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Production Line Control in Semiconductor High-Volume Manufacturing (2020-EU-45MP-472)
Monday, March 9, 2020
Guillaume BUGNON, Data Engineer Soitec Stéphane ASTIE, Yield Manager Soitec   Semiconductor companies are continuously challenged on manufacturing cost and quality. An effective yield management system is key to detect line drift and minimize scrap to reduce wafer cost and quality impacts. JMP has helped Soitec to improve yield since 2007 by providing an easy-to-use tool to visualize equipment data versus product yield data. Methods for performing a set of statistical tests adapted to the nature of the response distribution (normal, non-normal, binary) were developed in 2010 for the yield team to find the root cause of the issue (Yield Guard dedicated JMP/JSL application). In multiple products and a full fab environment, data quality and real-time analysis are critical for accurate drift root cause determination. Agile collaboration between IT and yield teams allows us to develop an automatic tool to reach those goals in order to control our production line. We would be proud to present our methodology from data collection to data analysis. The presentation will be done interactively with JMP and JSL codes.
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Introduction to JMP(R) Live: Use and Administration (2020-EU-TUT-529)
Monday, March 9, 2020
Brian Corcoran, JMP Director of Research and Development, SAS Dieter Pisot, JMP Principal Application Developer, SAS Eric Hill, JMP Distinguished Software Developer, SAS   You know the value of sharing insights as they emerge. JMP Live — the newest member of the JMP product family — reconceptualizes sharing by taking the robust statistics and visualizations in JMP and extending them to the web, privately and securely. If you'd like a more iterative, dynamic and inclusive path to showing your data and making discoveries, join us. We'll answer the following questions: What is JMP Live? How do I use it? How do I manage it? For background information on the product, see this video from Discovery Summit Tucson 2019 and the JMP Live product page.   JMP Live Overview (for Users and Managers) – Eric Hill What is JMP Live? Why use JMP Live? Interactive publish and replace What happens behind the scenes when you publish Groups - From a user perspective Scripted publishing Stored credentials API Key Replacing reports Setup and Maintenance (for JMP Live Administrators) – Dieter Pisot Administering users and groups Limiting publishing Setting up JMP Live Windows services .env files Upgrading Applying a new license Using Keycloak single sign-on Installing and Setting up the Server (for IT Administrators) – Brian Corcoran Choosing architectural configurations based on expected usage Understanding SSL Certificates and their importance Installing the JMP Live Database component Installing the JMP Pro and JMP Live components on a separate server Connecting JMP Live to the database Testing installed configuration to make sure it is working properly (view in My Videos)     (view in My Videos)   (view in My Videos)
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Automated Updates of JMP(R) Live Reports (2020-EU-30MP-501)
Monday, March 9, 2020
Dieter Pisot, JMP Principal Systems Engineer, SAS Stan Koprowski, JMP Senior Systems Engineer, SAS   Data changes, and so do your JMP Live reports. Typical data changes that involve additional observations or modifications to columns of data necessitate updates to published reports. With the first scenario, an existing report might need to be recalculated to reflect the new observations or rows of data that are used in the report. A second option is when you want to restructure the underlying data by adding or removing columns of information that are used in the report. With both situations you must update your report on a regular basis. In this paper we will provide practical examples of how to organize JSL scripts that facilitate the replacement of an existing JMP Live report with a current version. Prior to the live demonstration, we will discuss all key security protocols, including protecting credentials needed to connect to JMP Live. The code presented are designed to be reused and shared with anyone who has a need to publish or replace a JMP Live report on a predefined time interval, such as hourly, daily, weekly or monthly. With some basic JSL knowledge you can easily adopt them for your automated updates to any of your other existing JMP Live reports. Not a coder? No worries, we've got your back. Additionally, we will provide a JMP add-in that schedules the publishing of a new report or the publishing of a replacement report for those with little JSL knowledge using a wizard-based approached.
