finding clarity through complexity. But with JMP, you’re not just solving a puzzle, you’re unlocking a whole new level of insights with data visualization and interactive features. Isn’t it cool how data can guide us through even the most tangled paths?

      In this talk, I present helper functions that make working with JMP even easier. Toolbars allow users to access standard JMP and JSL functions with a single click; even complex custom functions can be added. The result? A toolbox for exploratory data analysis that enables users to create illustrative and convincing analyses in seconds.

       


      QuickSelect Toolbar  ,Graph Builder Toolbar , Normalization GUI 

      Presenter
      Room
      Ballroom Ped 3
      Skill Level

    • Automation of Data Processing for Accelerated Product Development

      During product development, reagent formulations for new sequencing platforms require multiple cycles of development, optimization, and robustness testing. The result? Significant amounts of data that need to be processed and compared using statistical analysis.

      These data handling tasks are often tedious, time-consuming, and prone to copy-and-paste errors. Automation of the data processing workflow represents the ideal solution to those issues.

      The Workflow Builder in JMP allows scientists with minimal programming experience to create their own scripts that automate otherwise repetitive data-handling workflows.

      In this presentation, a reuseable script is created for the analysis of an example: the results of a plate-based enzyme assay. Use of column formulas, column stacking, non-linear (exponential) fits, and combined data tables are demonstrated live in conjunction with basic functions of the Workflow Builder. The final workflow is debugged in real time and modified to make the script reuseable and robust, using simple JSL elements such as variables, and the “Current Data Table” and “Pick File” functions.

      The presentation culminates by showing how JMP scripts were leveraged to streamline data processing at Illumina, unlocking substantial time savings and faster insights for project development teams.

      Presenters
      Room
      Ballroom Ped 4
      Skill Level

    • Using JMP in the DMAIC Stages of Process Improvement in Semiconductor Chip Back-end Assembly

      Define-Measure-Analyze-Improve-Control is a Six Sigma methodology that is used to move methodically through the five stages for finding a solution for processing issues in semiconductor chip back-end assembly. Through each stage, data is collected and JMP is used to describe the data and provide inferences toward the effectiveness of actions and factors to move the yield trends toward a target. 

      The Define stage, with key contributions in the Project Charter, is aided by JMP Graph Builder to produce time series plots and bar charts that explain the need for moving toward a SMART objective. 

      In JMP, Pareto charting, linearity and bias, Gauge R&R, and process capability analysis are the main tools used in the Measure stage. Suitable sampling sizes are selected to match the confidence levels of the expected results. 

      Hypothesis testing, both in proportions and means for parametric and non-parametric distributions, are the primary tools used in Analyze stage. Equality of variance helps to understand the robustness of a process. 

      JMP DOE is the main tool used in the Improve stage, with time series plots used for monitoring the first short-term pilot batches. 

      Finally, JMP control charts are utilised to monitor the trends after launching the process improvements.

       

      Presenter
      Room
      Ballroom Ped 5
      Skill Level

    • Decision-Making with Prediction Intervals in JMP Profiler: A Case Study from Cencora-PharmaLex

      In this presentation, we explore the practical application of JMP Profiler, focusing on its improved feature of incorporating prediction intervals. Through a detailed case study from Cencora-PharmaLex, we demonstrate how prediction intervals provide a more robust framework for decision making by quantifying the uncertainty in model predictions. This added feature allows for more informed decisions, particularly in critical scenarios where risk management and precise predictions are essential. Through this case study, we will highlight the added value of prediction intervals in improving the reliability of data-driven decisions, ultimately leading to better outcomes in pharmaceutical and life sciences projects.

      We also review the current capacity of JMP’s simulator, which does not yet incorporate predictive distributions to support risk-based decisions in the same manner. By addressing this limitation with examples and formulas, we aim to highlight opportunities for further enhancements in future JMP releases, ultimately leading to better outcomes in pharmaceutical and life sciences projects.

