The following are customer impact quotes from success stories and Statistically Speaking events organized by subject. These quotes have been approved for public use. Remember to check back for updates and new content!
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ANALYTICS CULTURE / JMP WORKFLOW
"Some employees are more reluctant to embrace analytics. They might recognize we're on a data journey and they don't want to be left behind, so they get on board. However, the real value is in those who simply get it and their whole mindset changes after they experience insight at their fingertips just waiting to be uncovered."
-Kieran O’Mahony - Dairygold
JMP DS Americas 2021- Recording Producers - Data Explorers Spotlight gallery.mp4 - All Documents (sharepoint.com)
"Starting out, trying to understand data analytics, and using a new program like JMP requires a manager to say it’s OK to go slower in the short term with analytic problems so that people feel empowered to actually learn rather than resort to their old ways. When the house is on fire, people won’t naturally grab the tool that will slow them down at first, so that’s our job as change agents to let them know it’s OK to go slower at the outset to end up going faster and farther overall."
-Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
“We wanted to stop firefighting, so we started educating people around statistics, data analysis and why data is vital for decision-making.... People used to often ask, ‘Can you crunch these numbers for me?’ But now they’re doing it themselves. That saves me time. But it also means we’re spending less time on accessing the data, and more on what’s really important – decision-making.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Xaar success story
"So the key hallmarks there were the willingness to grow and adapt, the curiosity to learn how to do our work better, and the investment and the resources, both educational and technical, such as JMP software, by leadership to support people in upskilling, and it's very much a continuous growth activity."
– Loren Perelman, Vice President of Science, Riffyn
Statistically Speaking: Developing a shared vision for analytic excellence
"I first want to thank the team at JMP for inviting me here to speak today. And also for building a world-class product that really and truly changes the way I look at the world."
– Tim Gardner, CEO, Riffyn
Statistically Speaking: Reshaping the “Experiment” for 21st Century Science (link coming soon)
"[Using Query Builder to interface directly with data from a range of data sources] was a really, really big step for us because then you’re not relying on IT to give you access to the data. It’s starting a journey of digitalization.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Xaar success story
“Innovation through data is a key characteristic of leading organizations. The ability to analyze data and visually communicate results is critical to speeding up decisions with a low margin of error. At Vishay Semiconductor Italiana, analytics culture is part of our DNA – and it improves day after day. I believe that the best results are achieved when analytic skill and technical competence are merged, fostering Lean Six Sigma culture deployment.”
– Fabrizio Ruo Redda, Senior Manager of R&D, Vishay Semiconductor Italiana
Vishay success story
“The integration of the analysis platform with the scripting language is what makes JMP a very powerful tool. You can learn basic scripting very easily and use it at your convenience… With the new system it is now possible to perform detailed data analysis with minimum effort and lower risk of error. Analysis that in the past required days of work to integrate data from different systems can now be done in minutes, fostering innovation through data.”
– Fabrizio Ruo Redda, Senior Manager of R&D, Vishay Semiconductor Italiana
Vishay success story
“[Without JMP] I believe we would have continued to make incremental improvements, but not realized the same level of business impact.”
– Fabrizio Ruo Redda, Senior Manager of R&D, Vishay Semiconductor Italiana
Vishay success story
"JMP is a really attractive and easy-to-use software that also has interesting features like DOE and all the other statistical analyses I might need. To have this tool – and also to be able to code in this tool – is great. In JMP, you have a software where you can analyze the data and also build some of your own applications with scripts. With JSL, you can quickly learn a lot of things."
– Torsten Weber, Process Integration Engineer, Heliatek
Heliatek success story
“JMP can integrate everything into one unified platform… We can use JMP functions to import data from databases and multiple files, do an efficient cleaning or preparation using various JMP table functions. JMP provides many powerful platforms and tools for statistical analysis and modeling. The platform design is user-friendly and keeps details of the analysis process flow, providing high-level functions in JMP’s scripting language to run various analyses.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“We have the choice of building JMP applications on a dashboard using existing JMP templates or AI tools. We can choose to use JSL to write some UI from scratch. In terms of automation, JSL has a stable runtime environment. It is useful to create robust, reusable code for running complex analysis or interacting with the external systems.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“Now it is time to share this readout and observations with our end users. And JMP has very rich functions that can help us create graphs and wrap everything into an interactive JMP report. We just click a button and it will automatically generate a root-cause analysis report. This is a customized report – with the help of some JSL here, the graphs are sorted in the order of their contributions, given by the predictive screening. The best thing about this report is that it’s interactive. We can add in some Fit-Y-By-X charts for the users to interpret the difference between good and bad grooves. On the right, we’ve added another SPC panel in case our users are interested in some sensor trends.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“We’ll use one line to import our JMP file but in a real case we might want to connect JMP to some SQL query. In the second step, we simply define a list of these variables for the future functions to use. And we could add in or create many other data-cleaning functions here…. JMP has a very handy feature to help us save scripts from a JMP interface, so we can modify the existing code instead of creating them from scratch.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“We can let our analysts and domain experts work closely together using the same interface and share the approach to prototype our ideas using the existing JMP platforms or application builders… And we can use JMP scripting language to connect every piece of the process and develop some complex analysis applications. Further expansion of these applications allows us to interact with external systems, such as responding to the external database or publishing a report to our web system to benefit more users. That iterative approach is definitely boosting our data value extraction process.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“Nowadays, more and more organizations are looking to leverage advanced data analytics, or machine-learning technologies, to develop or maintain their competitive advantage. While the top-down strategy is helping identify some key areas, by evolving data infrastructures and the allocation of resources to accelerate the analytics process, we can proactively take a bottom-up approach, making use of technologies [like JMP] that streamline the implementation of ideas. More automated analytics projects enhance our analytics capabilities and benefit the front-line users as well.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“At NXP, we allow a bit of ambiguity with the word ‘dashboard.’ We would rather define a dashboard as any type of graphical interface which organizes and visualizes data to increase the ease of consumption by any type of audience. It should enable comparisons to goals and metrics or KPIs and provide additional insights that you can’t derive from the raw data itself. At NXP, we have what we call our ‘JMP Guides’ – our dashboards, mini applications. Each JMP Guide will run a JSL script which performs analysis on a JMP data table, generating summary statistics and it may include thresholds like spec limits. And it will obviously also include visualizations that go along with the statistics.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“At NXP, JMP is an enterprise application – meaning that there are processes to support the application across all domains globally across hundreds and hundreds of teams. With the centralization of resources under my team to support JMP [deployment], we’ve been able to consolidate the development of JMP dashboards. They’re hosted on an internal website that any JMP user will have access to.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“While the user’s data may be different from one [JMP dashboard] to another, the analysis is always the same. It’s standardized from one user to the next… from machine to machine, regardless of where you are…. [All JMP users at NXP] have access to the same version of our JMP [dashboards]. Essentially, it is that single version of truth for standardized analysis across the company.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“[One of the main benefits of creating a common culture of analytics is that] we all speak the same language. We’ve been able to develop a glossary of standardized terms and language around solving data analytics challenges using JMP. For example, we have people in Austin, Texas working in manufacturing who can reach out to our quality engineers in Tianjin, China. They’re both speaking the same language when it comes to working through problem solving using data analytics.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“Another benefit [of a common culture of analytics] is standardized analytics methods, which we consider an essential part of the change management process. That process prescribes the specific analytics which must be evaluated based on the change type – and type of data being analyzed. No one has an excuse to skip the analysis since they’ve already been standardized through coded dashboards in JMP.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“Standardizing our tools and methods allows us to standardize our training…. We have a pretty solid internal training program on applied statistics using courses we purchased from JMP. But this wouldn’t be possible if we weren’t able to standardize the methods by which we perform analysis.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“At NXP, we actually dashboard our dashboards. We have a dashboard to measure and assess the usage of all our JMP dashboards… One way we go about that is to have a background script running on our machines. Someone launches JMP and then they also use a [dashboard], it will measure their usage. So we’re able to monitor that and take an operations perspective to JMP dashboarding itself.”
Statistically Speaking: Make dashboards an essential part of the science and engineering workflow
– Alex Pamatat, IT Solutions Manager, Advance Analytics, NXP
“[Early adopters at Parker LORD] built cases to help show the power of [analytics], and it started to look more approachable. They were willing to help others learn – they became the experts. So we didn’t have to breakthrough with everyone, just with a few people and use them as lead adopters.”
– Tim Holtz, Process Engineering Manager, Parker LORD
Parker LORD success story
"We've spent a lot of time and money developing the automation infrastructure, the robotics that move the DNA and liquids around and put that DNA into the cells and grows them up. But also a lot of money on metrology, mass spectrometry, liquid chromatography, that sort of thing—equally as important. And we automatically design, build, and make these things. But we need to measure the output and see whether it's produced what we're interested in. And what we decided was that JMP would be an integral part of that process. And also in order to do that, building custom software and having that program capability inhouse has really helped to accelerate that process.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“Robotics allows you to start to measure more and more and more things, you're potentially losing the ability to keep track of all that and have that information be meaningful. I read a great term for this in Nature: datageddon. We have the ability to collect all this data, but we can potentially have problems in the interpretation of that data if we’re not thinking clearly about why and how we want to measure things.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“How do you build a culture of analytic excellence? I felt that there was a lack of really strong statistics within the field [of pharmacology and drug discovery]. I was aware that modeling and DOE were available. And I felt that there was a massive scope for increasing the reproducibility of biological research by moving in that way. But what I felt was -- particularly having introduced new ways of doing things, new instrumentation, new software -- that there also had to be an internal champion that could push that. Because it's really easy for things to just kind of fall by the wayside unless there's someone internally that someone can knock on the door and say, can you help me with this? I felt that I wanted to be that champion. Also we had a mandate. When it was decided that we were going to do this, the CEO mandated, as much as possible, that we were going to do design of experiments and statistical modeling.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“What we've been trying to do has been trying to build institutional knowledge. As more people get better at this and find out new things through their own continuing education, then more people can learn from that. So you can get an exponential increase of institutional knowledge, because I don't want to be the champion forever. I want there to be multiple champions and for everyone to be learning from each other and building up those capabilities that way.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“Some companies are beginning to explore the potential of big data by participating in initiatives like the Internet of Things and Industry 4.0. As far as I'm concerned, a lot of these things are actually head office initiatives at the moment when in reality a lot of the data is held by the maintenance manager, production manager, the R&D managers. And it is these people, I feel, who should have access to JMP and who can make most out of the data that's been generated in our factories.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“Many companies are at the beginning of this data revolution. I visited several companies within the last few months who are trying to work out how to use their data. Some of them are working out how to store it. There's a lot of concern about how to normalize it and get it into a state for analysis. And I do believe that JMP can do that – enabling your data to be extracted from several different sources and normalized within JMP.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“Enabling our engineers to have a proper conversation about data driven results with management is key. I've seen how some big companies – very big chemical companies now – are utilizing JMP for their manager meetings. Because the data is already available to them to answer questions that come from the management team: “What happens if X?” Well, you pull the data up, show what happens when you add more… modifier to the process. If you have the data there in front of you, you can show pictorially how it crucifies some of the important parameters. So some people are using [JMP] on a management level as a tool to hold production management and R&D management meetings.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“The challenge recently has been in making available the data that is siloed in different systems and requires manual work to be integrated… To overcome those data accessibility issues, like querying databases or multiple file import, joining data from multiple sources, and JMP has enabled us to do all that, but without having the need to program. I think more importantly than that is we've given those skills to engineers and subject matter experts, which are now doing it themselves and turning that access into active data, doing faster and better proactive analysis and improvement as a consequence.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Statistically Speaking: Why quality improvement is imperative in the big data era
“Our audience already understands the value of data, and they're definitely keen to move on to the next level to make better decisions. My recommendation is to evaluate the level of maturity the business is with regards to data and statistical methods, and maybe to find a plan to move forward to that next level.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Statistically Speaking: Why quality improvement is imperative in the big data era
“We are very often called in by management. And the engineers who have been trying to solve the issues [we were called in to fix] are often concerned about their career if we come in and solve the problems they should have solved. So it's therefore essential to make them understand that their knowledge is key to being successful with statistical methods. It's actually the combination of statistics and engineering that makes a difference. Statistics alone, or engineering alone, is not sufficient. We need statistical engineering. And the best way to ensure this happens is by training an engineer in statistics using software like JMP. After [learning to] solve the issue together with us, they are much better prepared to solve the [challenges] next time. They have increased their value for the company, and of course, also their own value in general, which it's very motivating for people.”
– Per Vase, Managing Consultant, NNE
Statistically Speaking: Why quality improvement is imperative in the big data era
“My advice there would be to stay open to new opportunities, to methods that are currently not being practiced or cannot be easily practiced at the company. For example, conducting a design of experiments can be disruptive, even costly to start with, for companies that can only collect their experimental data in their production lines. We actually went through that specifically a couple of years ago as part of an improvement project about a quality issue that was concerning us for several months. However, the benefits of well-planned DOE together with the process engineers’ knowledge brought the understanding needed to take appropriate actions, improve yields, and get that return on investment. I would say to encourage members of the team who already have that statistical mindset to come forward with the right skills, demonstrate the value of these methods, and communicate the impact on their work and on the business. I think this will create momentum and encourage other people, including managers themselves, to be more receptive to data analytics as the core skill, and be willing to incorporate it as part of their day-to-day decisions.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Statistically Speaking: Why quality improvement is imperative in the big data era
“Think big, start small, and scale fast. It's important to make early success stories to keep momentum on the long journey, to focus on one of the issues at your company and start by identifying the improvement potential and estimate money left on the table so you can get management’s attention. Then, to start fast, scale the issue down to sub issues that can be solved fast, and then train selected engineers in applied statistics and ensure they have support from experienced internal or external consultants. There's no time for failures due to lack of experience. And make sure that those who are trained afterwards can use a third – minimum – of their time working with these methods; otherwise, they will never get skilled to make continued success stories. This is essential to get the conversion of the company culture.”
– Per Vase, Managing Consultant, NNE
Statistically Speaking: Why quality improvement is imperative in the big data era
“If a company is currently using the most common statistical methods to quantify and control variation, such as measurement system analysis, capability studies, statistical process control, but still hasn't found the optimization required, maybe it's time to explore more advanced methods, like predictive modeling, to find possible relationships between valuables that one previously considered. Or maybe do a controlled experiment. Find those relationships outside the normal operating range by conducting a DOE.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Statistically Speaking: Why quality improvement is imperative in the big data era
“Machines don't actually learn very much that's outside of the data that you give them. So where
people are needed is in the domain knowledge. The most important thing is in defining the problem you want to look at, defining the parameters in which you accept good or not good data, and then also defining what the levels of success are. That's where human creativity comes in. Which problems are we even going to look at in the first place? A machine cannot tell you that. That's something that humans are for.”
– Bernard Ennis, Principal Researcher, Tata Steel
Statistically Speaking: Solving science and engineering problems with machine learning
"So over the years I think it’s fair to say that my job description has grown to be multifaceted, and all of it is tied closely to BIOTRONIK’s emphasis on analytics and our usage of JMP."
– Gerd Kopp, Quality Management and Reliability Statistician, Biotronik
Gerd Kopp talks advocating for analytics overcoming internal hurdles and building a relationship with the JMP organization
DISCOVERING INSIGHTS
“JMP plays a key role in that exploration by helping you explore and understand your data in a quick, efficient manner… It gives you the ability to not know what you want to do. JMP is a great tool – you can explore your data to really understand what works and what doesn’t.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“JMP was not only faster to use for every task, but when I showed how nuisance factors such as component measurements could easily be included in a DOE as covariates, this boosted our already existing efforts to make processes even more robust. So JMP was not only more efficient, but it added new possibilities for analysis.”
– Gerd Kopp, Quality Management and Reliability Statistician, Biotronik
Gerd Kopp talks advocating for analytics overcoming internal hurdles and building a relationship with the JMP organization
“JMP is an important tool. I get extremely positive feedback from internal customers when I show them the outcome of my analysis. It’s very impressive for them to be able to see the data so easily, in one place all together, while clearly distinguishing between potential critical factors, such as visit and dose level. Some of the [subject matter experts] now want to use JMP themselves.”
– Angela Manginelli, Senior Scientist, Biomarker Data Analytics, Merck KGaA, Darmstadt, Germany
Merck success story
“The most important measuring point is the ability to make a difference. The knowledge we can acquire with the analysis of data really impacts our clinical trials. It is therefore crucial to have tools like JMP available, which greatly support the purpose of my job.”
– Angela Manginelli, Senior Scientist, Biomarker Data Analytics, Merck KGaA, Darmstadt, Germany
Merck success story
“The value of JMP to Medtronic lies in being able to understand and make use of all of our data. More [efficacious analysis methods] mean we’re able to better understand the performance of our devices over time and ensure that we’re designing safe and effective devices. That’s our primary goal. And JMP helps us with that goal by enabling us to get all we can out of our data. It’s something we couldn’t do before.”
– Susan Roweton, Research Manager, Medtronic
Medtronic success story
“Data is worthless if we don’t have the right tools to work with it, so we need to be using the newest and most innovative means to manage this data. JMP Pro uses the most innovative and effective methods, and that’s very important.”
– Corinne Bergès, Manager, Risk Assessment, Statistical and Safety Analysis and Six Sigma, NXP
NXP success story
“We not only solved the problem, we solved the problem to the point where our customer came to us and said, ‘You guys have done so well on this, we'd like to tighten the specs. The old specs don’t reflect what you can do today.’”
– Jim Lamar, Quality Manager, Saint-Gobain NorPro
Saint-Gobain NorPro success story
“JMP is absolutely invaluable if you want to see what's going on within the data. We can learn from the data already available to visualize an outcome that works both in theory and in practice.”