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Industrial Biotech Case Study: Customized DOE for Process Optimization in Small-Scale Bioreactors (2020-EU-EPO-473)
Monday, March 9, 2020
Andreas Trautmann, R&D Scientist, Lonza AG Claire Baril, Scientist USP Development, Lonza AG   Design of Experiments (DOE) is a frequently used and time-saving tool in industrial biotechnology for optimizing microbial cultivation process parameters. Developing standard approaches for such experiments facilitate the transferability to customers as well as the comparability between similar projects.   In this case study, we show a general approach for the optimization of process parameters in small-scale bioreactors including a subsequent DOE model validation in pre-pilot scale. The software tool Custom Design in JMP was applied to generate an I-optimal response surface design with three center points. In total four continuous factors were chosen based on empirical knowledge and preliminary data from disposition runs.   One major goal of DoE approaches in industrial biotechnology is to increase yield, e.g. concentration or titer, of the final product. By applying the Custom Design in JMP we were able to increase product titer significantly. The generated model was able to accurately predict the output variable within the characterized range, even though three out of the twenty-four experimental runs were not successful. In addition, the JMP Data Exploration tools enabled a fast evaluation of the data quality as well as time-dependent factor correlations, which contributed to enhancing the DOE model. (view in My Videos)  
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Production Line Control in Semiconductor High-Volume Manufacturing (2020-EU-45MP-472)
Monday, March 9, 2020
Bugnon Guillaume, Data Management Engineer, SOITEC Baptiste Follet, Yield Engineer, Soitec Stephane Astie, Yield Manager, SOITEC   Semiconductor companies are continuously challenged on manufacturing cost and quality. An effective yield management system is key to detect line drift and minimize scrap to reduce wafer cost and quality impacts. JMP has helped Soitec to improve yield since 2007 by providing an easy-to-use tool to visualize equipment data versus product yield data. Methods for performing a set of statistical tests adapted to the nature of the response distribution (normal, non-normal, binary) were developed in 2010 for the yield team to find the root cause of the issue (Yield Guard dedicated JMP/JSL application). In multiple products and a full fab environment, data quality and real time analysis are critical for accurate drift root cause determination. Agile collaboration between IT and yield teams allows us to develop an automatic tool to reach those goals in order to control our production line. We would be proud to present our methodology from data collection to data analysis. The presentation will be done interactively with JMP and JSL codes.
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From Data Swamps to Knowledge Pools: A Simple Solution to Curating Science and Engineering Knowledge (2020-EU-EPO-464)
Monday, March 9, 2020
Bernard McKeown, JMP Client Manager, SAS   Industries like banking, online retail and marketing are data rich. They have genuine "Big Data": huge volumes of homogenous data that can readily answer important business questions. Their challenge is to scale their analytics.   Science and engineering, especially Research and Development, has a very different problem. The data is small in volume, because it is expensive to collect. It is difficult to aggregate it into a single source, because different projects bear little relationship to each other.   These "small data silos" present a different challenge: one of curating these knowledge pools to enable agility, speed and efficiency. By doing this companies can:   - Speed products to market by leveraging their existing knowledge, instead of wasting time and effort repeating what has already been done. - Fix problems faster downstream, because process knowledge becomes readily accessible across functions. - Reduce time wasted writing long reports that nobody reads or on a “Big IT Project” that nobody needs.   In this paper I will discuss how JMP Live can be used as a simple solution to curate knowledge into pools from experiments in a scientific or engineering organisation. (view in My Videos)  
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See Fer Yer Sen: The Importance of Data Exploration, a JMP(R) Live Showcase (2020-EU-30MP-458)
Monday, March 9, 2020
Phil Kay, JMP Senior Systems Engineer, SAS   People and organizations make expensive mistakes when they fail to explore their data. Decision makers cause untold damage through ignorance of statistical effects when they limit their analysis to simple summary tables. In this presentation you will hear how one charity wasted billions of dollars in this way. You will learn how you can easily avoid these traps by looking at your data from many angles. An example from media reports on "best places to live" will show why you need to look beyond headline results. And how simple visual exploration - interactive maps, trends and bubble plots - gives a richer understanding. All of this will be presented entirely through JMP Public, showcasing the latest capabilities of JMP Live.   In September 2017 the New York Times reported that Craven was the happiest area of the UK. Because this is an area that I know very well, I decided to take a look at the data. What I found was much more interesting than the media reports and was a great illustration of the small sample fallacy.   This story is all about the value of being able to explore data in many different ways. And how you can explore these interactive analyses and source the data through JMP Public. Hence, "see fer yer sen", which translates from the local Yorkshire dialect as "see for yourself".   If you want to find out more about this data exploration, read these two blogs posts: The happy place?  Crisis in Craven? An update on the UK happiness survey    (view in My Videos)     This and more interactive reports used in this presentations can be found here in JMP Public.