      Presenter
      Room
      Ballroom Ped 6
      Skill Level

    • Using JMP for Defectivity to Process Correlation: A Case Study

      The semiconductor industry faces significant challenges in extracting and leveraging data to improve yield. One of the Defectivity workshop's missions is to locate and identify physical defects on production wafers caused by the process. These defects can directly impact the product electrical performance, highlighting the need for a better understanding of the correlation between defectivity levels (defect count per wafer) and the process involved. Highly complex, those processes are driven by multiple parameters, which means their relationship to defectivity is a major enabler for process tuning toward yield improvement and cost optimization.

      In this case study, we demonstrate the efficiency of JMP as a tool for data management and formatting from process in line collection through the following steps:

      1. Visualizing the initial extracted data to verify a hypothesis about the worst process tool.
      2. Manipulating and extending the data set for a more in-depth analysis of a previously identified process parameter, enabling correlation analysis.
      3. Quantifying dollar gains based on the analysis.

      Finally, a clear results display is highly beneficial for management when considering a potential process change and making informed decisions regarding the cost-yield balance.

      Presenters
      Room
      Ballroom Ped 7
      Skill Level

    • Efficient Strategies for Selecting Minimal Solute Sets in Linear Solvation Energy (LSER) Models

      Linear solvation energy relationship (LSER) models are used in adsorption and chromatography to describe how molecular interactions influence solute retention or adsorption. These models relate the partitioning coefficient of a solute to various molecular properties, enabling predictions based on solute descriptors, which can be looked up or calculated via quantum chemistry.

      Mathematically, LSER models are expressed as linear equations, with coefficients obtained through multiple linear regression of experimental data from a set of solutes. Since obtaining data for solutes is labor-intensive, and solutes may have limitations (e.g., low solubility, high cost, or instability), selecting an optimal minimal set of solutes becomes important.

      This study discusses strategies for selecting a chemically diverse minimal solute set that minimizes the standard error of the model's coefficients. Monte Carlo simulations (performed in JMP via Python integration) are used to explore potential solutes, considering cases where solute descriptors span a limited range. Theoretical upper and lower bounds for the standard error are presented. Both homoscedastic and heteroscedastic LSER models are considered. Finally, the impact of interdependencies among solute descriptors on the statistical robustness of these strategies is discussed.

      Presenters
      Room
      Ballroom Ped 8
      Skill Level

  • 12:00-12:30

    • How to Decode Rainbows: The Best (and Least Confusing) Ways to Analyze Spectral Data

      Analyzing spectral data is a bit like trying to decode a rainbow -- it’s beautiful but full of tricky surprises.  Spectral data presents unique challenges due to its highly correlated nature, which renders many conventional techniques ineffective.

      In this talk, we identify these challenges and explore advanced methods tailored for handling such data. Specifically, we dive into three powerful techniques: principal component analysis (PCA), partial least squares (PLS), and functional data analysis (FDA). By comparing these methods, we highlight their strengths, limitations, and practical applications, offering insights into choosing the best approach for analyzing highly correlated spectral data. We show you how to transform your data into a vibrant spectrum of success even if you never look at a rainbow the same way.

      Presenters
      Room
      Ballroom Ped 2
      Skill Level

    • Work Smarter, Not Harder: Git on Board!

      In the dynamic landscape of script development, maintaining efficiency and coherence in the middle of frequent updates and collaborative input can be frustrating. This presentation shows the power of Git software for collaborative work on scripts, offering solutions to common issues such as accidental deletions, version control, and the hassle of manual backups.

      Git is an essential tool for teamwork, allowing multiple contributors to work simultaneously on a project. With Git, every change is automatically versioned, providing a robust history that empowers teams to track, revert, and review modifications effortlessly. Furthermore, Git's automatic backup creation ensures that your work is safeguarded in real time, eliminating the risk of data loss and enhancing overall productivity.

      After explaining the structure of Git, as well as how it is set up and used, this presentation demonstrates how to develop a script.

      Presenters
      Room
      Ballroom Ped 3
      Skill Level

    • Drag, Drop, and Deploy: Can JMP Really Play a Significant Role in the Future of AI/ML?

      We provide some friendly banter and debate on the role that JMP and JMP Pro are likely to play amidst modern artificial intelligence (AI) and machine learning (ML) trends. In the evolving landscape of AI/ML, scientists and engineers across industries need intuitive tools for data exploration, model building, and deployment.