– Egon Gross, Master Technologist, Symrise
Symrise success story
“Every Monday morning, we have an all-engineers-on-deck meeting on various topics and there are usually two or three executives in the room, and they’re able to see what we can do with JMP; it’s definitely recognized. The VP for R&D is a JMP user. It’s used throughout the organization.”
– Jed Campbell, Quality Manager, US Synthetic
US Synthetic success story
“It’s very difficult culturally to get people to change the way they manage their data, but if you’re able to really distill out insights from the data – and make critical business decisions based on those insights – then you should be sure that those decisions are right. There are some very simple things that can help you do that. And for me, JMP is one of those tools.”
– Owen Lozman, Vice President, M Ventures, the strategic capital arm of Merck KGaA
M Ventures success story
“JMP enables knowledge discovery across our entire business and allows Whirlpool to analyze data that would not have been possible otherwise. The applications are endless.”
– Aaron Youngstedt, Operation Excellence Program Deployment Lead, Whirlpool
Whirlpool success story
“Regardless of whether someone is across the hall or across the ocean, if you’re looking at a JMP file together, you can drop some scripts in and find ways to extend the analysis.”
– Dave Sartori, Senior Data Scientist, PPG Industries
PPG Industries success story
“JMP is a very useful tool for our method-development stage because it gives us the best answers to what we’re looking for.”
– Camilla Liscio, Applications Chemist, Anatune
Anatune success story
EASE OF USE / LEARNING STATISTICS
“[JMP] is very intuitive for new users because you don’t need to understand anything to do with analytics to be able to extract insights from your data via the simple drag-and-drop environment. That’s hugely beneficial for new users and something that people really like.”
- Kieran O’Mahony, Data Science and Analytics Manager, Dairygold
Dairygold | JMP
“It helped that there was a lot of information on the JMP website, including a video on how to set up the software quickly. So I could do a quick trial run and see how easy it is to implement JMP and how well it works…. It was just a matter of exploring various models and options I could use…. I found JMP to be straightforward to use and quite powerful.”
– Alin Ionas, Analytical Chemist, Technical University of Denmark
DTU success story
“I often notice that new users are surprised how easily graphs can be created and rearranged in a drag-and-drop manner. This not only saves time, but it also helps with the perception of statistics as being an asset rather than an obstacle.”
– Gerd Kopp, Quality Management and Reliability Statistician, Biotronik
Gerd Kopp talks advocating for analytics overcoming internal hurdles and building a relationship with the JMP organization
“There may be a general lack of understanding of the power of modern statistical methods and the tools that are now available that can help scientists and engineers. These tools might seem a bit daunting to people if they're not used to using statistics, but from my understanding of the software that JMP provides, you don't really need to know how these statistics work. You need to know how to use JMP and apply them to the data that you have. And JMP enables you to visualize the potential outcomes beyond the visualization software that I believe is already well-known within the industry.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“One of the things that came across very clearly when we visited some companies in the process sector is that the people there have the capability to use this software… The ability to use JMP is acquired within a couple of days. You don't need months of training here, especially since it's a little bit like click and point in Microsoft. You can start to use it very quickly.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“JMP provides tutorials online and also occasionally a little bit of personal help when you first contact or meet your sales advisor. Most people that I [visited] who I knew were not using JMP – I can tell you that every one of those companies has since purchased the software, which is some kind of indication of the impact of the case studies we showed them, I think.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“Our engineers and scientists don’t get paid to do statistics. We get paid to make recommendations and decisions. So the ability to teach them how to use the data to get there is really what we do internally to establish that foundation.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“JMP has amazing tutorials that handhold you through this. You can just click your way through to new levels of learning.”
– Timothy Gardner, Founder & CEO, Riffyn
Statistically Speaking: The power of structured experimentation in science and engineering
"I would start with a platform like JMP, honestly. It just comes with an amazing amount of onboard learning opportunities and really practical information to start driving that process forward. It can be really helpful. There's sort of an industry around JMP and similar platforms for consultants who can come in and provide that training. And so you can really extend your network by reaching out to experts, bringing them in for specific problems, and kind of learning through iteration that way as well."
– Loren Perelman, Vice President of Science, Riffyn
Statistically Speaking: Developing a shared vision for analytic excellence
“Because JMP is so intuitive, anyone can achieve useful analytics.”
– Corinne Bergès, Manager, Risk Assessment, Statistical and Safety Analysis and Six Sigma, NXP
NXP success story
“What I like about JMP is that it’s a simple, self-explanatory tool – you don’t have to be a statistician to work it.”
– Owen Lozman, Vice President, M Ventures, the strategic capital arm of Merck KGaA
M Ventures success story
“The thing that was most enticing to me: the user-friendliness of JMP. The platform is so easy to navigate for someone who doesn’t have a ton of experience with statistics. It’s really helped me develop my skills.”
– Tyler Dunaway, Process Engineer, US Synthetic
US Synthetic success story
“Once we made analytics more a part of the problem-solving culture, we actually did see problems beginning to subside.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“JMP is sometimes too smart for its own good but it will direct you towards the analysis that fits your data.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“For us, it was when [management] understood that buying these JMP licenses would lower the bar for when we can use our data. You don't have to be a statistician or data expert. You can use it right away, and that was that was actually the turning point.”
– Søren Pehrson, Catalyst Manufacturing Scientist, Haldor Topsoe A/S
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“JMP’s interactive graphics are very easy to understand. The entire representation is very intuitive and clear. All my colleagues agree."
– Guifu Wang, Principal Reliability Engineer, ATL
ATL success story
"I’m a chemist, not a statistician, and I’ve come to really appreciate how JMP makes things so quick and easy."
– Jim Dailey, Global Technical Key Account Manager, BASF
BASF success story
“On our team, we have folks who are completely unfamiliar with statistics and folks who are more advanced, and one of the values we see with JMP is the ability to use it to communicate, to everyone on the team, simple or complex interactions that we uncover during the design cycle.”
– John Allen, Engineer, W.L. Gore Elixir Strings
W.L. Gore Elixir Strings success story
“JMP is graphical and a perfect tool for communication that helps shorten our design cycles. I also think its ease of use is a really powerful attribute. I mentor new hires, and I’ve seen them come up to speed on JMP quickly, and thereby contribute to the team almost at the get-go.”
– John Allen, Engineer, W.L. Gore Elixir Strings
W.L. Gore Elixir Strings success story
“I’ve found that JMP is very suitable for non-statisticians. The software’s clear and intuitive visual display is ideal for statisticians and clinical experts alike, as it's very easy to understand. And it allows software operators to better understand the correlation and structure of statistical methods, rather than looking at them separately."
– Feng Guo Shuang, Director, Big Data and Engineering Research Center, Beijing Children’s Hospital
Beijing Children’s Hospital success story
“With the same software, I can connect to our internal database, extract selected data by using filters, combine data tables, clean up my data, visualize it and apply powerful statistical techniques to analyze it. Today at CEA, scientists may use several different tools to visualize their data, design experiments or perform data preparation and statistical analysis. But I am always very enthusiastic to try to convince them that all of this can be done with one single software.”
– Damien Perret, R&D Scientist, CEA Marcoule
CEA Marcoule success story
“I am originally a chemist with almost no education in statistics, but I developed an honest background knowledge in stats just by using JMP. Obviously, the process of acquiring knowledge is infinite… but we have the JMP community of users and developers, which is a great help to progress.”
– Damien Perret, R&D Scientist, CEA Marcoule
CEA Marcoule success story
“From an engineering point of view, JMP is great because it gives you the speed, connectivity and analysis very quickly. And it handles lots of data and I like the fact that JMP is not intimidating.”
– David Seniuk, Senior Product Engineer, Ciena
Ciena success story
“I like to use JMP because it's so interactive. It's very user-friendly, and there are so many tools available. Researchers from other disciplines all have different levels of programming skill and statistical knowledge. Many ongoing research projects are interdisciplinary in nature and require effective collaboration from both domain expertise and statistical computing.”
– Jiangeng Huang, PhD, Virginia Tech
Virginia Tech success story
“JMP makes it so easy for researchers to analyze their own data. I think that's the really cool thing about JMP. You advance research, but you're also learning in this process; you're learning skills that you can apply when you move on to do something that's not pure statistics.”
– Jiangeng Huang, PhD, Virginia Tech
Virginia Tech success story
“JMP allows people who aren’t data savvy to engage.”
– Michelle Woolfolk, Water Resources Engineer, City of Durham, North Carolina
City of Durham success story
“I gained valuable knowledge of the theoretical elements of statistics. JMP has certainly made me more confident, and I think that is the best advantage of all.”
– SungSoo Lee, Innovation Group Manager, Daeduck Electronics
Daeduck Electronics success story
“I was intrigued by how analytics, models, project management, process – they all share a similar nature. Analytics isn’t just about number crunching. You can use analytics on a daily basis to drive project and team management and performance. And with JMP, you can learn to use the tool and make decisions right away.”
– Tyler Wise, Project Analyst in Global Business Services, Diebold Nixdorf
Diebold Nixdorf success story
“With JMP, we can present statistical concepts in a clear, approachable, relevant and fun way that allows veterinarians to deliver relevant information to their customers devoid of ‘statistical gobbledygook.’ I knew that JMP would allow [my clients] to see patterns, trends, things of value for their customers, without requiring a statistical background.”
– Andy Liddle, Director, Process Insight Consulting
Elanco success story
“JMP is really easy to learn and to begin to use. Often students are here for only about a year, so it’s important to have them operative in a short time.”
– Mauro Tettamanti, Epidemiologist, Mario Negri Institute
Mario Negri Institute success story
“I'd like for the statistics discipline to really move in a direction where both statistical science and the fundamentals of using these tools are something that people are solid on, but then also, the engineering part becomes much more emphasized.”
– Christine Anderson-Cook, Statistician, Los Alamos National Laboratory
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“JMP is very powerful. The interface is clean and tidy, and it is very intuitive. Even without training, JMP is easy to use, especially for engineers.”
– Vinson Wei, Run Plant Engineer, DuPont Taiwan
DuPont | JMP
“JMP outputs are easy to understand, concise and visually explain the project clearly and easily”
– Jerry Huang, Analytical Lab Senior Engineer, DuPont Taiwan
DuPont | JMP
“It’s hard to make errors in JMP. The interface helps you set up and run tests, and the data visualization makes it easy to see the results right away.”
- Jennifer Verdolin, PhD, University of Arizona
University of Arizona | JMP
COST SAVINGS
“The benefits were very rewarding. We definitely saw a return on investment. We also gained confidence that applying statistical methods to real-life problems can benefit the company in the long term.”
– Vasco Cachaco, Principal Six Sigma Process Champion, Xaar
Xaar success story
“We used JMP’s mixture design module and unlocked better insight with dramatically fewer batches than our previous method. Had we not had JMP, we would have sent that [instruction] to the plant and kept adding more [of the ingredient] as we have for the previous 10 generations. There’s a direct impact on our costs of goods sold. It would have meant extra cycle time eaten up trying to get that thing at the tolerance. Worse case, it would have been scrapped or delivered late to the customer…. That was the first time I thought ‘wow.’”
– Bernard Goguelet, Quality Leader, Parker LORD
Parker LORD success story
“What we've clearly been able to demonstrate when we've been to see others in our sector is JMP can help to find better, less costly solutions more quickly not just in the factory, but in R&D and customer service. It will help drive continuous improvement and use simplified analytic methods to help you understand where the important factors are in your saved data. It will give insight to the heart of management process funds, and certainly help department and team discussions move from the anecdotal and the experience through to data driven decisions.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“JMP can help you find better, less costly solutions more quickly. JMP puts data [behind] the knowledge it brings to site management and departmental team discussions, removing the anecdotal [noise] and helping to derive continuous improvement and increase OEE. This can be done by using simplified data analytical methods and standards with software like JMP.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Capturing value from your data to accelerate innovation (link coming soon)
"We were working for a company doing injection molding where we had gone almost all the way in our six-step model. They used lots resources on end product testing, like dimension, flashes, incomplete moldings, and they had heat issues because they found out too late when something was wrong. By using design of experiments [in JMP], we made relations between process indicators, which is something they can measure in real-time while doing it, and in product quality. So they could actually implement real-time release testing, thereby skipping end product testing. They actually saved 2 million euros per year in reduced QC costs. And in addition, these early warnings can also be used to predict maintenance. They reduced scrap by 70%.”
– Per Vase, Managing Consultant, NNE
Statistically Speaking: Why quality improvement is imperative in the big data era
“Before using these methods [in JMP] so routinely, there were a lot of repeated experiments going on. A lot of situations where we would get to the end and go ‘what should we do next?’ That would often cause us to redo work… And the cost of repeating those experiments was phenomenal”
– Teena Bonizzi, Staff Scientist, SABIC
Statistically Speaking: Solving science and engineering problems with machine learning
“You can do things with advanced analytics on large sets of data [in JMP] that you wouldn't normally be able to produce in a lab. I can share an example where we are using a deep learning neural network technique to predict when certain ovens will need maintenance. The product is in the oven for a very long time – about 48 hours. And after it's been processed, it's pushed out of the oven from one side to the other. If the oven lining is not in very good condition, then the plunger can stick to the walls and all of the product will be lost. It means the oven has to be stripped out and refitted, which costs a lot of time and money. It's not something that we were able to pinpoint before because there was no real way of saying when it was going to happen, even though it happens maybe once every two or three weeks. But by using machine learning algorithms, we saw patterns in the force of this plunger to help us predict when it's starting to stick and therefore be proactive. So we've reduced the amount of stickers and saved quite a lot of money and time in the process.”
– Bernard Ennis, Principal Researcher, Tata Steel
Statistically Speaking: Solving science and engineering problems with machine learning
“We were looking at ways of using statistical techniques [in JMP] to identify the onset of engine issues in service. When you're using these analytic methods, you need training data to establish a model. And if you're trying to detect faults, you need adequate examples to train the model. But we were dealing with high integrity assets. So the volume of data just simply wasn't there. One of the things that we did was to look at novel detection approaches, constructing a model of the normal [function] and using it to understand the bounds of what normal represents. We then locked down the deviations outside of normal. That way, we could identify early indicators of potentially emerging problems. That approach is now well-established in the health monitoring aspects of delivering what's referred to as total care of engine assets flying around the skies. And of course, what that means is that we will avoid disruption to operators. So you only do maintenance when it's necessary, not scheduled.”
– Steve King, retired Engineering Associate Fellow, Advanced Analytical Methods, Rolls-Royce
Statistically Speaking: Solving science and engineering problems with machine learning
“You need to showcase ‘look, we're spending so much time doing x, y, z or the quality of what we're getting is not as good. You know, let's look at the opportunity cost of making this investment and what we're going to get.’ And it comes back down to money… tying it back to either your operating expenses or to your revenue… and having the metrics to be able to support it.”
– Sarah Kalicin, Data Scientist, Intel
Statistically Speaking: Lead your organization to analytic excellence
“If you're going to build a house, you're going to want to give hammers and nails to your carpenters. If you're going to have a technology company, you're going to need to have some software, some training, some statistics, some analytical capabilities amongst your technical staff. So I think [building out an analytics culture with JMP] truly can be demonstrated to pay for itself.”
– Ted Ellefson, Managing Principal Engineer, Seagate Technology
Statistically Speaking: Lead your organization to analytic excellence
“With the cheapness of hard drives today, there is no reason to throw data away. We may not know how to use it right now, but this idea that we're going to throw it away because it's somehow expensive is crazy. And the second thing is you're going to pay, what? $150, $200k for a professional in this field, and you're going to argue with me about how big their laptop is? Like whether it has 32 gigs of RAM? Get them a good computer. Buy good software. If they need JMP Pro, get them JMP Pro. That kind of money is small money when analysts return 10 times their results.”
– Tom Lange, retired Director or Modeling & Simulation, Corporate R&D, P&G
Statistically Speaking: Lead your organization to analytic excellence
“I think [Corning’s culture of analytics] certainly has contributed to push innovation and experimentation back further into the early stages [of development]. And as a result, the more you can kind of trim your choices, your selections, early on in the process, the more money you can save the further down the road.”
– Michael Anderson, Senior Statistician & Measurement Development Engineer, Corning
Statistically Speaking: The power of structured experimentation in science and engineering
“Medtronic invests a lot of money in R&D to generate data, with all the testing needed for new product development. We want to make sure we’re using those resources well. We don’t want to waste any part of that data. And with JMP, we’re able to extract more information than with other methods.”
– Susan Roweton, Research Manager, Medtronic
Medtronic success story
“I use JMP to design and analyze experiments that we generate for a formulation robot. Instead of the usual two or three samples people can make in a day, we use the system to make two to three hundred. Handling all that data is quite important, and we use JMP to do it.”
— David Barnett, Senior Formulation Chemist, Data Scientist and Robotics Chemist, Syngenta
Syngenta success story
“The big advantage of JSL [JMP scripting language] applications is that users don’t need any additional software to solve even the most complex problems. And that saves us a lot of money and time by enabling us to invest in new developments while staying within one software package. It’s a huge advantage.”
– Maria Lanzerath, Global Head of Statistics, W.L. Gore
W.L. Gore success story
“I'll say, ‘Oh, we did this variance components analysis and used these analytical variability charts. It was so cool. We figured out where the variability comes from.’ But [management] will say, ‘But you know what? When you did that, the outcome for the business was that when we understood where the variability in the analytical method came from, it resulted in being able to cut the number of tests in half.’ These are very expensive tests to run, they're biological tests.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“During a session with a potential funder, [some colleagues] presented one of these JMP graphs showing how they had figured out the assay analytical variability and come up with a more efficient testing scheme that was giving them better data. That was a selling point for the investor who thought that it was such a great insight. They ended up getting some nice funding.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“Every percentage-point improvement we have in our processes is literally worth hundreds of thousands of dollars to us. The modeling and data analysis capabilities in JMP guide us in improving process yields, thereby reducing costs, which eventually translates into more affordable and clean energy for the world.”