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Determining Confidence Limits for Linear Combinations of Variance Components in Mixed Models (2020-EU-EPO-455)
Monday, March 9, 2020
Hadley Myers, JMP Systems Engineer, SAS Chris Gotwalt, JMP Director of Statistical Research and Development, SAS   Generating linear models that include random components is essential across many industries, but particularly in the Pharmaceutical and Life Science domains.  The Mixed Model platform in JMP Pro allows such models to be defined and evaluated, yielding the contributions to the total variance of the individual model components, as well as their respective confidence intervals.  Calculating linear combinations of these variance components is straightforward, but the practicalities of the problem (unequal Degrees of Freedom, non-normal distributions, etc.)  prevent the corresponding confidence intervals of these linear combinations from being determined as easily.  Previously, JMP Pro users have needed to turn to other analytic software solutions, such as the “Variance Component Analysis” package in R, to resolve this gap in functionality and fulfill this requirement.  This presentation is to report on the creation of an add-in, available for use with JMP Pro, that uses parametric bootstrapping to obtain the needed confidence limits.  The add-in, Determining Confidence Limits for Linear Combinations of Variance Components in Mixed Models  , will be demonstrated, along with the accompanying details of how the technique was used to overcome the difficulties of this problem, as well as the benefit to users for which these calculations are a necessity. (view in My Videos)  
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Variance Components Analysis (2020-EU-EPO-438)
Monday, March 9, 2020
Phil Greaves, Staff Scientist, Fujifilm Diosynth Biotechnologies Amy Woodhall, Research Scientist, Fujifilm Diosynth Biotechnologies   With cost and timeline pressures for process development there is a drive to use high throughput methods.  Recent commercial availability of small scale down system hardware when coupled with design of experiments software enables the potential discovery of the few critical process parameters from the many, in the short timelines required.  For process evaluation, early phase development of fermentation recombinant protein processes often uses generic assay methods, but the high throughput methods are not necessarily optimised for accuracy and precision.   Thus the overall process analysis is the sum of process variability and that of the measurement system.  The estimate of experimental error (noise) will determine the size of response difference (signal) that can be readily detected in the experimental design.  JMP software contains process quality analysis tools such as gauge analysis and measurement system analysis that can be used to identify the sources of variation.  For expression of an intracellular model protein in E coli the work presented here will show how JMP can carry out variance component analysis on a nested design of the overall process analysis.  This information can then be used in the subsequent JMP design of experiments evaluation.
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Performance Evaluation and Prediction for Quenching Oils in Production Using JMP® Pro (2020-EU-EPO-436)
Monday, March 9, 2020
Victor Guiller, R&D Engineer Quenching, Forming and Hydraulic, Fuchs Lubrifiant France S.A.   Quenching is a very common method in the heat treatment of materials. In this process, a heated workpiece is rapidly cooled within a fluid to obtain certain material properties, i.e. specific hardness of metals. In the production of quenching oils, a time-consuming performance test evaluating the cooling speed and characteristics of the fluid is used to assess if the product meets the specifications. The evaluation and comparison of performances is realized through the analysis of six parameters obtained from the cooling curves. The study’s objectives are to create a mathematical model of the cooling curves with the Functional Data Explorer on JMP® Pro, predicting conformity or non-conformity of the production batches, and being able to classify the different products. The targeted additivation of these fluids has a major impact on their performance, such as the cooling rate. As a result of this study, the creation of the mathematical model leads to a correct product and conformity status identification, and the prediction model enables to plan correction of non-conform batch with the optimal amount of additive. The developed models make a significant contribution to making the fluid development more efficient. (view in My Videos)  