      JMP and JMP Pro, as no-code platforms, simplify and speed up the process from raw data to AI with visual, interactive capabilities that enhance understanding and efficiency. This presentation highlights their strengths through real-world examples, including deep learning for tabular, text, and image data, showcasing how these tools enable seamless, AI-driven insights with minimal coding expertise required and potentially significant savings in time and energy.

      Presenters
      Room
      Ballroom Ped 4
      Skill Level

    • Wafer Sheet Resistance WIW-NU Study

      In the semiconductor industry, within wafer non-uniformity (WIW-NU) has been the most critical indicator to quantify film performances. Produced wafers should meet the desired film properties homogeneously. We are facing a huge challenge to improve process tuning due to lack of smart WIW-NU metrics.

      The baseline WIW-NU metric can only provide the wafer map and basic stats (e.g., mean, stdev., and range), which cannot identify the true root cause. There is an increasing demand to establish a smarter WIW-NU metric to shorten process tuning cycles. 

      JMP is used to breakdown the total WIW-NU variations into four process failure modes: radial, angular, off-centering, and outlier. The following methodology was deployed:

      • Transform raw data to create desired descriptive statistics.
      • Define WIW-NU criteria.
      • Conduct hypothesis mean test, normality test, and normality violation modes.
      • Conduct GRR and Cpk/Ppk (wafer is the rational subgrouping).
      • Establish I-MR by Phase (wafer), Xbar-R to capture special variations.
      • Utilize modern data mining to extract WIW-NU insights.
      • Derive predictive models to guide film process tuning.

      This JMP WIW-NU project has significantly improved process tuning efficiency and shortened the process development cycle time. We are proliferating these methods across all internal organizations to improve overall performances and enhance competitive advantage.

      Presenters
      Room
      Ballroom Ped 5
      Skill Level

    • ANOVA and Beyond: Turning Data Chaos into Statistical Bliss with JMP

      This work aimed to create a detailed JMP Journal to improve the effectiveness and consistency of data analysis across our team. This journal ensures that all analyses are performed consistently and accurately by providing a step-by-step guide from initial data cleaning to the presentation of final results. This journal provides comprehensive instructions for analyzing the normality of the data and homogeneity of variances, i.e., prior conditions for ANOVA. Alternative statistical tests are recommended if the data do not meet the required assumptions.

      By following this methodical approach, our colleagues can drastically reduce the time required for analysis while achieving high levels of accuracy and reproducibility. In addition to improving the overall quality and reliability of our research results, this project accelerates the analytical workflow, thereby enabling faster publications. This work presents a use case of the JMP Journal, emphasizing how it is helping to create consistent and effective data analysis processes.

      Presenters
      Room
      Ballroom Ped 6
      Skill Level

    • Automating Control Charts with JMP Live

      Precise control of the batch parameters is crucial. JSR Micro NV manages batches with 20 to 400 parameters across approximately 100 different products. Additionally, raw materials require control.

      Most of our data is stored in the SAP HANA database and the OSI PI data historian. JMP queries data from SAP HANA using an ODBC driver and accesses the OSI PI server directly. With JMP Live, we have continuous access to the latest data from these databases.

      JMP gives us complete flexibility in processing and utilizing the data according to our needs. This entire process streamlines monitoring and enhances our operational efficiency and quality control.

      Presenter
      Room
      Ballroom Ped 7
      Skill Level

    • Syensqo Industrial Analytics Academy

      The objectives of the Industrial Analytics Academy (IAA) at Syensqo are to reinforce the culture of data-driven decisions, to upskill Syensqo workforce so that we keep pace with our digital transformation, and to democratize the use of advanced analytics.

      To do that we have set up a frame for self-service analytics with a clear learning path, skill set, and certification using JMP. Additionally, we offer coaching from corporate industrial advanced analytics experts.

      The target population is our colleagues from the industrial sites, such as process and production engineers, APC engineers, quality engineers, reliability engineers, and operators.

      The academy was first introduced in 2023 and so far it has had very positive feedback from different business units and production processes. The IAA generated significant value for Syensqo's engineering community, which can be replicated across all Syensqo sites.