– David Trindade, Fellow, Bloom Energy
Bloom Energy success story
“When we use JMP… we calculate the business case. And those analyses have proved to be a good investment. Management also recognizes this, or they wouldn’t allow us to invest the time and resources on those analyses.”
– Lise Isachsen, Senior Process Specialist, Coloplast
Coloplast success story
“In manufacturing, we typically start out on one machine and make sure it’s functioning perfectly. If we can ensure that the first machine is perfect in function and processes are on target, it decreases costs for the next 10 machines. And the cost savings of that are huge.”
– Lise Isachsen, Senior Process Specialist, Coloplast
Coloplast success story
“When we develop new products, we’re not focused on trying to save time or money, but on creating a quality product for patients. By performing our DOEs and investing the time and money upfront, we’re saving time and money by avoiding problems later. The result is that the quality of the product is optimized right from the start. We can immediately identify any problems, and we know when we hit the target. It’s so cool.”
– Lise Isachsen, Senior Process Specialist, Coloplast
Coloplast success story
“JMP has the potential to simplify the software that’s in the product and improve the finished-goods yield, which is one of the most important parameters that affect the cost of goods sold.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Siemens Healthineers success story
“We expect JMP to help us continuously improve the precision and accuracy of our analytical values. That benefits us, but in the end, it also benefits our customers.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Siemens Healthineers success story
“Our top-level management understand… and buy into [using JMP for DOE]. You tell them, I'm going to improve the speed of learning and I'm going to improve the position and accuracy while saving you money in the experimental run cost and the time, they're like, I'm totally in.”
– Sidharth Bhatia, Director of Data Science and Analytics, Applied Materials
Statistically Speaking: Leading companies innovate with statistically designed experiments
“We get our annual license cost back dozens of times every year in project speed, discovery horsepower and document robustness. We can't imagine not having JMP.”
– David Herridge, Vice President of Regulatory, Quality, and Engineering, Incisive Surgical
Incisive Surgical success story
“This way we’re saving an incredible amount of time, and so, of course, we’re saving money. And we’re teaching statistics behind the scenes. It’s been a great success.”
– Mike Cramer, Director of Operations, Research for Worldwide Restaurant Innovation, McDonald’s
McDonald’s success story
“It’s not economically feasible to have a very, very high sampling rate because the more we sample, the more we waste. We have a direct material cost to our sampling… We've been able to use JMP to create some pretty slick models. By lumping our relatively small sample sets together, and interpreting them properly, we can effectively [gain] much better confidence about our product quality without destroying a large percentage of what we create.”
– Greg Mozsgai, Research and Development Manager, Samson Rope
Samson Rope success story
“The outlier effect is a representative example. In addition, visualization tools are highly necessary for more efficient communication with on-site engineers. I believe that JMP is an intuitive and fast tool for this purpose.”
– Yonghan Ju, Head of Data Innovations, SK Hynix
SK Hynix success story
“Labor could easily be half the cost of the actual product. That’s why optimizing things like labor productivity gives immediate return… JMP data visualization and interactivity has helped managers see the unexploited possibilities to make the business more efficient. And changing [former ways of thinking] is not easy… But JMP is good because it allows you to easily connect to various data sources and integrate and clean data, that’s a big thing.
– Serguei Roupassov, Data Science Group Leader, Valard Construction
Valard Construction success story
TIME SAVINGS
"For us, JMP was the right tool to speed up statistical analysis and share results through visual information."
Fabrizio Ruo Redda, Vishay
JMP DS Americas 2021- Recording Producers - Data Explorers Spotlight gallery.mp4 - All Documents (sharepoint.com)
"The amount of information that you can extract from JMP in such a short time – and in particular, the multivariate element of it – is amazing. You can quantify the amount of variation that multiple factors will have on the output. That’s gold from a new user’s perspective."
- Kieran O’Mahony, Data Science and Analytics Manager, Dairygold
Dairygold | JMP
"At the end of the day when [the engineers] are done measuring some parts, I'll go in and run a script that will take me 15 seconds. Doing this by hand [as we did in the past] takes me at least 30 minutes.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“The real kick then is when you say, ‘Okay, you've done it once and you know you're going to need to do the exact same thing next week…. So just click that little green script button in JMP, and you've got your analysis done again...And they see you've just knocked three or four hours off the day’s work very quickly… so they say, ‘JMP is worth switching to because we get a long-term benefit.’”
– Brendan Malone, Senior Process Engineer, Johnson & Johnson
Johnson & Johnson success story (link soon)
“Tasks like compiling data over years were practically impossible in the past. [With JMP,] we’ve cut the time from days or weeks to a few minutes or hours. And that opens up analysis that wasn’t possible before.”
– Tom Walk, Breeding Pipeline Database Manager, North Dakota State University
North Dakota State University success story
“If I’m doing a standard size grid using definitive screening and graph building options on top of that, I can analyze [my data] in an hour and know what I’m doing next. Without JMP and design of experiments, you’d be looking at about a day…I’ve always got the window open because there’s always the question, ‘Well what if we change this?’ You’re literally three or four clicks and you’ve done it. I’ve been able to demonstrate to leaders effectively that [JMP] is something that has a huge benefit.”
– Jon Armer, Formulation Lead Senior Scientist, F-star Therapeutics
F-star Therapeutics success story
“Time is equal to money on computational platforms. So when I moved to Bionano, one of the first things I wanted to do is make sure I got a JMP license…. Now I can't think of how [without JMP] we would be able to pull as many variables in to come up with some sort of expression that would take that data and turn it into predictive computational hours. We likely would have had to take the quantity of DNA and [desired] coverage and do a linear regression.”
– Pat Lynch, Director of Systems Engineering, Bionano Genomics
Bionano Genomics success story
“With quantifiable time savings across the organization, a clear strategy and support from the senior management team, we are pushing for even more benefits in terms of time to market and enabling new technology for our customers. We need to do the cost/benefit evaluation on everything from experimentation to process and hardware development, we want to make sure we do enough but not more. Be effective and efficient, JMP allows us to find that sweet spot.”
— Paul Deen, Statistical Methods Program Manager, ASM International
ASM International success story
“Our aim in using JMP is to save researchers time, which they can devote instead to reflecting on the meaning of the statistical analyses. These applications free up time for carrying out more value-adding tasks and there are gains in terms of quality too. Researchers can now explore avenues they wouldn’t have even considered in the past.”
– Jean-Luc Blachon, Head of Statistical Research, Bioinformatics, Ipsen
Ipsen success story
“JMP can answer your question in a matter of seconds. A customer comes to visit with a question, and I no longer have to say, ‘I'll get back to you with an answer.’ I'll say, ‘Let's look.’ And I can put it on the screen.”
– Jim Lamar, Quality Manager, Saint-Gobain NorPro
Saint-Gobain NorPro success story
“Now when I use JMP, I think about how it could have taken days to do what now takes merely seconds.”
– John Cunningham, R&D Manager, G3 Enterprises
G3 Enterprises success story
“JMP has become an essential tool for optimization activities. The statistical results facilitate the choice of solutions, and the speed with which they are produced frees up more time for innovation and imaginative thinking. JMP allows us to work faster and perform more effectively, increase our customer satisfaction, and, in the case of customer feedback, provide more relevant answers and embrace a process of continuous improvement.”
– Annick Vuillermoz, Yield/Process Control System Senior Manager, Soitec
Soitec success story
“It's really nice to see how others approach problems, even problems in other fields of interest, with more or less the same optimization strategy: reducing time to market or boosting the speed of experiments. And it's really inspiring to see that you can always dig deeper yet into JMP.”
– Egon Gross, Master Technologist, Symrise
Symrise success story
"JMP is really, really quick. In other software you can do the work, but everything is not in one place. With JMP, you can use just one piece of software to do everything, where previously we’d be using three or four different pieces of software."
– Paula Kitts, Computational Chemist, AstraZeneca
AstraZeneca success story
“The problem had been going on for two years. But once the data was gathered and we began a focused dive into it, we had it in three weeks.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“It's a mindset changer. People see that entire weeks of work can be done in seconds and then it leaves them plenty of room for thinking, for framing the question in a different perspective.”
– Alfredo Lopez Navarro, Omnichannel Data Lab Manager, Telefónica Germany
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“Having the interactive graphics that JMP puts at your fingertips is a great way to get an interdisciplinary discussion going. I like having good, well-constructed graphs in front of an audience that [represents] a whole bunch of different perspectives discussing the issue. It’s very valuable.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“In the past, when we used Excel. We spent a lot of time and energy because it was a tedious and error-prone process. Now with JMP, we let the data run overnight and see the output the next day. This saves us a lot of time, whereas with Excel, I needed two or three days to decipher it.”
– Guifu Wang, Principal Reliability Engineer, ATL
ATL success story
“The big benefit is time savings. You have to be responsive in the electronics and semiconductor industry, which is quite fast-paced and competitive. JMP lets us do that.”
– Petra Schreier, Engineer, Atotech
Atotech success story
“There are analysis tasks I can now perform in 90% less time, which means that I can do so much more in the same workday. In other words, JMP’s cost-benefit ratio is very high.”
– SungSoo Lee, Innovation Group Manager, Daeduck Electronics
Daeduck Electronics success story
“It sounds simple, but only because JMP made it simple. JMP gave us the answer literally in seconds … So instead of spending all that time trying to find the why or the what, we dove right into what was [driving the problem] and what we could do to fix it.”
– Tyler Wise, Project Analyst in Global Business Services, Diebold Nixdorf
Diebold Nixdorf success story
“JMP is the workhorse of our group. It makes us much more productive – and we’re constantly learning new ways to use it.”
– Kevin White, AI & Machine Learning Technology Manager, Eastman Chemical
Eastman Chemical success story
“If I wanted to look at all the manufacturing defect areas, it would probably take two people an entire week to do that task and do it right. With JMP, I can do it in 15 minutes, then do it over and over, play with it, and look at it a lot of different ways.”
– Dan Fortune, Global Business Excellence Manager, FMC Technologies
FMC Technologies success story
“In six weeks, we went from scraps to the interface. JMP was the ideal tool. It’s the only Six Sigma tool that’s real-time and interactive.”
– Laura Zambianchi, Senior Filtering Material Specialist, Fresenius
Fresenius success story
“I used the stack command and in 10 minutes was able to say: ‘Here you go. All done.’ He was impressed. And so was I. JMP is just so powerful.”
– Peter Mroz, Senior Statistical Programmer, Janssen
Janssen success story
“We used to have three people doing it full time. Now we’ve got two people doing it part time, freeing them up to do additional testing.”
– Jason Lott, Research Engineer, US Synthetic
US Synthetic success story
“We were able to identify the three factors that were most important, and we really dialed in the process from there. We created a machine based on those results that we’re now using on the floor. It took about six months from the beginning of the process to the implementation of the machine, which is pretty screaming fast for that kind of project.”
– Matt Brown, Process Engineer, US Synthetic
US Synthetic success story
“I now save considerable time because JMP can assign properties to the products that transport across all the graphical routines.”
– Jeff Stagg, Principal Scientist, Kraft Foods UK
Kraft success story
“I fine-tuned how we collect our scrap data, because if you’re dashboarding on bad data, there’s no point. Now we have data collection set up to be ownership-oriented, and we can track scrap across our entire facility and view the data in the same way across all groups. Before [we had JMP], groups used to manually pull data from a SQL database and make their own charts; you can see how time-consuming this would be if it needed to be done weekly.”
– Evan Ngo, Senior Electrical Test Engineer, Polar Semiconductor
Polar Semiconductor success story
“When our engineers come to work in the morning, they start a script, receive a report and can decide immediately what to do without spending a lot of time on analysis. They have more time for doing more important things. JMP helps them optimize the production process. That’s why I say, ‘Let JMP work for you.’ Just write a script and tell JMP what to do for you. Then you can start with more interesting things, not always the same analysis again and again.”
– Katharina Lankers, Research Scientist, SCHOTT
SCHOTT success story
“Working with x-ray tomography, we could actually use JMP’s statistical function and obtain a distribution of the void space in high explosives that would previously have taken [my colleagues] three or four days to process. But it took me about a half an hour with JMP. LANL management was really happy with that result.”
– Trevor Shear, former Los Alamos National Laboratory, now a PhD candidate at University of Oregon
University of Oregon success story
“Just going through that in JMP really sold them. It is not just about the analysis, but also the graphical representation of your data. Other software might be able to do this, but it takes them three or four hours where the same thing would take literally one click of a button in JMP. That made the group excited.”
– Trevor Shear, former Los Alamos National Laboratory, now a PhD candidate at University of Oregon
University of Oregon success story
“I went from at least a week of work to maybe 10 minutes. It was pretty considerable.”
– Dirk Jordan, Senior Reliability Engineer, National Renewable Energy Laboratory
US Department of Energy success story
“With JMP, I can reanalyze the data really quickly in either exactly the same way, or in a different way to see what else it is trying to tell me.”
- Jennifer Verdolin, PhD, University of Arizona
University of Arizona | JMP
BOOSTING PRODUCTIVITY & QUALITY
"There's always hesitation whenever we are asked to try something new; that's just human nature. When we saw that we could integrate JMP Live with Microsoft Team, we were really excited to use this combined tool for project collaboration. Now that JMP Live is a part of Microsoft Teams, scientists feel more comfortable; they don't have that hestiation to start a new project."
-AJ Kaur, Perrigo
JMP DS Americas 2021- Recording Producers - Data Explorers Spotlight gallery.mp4 - All Documents (sharepoint.com)
"Initially, there is a huge energy barrier for a reaction to take place, but by adding in an appropriate catalyst, that energy is greatly reduced. I see JMP – and the support structure that comes with the software – as that catalyst for our company."
-Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
With our alarm board, you can quickly see when a parameter is out of control. And even if you have multiple parameters, it’s still really easy – not just with the alarm board but also when you think about Graph Builder... The alarm board probably improved yield about 1 to 5% in our pilot production. It depends on the task, but based on a set of experiments and runs, I think 1 to 5% is a huge improvement.”
– Torsten Weber, Process Integration Engineer, Heliatek
Heliatek success story
“One of the ways we use [machine learning applications in JMP] at Procter & Gamble is detecting defects on a manufacturing line. On the manufacturing line, there are often sensors that generate the necessary data to develop a machine learning model which will then classify an individual unit going down the packing line either as a defect or not a defect. A second example is what is called predictive maintenance – to be able to predict disruptions to the production line before they actually occur. And the final area is in consumer research. This is to classify consumers into defined segments based on demographics, and this allows companies to focus marketing and advertising.”
– Bill Myers, Principal Statistician, Procter & Gamble
Statistically Speaking: Applying machine learning to the right problems
“There are statistical techniques that can handle missing values – what is often called dimension reduction. If you have a categorical variable with lots of categories, you might want to reduce the number of categories. And the benefit of dimension reduction is it can often allow more efficient model building. It can be easier to understand and explain the model. And also it might be easier to visualize. Data visualization tools [like JMP] are a great way to do this exploration.”
– Bill Myers, Principal Statistician, Procter & Gamble
Statistically Speaking: Applying machine learning to the right problems
“JMP will help increase overall equipment effectiveness, I believe, because it will identify the issues that are really important. Now, quality management and other methodologies can be a means to manage, and they will help measure and identify potential improvements. But the technical decisions that have to be
made still rely on intuition, anecdotes, and of course, the experience of people who are managing the process. I think this is where JMP and the statistical analysis of the data behind your factory will help you identify the key parameters which will help control your process deliver better output and, for example, help you manage maintenance better.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“This is an opportunity to extract knowledge and insight from a sea of data that's being produced in our factories. Most unit operations these days are producing hundreds if not thousands of data points per second and using all that data to identify how to better control our processes can be done within JMP. It will help you make the right decisions more quickly and eliminate group discussions about what may or may not be important.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“In a recent survey, PWC noted that growth for the most innovative specialty chemical companies was 16% higher than the least innovative. Top innovators forecast that the red group will be 3 times higher than their least productive competitor. I would add to this that getting these tools to the hands of operators is the way forward. We have to move them out of the IT departments and out of the head offices into production, R&D, and business development. That's where we'll get most benefit. And I do believe that JMP can be learned very quickly and can start bringing results much more quickly than you might think.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“The UK government has already stated that the UK economy is not very efficient. And a lot of its initiatives are going to be about improving efficiency. I think JMP can help getting products to market quicker. That's a great value we can bring: reducing manufacturing failures, more consistency in the quality of production.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Uncover the key data necessary to reduce your hidden factory (link coming soon)
“We had nine factors and could only estimate a few interactions. And we could only afford 21 experimental runs. [That’s when I learned about Definitive Screening Designs in JMP]: a design to identify the key process knobs. What are the driving factors? In this example, the concrete savings for us running the DSD with 21 runs versus the traditional approach that would have been 28 runs… plus maybe another 20 for the optimization phase.”