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A Practical Road Map for Implementing Industry 4.0 and Data Analytics Using Custom SQL and JMP(R) (2020-EU-45MP-429)
Monday, March 9, 2020
Joseph Beauchemin, Jr., Director of Quality (Non-Ferrous), Hitchiner Manufacturing Philip J. Ramsey, Professor, University of New Hampshire   Industry 4.0 (I4.0) is an initiative to transition organizations to the digital revolution and guide the creation of smart factories. Despite confusion about I4.0, one consequence is the proliferation of large amounts of data within organizations. We present a framework to manage many data streams resulting from I4.0 and propose standardized workflows for analytics to drive process improvements. JMP statistical software is a solution for data analyses and reporting. Standard Work and JMP are used to: Identify data requirements and collection strategies; create custom SQL databases; perform standardized analyses in JMP via custom scripts; and create dashboards with JMP for management reviews. The range of analytical capabilities and scriptability of JMP make it key to Standard Work. The JMP Query Builder is essential in accessing SQL databases that link all of the relevant data sources, while automatically integrating various JMP data analyses. A case study using JMP and Standard Work shows an advanced manufacturing company reduced data correction and formatting time from 90% to less than 10% of project time and increased yields from 10% to over 90%. JMP platforms used are: Query Builder, Process Screening, Predictor Screening, Response Screening, Model Driven Multivariate Control and Functional Data Explorer.   (view in My Videos)
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Simple Process Monitoring of Multiple Parameters Using JMP(R) (2020-EU-30MP-404)
Monday, March 9, 2020
Torsten Weber, Engineer Process Integration, Heliatek   The paper will discuss the application of JMP at Heliatek in terms of process monitoring in a pilot production environment. In a running production it is a challenge to keep track of multiple control charts simultaneously. The contribution describes an alarm dashboard compiled in JSL, which monitors multiple process parameters and visualizes critical limit exceedings in a simple way. The application counts the limit violations in a defined time frame of multiple components and depicts the results in a heat map. Database queries continuously update the data. This presentation will explain in detail: How to run this application in a loop to react as soon as possible to critical process variations. How to solve issues regarding automatic alarm emails without using the „mail()“ or „alarm script()“ function of JSL How to share this alarm board in a network via customized HTML report to minimize interrupting „hangover“ while updating the data. This application helps to enhance the production yield of organic-PV products by a simple visualization of multiple process parameters and therefore shortens the response time.   (view in My Videos)
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To Drink or Not to Drink? That Is the Question. Analyzing Alcohol Data With JMP(R) 15 (2020-EU-EPO45M-395)
Monday, March 9, 2020
Mandy Chambers, JMP Principal Test Engineer, SAS Melanie Drake, Principal Systems Developer, SAS/JMP   Did you know that 30% of Americans are teetotalers, while another 24 million consume on average about 10 drinks per day?   In 2017 alone, alcohol sales in the United States amounted to approximately 234.4 billion dollars. For this presentation, we narrowed our scope to explore alcohol sales data in North Carolina.   Our objective was to help plan marketing strategies for increasing vodka sales for a local distillery by analyzing sales patterns in different counties. We began with using the new PDF import in JMP 15 to pull data from the web, as well as taking advantage of the data table features to enhance visualizations, clean up the data, and prepare it for our analysis.  New features such as header graphs in the data table help determine which columns to use in evaluations. JMP allows comparative trends, fit analysis and forecasting to gather possible future sales scenarios, and JMP Live publishes these reports for everyone to view. When we are finished, not only will you know interesting statistics about North Carolinians concerning the amount they drink or the types of alcohol they consume, but you will also be able to use new JMP features. You will learn how painless JMP 15 makes it to analyze your data more effectively, create graphical visualizations attractive to the eye, and share your results more easily.