      Presenter
      Room
      Ballroom Ped 8
      Skill Level

  • 12:30-13:45

    • Lunch

      Room
      Hall Berlin

  • 14:00-14:45

    • Navigating Statistical Limits: Understanding Confidence, Prediction, and Tolerance Limits

      In this presentation, we explore the crucial role of predicion, confidence, and tolerance limits. Given that they are easy to calculate and appear in most textbooks, they are commonly used; however, that also means that they are often misused or misinterpreted. One of the current issues lies in accurately estimating these limits for mixed models, which is a serious limitation in most industries. 

      While JMP uses the best option available to estimate these limits (based on a paper published in 2019 in the journal, Wiley Medicine), we can run into trouble if we're not careful, as the method does have some shortcomings. As a consulting company, the methods we use need to be solid. Therefore, we decided to delve deeper into the issue and evaluate these limits through simulation in JMP. 

      The presentation focuses on the storytelling of the problem, making people across industries aware of these statistical dilemmas, along with a demonstration of how JMP was used to simulate different scenarios and how models were evaluated. It  showcases a graphical interface made for this purpose, as well as model building and producing an interpretable output. 

      Presenter
      Room
      Salon 1-Moscow
      Skill Level

    • The Application of JMP in the Six Sigma Methodology: Practical Examples

      This presentation focuses on the integration of JMP software within the Six Sigma DMAIC methodology. This powerful combination streamlines various aspects of the Six Sigma toolbox, making it easier for teams to implement data-driven improvements efficiently.

      JMP provides advanced statistical analysis and visualization tools that enhances the DMAIC phases. Key components of JMP that will be highlighted include measurement system analysis for assessing measurement accuracy, descriptive statistics for summarizing data characteristics, Pareto charts for identifying the most significant factors and regression analysis for understanding relationships between variables. Furthermore, it includes Graph Builder, which allows for intuitive data visualization, the optimal design of experiments (DOE) feature, which helps in planning efficient experiments and control charts, essential for monitoring process stability over time. Using these tools, practitioners can quickly identify key variables, analyze data trends, and visualize results, accelerating decision-making processes.

      The presentation also demonstrates how JMP simplifies data collection and analysis, allowing teams to focus on valuable insights. Real-world examples where JMP has been successfully utilized in Six Sigma projects, highlighting its impact on project outcomes, are showcased.

      Presenter
      Room
      Salon 3-Rome
      Skill Level

    • Thanks to JMP DOE for the Next Level of Assay Development

      Bioassays are a key analytical technology in the biopharmaceutical industry, ranging from basic research to drug discovery. Due to their complexity and the multiple steps needed to handle assays, it is crucial that they be optimized to develop a fit-for-purpose bioassay, especially since the assays must perform robustly if they are to be used for the release of biopharmaceutical product.

      For process optimisation, design of experiments (DOE) has long been established as a more powerful strategy than a one-factor-at-a-time approach. Nevertheless, while it is known that complex interactions often exist, DOE is not widely used due to the perceived costs, effort, and complexity.

      In this presentatin, we share how the implementation of DOE as a regular technique in bioassay optimisation is facilitated by identifying a key user group and providing training on JMP's DOE platform.

      Presenter
      Room
      Salon 5-London
      Skill Level

    • Extending Data Connectors with Python: Query Builder Integration and More

      JMP 19 will expand on the Data Connector feature introduced in JMP 18 to allow uniform access to more data sources. Besides adding more built-in connection types, JMP 19 will also allow users to define their own connection types by means of an exposed Python API. Like the built-in types, these types will share in the benefits of the Data Connector feature, including its configuration management and Query Builder integration. And because they're implemented in Python, they can make use of existing Python libraries. In this presentation, we describe the API, discuss the concepts involved in it, and show it in action.

      Presenter
      Room
      Salon 7-Vienna

  • 15:00-16:15

    • Closing Plenary

      Room
      Grand Ballroom

  • 16:15-17:45

    • Discovery Expo and Meet the Author

      Room
      Grand Ballroom

  • 18:30-21:30

    • Dinner Mingle

      Room
      Hall Berlin
Times subject to change
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