– Maria Lanzerath, Global Head of Statistics, W.L. Gore
Time to Innovate: Optimizing production processes with design of experiments (link coming soon)
“If you're a specialty chemical company, quite often you'll receive a process from your customer. Going back to the customer with this type of data, showing them that you have a deeper understanding of what's going on than they do, might well cement you the business.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Capturing value from your data to accelerate innovation (link coming soon)
“As an R&D manager, using the design of experiments methodology should enable you to be very predictable about that length of time it will take you to do this R&D work. And giving much more confidence in the business managers relationship with the customer.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Capturing value from your data to accelerate innovation (link coming soon)
"If you’re a construction company and you’re building houses, do your workers get hammers? Yes. Our engineers, if they analyze data, then yes, they typically use JMP.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“The role that JMP plays is to really help us explore that data in the beginning… to determine which parameters and interactions are the most important. And then explore which types of SPC monitoring tools might be really effective… Once you determine that, now you can deploy a specific technique with an automated information system or MES system, and then that can run real-time all the time.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“Going from low knowledge to high knowledge – that is what design engineering and process engineering are all about! You’re continuously delivering knowledge to the company. That’s what data analysis in JMP can do.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“One little button in JMP can create a graph based on millions of lines of codes, 40 or 50 files of data – it's just amazing. With a click of your JMP script you can produce results and graphs that help you make a decision… Whereas 20 years ago it would just never happen.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“It's relatively easy to imagine a set of single-parameter experiments that eventually map out the space. But you can get to that place much more quickly with the DOE. We’ve definitely improved on efficiency. JMP gives you very good measures of how reliable the coefficients of your model will be based on the uncertainty of your input data. There are lots of tools within JMP to help you understand what you can do with your data in a reliable way.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“Interaction effects are becoming more and more important. Single-parameter issues that the quality engineer has to deal with are being taken care of quickly, and more multivariate approaches are becoming more and more important.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“We certainly use a lot of the data when it comes to things like periodic calibration and continuous improvement activities, although even there, access time and time to clean up data is significant. So we're trying to push towards using it in a more active, forward-looking way – for example, with smarter, more automated control charting to avoid the issues in the first place.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“One of the neatest developments in the past couple of years has been the proliferation of more automated inspection tools that can characterize our products to a much finer extent than we could ever do before. They're recording massive amounts of data, and it's become a really powerful tool to understand the quality of our product as we are generating it. But then you've got the issue of what to do with all these gigabytes and terabytes of data. Once you've used it to establish the quality of your product, then how do you archive it? Do you start onsite or offsite? Do you have enough bandwidth to store it offsite? Do you have enough bandwidth to retrieve it when you need to analyze it again? These sort of practical problems really need to be thought of in advance when designing new tools or new processes because they can become real limitations to the factory if you don't anticipate the problems.”
– Ed Hutchins, Product Engineering Manager, Wolfspeed
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“In the next 10 years, I'd really like to see us evolve from being very focused on big data to developing some strategies that connect big data to little data. Big data we would be using as an exploratory tool to identify those trends that show potential for us to leverage, but then using designed experiments and small collections of data that are much more focused on establishing causality to be able to confirm what we're seeing in the big data. If we can build that bridge, then I think we're going to get maximum advantage of the big data that we have and then also feel much more confident in our decisions going forward.”
– Christine Anderson-Cook, Statistician, Los Alamos National Laboratory
Statistically Speaking: Stay relevant and competitive with modern quality engineering
"With JMP and DOE, now everyone recognizes the value of data and how it’s improving processes and product quality. And now that we’re using JMP, we can test multiple interactions. Then we can go back to our technicians and show them the data."
– Lise Isachsen, Senior Process Specialist, Coloplast
Coloplast success story
“JMP has really helped ensure consistent [methodologies] across the group – and consistent reporting of results. It has really bolstered our evidence generation capabilities.”
– Susan Roweton, Research Manager, Medtronic
Medtronic success story
“Analytics allowed us to build consensus, and to start listening to the process technicians who had been saying all along what they saw. The data helped us all to focus on what they had been trying to tell us and so it was a very successful project.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“We applied a number of different approaches to solve this issue, and what happened was – I remember the moment…. We used a form of Bootstrap Forest as we have in JMP to come up with a variable importance plot. And when that plot showed up on the screen that everyone was looking at, there was this moment of silence and this big ‘aha!’. The whole investigation turned at that point.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“If you have process understanding, then you are able to look at the cost structure and make optimizations both quality and cost-wise.”
– Søren Pehrson, Catalyst Manufacturing Scientist, Haldor Topsoe A/S
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“One of the lessons I've learned over the years is to find a big problem to solve. Don't let a crisis go to waste! I was involved quite a bit with the implementation of total quality and Six Sigma – trying to get statistical thinking ingrained into the work. When we started getting real traction was when we began tackling some of the most challenging problems using analytics approaches. It was higher risk, a lot more exposure, but that can be a good thing if you find the answer.”
– Julia O’Neill, Founder & Principal Consultant, Direxa and former Director of Engineering, Merck
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“[One of our biggest customers] complained that the variance in our process was excessive. So we formed a team to resolve the issue using Six Sigma. JMP was very useful in helping us solve this problem because of its ability to handle large amounts of data – and because of its interactivity. We’ve now reduced variance by around 50 percent. Our bosses and customers were very pleased with the result.”
– Henry Qiao, Six Sigma Deployment Manager, ATL
ATL success story
“Celltrion uses QbD extensively for product development…. It’s about fully leveraging statistics in product development. This is how we approach our work…. The tool we rely on most is JMP. We use it to optimize method development and processes. JMP is easy to use and essential for our data analysis.”
– Minjoo Choo, Head of Chemistry, Manufacturing and Control Statistics, Celltrion
Celltrion success story
“When 30 percent of your product is being thrown out at a cost of $1,500 to $2,000 an assembly, you can imagine our scrap bill was crazy. [Eventually we identified disparate yields between two suppliers as the source, with one supplier achieving 100 percent yield and another achieving 70 percent] and saw that our daily production didn’t use 50/50 of each supplier. We used material from one supplier, then moved to the material from the other supplier. [Yield] would cycle related to which supplier we were using. It taught us a lesson that we have to parse the data based on suppliers.”
– Melisa Buie, General Manager, Coherent
Coherent success story
“I explained to my boss that if I use JMP, we can get more data and we can more easily incorporate QbD into all aspects of our projects.”
– Kyoung-Yun Kim, NABOTA Research Group Manager, Daewoong Pharmaceutical
Daewoong Pharmaceutical success story
“JMP is fundamental to what we do in our Six Sigma program. It’s woven into the fabric of the training program…. It’s a fantastic platform for us to build on because it offers a whole new area of opportunity for us across the businesses.”
– Kevin McCarron, Six Sigma Service Leader, Dow
Dow success story
“We’re seeing so many successes across the board with JMP that it’s hard to single out an individual example. It’s just such an essential tool for us.”
– Howard Rauch, Statistician, Eastman Chemical
Eastman Chemical success story
"It became apparent very quickly that those weight changes weren’t measuring what [we] thought they were measuring. We would never have known that without JMP."
– David Payne, Site Director, Johnson Matthey Fine Chemicals
Johnson Matthey success story
“What we’re seeing on-site is the use of JMP expanding more and more to solve all kinds of problems. JMP is our chosen statistical analysis tool.”
– David Payne, Site Director, Johnson Matthey Fine Chemicals
Johnson Matthey success story
“JMP has been significant in allowing us to unravel the key facts from all the noise in our complex manufacturing process.”
– Geoff Beatham, Quality Director, Kodak
Kodak success story
“Using JMP, I can identify bottlenecks very easily. Being able to identify where your main issues are – where you should focus your attention – is, obviously, very helpful. The processes we have are multifarious and fairly complex, and one of the useful things is to know where to go to apply your Lean tools. JMP targets my efforts.”
– Peter Blum, Quality and Technical Manager, Kodak
Kodak success story
“The development of this methodology allowed us to rapidly change airplane parameters and flight conditions and then look at the resulting impact on the noise for the community… This process is just much quicker and easier using JMP. Not only does it reduce manual input errors, but we can accomplish in seconds what used to take hours or days to perform previously.”
– Jeanette Elliott, Systems Engineer, Lockheed Martin
Lockheed Martin success story
“With the limited resources that we have – we have only about a dozen chemists, and we’re trying to push forward multiple product efforts – JMP has helped us screen different reaction conditions in a very logical way to give us a starting point for optimization. If we didn’t have a very deliberate, structured way of doing that, these new application-development efforts would really suffer.”
– Scott Allen, Vice President of Catalyst Development, Novomer
Novomer success story
“This was the beginning of the development of a robust formulation for the Brite Amber panel. It could only have been done with JMP. JMP is just an amazing tool.”
– David Lee, Director of Quality and Reliability, OLEDWorks
OLEDWorks success story
“With JMP, we identified a lot of improvement opportunities for the folks who do the service calls and who keep the records of these calls. We made the process better.”
– Ron Zhang, Senior Chief Engineer, Oshkosh Corporation
Oshkosh Corporation success story
“We found a new methodology that allows us to process our information in a way we hadn’t. It was an epiphany. JMP allows us to do all this; it allows us to break new ground.”
– Alberto Colombi, Corporate Medical Director, PPG Industries
PPG Industries success story
“Thousands of other companies are in the same situation: They have SAS in place, and if they added JMP, they would get substantial additional benefit… JMP’s graphical abilities and ease of use make it terrific for designing experiments and exploring data. SAS pulls data from just about any source and does the heavy analytical lifting. And JMP integrates with SAS. It’s a powerful combination.”
– Wayne Levin, President, Predictum
“One of the things we've been able to do with JMP is to transform our thought process from a discrete world into a more continuous time-scaled world... Once I started using JMP, there was no comparison.”
– Greg Mozsgai, Research and Development Manager, Samson Rope
Samson Rope success story
“JMP equips us to study things that we took for granted for 140 years. We’ve challenged a lot of status quos and a lot of entrenched habits, and JMP has helped us accelerate that process.”
– Steve Czupryna, Quality Process Engineer, Samson Rope
Samson Rope success story
“JMP was already being used by 10 or so users to produce statistical analyses for monitoring yield and product quality, across their various product lines. After two years of using it, the only constraint they faced was the fact their colleagues kept asking them to do their searches, tests, and calculations! Because innovation and quality are our leitmotif, the whole company needed to increase its efficiency. This initial experience gave us every opportunity to be persuaded by the power, potential, and user-friendliness of the tool. As a result we didn’t hesitate to extend its use, at the appropriate time, to support the development of our activities to the whole of the business.”
– Annick Vuillermoz, Yield/Process Control System Senior Manager, Soitec
Soitec success story
DESIGN OF EXPERIMENTS
“There is so much evidence that DOE is great. But many organizations, so many people, are still not using it. Why? There must be something else beyond that rational knowledge. In my view, it must have something to do with trust or fear of doing something wrong… the trust people have in this new method of changing more than one thing at a time… So the easiest way to [change attitudes] is to give your organization a chance to see the success you have with DOE. You can carry out some simple experiments and show the results. I tell my colleagues ‘trust me. I will help you. And you can follow me on the journey through DOE. While we’re working together, I can help them see and feel how all these seemingly complicated things come together and add up to great knowledge and insight. Be a good role model and make them feel the success. If you can do that, you will gain allies and people will start accomplishing good results with DOE.”
– Olaf Bürger, Quality Manager Systematic Improvement Process, Mahle
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“As Adocia’s products have matured and as ICH guidelines incite pharmaceutical companies to deeply understand product-process parameter linkages, we are increasingly using statistical approaches like design of experiments in our laboratories.”
– Thomas Cochet, Head of Process Chemistry
Adocia success story
“We had the increasing feeling that we would need to implement statistical approaches like DOE in our process developments, and one of our CMO’s project leaders recommended JMP.”
– Thomas Cochet, Head of Process Chemistry
Adocia success story
“I didn’t show my management any data in the sense of values or tables. I visualized the data. And if you can do this interactively and show what’s happening if you move one control and see your response move… if they see what the process looks like when you change things and you show them the interactions and how the system behaves… they will start trusting in your [DOE].”
– Olaf Bürger, Quality Manager Systematic Improvement Process, Mahle
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“The complexity of DOE fits well with the complexity of the efforts we now have to make [given increased automation and larger data streams]. At the end, you get a very good and deep understanding.”
– Egon Gross, Biotechnology Engineer, Symrise
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“For many people, DOE is not a common code of practice. There are still many people not familiar with it and therefore not using it. And they’re losing a lot of time and money and insight to the tasks they’re trying to solve.”
– Egon Gross, Biotechnology Engineer, Symrise
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“A good way to overcome some of the common [sources of resistance to DOE] – training, complexity and so on – is to find a good teacher, or a good textbook if you prefer. Even more, find a good coach. This could be a colleague who is knowledgeable. And then start with small questions you may have, perhaps with two or three or four factors. Start small and grow with your experience and knowledge. If you can find a coach, you’re on a good path.”
– Markus Schafheutle, Consultant and Former Global Improvement Manager, Allnex
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“DOE is not just a thing which can be used by only a few industries. DOE is really for everybody.”
– Markus Schafheutle, Consultant and Former Global Improvement Manager, Allnex
Statistically Speaking: Fast-track innovation and process improvement with designed experiments
“Compared to traditional multivariate methods, the [JMP Predictive Screening Platform] can handle both continuous and categorical factors in response. It is very useful when there are interactions and other complex relationships among those factors.”
– Miao Chen, Data Scientist, Tokyo Electron Limited
Statistically Speaking: Applying machine learning to the right problems
“Being able to set a relative (numerical) importance for the optimization, as is possible with the implementation of design of experiments in JMP, proved invaluable.”
– Alin Ionas, Analytical Chemist, Technical University of Denmark
DTU success story
“I’ve worked on projects where we tried to optimize a general method for a few hundred compounds. And then we had to try to find a set of instrumental parameters that were good enough to get reasonable performance for most of the chemicals we were looking at. That’s a daunting task, so I felt that using the DOE platform from JMP was the solution.”
– Alin Ionas, Analytical Chemist, Technical University of Denmark
DTU success story
“Once you have some experience running these kinds of experiments, you can just set it and forget it. You set up the experiment, let the instrument do its thing, validate the way you extract data, and make sure you get high-quality data from your data files. Then it’s not much work to put it back into the model and generate the next iteration.”
– Alin Ionas, Analytical Chemist, Technical University of Denmark
DTU success story
“JMP will deal with an unbalanced data set. It will tell you what the risks are, deal with it, and let you make an intelligent choice rather than just saying no.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“JMP is a competitively priced product that is a mix of both common use for any engineer and advanced-use statistical tools that allow you to look at any problem that someone in the engineering and research fields would have to deal with on a daily basis.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“Truism in materials: there's no one-size-fits-all. If you increase one [factor], typically you compromise something else. We wanted our coating to be as transparent as possible but also to be able to survive whatever processes it goes through [when it is filled], for example. The amount of formulation and response surface and mixture DOEs that we have done in order to optimize component ratios to figure out sub-components within those components is tremendous.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“Being able to fail is expensive, but it’s a critical part of experimentation. And at Corning, I have never encountered any significant resistance around conducting experiments. We want to learn as much as possible for the money that we’re spending. When I say, ‘I want to do this as a fully designed experiment,’ I never hit resistance. [DOE] is bred into Corning at this point.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“Breaking through that initial skepticism was the hardest part – but also the inflection point… Our scientists learned how the way you choose to set up the experiment has a huge impact on the quality of the data you get… JMP’s Custom DOE module helps us make sure each experiment yields the most information.”
– Bernard Goguelet, Quality Leader, Parker LORD
Parker LORD success story
“We’re already seeing signs of people integrating [DOE] into their daily thinking: I’m going to develop a new product and use my intuition to get close – that’s the subject matter expertise. Then I’ll use JMP to define the region and to optimize it. They’re learning things about their models that they wouldn’t otherwise have known.”
– Bernard Goguelet, Quality Leader, Parker LORD
Parker LORD success story
"We use JMP to do custom designs. The Custom Design platform is a really great way of describing an experiment that you're interested in. We use our own custom applications that are web applications that take that design and then convert that into a type of instructions that can be run by robots. So we've been very quickly able to rapidly prototype and build biotechnology capability by having that key thing in the middle that can connect the JMP to the robots and close that loop in the ability to design, build, test, and measure experiments, completely statistically balanced using design of experiments from JMP.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“DOE in advanced data analytics helps us put our data together and use these data more efficiently. That means extracting more insights from our data. But design of experiments also has a role in the prior step in which we need to obtain the right data and do it in an efficient way… because normally we are always short of resources and time.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Everyone can do DOE… and the best way to start with DOE if you haven’t done it before is just to jump in.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Someone who has a traditional mindset would try to get the optimal setting from the beginning. They’d aim for the target, even though as humans we struggle to visualize a multivariate experimental space. By contrast, someone who has a DOE mindset will sit down and spend more time planning, spend more time trying to think of all the different variables that could be having an effect in this experiment. They’ll already be thinking in ranges… and be interested in exploring a big range of experimental space to try to find the optimum. This is the big difference between a traditional and DOE mindset.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Design of experiments is a tool, but it's not a magic tool. It requires your expertise. It's a tool to complement what you already know.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Every system in which you have more than one factor that can be having an inference, you can use a DOE. In my experience during my 15 years, I never have seen a case in which there is only one factor. As we know, science is complicated. So I think design of experiments should be used all the time.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Some people think DOE is a black box. And some think that to use DOE, you actually need to know a lot about statistics. While maybe these things were true in the past, now applying DOE is really easy. It's really
quick. And it's really visual. The visual part solves the problem of the black box. And the easy and quick part solve the other issues. Now we have user-friendly software packages like JMP that help you to design the DOE really quickly and analyze the results very quickly. And very importantly to do it in a visual way so that you will be able to understand the analysis clearly. And then you will be able to explain it to other people. Everyone can do it.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“What are the disadvantages [of DOE]? None. For me, there are no disadvantages of using design of experiments because actually now having software like JMP, it's really easy. Even for those people who are afraid – in reality, you don't make more mistakes that you would normally because if the results come out with settings that says that is the optimum, you just have to go to the lab and do the experiment and check. So you can always evaluate the conclusion. So actually, I don't think you can make mistakes. My message is: go and try it for yourself.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“[Using JMP for Definitive Screening Designs] led to more process insight and all this work was done within a week… Within this platform, we can choose the distribution, and we can choose the way we want to build the model, and there are also validation methods available. And just by hitting Go, JMP creates a model with nine non-zero parameters, and then you can look for different models, let them run, and then evaluate your models based on the factors you need based on the R square or whatever parameter you want. And that's quite easy.”