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NXP Structured Problem-Solving Approach: Two Case Studies in Semiconductor Test and Design for Automotive (2020-EU-EPO-384)
Monday, March 9, 2020
Corinne Bergès, Six Sigma Black Belt, NXP Semiconductors Kurt Neugebauer, Analog Design Engineer, NXP Semiconductors Da Dai, Design Automation Engineer, NXP Semiconductors Martin Kunstmann, R&D-SUP-Working student, NXP Semiconductors Alain Beaudet, Product and Test Engineer, NXP Semiconductors   Structured Problem Solving (SPS) is one of the three pillars of NXP Six Sigma system, with Quality Culture and Continuous Improvement, and demonstrates still more NXP Quality system maturity. Some key approaches in NXP SPS are fitting with the DMAIC/DMADV, 8D or 5-Why frameworks. They widely use statistics to change assumptions into evidences, necessary for a real defect root cause elimination: modeling, DOE, multivariate analysis, …Two specific statistical analysis are described. In design for automotive, about simulation of parametric, hard or soft defects, purpose is to implement the best algorithm to reduce number of simulations, without impacting test coverage or failure rate estimation precision: for this, JMP provides interesting options in clustering. NXP experiments will result in an algorithm and in some recommendations for the new IEEE standard on study about defect coverage accounting method. Now, downstream in manufacturing, when it deals with capability index computation, and with normality test, to bypass high sensitivity of these tests for a slight abnormality, a methodology was designed in JMP to quantify shift from normality, by using the Shash distribution and its Kurtosis and Skewness parameters. A script was implemented to automate it on the more than 3000 tests for an automotive product.  (view in My Videos)  
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How JMP(R) Can Reduce Unutilized Engineering Talent and Save Money (2020-EU-EPO-375)
Monday, March 9, 2020
Fabrizio Ruo Redda, R&D Senior Manager, Vishay Semiconductor Italiana   Semiconductor devices development requires an extensive work of testing and data analysis. Electrical characterization laboratory provides a large amount of data to development engineers, coming every time in different formats according to specific products under test. Usually development engineers spend a lot of time putting together the data, while a limited fraction of time is dedicated to value added activities like analyzing statistically the data and drawing sounding conclusions. In the frame of a lean six sigma project, it was possible to show the economic advantage achievable by eliminating the tedious data preparation or better “data moving” process. JMP scripting capability is used to manage complex data files from different testers so that they can be easily uploaded in a SQL database. Development engineers can now use JMP to download data directly from SQL in a format ready for analysis. The typical engineering analysis time has been reduced by more than 85%, but more important, it is now only dedicated to value added analysis without any intellectual waste. Furthermore, the quality of the analysis and conclusion is now improved, considering also the possibility to make quick comparison among products or with previously collected data.
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Data Exploration and Discovery in Multi-isotope Imaging Mass Spectrometry (MIMS) in Cancer Research (2020-EU-30MP-371)
Monday, March 9, 2020
Greg McMahon, Principal Research Scientist, National Physical Laboratory   Multi-isotope imaging mass spectrometry (MIMS) combines stable isotope labeling of biological samples with high spatial resolution (sub-cellular) mass spectrometry imaging and extensive statistical analysis of the resultant image data. The images are rich in information, and use of JMP allows a quick and easy method of analyzing the data for information that is either just subtly contained within the image, or other information that may be below the first "obvious" layer of information. Combining Graph Builder with simple data distributions and local data filters provides a wealth of information. The approach can be extended by application of cluster analysis and multivariate statistics. In this presentation, we will use an example tracking the metabolic fate of 13C and 18O stable isotope labeled glucose in mouse breast cancer tumors engineered to contain cells with either high levels of the Myc oncogene, which is a driver for aggressive breast cancer growth, or low levels of the Myc oncogene. We will finish with a few comments about the significance of the results in terms of cancer research for the non-expert.
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Using Simulation Methods in JMP® to Prevent Supply Chain Fires (2020-EU-30MP-356)
Monday, March 9, 2020
Stephen Pearson, Specialist Data Scientist, Syngenta   Many powdered materials slowly oxidize with time which generates heat. If in a bulk form (such as during transport or storage) then heat generation can exceed heat loss, leading to ignition. Climate control and limiting packing amounts can reduce the risk, but this increases the costs for the consumer through reduced logistical options, larger shipping volumes and disposal of additional packaging. Laboratory tests are well established to determine a safe packing size. However, they are costly, especially for new products where limited amounts of material are available. The physics of the oxidation process can be simulated provided all the material properties are known. Using JMP® we will demonstrate how to combine these two approaches to reduce the amount of thermal stability testing required: 1) generate a constrained spacing-filling experimental design; 2) control the simulation software (COMSOL Multiphysics®) via JSL; 3) build meta-models; 4) simulate the outcome for new materials. By obtaining estimates of different material properties with each test, the prediction uncertainty can be updated to suggest the range of suitable packaging given the available data. This enables a data-driven approach to the selection of laboratory tests.   (view in My Videos)
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Calculating Nonparametric Tolerance Intervals for Small Sample Sizes (2020-EU-30MP-343)
Monday, March 9, 2020
Oliver Thunich, Consultant, STATCON GmbH   In industry production, tolerance intervals are widely used to determine the quality of a process.The commonly used tolerance intervals, however, assume normal distribution of the data, which is problematic for many processes. Following a request of a client, we came up with a possibility to calculate nonparametric tolerance intervals by calculating confidence intervals for quantiles using the nonparametric empirical likelihood approach implemented in JMP. As the desired sample sizes become very small, the traditional nonparametric confidence intervals tend to return unstable results. Therefore, we developed a JSL script that extends the empirical likelihood method and is able to generate stable, nonparametric tolerance intervals for a large proportion of the population even with small samples. A simulation study evaluates the performance of the approach in comparison to existing methods using production data as well as survival analysis data. We found that the proposed method is much more stable than existing methods, especially when the data heavily differs from a normal distribution. Using JMP in combination with the implemented method, we are able to assure quality of processes where measuring quality is very costly and/or time consuming.