– Egon Gross, Master Technologist, Symrise
Time to Innovate: Mastering complex processes with design of experiments (link coming soon)
“I joined GSK as a process development chemist. I spent a number of years developing roots and processes and scaling them up into full-scale manufacture. And then I discovered design of experiments. And that definitely changed the way that I work…. The one factor at a time approach -- this is the way that I was
traditionally taught…. The downside of this, though, is it doesn't very efficiently cover the space that you
could be operating your process in. And it could miss the optimal solution. And be resource-intensive.”
– Gill Turner, Quality by Design Lead, GlaxoSmithKline
Time to Innovate: Optimizing your experimentation process using JMP (link coming soon)
“If you're doing design of experiments, and you're going to the trouble of designing the experiments, and carrying out this work systematically, try to measure as many things as you think you're going to need in the future. Don't just measure one thing. You will always regret not having done good analysis and having that data available in the future will drive further decision-making... Those people who go out there and really understand the noise and the reproducibility of their system first have got the best opportunity to then understand how they might improve that process in the future.”
– Gill Turner, Quality by Design Lead, GlaxoSmithKline
Time to Innovate: Optimizing your experimentation process using JMP (link coming soon)
“DOE certainly does [reduce cost and time]. You know what cost and time you are going to commit to this upfront as well. So when you choose to design an experiment, you know you're going to get some result at the end of it. If you choose not to take this approach, then the idea of generating any sort of understanding around the cost and time is a very open-ended question. You could be doing 100 experiments before you've got any sort of understanding of what's going on, or you could be lucky and hit on a good result with one. But you won't necessarily have the understanding. So absolutely, I believe [DOE] fully reduces cost and time, and it is the standard technique that we use for all our process development.”
– Gill Turner, Quality by Design Lead, GlaxoSmithKline
Time to Innovate: Optimizing your experimentation process using JMP (link coming soon)
“Even if you think you've got a perfect process, once you've got the understanding that sits behind that, then when something goes wrong in the future – which it inevitably will do – you've got the data and the information that can allow you to bring your commercial process back in line as well. So it is really important to have this type of understanding.”
– Gill Turner, Quality by Design Lead, GlaxoSmithKline
Time to Innovate: Optimizing your experimentation process using JMP (link coming soon)
“Another reason I like design of experiments is how it applies to the experimentation cycle. With DOE, we're trying to hypothesize what are all the important variables that are at play in our system at the start…. DOE is a multivariate approach. We'll run that experimental cycle, testing a whole set of variables at once. And we'll get some knowledge about the system.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“I'm pretty confident that design of experiments could give you some benefit in almost any field of chemistry.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“Having a good software package really helps. I showed some scripting, but you don't need to know [how to do any coding]. The software handles all of that for you. The modeling is all handled under the hood. You can dig as deep as you want, but if you just want to see what's on the surface, [JMP] will present the core summary statistics to you. JMP is a very good package for this. You don't need to understand how to write your own code. You don't need to know how to do modeling. It does it all for you. It's point and click.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“[In a conventional laboratory] there are things you don’t realize you’re doing and things you don’t record that might be critical to the process. So other scientists can’t later understand what you did. Studies show that it’s rare to find enough information in a published paper to reproduce an experiment.”
– Michael Sadowski, Head of Bioinformatics Synthace
Synthace success story
“We have examples where we show that the success of the process we engineered was critically dependent on a three- or four-factor interaction. It's very hard to imagine how you’d get there without DOE. You need either a big flash of insight or serendipity if you're doing things in a more traditional way.”
– Michael Sadowski, Head of Bioinformatics Synthace
Synthace success story
“Biologists are at the beginning of a transition to a new way of conducting experiments. Up to now, they’ve had to approach experiments in terms of what each individual can do personally. But that’s changing – and rapidly. Once you have computer-aided experiments, with software assisting with the planning, execution and analysis, it encourages you to think bigger. You don’t merely look at questions in terms of the limited solutions you can implement by hand. You can focus on more problems and make sure the questions you’re asking are the right ones. Computational tools like JMP and Antha are the way we can get there.”
– Michael Sadowski, Head of Bioinformatics Synthace
Synthace success story
“There’s something of an AI bubble in the industry at the moment, but the first problem you hit when trying to apply more sophisticated methods is the lack of clean data with the right structure and annotations to support ever more complex modeling. Implementing sophisticated designed experiments is essential for that to happen.”
– Michael Sadowski, Head of Bioinformatics Synthace
Synthace success story
“I first stumbled across JMP in design of experiments a couple of years into my career. And it revolutionized the way I approach problems. It changed the way I design experiments and analyzed my data. And ever since then, I have been hawking it to every company I move to.”
– Jon Armer, Formulation Lead Scientist, F-star Therapeutics
Statistically Speaking: Achieve rapid innovation with design of experiments
“If we imagine the experimental space as a room, we see traditional trial and error as a spyhole on the door through which you can try to see the content of the room. You can make another hole, but you won't see very well through it either. You're trying to figure out what is inside the room with a couple of holes in random places. However, for me, a smart trial and error or design of experiments is just opening the door and looking around. At the end of the design of experiments, you have a model that represents your experimental space well. And that gives you a lot of confidence.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Statistically Speaking: Achieve rapid innovation with design of experiments
“I have to say it’s shocking that about 30% of people only use [DOE] in special occasions. I have to say I encounter that quite a lot of times. I think you can us design of experiments every day for every assignment.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Statistically Speaking: Achieve rapid innovation with design of experiments
“Lately the demands of one of our products increased substantially. We needed to build a new facility to produce this product, but first we had to work on the route to get one of the precursors of this product. That’s a lot of uncertainty when you are going to build a new facility. You want to actually control your process very well. And you are under stress because if you want to be able to cover this new increase in demand, you have to be very quick. So we were able to help our development team and actually come out with a quicker DOE that helped us to obtain a very good model. It is a model that is helping them to scale up the process and plan building these facilities as fast as possible and also with the trust that it is going to work.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Statistically Speaking: Achieve rapid innovation with design of experiments
“From the beginning, we were able to quantify how much value DOE adds for us. We were using DOE in several internal cases and we were able to actually quantify our savings of at least 50% of the time and resources. In some cases it was even 70%. So, management has always been aligned with us [using DOE in JMP].”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Statistically Speaking: Achieve rapid innovation with design of experiments
“I stumbled across design of experiments by chance. The company I was working for at the time had a specific problem we were trying to get around. We were using a more traditional one-factor-at-a-time approach that generated a lot of data. We were in the lab for months trying to crack this problem and we went down a number of dead ends. Eventually, we had a prodigious amount of data, I mean, the kind of Excel sheet that'd make your head spin. And we just didn't know where to go. As far as we were concerned, we had explored every avenue, we'd looked at every possible combination, and we were no closer to finding the solution. So, we ended up hiring external consultants, JMP partners, to have a look. Brilliantly, but also massively frustratingly for us at the time, they put the data into JMP and found the solution in about eight minutes. We had been banging our heads against this problem for the best part of two months! JMP was able to look at the data, see where the optimums were, the little bits we'd missed when we were stepping through the experiments, and show us what we needed to do next… We learned that if we'd used the more structured and thorough design of experiments approach to begin with, we could have done [what had taken us 100 experiments] in under 30 experiments. That was a huge hook. One, [DOE in JMP] solved our problem. Two, it showed us that we used more time than we necessarily needed to on the problem, including resources and money, of course. And three, we now have much more confidence in the answer…. ever since that point, I've been using DOE for pretty much every experiment on every type of analysis I do. It's a game changer.”
– Jon Armer, Formulation Lead Scientist, F-star Therapeutics
Statistically Speaking: Achieve rapid innovation with design of experiments
“The efficiency you gain from using this kind of approach just has a huge knock-on effect, especially on things like timelines and predictability. You can confidently say this stage will take this amount of time.”
– Jon Armer, Formulation Lead Scientist, F-star Therapeutics
Statistically Speaking: Achieve rapid innovation with design of experiments
“I'm a biologist, and I partially went into biology because I was terrified of statistics and anything to do with math. And yet, I've turned into the company expert on design of experiments and JMP. It's very, very straightforward to use. And the online learning and the tutorials inside the software are excellent.”
– Jon Armer, Formulation Lead Scientist, F-star Therapeutics
Statistically Speaking: Achieve rapid innovation with design of experiments
“It's very frustrating to do an experiment and then not know why it's failed or why it's not giving you the right answer…. That's one of the other big benefits [of DOE in JMP]. Even if an experiment fails when you're doing a design of experiments approach, it points you where to go. So every experiment is useful. You don't feel like you're wasting effort. I honestly can't imagine not using design of experiments in my day-to-day life. It's unthinkable for me now.”
– Jon Armer, Formulation Lead Scientist, F-star Therapeutics
Statistically Speaking: Achieve rapid innovation with design of experiments
“Once you’ve done [DOE], you’re not doing repeats. You don’t have inconclusive data that you’ve got to figure out. It's moving away from the more academic mindset towards a more engineering mindset.”
– Jon Armer, Formulation Lead Senior Scientist, F-star Therapeutics
F-star Therapeutics success story
“As scientists and engineers, we’re really good at collecting data, but not so great at using it. One of the things I liked when I first started using DOE, in JMP specifically, was that it forced you to think about your experiment – why am I testing this? Do I need to? Having that framework as you’re working through the experiment and analysis really helps.”
– Jon Armer, Formulation Lead Senior Scientist, F-star Therapeutics
F-star Therapeutics success story
"The three things that really strike me as the key to why industrial experimentation is so important are: the ability to see interactions… and also the ability to deal with random effects – things that could otherwise really block you from learning about your process by overwhelming your one-factor-at-a-time experiments. And finally, it will highlight places where the experiment will just fail either for product or safety reasons. So you need to be able to constrain your experiment.”
– Michael Anderson, Senior Statistician & Measurement Development Engineer, Corning
Statistically Speaking: The power of structured experimentation in science and engineering
“Whether you're a manager, a director or an engineer, you want to know as much as you can as efficiently as possible. Whether that efficiency is your raw materials-- it may be that the world's supply of a certain raw material is in the microgram scale, so you really can't afford to waste anything. And it may be time. You may have just that one sliver of time, a shift overnight, on your manufacturing line that's running 24/7. You only have those 8 hours every month. You'd better make it worthwhile. And then of course, the other one is just straight up money. All of these things cost resources across the board. So again, the ability to learn about multiple variables and interactions between non-linear effects, all in one tight experiment in a short amount of time with as few resources as possible is really attractive to people on all ends of the cost spectrum.”
– Michael Anderson, Senior Statistician & Measurement Development Engineer, Corning
Statistically Speaking: The power of structured experimentation in science and engineering
“The ability to bring together [high-dimensional multi-factor experiments through definitive screening designs in JMP] in a very efficient experimental system, where you get your absolute certainty and your main and two order effects, and then also the ability to get quadratics in a real tightly bound up experiment system was really a big development for Corning.”
– Michael Anderson, Senior Statistician & Measurement Development Engineer, Corning
Statistically Speaking: The power of structured experimentation in science and engineering
“Here’s an example: we were dealing with early research physics… looking for phase transitions. We designed this screening experiment, for which we cleverly tried high-throughput testing because we had so many different factors and combinations…. In JMP, we basically were able to identify the phase transitions; we found where it works. And now we can actually start developing the measurement protocols for the quantitative outputs that were based on those discovered models… now we can make progress. Big progress.”
– Vicky Svidenko, Partner, Quantum Data Sciences, Microsoft
Statistically Speaking: The power of structured experimentation in science and engineering
“In another example, we looked at a screening experiment where we wanted to create 8 factors. We were having a hard time studying one of the levels as intended. That factor was dilution rate. And we were trying to control the dilution rate as it was changing by the concentration of the agent. It was starting to be difficult, but we found that… we needed to change the water amount we add to the dilution. And this actually resulted in across-the-board better uniformity, stability and quality of the resulting fabricated structures. It wasn’t necessarily what we intended going into the designed experiment, but just following the process and looking for the opportunity space, we’re finding additional learning opportunities. This whole framework [in JMP] has been providing pretty valuable and effective ways to spread the culture of experimentation.”
– Vicky Svidenko, Partner, Quantum Data Sciences, Microsoft
Statistically Speaking: The power of structured experimentation in science and engineering
“With the power of DOE, it’s extraordinary what I can get done. It’s pretty much a universal outcome of using DOE for the first time. I had that experience first as a professor at Boston University when a student tried it and was able to rapidly optimize a PCR process to get high performance, high stability, highly robust results in just a matter of an experiment or two instead of the months we’d spent unsuccessfully achieving that with a one-at-a-time approach.”
– Timothy Gardner, Founder & CEO, Riffyn
Statistically Speaking: The power of structured experimentation in science and engineering
“[DOE] gives you freedom. In pharmaceutical development in particular, getting locked into a particular operating regime is problematic because, once you get approval, it's hard to change course. Designed experiments can allow you to explore what's called the design space, the allowable range, in a very systematic way. And it is actually buys your freedom. It opens up the operating regime that you can safely perform with quality and get that larger space approved. It allows your manufacturing team to make adjustments without having to go back to the drawing board. That's incredibly valuable.”
– Timothy Gardner, Founder & CEO, Riffyn
Statistically Speaking: The power of structured experimentation in science and engineering
“DOE in [JMP] is used in operational qualifications as part of our validation efforts, and it’s also used in continuous improvement efforts. It is very rewarding to see how our engineers and scientists respond to the results of these design experiments. The models that are created and verified in our DOEs are very useful, and they invariably end up helping the engineers and scientists with their future work as well.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“The most successful experiments are those that are thought through carefully upfront with particular attention paid to the design and the measurement aspects of it. Too often, an experiment can fail because there is variation we don't expect or that you're not clearly measuring the outcome that you need to measure.”
– Ed Hutchins, Product Engineering Manager, Wolfspeed
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“I am a fan of keeping it simple, focusing on major effects, using your expertise and your experience to study the things that are most likely to be outcomes and then building upon that with subsequent experiments to refine and optimize further.”
– Ed Hutchins, Product Engineering Manager, Wolfspeed
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“We're doing a lot of different experiments in different capacities. So one of them is sequential design of experiments. And so this is shifting from the agricultural paradigm where you would set up a big experiment, and then you'd have to wait a whole growing season to be able to see how the results were. And I think in a lot of industry applications, there can be a much quicker turnaround of getting results from experiments. The other category is we're now starting to see multiple streams of data, and we want to have a way of designing an experiment to combine those. So perhaps we start with a computer model that captures the science that we know for a particular process, and we run a computer experiment to explore how that model behaves throughout the input space that we have. And then we have the opportunity to collect physical data which we then need to dovetail in with what we know about the model that we've explored through that previous experiment.”
– Christine Anderson-Cook, Statistician, Los Alamos National Laboratory
Statistically Speaking: Stay relevant and competitive with modern quality engineering
“We need to move to data driven science, which just sounds completely ridiculous because everybody assumes that science is data driven. But the truth of it is there's a lot more mysticism, guesswork, and bad decisions being made in science because the data either is inaccessible because it's in silos. It's not collected in a meaningful way because it doesn't have the context and annotations that define what it is or how it relates to a process. Or it's just really bad quality because the process that generated it, the experiments or the measurements, were not controlled. Data-driven science means getting your head around the process we use to generate data so that we can do it better. And that takes an investment upfront that seems too high, but in the end it pays off in multiples. You go many times faster.”
– Timothy Gardner, Founder & CEO, Riffyn
Statistically Speaking: The power of structured experimentation in science and engineering
“As you can imagine, the DOE tools provided by JMP in particular were very helpful [in this case] and are often used in this R&D group since a lot of parameters are unknown for the processes we're investigating.”
– Andreas Trautmann, Process Expert for Upstream Processing, Manufacturing Science & Technology, Lonza
Lonza success story
“JMP is most helpful because it's much more efficient to investigate several factors at a time instead of investigating only one by one. It saves a lot of time to use statistical methods like this…. We used JMP for designing, planning and evaluating the experiments from the very beginning in small-scale bioreactors. Then, we continued the work in medium-scale bioreactors and by the end of this year it will be transferred to a production scale. It doesn't happen so often that very new processes from lab-scale reactors will end up in manufacturing. So it was a very nice success story.”
– Andreas Trautmann, Process Expert for Upstream Processing, Manufacturing Science & Technology, Lonza
Lonza success story
“Without using the Custom Design tool, we would have needed at least double the amount of time to investigate these factors. Instead of two weeks, probably four weeks at least… The customer was really happy, and we were really happy as well.”
– Andreas Trautmann, Process Expert for Upstream Processing, Manufacturing Science & Technology, Lonza
Lonza success story
“JMP has been valuable because a lot of times when you’re doing a DOE, you don’t spend time looking at every data point. [After improving the overall yield from 1 percent to 5 percent], we had so many lasers we didn’t know what to do with them. At that time, I really didn’t see any other software that could do that so easily, so I was a JMP fan from that point on.”
– David Seniuk, Senior Product Engineer, Ciena
Ciena success story
“Not every company has the luxury of having a statistician on staff, but everybody can afford a JMP license. Even just a basic understanding of statistics and how to use it can help people to really add more value to the experiments they’re doing. It’s only once we’ve found those answers that we can have confidence in our research.”
– Owen Lozman, Vice President, M Ventures, the strategic capital arm of Merck KGaA
M Ventures success story
“With JMP, you can do a definitive design run and get great results in approximately the same number of runs. A properly designed experiment will yield better results every time. There’s a strong tendency to vary one factor at a time, and to think you’re doing real science, when in fact you’re just basically hunting and pecking to find something that works. Without formally designing an experiment, it’s easy to say, ‘Well, I guess we’re at the sweet spot; let’s move on.’ And you end up with an inferior product.”
– Jed Campbell, Quality Manager, US Synthetic
US Synthetic success story
“We call in JMP to dive in, decipher it, design an experiment to solve it and move forward. With JMP, there’s a graphic and number for everything, and it guides you right through the data exploration. It’s so easy to use.”
– Jed Campbell, Quality Manager, US Synthetic
US Synthetic success story
“We may have 10 or 20 variables for which we need to consider interaction effects. [Traditionally, engineers] did not use statistics [to guide experiments] because a lot of it dealt only with relatively simple data sets. But when we began using JMP for DOE, we were able to dramatically reduce the required sample size of the experiments so that we could apply appropriate statistical analyses. This has really improved time spent on analysis.”
– Yonghan Ju, Head of Data Innovations, SK Hynix
SK Hynix success story
“For a long time, we did some blind experimenting and a bit of hit-and-hope. Previously, we got away with it because you can re-mill particles that are too large. But you can’t make particles milled too small bigger again without reprocessing – and that can be very costly…. We’re building up a picture – a model – for any starting particle size we might need. We’re now able to set the mill up to hit the target size we’re looking for. Once you’ve built that model, almost anyone can plug the data in… In fact, the success of this [particle milling] project, and specifically using JMP within it, elevated the importance of JMP at this site.More and more projects will be using JMP to do this sort of analysis going forward.”
– David Payne, Site Director, Johnson Matthey Fine Chemicals
Johnson Matthey success story
“JMP is being used throughout the entire scientific method process. A lot of the data that is necessary to make [strategic business] decisions doesn’t exist yet. So we use JMP to help us in planning our experiments in an efficient and effective way, all the way through the modeling of the resulting data.”
– Jeff Sweeney, Senior Research Statistician, Dow
Dow success story
“There was no way we could look at all those process variables without using DOE – we’d have to run so many experiments, and we would have missed interaction effects. JMP made the design, analyses and presentation of the experiments easy. Essentially, a DOE approach was the only way we could have solved the problem in a reasonable amount of time using minimal resources.”
– John Cunningham, R&D Manager, G3 Enterprises
G3 Enterprises success story
“My own experience using DOE… has shortened my method-development times by 50 percent or more. And I love the certainty of knowing I’ve found the best possible combination of parameters.”
– Camilla Liscio, Applications Chemist, Anatune
Anatune success story
“With JMP, anyone can use DOE, because the software does the difficult work. JMP provides a structural and very elegant way to tackle very complicated analytical challenges.”
– Camilla Liscio, Applications Chemist, Anatune
Anatune success story
“With JMP, we don’t need to run as many wafer experiments to evaluate tool performance… The way JMP enables us to examine ‘what if’ scenarios is really powerful.”
— Paul Deen, Statistical Methods Program Manager, ASM International
ASM International success story
“I really like being able to translate what I know to be good, strong science, very well-designed experiments into something that actually supports sustainable agriculture. The sustainability of animal production is so important. And being able to do good science that gets peer reviewed and published – that actually has some real value for people who are producing animals to feed the world – that’s what drives us.”
– Nell Masey O'Neill, Research Manager, AB Vista
AB Vista success story
“And we know this from the predictive modeling we’ve done with JMP. The really important point here is that we wouldn’t have been able to divine that from one single experiment.”
– Nell Masey O'Neill, Research Manager, AB Vista
AB Vista success story
“As you work with JMP, you learn to trust the software. You can test multiple factors at the same time and reduce the number of test runs.”
– Petra Schreier, Engineer, Atotech
Atotech success story
"I’m often unable to draw clear conclusions after one-variable-at-a-time experiments, but with models developed using the JMP Custom Design platform, I can clearly visualize sweet spots in performance, and I’m always confident in my conclusions."
– Jim Dailey, Global Technical Key Account Manager, BASF
BASF success story
“For us, the speed of learning is very, very important. We have to embody the principles of SWOT trial and experimentation to differentiate between the signal and the noise. So for the matter of time, we have to use smart trial and error methods.”
– Sidharth Bhatia, Director of Data Science and Analytics, Applied Materials
Statistically Speaking: Leading companies innovate with statistically designed experiments
“In a complex system, how do I gain maximal information with minimal number of runs? That's the key. Once I actually go do an experiment in a structured and controlled manner, I can see that I would not have found those ideas from just doing one factor at a time.”
– Sidharth Bhatia, Director of Data Science and Analytics, Applied Materials
Statistically Speaking: Leading companies innovate with statistically designed experiments
“My goal is to develop a process that has the highest possible yield and to lower the cost of goods for how we make it. So we need to understand our process and know which factors could affect it when we're doing a small-scale experiment. We know our equipment really well and it may work really reproducibly every time. But when we take it to the large scale, we want to know which factors are important so that it will be reproducible when we have it made there.”
– Patti McNeill, Associate Director of Cell Culture Development, Lundbeck
Statistically Speaking: Leading companies innovate with statistically designed experiments
“We had a problem that had been plaguing us for roughly 15 years. Our customer was complaining about it and it was costing us a lot of money. Finally, we set up a designed experiment where we used a strategic approach to varying the parameters. Within 6 weeks, we were able to identify what the problem was and then we were able to address it. And we've since been able to extend the [product] lifetime.”
– Melisa Buie, General Manager, Coherent
Statistically Speaking: Leading companies innovate with statistically designed experiments
“DOE is so much more powerful than a one-factor-at-a-time [approach]! After all, most of us don't live in a one-dimensional world. Realistically, our experiments are not based on one dimension. It's important that we build a model that reflects the true experimental space that we're trying to optimize our process or make a discovery in.”
– Melisa Buie, General Manager, Coherent
Statistically Speaking: Leading companies innovate with statistically designed experiments
“I was able to do a definitive screening design [in JMP] and for 10 factors, I was able to test three different concentrations of each of those 10 factors in 24 shake faucets in one experiment. And at the end of that experiment, using the Profiler and fit definitive screening designs, it came up with a new recipe that was not tested in one of those experiments. We put that new recipe together, ran the cells using that new medium, and increased our yield 25% to 30%.”
– Patti McNeill, Associate Director of Cell Culture Development, Lundbeck
Statistically Speaking: Leading companies innovate with statistically designed experiments
“It's okay if some runs fail because that means I've found a factor that's super important for this response. I get really excited when runs fail because then I know that I’ve found something important.”
– Patti McNeill, Associate Director of Cell Culture Development, Lundbeck
Statistically Speaking: Leading companies innovate with statistically designed experiments
“If we had run eight reactors, which would have been a 14-day experiment, we could have predicted the same pH instead of taking nine months to predict that same pH. And I had a lot more ‘aha!’ moments from the audience doing it [with DOE]. Sometimes there's this fear that we're not testing the pH setting that we think is going to be the right pH setting. So we want to do five levels. But we don't need five levels. We can just do three levels. Even though that level isn't the one that we know is best, it still predicts the one that's best.”
– Patti McNeill, Associate Director of Cell Culture Development, Lundbeck
Statistically Speaking: Leading companies innovate with statistically designed experiments
“DOE's a toolbox and you can make it fit wherever you are in the process. Sometimes you're screening, sometimes you're optimizing, sometimes you're exploring the operating space.”
– Patti McNeill, Associate Director of Cell Culture Development, Lundbeck
Statistically Speaking: Leading companies innovate with statistically designed experiments
“When I first started at Coherent, we didn't have a strong culture of DOE. It’s something that we've developed over the years, and there is still a strong reliance on the physics. We never want to leave that knowledge of the subject matter behind. We want to incorporate it into our design and use that expertise to build a stronger model and make our theories and knowledge of the subject matter even more relevant to the actual situation.”
– Melisa Buie, General Manager, Coherent
Statistically Speaking: Leading companies innovate with statistically designed experiments
“Once you no longer have to think about an experimental design and how to set it up, analyzing the results takes the spotlight and there the sparks are to be found that inspire and drive the breakthroughs. Not all of our data, however, comes from plug-and-play experiments; sometimes we are dealing with large volumes of data – where interrelated factors are numerous – and this introduces its own challenges. JMP, with its multivariate capabilities, comes in handy with our high-throughput equipment, which generates a lot of this data type. It’s much better if we can match our high-throughput data generation with high-throughput, statistically significant data mining techniques. JMP gives you lots of options, and that allows you to do more.”
– Andrew Kaja, Biologist & Bioprocessing Team Leader, Johnson Matthey Biologics
Johnson Matthey Biologics success story
“We use JMP to give us confidence in our decision making. If we weren’t using DOE and JMP, we wouldn’t be able to characterize our processes and demonstrate that we’re minimizing our failure rate and increasing our robustness. The designs show us that our processes are robust and ready for transfer. And because more and more of our partners are talking in terms of DOE, they can trust our processes.”
– Andrew Kaja, Biologist & Bioprocessing Team Leader, Johnson Matthey Biologics
Johnson Matthey Biologics success story
"We have an interface that … enables a high level of experimental design and statistical analysis in JMP. The system makes it easier to across the board increase standards, reproducibility and data interpretation for genetic engineering applications… It’s not just doing more replicates but doing smarter replicates and building models which have much more value.”
– David McClymont, Head of Automation, London DNA Foundry
London DNA Foundry success story
“When you can take a process that may take weeks – [not to mention] all the thinking and focus that it requires – and you turn that into a ‘click and go’ operation, it’s such a liberating thing for us as scientists. You can focus your mind on more important things like interpretation, modeling, thinking of your next experiment. And you can essentially let the software and the robots do all that for you.”
– David McClymont, Head of Automation, London DNA Foundry
London DNA Foundry success story
“JMP is based on how scientists or biologists would interact with it. I really liked the way it seemed to be aimed at getting things done rather than spending all your time trying to figure out what the software is trying to do. [JMP] did what I wanted it to do and we just jumped straight in.”
– David McClymont, Head of Automation, London DNA Foundry
London DNA Foundry success story
“DOE is just so fundamental. It’s a springboard for other experiments, other analyses, understanding sensitivities. I can’t overstate how important it is. JMP is a phenomenal tool for extracting information on multiple factors in a single experiment.It needs to be set up with the right questions in mind and, of course, is only as good as the assumptions behind the experiment. But DOEs are critical to getting independent information on multiple factors.”
– David Lee, Director of Quality and Reliability, OLEDWorks
OLEDWorks success story
“DOE has begun to play a bigger and bigger role within the company. If you can afford to do big consumer studies, then you’ll usually get the answers. But nowadays most departments [prefer to use resources more efficiently]. In fact, [with optimal DOE] you may even find that there are different solutions which vary greatly in cost to the company. And if you have two or three optimal solutions, you can pick the cheaper one. Only DOE will give us these kinds of options. So in every single step, we try to use DOE wherever possible.”
– Beatrice Blum, Statistician, P&G
P&G success story
“You might ask ‘why automation and experimental design?’ Well, I believe strongly in the power of multidisciplinarity. DOE is actually a very powerful statistical tool that can help you find the best method within the experimental space you're exploring. Automation is a very powerful analytical tool that can overcome human error and give you very good analytical performance. If you combine the two, that actually leads us to what we might call high-performance method development because it's going to give you the best method with the best performance.”
– Camilla Liscio, Applications Chemist, Anatune
Time to Innovate: Data analytics for analytical method development (link coming soon)
“DOE is a very elegant way to establish the relationship between the factors we are investigating and the response we’re interested in. Once you establish this relationship, you are in control of the process. You understand the process. And then you can steer the process in the direction you want it to go… DOE pushes the boundaries so that you can explore the extreme of your experimental space and you can actually find where your optimum lies.”
– Camilla Liscio, Applications Chemist, Anatune
Time to Innovate: Data analytics for analytical method development (link coming soon)
“When using DOE, JMP provides so much power – and complete model predictions, which can help us find optimal conditions very accurately and quickly. This is not available in other software.”
– Jerry Huang, Analytical Lab Senior Engineer, DuPont Taiwan
DuPont | JMP
ACADEMIC TEACHING & LEARNING
"Be curious, use the "Help" section of JMP – and do so frequently! The documentation within JMP itself is the best place to begin, and the sample data sets and books are a treasure. From there, check out the resources online at jmp.com, check the training schedule and get engaged with a users’ group – try virtual if in-person is not available in your area."
- Don Kent, manger of full-stack data science team, Intel
Intel’s Don Kent on what makes a mature analytics organization, the importance of mentorship and joining the “JMP family” at Discovery Summit
“DOE is an essential skill. When we were talking, when we were setting out CDT, we did a lot of consultation with the chemical industries in the UK. And it was almost unanimous. We were told that chemical industries in the UK, be it research or process, they want their chemists to have design of experiments skills.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“In DOE we start to think about only testing two or three levels and trying to predict what's happening in between. There's a lot of built-in testing of variable interactions. And then there's also all the statistical analysis and predictive modeling that comes from the DOE approach. That's why several years ago, when we got started in this journey with ROAR and the REACT CDT, we were so keen to build DOE into our training courses. We didn't have a lot of experience, initially. So we went over to JMP.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“Students come along to our [DOE] course. They learn about this wonderful tool, [JMP]. And then they go back. And if their supervisor's not on board, they're not really going to be able to use it… We're also very aware that if we send students with some DOE knowledge [back to their labs] and they have a bad experience, that could potentially put a whole research group off trying DOE again forever. So what are we trying to do to solve this barrier? We need to get the supervisors along to the course as much as possible.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“We also make sure we present relevant case studies. And we tell the students honestly about what the limitations of DOE are. It's not a magic bullet. It's not going to solve every optimization problem. We try and give the students as many examples where DOE can be an advantage, but also tell them where, look, it's not going to solve that problem for you.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“We find that the students coming into our courses don't have a lot of statistical vocabulary, but this is not actually a problem. DOE and JMP in particular don't require a lot of prior knowledge. You don't need to know a huge amount.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“I think DOE will come to undergraduate courses over time. One of the major barriers we have at the moment is what we think of as a traditional undergraduate chemistry lab experiment. It's more about teaching them practical aspects of handling the glassware, so there are fewer opportunities to run a whole series of experiments and build up a data set and test parameters. But I think we're updating and modifying our experimentation courses and we're moving much more toward research experience earlier in the course, so that students learn about it earlier. And that's where DOE would fit nicely.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“The use of statistics is generally taught when you study chemistry. But over the last 10 or 15 years, most of us haven't realized that statisticians have been getting on with their job. They've improved these techniques. And you've also got people like JMP who are taking those techniques and turning them into something that is easy to use… If you can use Microsoft products, you can use [JMP].”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Capturing value from your data to accelerate innovation (link coming soon)
“We opted to focus on JMP as a platform because it coincides with the democratization of data. Of the software available, JMP seemed to be much easier than alternatives, intuitive, excellent interface and superb online support tools both for the software and statistics in general. With JMP, even if you stick with the defaults, the software is rich and intuitive enough that you can adapt, learn, play and get a result.”
– Stan Maklan, Professor of Marketing and Technology at Cranfield University
Cranfield University success story
“That's where JMP is very strong: you can look at customer defection, for example, and run through multiple methods, and literally in a couple of hours the student will understand that different methods might give you different answers– and that your job is to understand the differences and make an informed choice.”
– Stan Maklan, Professor of Marketing and Technology at Cranfield University
Cranfield University success story
“It was through [our corporate partners’] feedback, and through my assessment of early career positions, that we decided that a design of experiments (DOE) course using an industry standard software such as JMP was going to give our students an edge. They’re only here for a finite amount of time – and we have a finite amount of resources to work with. So [adding a DOE course] was also a strategic decision: what would have the largest return on investment in terms of students' marketability?”
– Stacey York, Director of the Master’s Industrial Internship Program, University of Oregon
University of Oregon success story
“I project my screen whenever we’re using JMP in class. That way, I can show them how to do the [exercises assigned to them] and they can follow in real-time with the JMP software installed on their laptop... Once they have the basic statistics and principles of DOE down, we then move on to class projects that most people can easily relate to.”
– Fuding Lin, Manager of the Master’s Industrial Internship Program, University of Oregon
University of Oregon success story
“Think of it as a customized tool for interacting with the data, rather than just as producing something you’re going to print on a piece of paper. Once business students start thinking about the graphics as analytic tools, JMP becomes an enormously powerful asset for them. There are all kinds of little things that this software does to change the way you think about interacting with data, and at the end of the day, that’s one of the big jobs we are sending our graduates out into the world to do.”
– Robert Stine, Professor of Statistics, The Wharton School of the University of Pennsylvania
University of Pennsylvania success story
“The graphical displays are very, very important to us… JMP basically forces students to look at pictures, and we think that’s so important to having a good model.”
– Robert Stine, Professor of Statistics, The Wharton School of the University of Pennsylvania
University of Pennsylvania success story
“I expect good software to be easy to use and also to offer all the possibilities that you will need in your daily work. I like to use JMP in my classes because it fulfills both requirements and in addition it’s visual and interactive, so in the end fun to use.”
– David Meintrup, Professor, Ingolstadt University of Applied Sciences
Ingolstadt University of Applied Sciences success story
“It’s so important to calibrate with the industry. I thought it would be a good idea for our students to know how to use JMP because so many of them will go on to work for these companies in California. We provide the training, and with their knowledge of the software once they graduate, our students will have an advantage.”
– Jianbiao John Pan, Professor of Industrial and Manufacturing Engineering, California Polytechnic State University
Cal Poly success story
“We used Six Sigma processes in class to analyze team projects. The project definitely helped me learn statistical process control and a number of different quality modules, looking at the charts that were available. The interface that JMP has is very nice—you can use the raw data to create charts and analyze and interpret the data.”
– Kyle Batman, Undergraduate Student, California Polytechnic State University
Cal Poly success story
“I know that in the manufacturing field, whenever you’re dealing with analysis with multiple dimensions, statistical software like JMP will play an integral part in helping me analyze large data sets. I’m told in manufacturing, JMP is what everyone is using for analysis.”