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Pictures From the Gallery 5: Select Advanced Graph Builder Views (2020-EU-45MP-320)
Monday, March 9, 2020
Intro Video: Click Here First (view in My Videos)   Main Presentation Video: Click Here Next (view in My Videos)   The Pictures From the Gallery 5:   A picture is said to be worth a thousand words, and the visuals that can be created in JMP Graph Builder can 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, the Gallery 5 presentation and journal features new views available in the latest versions of JMP. We will feature several popular industry graph formats that you may not have known could be easily built within JMP. Views such ridgeline density plots, bag plots, informative box plots and more can help breathe life into your graphs and provide a compelling platform to help manage up your results.   Attached: - Pictures From the Gallery 5 JMP Journal (Includes Pictures, Instructions and Scripted Sample Data so you can learn, practice and replicate all the graph views from the gallery!)   Bonus: See past Pictures from the Gallery Journals: - Pictures from the Gallery 4 (Pictures From the Gallery 4) - Pictures from the Gallery 3 (Pictures From the Gallery 3) - Pictures from the Gallery 2 (Pictures From the Gallery 2) - Pictures from the Gallery 1 (Pictures From the Gallery 1)  
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Large-Scale Process Monitoring Using JMP(R) (2020-EU-TUT-315)
Monday, March 9, 2020
Laura Lancaster, JMP Principal Research Statistician Developer, SAS Jianfeng Ding, Senior Research Statistician Developer, JMP   In this age of big data and complex manufacturing there is often an enormous amount of process data that needs to be monitored and analyzed to maintain or improve quality. JMP has several tools to help the analyst quickly and efficiently increase the scale of their process monitoring. The Process Screening platform allows users to easily scan processes for stability and capability and enables them to focus attention on processes needing improvement. The platform initially computes a summary report based on control chart, capability and stability calculations, and creates several graphs for quick visual assessment of process health. Based on these initial results, it is easy to select the processes needing attention and explore them more in depth with access to Control Chart Builder and Process Capability. The Model Driven Multivariate Control Chart (MDMCC) platform, new in JMP 15, allows users to monitor large amounts of highly correlated processes. This platform can be used in conjunction with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts, and suggest probable causes of process changes. We will use case studies to demonstrate how to use JMP to monitor and analyze many processes for fast and efficient improvement.
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Recent Developments in JMP(R) Quality and SPC (2020-EU-45MP-306)
Monday, March 9, 2020
Laura Lancaster, JMP Principal Research Statistician Developer, SAS Jianfeng Ding, JMP Senior Research Statistician Developer, SAS Annie Zangi, JMP Senior Research Statistician Developer, SAS   JMP has several new quality platforms and features – modernized process capability in Distribution, CUSUM Control Chart and Model Driven Multivariate Control Chart – that make quality analysis easier and more effective than ever. The long-standing Distribution platform has been updated for JMP 15 with a more modern and feature-rich process capability report that now matches the capability reports in Process Capability and Control Chart Builder. We will demonstrate how the new process capability features in Distribution make capability analysis easier with an integrated process improvement approach. The CUSUM Control Chart platform was designed to help users detect small shifts in their process over time, such as gradual drift, where Shewhart charts can be less effective. We will demonstrate how to use the CUSUM Control Chart platform and use average run length to assess the chart performance. The Model Driven Multivariate Control Chart (MDMCC) platform, new in JMP 15, was designed for users who monitor large amounts of highly correlated process variables. We will demonstrate how MDMCC can be used in conjunction with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts and suggest probable causes of process changes.
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