– Kyle Batman, Undergraduate Student, California Polytechnic State University
Cal Poly success story
“I focus on materials engineering and there is a lot of experimentation that is required in that field. Fortunately, the whole design of experiments process is facilitated by JMP—for example, if you’re doing some kind of fractional factorial, it’s really easy to set it all up to be generated in JMP. Since it’s so interactive and intuitive, JMP makes it a lot easier to look at the data and work with it on the fly. For someone like me who’s a visual learner, that’s really useful.”
– David Otsu, Graduate Student, California Polytechnic State University
Cal Poly success story
“JMP reinforces a holistic approach. Students learn how to understand the scope of a project. They learn how to question a problem statement. And how to reformulate. They learn to ask: ‘How can we shift a project that's scoped in the wrong way with the wrong people?’”
– Peter Hammersberg, Senior Lecturer in Engineering Materials, Chalmers University of Technology
Chalmers University of Technology success story
“Not many projects end up being very specific on the definition of control charts and control limits, for example. But understanding the concept of control and the role of a control chart allows me and the students to talk about noise [versus] something which contains signals. They really understand [these concepts] when they’re forced to dig into the data themselves.”
– Peter Hammersberg, Senior Lecturer in Engineering Materials, Chalmers University of Technology
Chalmers University of Technology success story
“If my students have a chance to become more used to visualizing data – and I think that's the important first step, getting used to the data – they’ll see that it's simple to create graphs and look for patterns and do exploratory data analysis. JMP has good supporting features. In any course where I teach measurement system analysis and all basic graphical stuff, JMP has modules that make it easy for students to use.”
– Peter Hammersberg, Senior Lecturer in Engineering Materials, Chalmers University of Technology
Chalmers University of Technology success story
“Since I've been in industry, I see that my students will need [to know how to use JMP]. Many new hires arrive at their jobs and they're not well-trained. I’m trying to fight that war to get students better prepared with the skillset they need. And I’m gaining ground, slowly, step by step, more and more.”
– Peter Hammersberg, Senior Lecturer in Engineering Materials, Chalmers University of Technology
Chalmers University of Technology success story
“Students have to learn how to handle all the data [that’s collected in a factory]. They have to find out what's important without doing a theoretical statistics report. So teach it early. Make JMP a general tool for them, equally with the other software they use for solid mechanics or math. Students should have a good platform for data analysis too.”
– Peter Hammersberg, Senior Lecturer in Engineering Materials, Chalmers University of Technology
Chalmers University of Technology success story
“The first thing we've got to do is get people educated about DOE. And as you can see from the panel up here, we didn't know about DOE coming out of graduate school. And so we were experts in our field! Because this really valuable tool was not available to us in graduate school. I think with a lot of scientists and engineers, as they're entering the workforce, we need to make sure that people are educated and have the tools that will allow them to be successful. And I think JMP does a great job providing that to people, the education as well as the tool.”
– Melisa Buie, General Manager, Coherent
Statistically Speaking: Leading companies innovate with statistically designed experiments
“The involvement of practitioners and regulators with experience at the cutting edge of the industry enables students to access the best quality by design practice from around the globe. We have been working closely with industry for over 7 years to develop efficient and rapid methodologies to produce high quality medicines and innovative approaches to teaching. The use of JMP enables us to teach students how to organize research data, apply visualization and perform data analysis. Data management and data integrity are key within product development and manufacture.”
– Walkiria Schlindwein, Associate Professor of Pharmaceutics, De Montfort University
De Montfort University success story
“JMP encourages trial and error, and that’s key to the learning process… By familiarizing students with software like JMP, it makes statistical thinking less intimidating – they start to think of statistics as a routine, professional thing. With JMP, it feels like the threshold for statistical problem solving is actually very low – and that’s incredibly valuable.”
– Kai Xu, Professor of Exercise Physiology, Nanjing Sports Institute
Nanjing Sports Institute success story
“What my students like about JMP is that it’s very easy for them to learn. They like that after each step, there are graphics to present the results. So it’s intuitive, and they get feedback. If they go in the wrong direction, they can easily erase the vector and add a new method to try again. This ease of use motivates them to study further. They find studying with JMP to be very interesting.”
– Kai Xu, Professor of Exercise Physiology, Nanjing Sports Institute
Nanjing Sports Institute success story
"JMP is already in the industry, so if I produce students who already have JMP skills [by the time they begin their career], they can hit the ground running. Employers don't have to teach them JMP. They already know it."
– Goutam Chakraborty, Professor of Marketing Analytics, Oklahoma State University
Oklahoma State University success story
“JMP is where we start. It’s a great way for us to start teaching students the application of analytic statistics, visualization, exploration. I don’t lecture in class. Rather, the classroom is for discussion, interaction and exploring, so we’ll pull up JMP. We can do it on the fly. Can I do it on the fly with other programs? Yes, but it will take me more time, and even if I can do it, the students are not ready [to use software with a higher learning curve] at the beginning.”
– Goutam Chakraborty, Professor of Marketing Analytics, Oklahoma State University
Oklahoma State University success story
“We use SAS Enterprise Miner and JMP in a variety of courses. And when students graduate, they get good jobs at good companies because they’re familiar with the leading software packages for data mining and visualization.”
– Mark Johnson, Professor of Statistics, University of Central Florida
University of Central Florida success story
“Students used to struggle with things like regressions and decision trees because there were no really good, accessible tools available. They were required to have a fairly sophisticated knowledge of programming – and that’s what turned off the students. JMP has changed that dramatically. I think JMP is a tool that’s really amenable to business students – and I saw that right off the bat.”
– Ram Gopal, Professor of Business, University of Connecticut School of Business
University of Connecticut success story
“My personal feeling is that JMP is a great jumping-off point for business students to get introduced to the world of business analytics. The tight integration between JMP and SAS will enable students to delve deeper and explore opportunities in text mining and other useful aspects of SAS. Another promising area with JMP is in visualization. I think for business students that’s a must-have, and I’m really looking forward to incorporating more business visualization with JMP into my courses.”
– Ram Gopal, Professor of Business, University of Connecticut School of Business
University of Connecticut success story
“I’m amazed at the level of sophistication of different techniques that can be executed with JMP. I think it’s superb.”
– Ram Gopal, Professor of Business, University of Connecticut School of Business
University of Connecticut success story
“We’ve invited companies to provide data or present problems they’re facing related to data science and business analytics – and we pair students with these companies to work together on capstone projects. We’re really focusing on getting students close to the business side of data…. It’s especially critical that business analytics students get firsthand practical experience in data science.”
– Christian Hildebrand, Assistant Professor of Marketing Analytics, University of Geneva
University of Geneva success story
“The ability to learn and observe patterns in the data during the visualization process is a feature that is unique to JMP. Students can produce a graph and ask ‘are there certain patterns in the data that I haven’t thought about previously?’ Having the opportunity to actively learn during the creation of a graph or figure in JMP is what makes it so unique. It’s a feature that I can’t talk enough about – visualization makes everything click.”
– Christian Hildebrand, Assistant Professor of Marketing Analytics, University of Geneva
University of Geneva success story
“I could see it in the comments that we received from students in their evaluations. They were saying, ‘One of the main takeaways is having learned a new statistical program that really helped speed the analytics workflow.’ There were many comments that students were thankful for having had the chance to develop powerful, effective visualizations in a very short period of time… and that JMP really helped them understand the statistical part better.”
– Christian Hildebrand, Assistant Professor of Marketing Analytics, University of Geneva
University of Geneva success story
“Without visualization, all we as instructors can do is introduce concepts and discuss how an algorithm works in theory. Students would have to be very, very motivated to really follow along and say ‘OK, now I’m curious to see what the application is.’ But if students can visualize the end result from the start and go back and make a deep dive into the methodology, [they] say ‘oh, that looks really neat – I want to produce the same analysis and powerful statistical graph. And I’m curious to understand how this methodology really works.’”
– Christian Hildebrand, Assistant Professor of Marketing Analytics, University of Geneva
University of Geneva success story
“I think what’s most brilliant about JMP is that it’s great for both the novice and the advanced user. Whenever you teach with statistical software, you anticipate spending a lot of face-to-face time with students as they try to work through issues. That can be a delight, but it also requires a lot of extra hours. [With JMP] you don’t spend your time teaching software – you spend it teaching statistics.”
– Peter Lenk, Professor of Operations Management Sciences and Marketing, University of Michigan
University of Michigan success story
“Students love JMP. I hear that from both undergraduate and graduate students. It’s so easy to work with – and so powerful.”
– Robert Nydick, Professor of Management and Operations, Villanova University
Villanova University success story
“For design of experiments, JMP is probably the cutting-edge software. Plus it’s easy. It’s fully integrated with the analysis.”
– Phil Ramsey, Principal Lecturer, University of New Hampshire
University of New Hampshire success story
“These students have all heard how it really helps to have a design of experiments course on your resume when you’re applying for jobs, and they’re coming to realize – having heard from the students who’ve graduated before them – how important it is to know statistics.”
– Phil Ramsey, Principal Lecturer, University of New Hampshire
University of New Hampshire success story
“JMP helps me bring three disciplines of students together and then demonstrate to each group how the others experience data mining. And it’s all because of JMP’s visualization tools.”
– Arif Ansari, Assistant Professor, University of Southern California Marshall School of Business
University of Southern California success story
“For these students, the visualization JMP offers really opens their minds. Often, metrics can be misleading. Two different models with similar metrics can have totally different fits. The JMP profiler has helped me show students how nonlinear models behave and how to select the best model by visualizing the data.”
– Arif Ansari, Assistant Professor, University of Southern California Marshall School of Business
University of Southern California success story
DATA VISUALIZATION
"It was obvious that I should introduce non-technical people to JMP via the graph builder platform"
-Kieran O’Mahony - Dairygold
JMP DS Americas 2021- Recording Producers - Data Explorers Spotlight gallery.mp4 - All Documents (sharepoint.com)
"Graph Builder and the Profiler are the Rosetta Stone for domain experts and JMP nerds. The capability for dynamic visuals basically allows domain experts to see what the data is saying across different settings and in different graphical environments. That’s really the “ah-ha” moment because they see what they have in their head – and JMP allows them to explain the links between process elements very clearly to other team members."
-Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
“I started exploring JMP and having conversations about it, particularly with regards to the Graph Builder. I thought, wow, this is new. This is different, and it seems very flexible and interactive.”
– Vasco Cachaco, Principal Six Sigma Process Champion
Xaar success story
“Nobody has yet convinced me there’s anything out there that can give me the visualizations I get in JMP as easily. That’s probably where the buy-in from the decision point came with the software: it’s a visual tool, so when you show people, they’re very, very quickly on board with it because they can see the capability.”
– Brendan Malone, Senior Process Engineer, Johnson & Johnson
Johnson & Johnson success story (link soon)
“It’s so important to teach how to read graphs. Education on data visualization is critical to convey an accurate message – we often don’t give it enough credit.”
– Branca Almeida Silva, Formulation Development Coordinator, Bluepharma
Statistically Speaking: Why interactive data visualization is critical to industry
“These days, data visualization is more concentrated in some groups within the organization. Even if most people tend to use data and produce graphs, it’s often still just a few people who are working with the more powerful tools. It’s important that we have more discussions about it because you can see how easy it is to work with software like JMP. It’s very versatile. We need to motivate people to try exploratory data analysis and visualization without worrying they don’t have a specific skillset.”
– Branca Almeida Silva, Formulation Development Coordinator, Bluepharma
Statistically Speaking: Why interactive data visualization is critical to industry
“With data visualization, we can save time. [At Bluepharma,] we use data visualization to do all kind of analyses and more in-depth modeling. If we start with visualization, we can select factors. That way, we save time for further studies. And once again, saving time is the same as saving money because we can do things much faster. I use data visualization and JMP on a daily basis.”
– Branca Almeida Silva, Formulation Development Coordinator, Bluepharma
Statistically Speaking: Why interactive data visualization is critical to industry
“People are afraid of doing data visualization because we associate it with very specific skills related to statistics. But that’s not always the case. Everyone can perform data visualization without being an expert.”
– Branca Almeida Silva, Formulation Development Coordinator, Bluepharma
Statistically Speaking: Why interactive data visualization is critical to industry
“Sometimes when I’m doing a data visualization, I start with a goal but then during the process, find something interesting and totally forget my original goal. I might find some new pattern that is very interesting to explore as well. So we definitely need time to go through the data visualization process. And the tools are really important. We have to be confident with the support we can get when we select a tool to work with. And JMP has many webinars that we can explore in training.”
– Branca Almeida Silva, Formulation Development Coordinator, Bluepharma
Statistically Speaking: Why interactive data visualization is critical to industry
“The more we look at data in a professional way, the more intuition we will have about the content of our data. When we present the results of the analysis, it’s good to convey the message in a graphical way. People will understand it much better than just statistical details.”
– György Trefán, Process Developer, Coloplast
Statistically Speaking: Why interactive data visualization is critical to industry
“I will share an example: we ran into a problem with our machines where the machine produced too much scrap. When we looked at the data to find out why, we started first by looking at the machine and then at the material. We looked at the data over the three previous years to see how much scrap each shift produced. What we found is that just plotting the data over time revealed nothing interesting. But doing statistical analysis, we saw that the second shift had significantly higher scrap than the other two shifts. Once we discovered that, I had to show it to management. I decided to visualize it [in JMP]… the management reaction was ‘wow this is a shocking difference.’ And they looked into deeper causes. What we found after measurement system analysis is that the operators used a different way of judging whether something was scrap or not. So we brought the problem to the operators to hear their judgment.”
– György Trefán, Process Developer, Coloplast
Statistically Speaking: Why interactive data visualization is critical to industry
“When you analyze a situation, you can see it from a different point of view. Every visualization generates different perceptions, different points of view. Seeing the same situation from different points of view can generate a lot of added value.”
– Simone Cianchini, WW Innovation and Improvements Quality Manager, MARELLI
Statistically Speaking: Why interactive data visualization is critical to industry
“We need to make decisions objectively and not subjectively. Data visualization can help improve this path. It’s very important that the data be readable and understandable to the person in front of you… If we can transform the problem – and the data – with visualization, they will understand the problem better. It’ll be in their comfort zone. Also it’s very important to keep in mind the target of data visualization. You need to understand how to best visualize information for R&D, for your CFO, others.”
– Simone Cianchini, WW Innovation and Improvements Quality Manager, MARELLI
Statistically Speaking: Why interactive data visualization is critical to industry
“Some organizations don’t have a deep culture of data visualization. They don’t understand the real powerful parts. But JMP is a shortcut between data visualization and a complex statistical point of view. Some organizations believe that to have data visualization, people need to have a very high level of background statistical expertise. But this is a big error because it depends on what you want to do with data visualization. For the day-to-day baseline analytics, you don’t need to have a very high level of statistical expertise. So these are the two key errors people make: lack of understanding of the power of visualization and the perception that to have interactive data visualization, you need to have a background in statistics.”
– Simone Cianchini, WW Innovation and Improvements Quality Manager, MARELLI
Statistically Speaking: Why interactive data visualization is critical to industry
“We've also seen that JMP is used as the presentation format for site meetings. Because as we all know, you go to site meetings and people are throwing around ideas. Well, now you have the chance to say, ‘okay, let's see what the data says by increasing the temperature, boss.’ And you increase the temperature on the screen and you can tell him straight away, ‘boy, that is a good idea.’ Or more likely it doesn't work. There are lots of opportunities to do things with the data when you have it at the heart of your production meeting.”
– Stan Higgins, Former CEO, Northeast of England Process Industry Cluster (NEPIC)
Time to Innovate: Capturing value from your data to accelerate innovation (link coming soon)
“Visualization is absolutely essential. With experimental data, the first thing you have to do is visualize it. In some cases, it’s the only thing you can do. We’ve had several cases where the first runs were noisier than expected. You couldn’t build any model. But with visualization [in JMP], you could see things that looked interesting and design your next experiment to determine more rigorously if they are real effects.”
– Michael Sadowski, Head of Bioinformatics Synthace
Synthace success story
“My manager from early in my career would always say that the first step in data analysis is to plot the data. In the ensuing years, I saw many people make that mistake. Instead, some go straight to the tables and miss out or delay the insights that can be quickly identified from [JMP] Graph Builder. Anytime I see raw data spreadsheets in meetings, I ask for the file and am able to generate insightful analyses in a matter of seconds.”
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
“[With JMP,] we can color-code [our outputs] to point out outliers. Users prefer these visualizations because it accelerates decision making. They can see if the distributions fit their assumptions and get an overview of their experimental designs and how they ran their experiments.”
– Tom Walk, Breeding Pipeline Database Manager, North Dakota State University
North Dakota State University success story
“An effective graph drives the right decisions. An effective visualization informs the viewer and helps them make the right recommendation and decision for the organization. You've got to put things together visually to really deliver [meaning] to your data consumer.”
– Ted Ellefson, Managing Principal Engineer, Mechanical R&D, Seagate
Seagate success story
“There are a lot of areas where people can develop a small model or learn something which can give them direct value. And you can share that direct value – look, I've made this model, I've got these results, and visualized it. Visualization is an excellent way of getting other people on board. When you visualize results, other people say, oh, that's cool. I want to do that as well.”
– Bernard Ennis, Principal Researcher, Tata Steel
Statistically Speaking: Solving science and engineering problems with machine learning
“When I first started at Gore, I started off as a polymer chemist. And the very first thing I did was sit down with a statistician and design a very large experiment around polyurethane synthesis. In this experiment, we varied many factors within the formulation of our polyurethanes I ran a lot of lab experiments, collected my data, and I created some visualizations where we had response surfaces, and many graphs showing the different trade-offs and properties that we could get with the different formulations. So with that, we were able to convince our manufacturing team that we wanted to go off pilot and create a little larger batch… I had a prediction. We went off and we ran that formulation. And we came back. And lo and behold, the results didn't match the prediction. And so there was a lot of disappointment…. And so I sat back down with the statistician at that time-- of course, I didn't have any experience with data visualization. And I said, what happened here? Why didn't my experiment work? And what he did was bring up a graph where we actually put the prediction interval on the graph itself. And we could see then that my predictions actually were OK. In fact, we had made material that fell within the prediction interval. And just that graph alone, we took that back to the supply chain. By showing them the prediction graph, and showing them we actually understood what we were doing and we had made what we set out to make, we were able to convince them that we could move to a production scale batch. And when we did that, we found again that our predictions were sound, and we fell within our prediction interval. And it was that graph that really helped change their minds to know that, yeah, we did know what we were talking about.”
– Chris Chen, Enterprise Analytics Champion, W.L. Gore
Statistically Speaking: Data visualization best practices can transform the work of scientists and engineers
“At Gore we've equipped all of our engineers and scientists with JMP. So they have access to the tools, but we don't just hand it to them and let them go off and run. I think a big piece of an engineering and science community is teaching them how to use the tool -- teaching some of these design tips and tricks. What makes a good graph, what makes a useful graph, is very important.”
– Chris Chen, Enterprise Analytics Champion, W.L. Gore
Statistically Speaking: Data visualization best practices can transform the work of scientists and engineers
“Collectively engage in this graphing and data visualization effort. And a lot of times in the scientific world, that looks like peer review. So get in a room. Show your peers the graph that you've made. And then have some discussion.”
– Chris Chen, Enterprise Analytics Champion, W.L. Gore
Statistically Speaking: Data visualization best practices can transform the work of scientists and engineers
“If you create 100 graphs out of the data that you have, about 80 of them are going to be interesting, but they'll be things you know. And they won't have any deep insights in them. But those other 20% are going really show you what's going on. They're going to provide some really interesting insight into your data.”
– Chris Chen, Enterprise Analytics Champion, W.L. Gore
Statistically Speaking: Data visualization best practices can transform the work of scientists and engineers
“Data exploration is an important tool in JMP, because very often you're able to reveal some interactions which you wouldn't have realized without the explorative data evaluation. With the human eye, it's very hard to see the differences or the impacts [from a data table alone].”
– Andreas Trautmann, Process Expert for Upstream Processing, Manufacturing Science & Technology, Lonza
Lonza success story
“I'm a big proponent of looking at your data right at the start, because if there's something wrong with it, it will stick out like a sore thumb. [JMP] is visual and intuitive. The true power of JMP lies in data integration and being able to learn from the data set.”
– Court Sandau, Forensic Chemist & Founder, Chemistry Matters
Chemistry Matters success story
“We’ll do a principal component analysis or a hierarchical cluster analysis and start color-coding it… That’s my favorite thing about JMP – the interactivity. We truly do learn about our data through JMP.”
– Court Sandau, Forensic Chemist & Founder, Chemistry Matters
Chemistry Matters success story
“Data can be displayed however you want, but I have to prove that I’m doing it in a scientific fashion. And I have to be transparent about how I came to my conclusions by communicating visually. That’s where I get my clients involved with JMP: to be able to load my data into JMP and click on a spot and show them where it is and how [we arrived at our] interpretation. That’s the power of visualization: communicating the data, learning from the data and allowing our clients to see where the data comes from.”
– Court Sandau, Forensic Chemist & Founder, Chemistry Matters
Chemistry Matters success story
“We use JMP as a teaching tool because it enables us to show chemical fingerprinting in ways that [those without a background in chemistry] understand. JMP allows us to communicate to lay persons the connectivity of information so that they understand our conclusions…. And because I can do this in real time and interact with the data [in platforms like Graph Builder], it's much more convincing. They start to think, ‘I may not understand what he's doing, but I certainly see how he has connected all those dots.’ Now I’m telling a story rather than trying to explain multigrade statistics.”
– Court Sandau, Forensic Chemist & Founder, Chemistry Matters
Chemistry Matters success story
“Visualization gives us clarity on the information contained in the data, which then leads to a set of clear actions to take."
– Melisa Buie, General Manager, Coherent
Coherent success story
“We have a much deeper understanding and are able to work much faster thanks to JMP’s visual approach to statistically backed data analysis.”
— Paul Deen, Statistical Methods Program Manager, ASM International
ASM International success story
“There is a magic effect in ‘touching the data’. Sometimes visualization is enough, but storytelling with data is key. JMP Graph Builder is king here. We do a lot of modelling and multivariate analysis and the new Project feature in JMP 14 makes it very easy to document complex processes…. The Project feature is a real game-changer… People are excited. We can now use JMP like a PowerPoint.”
– Alfredo López Navarro, Knowledge Manager, Digital Command Center, Telefónica
Telefónica success story
“As a chemist coming into the world of agronomy, it’s less data-based and a bit more anecdotal. This is where JMP comes in – why I use JMP. The only way to [illustrate the case for data-driven decision making] is telling a story with the data and with graphics and pictures. That’s where JMP is really helpful: turning data and numbers into graphics [that production managers] can see and better understand.”
— Dirk de Bruyn Ouboter, Global Measurement Science and Process Performance Lead, Syngenta
Syngenta success story
“JMP has given me so many ideas – new ways of making my data more interactive and not just in terms of visualization but showing people the thought process behind how I analyzed it.”
– Anderson Mayfield, Assistant Scientist, US National Oceanic and Atmospheric Administration
Khaled bin Sultan Living Oceans Foundation success story
“Visualization is one of JMP’s great strengths. There’s a deliberate emphasis on graphs, which provide a very easy starting point for proposing hypotheses.”
– Jean-Luc Blachon, Head of Statistical Research, Bioinformatics, Ipsen
Ipsen success story
“The great thing about JMP is the visualization. It’s easy to use, and you can play around and see the results from different views. You can use filters, you can do overlays, you can have parallel plots. There are several ways of showing data and comparing it and focusing on certain points. This is really useful.”
– Petra Schreier, Engineer, Atotech
Atotech success story
“JMP was new to me when I first joined USound, and the biggest advantage I’ve found is that it has a very intuitive approach and a very intuitive way to get started. You can, for example, just take Graph Builder and play around a little bit and will instantly get a reasonable data representation. Then you can figure out how to use the data best to start problem-solving.”
– Florian Iglisch, Product Engineer, USound
USound success story
“In JMP, you just click and drop, and you have the graph.”
– Angela Manginelli, Senior Scientist, Biomarker Data Analytics, Merck KGaA, Darmstadt, Germany
Merck success story
“JMP has all the tools you need. Graph Builder is one of my absolute favorite tools. It does bar charts, line charts, heat maps… You can develop a graph and very easily tweak it, and tweak it again, to get it precisely the way you want it to look. Then you click on the little red triangle, and there’s all the JSL code. JMP has created the whole program for me.”
– Peter Mroz, Senior Statistical Programmer, Janssen
Janssen success story
“I am a big fan of the Graph Builder platform, which is something I have never seen in other software packages. To me, this platform is extremely innovative.”
– Damien Perret, R&D Scientist, CEA Marcoule
CEA Marcoule success story
“Our clients go crazy over JMP because they've never seen software with such interactivity between data and graphics. They really like that they no longer require one tool for data manipulation, another for statistics and yet another for reporting. JMP does it all.”
– Peter Bewerunge, Head of Life Sciences, Accantec
Accantec success story
“Data visualization is a very powerful language. We are visual creatures, but it's empty unless it's supported by [sophisticated analytics]. You need to ground all that theory in a way that is scientific and reproducible. And this, for me, is one of the beauties of JMP.”
– Alfredo Lopez Navarro, Omnichannel Data Lab Manager, Telefónica Germany
Statistically Speaking: Does your organization have a strategy for building analytic excellence?
“Data mining and data visualization are two of the things we use JMP for very extensively. This is a good example of the power of visualization with JMP: there was a false signal and a real signal. The false signal was how quickly the failure rate escalated. It had been zero, then jumped to 20 or 30 percent in a matter of just a few days. But that was a red herring.”
– Ed Hutchins, Product Engineering Manager, Cree
Cree success story
“Seeing the interaction so clearly in this model, as visualized in JMP, was the first time we’d ever contemplated that there might be this type of interaction. The benefit [of JMP] is not in finding the answer to the problem, but in pointing you in the right direction so that you know where to look to find the problem. I was able to understand the differences… that I may not have appreciated before. By knowing where to look and then looking there, we were able to drive the failure rate for that defect back down to zero.”
– Ed Hutchins, Product Engineering Manager, Cree
Cree success story
“I prefer JMP because it’s so powerful in visualizing data. It allows you to generate the types of control charts, graphs and displays that then allow you to understand that data. The most important thing is to get the right data; the second most important thing is to understand what that data is telling you. And to understand it, you have to look at it as a function of a number of parameters, so that you understand the space in which you’re operating. With just a table of data, you can’t do that.”
– Ed Hutchins, Product Engineering Manager, Cree
Cree success story
“I have no doubt that our success is due to our ability to continue to innovate and improve, and that our ability to innovate and improve is greatly enhanced with tools like JMP.”
– Ed Hutchins, Product Engineering Manager, Cree
Cree success story
“I used the graphical capabilities of JMP to demonstrate the results to many people in the company who had no concept of statistical techniques. I was able to show them that there were enough questions that we should have an evaluation by a third party.”
– Dan Fortune, Global Business Excellence Manager, FMC Technologies
FMC Technologies success story
“We are benefiting tremendously from the data visualization which JMP provides. We can now represent complex data clusters as colored maps, for example. We simply place various maps next to each other in order to interpret distinctions and outliers. This way we are immediately in a position to identify whether a mathematically correct model is actually plausible.”
– Anya Simon, Information Management Department Project Manager, Lufthansa
Lufthansa success story
“With JMP we are moving away from the blind trust in numbers. There are always situations where model generation results in mathematically perfect but unrealistic solutions. The visualization means that we can now identify these immediately. Visualizations are a very valuable instrument for making the right decisions.”
– Anya Simon, Information Management Department Project Manager, Lufthansa
Lufthansa success story
“The higher the volume of data we collect, the greater the chance we will get some signals from them that add value and build knowledge. But some data are just not that meaningful and it’s a big challenge to sift through and focus only on what will maximize value…. It can be complicated to visualize. I really like JMP for this, especially Graph Builder, which is very practical and efficient.”
– Augustin Cathignol, Principal Engineer for Product Reliability and Data Science, Lynred
Lynred success story
“JMP helps people focus. You can easily grab things, just by pointing and clicking. It allows you to keep people visually engaged.”
– Mike Cramer, Director of Operations, Research for Worldwide Restaurant Innovation, McDonald’s
McDonald’s success story
“Every time I have to present a project or if I have designed an experiment, I use JMP because it offers graphic visualization to show my tasks, to show my needs, and I get a better response from my management.”
— Paolo Nencioni, Formulation Development Technician, Menarini
Menarini success story
“JMP has a really strong practical interface that allows you to plot data intuitively with Graph Builder – anyone can do it. As long as your data is structured, Graph Builder will help you to understand what is really there before you even start analyzing – that’s what I absolutely love about it.”
– Beatrice Blum, Statistician, P&G
P&G success story
“Visualization is the key to everything because this is usually what people understand – particularly in management. They don’t want to deal with numbers. Give them a picture that tells the story instead. It doesn’t have to be the most precise picture; it just has to tell the story that holds the conclusion. This is also where I must say I see there is strength in JMP to deliver those graphics and thoughts. And the interactivity.”
– Beatrice Blum, Statistician, P&G
P&G success story
“Excel has very limited visualization possibilities, especially when you start getting into multiple dimensions, so that's where JMP comes into play.”
– Kenneth Hess, President & Founder, Science Buddies
Science Buddies success story
“We take our 78 dimensions and reduce it down to three as an approximation, and feed that into JMP, where I can do a 3D scatter plot, rotate it around and change the colors. JMP is an invaluable tool to see what's actually going on as we do our development work. When I do this kind of design and development work, proving things out for a sophisticated piece of software, I think visually. JMP has always been an important part of that visualization. The graphical capabilities in JMP let us take nearly 1TB of data and spin it around and see what’s going on. And the performance is unbelievably good.”
– Kenneth Hess, President & Founder, Science Buddies
Science Buddies success story
“With JMP, once you’ve synthesized huge amounts of data into one chart, it’s very easy to give the right message to your audience. You can prepare a graph showing just the data you want to show and how you want to show it. I’ve really appreciated the way you can manage data to show it from different views in a very compelling way.”
– Matteo Patelmo, Engineer, STMicroelectronics
STMicroelectronics success story
"There is tremendous power in storytelling with graphs and I find that I can tell a lot of stories by utilizing the many features within Graph Builder."
- John Szarka, Statistician, W.L. Gore
John Szarka on JMP Pro, reproducibility and why being active in the JMP Community and Early Adopter program helps users at W. L. Gore get more from their investment in the software
STATISTICS & PREDICTIVE MODELING
“The focus in drug discovery at the moment is figuring out how to go around this cycle as few times as possible to reach our destination and thereby accelerate our process of drug discovery and find drug-like molecules faster. And at every stage of this process, there is something we can do in terms of data analysis which will speed us up.”
– Graeme Robb, Associate Principal Scientist, AstraZeneca
Time to Innovate: Future chemistry: exploring the possibility space (link coming soon)
"I had to go back and learn statistics and go back to the customer again to be able to demonstrate the improvement. It changed the way that my career proceeded because of the ability to apply statistics. And, of course, I'm here today, JMP been an instrumental tool for me to do that, an enabling tool."
– Andre B. Argenton, Vice President Core Research and Development, Dow
Statistically Speaking: Developing a shared vision for analytic excellence
''For me, JMP is not only a tool for data analysis; it is also a communication method with which to transfer difficult statistical thinking to a simple graph or profiler and enable others – especially non-statisticians – to understand the data and models."
– Zhiwu Liang, Principal Statistician, Procter & Gamble
Procter & Gamble's Zhiwu Liang explains how nurturing a dynamic relationship with JMP Development helps the company get more from its investment in a software that is constantly being improved
"At P&G, we have many statistical courses which require participants to use JMP. You can see people's excitement and satisfaction when they get the correct results so easily for even the most complex models. In fact, my colleagues are also now using JMP daily. They like the visibility function in JMP because it makes it so easy to show the results to their manager."
– Zhiwu Liang, Principal Statistician, Procter & Gamble
Procter & Gamble's Zhiwu Liang explains how nurturing a dynamic relationship with JMP Development helps the company get more from its investment in a software that is constantly being improved
“I was keen to develop an application that analysts could use to investigate their methods and visualize the results without having to be concerned with the calculations. And it's easy in JMP to develop a scrape or an add-in to automate certain calculations.”
– Christel Kronig, Senior Analytical Scientist, Dr. Reddy's Laboratories
Time to Innovate: How to make analytical methods fit-for-purpose (link coming soon)
“By understanding the sources of error, it is possible to determine where replication should be used to reduce random error. Using an analysis of various methodology, it is possible to separate the effects from different injections, sample preparations, or series. And it's possible to package these into an add-in script and automate the analysis in JMP. The add-in helps us to understand the error components and generates a visual comparison of the error for different replication approaches. This helps us to identify the biggest error contribution and make an informed decision on the replication strategy.”
– Christel Kronig, Senior Analytical Scientist, Dr. Reddy's Laboratories
Time to Innovate: How to make analytical methods fit-for-purpose (link coming soon)
“Our JMP add-in allows the analyst to evaluate the impact of increasing or reducing the number of standard preparations or injections on the intermediate precision of the method, and therefore to make an informed decision, how to optimize the calibration format.”
– Christel Kronig, Senior Analytical Scientist, Dr. Reddy's Laboratories
Time to Innovate: How to make analytical methods fit-for-purpose (link coming soon)
“There is a lot of scrutiny placed on analytical methods and that's increasing in the pharmaceutical industry and there's more emphasis to ensure that methods used are fit for purpose. There are tools and calculations that exist to evaluate precision and bias of methods and this has been the subject of many articles in the past few years. And it is possible to automate these calculations and visualize the result, giving the analyst easy tools that they can employ to method development.”
– Christel Kronig, Senior Analytical Scientist, Dr. Reddy's Laboratories
Time to Innovate: How to make analytical methods fit-for-purpose (link coming soon)
“This is a very hot topic today: data science in general. But actually, in chemical engineering and process engineering, we have been using a method of machine learning before for years. The only thing is we didn't call it that. Anytime we were looking at certain critical correlations to estimate, for example, the heat transfer coefficient of the system, where we have to use part of the physical knowledge and then the other part, make an adjustment, a fitting regression of those data, this is supervised machine learning method.”
– Francisco Navarro, Chemical Engineer, Solvay
Time to Innovate: Industrial data science for chemical process perfection (link coming soon)
“The main problem we faced in the beginning was this big data abstraction and connection to different databases. There was always a delay. But JMP is able to directly connect to the databases... and the servers in our plant. I have to emphasize that having this direct access from the analytical tool to the database is a huge deal for everyone, because it means that you don't need to do any programming or data preparation just to make a table. So even the process engineers, any person interested can have access to this huge data set in an interactive way.”
– Francisco Navarro, Chemical Engineer, Solvay
Time to Innovate: Industrial data science for chemical process perfection (link coming soon)
"Too many businesses have fantasies about AI. It is regrettable that the importance of AI’s foundation in data analysis seems to be so often overlooked…We need to understand exactly what our data means, so we can select and apply the appropriate algorithms. I think people will come back to basics when the current hype around machine learning passes.
– Jong-Seok Lee, Professor, Sungkyunkwan University
Sungkyunkwan University success story
“I was very surprised when I first saw the column property function of a data table in JMP. It was so innovative. The interaction between graph and data selection is also a differentiating factor.”
– Jong-Seok Lee, Professor, Sungkyunkwan University
Sungkyunkwan University success story
“JMP is fast and convenient when it comes to checking distributions, identifying relevance using Scatter Plot Matrices and checking for outliers. Not to mention, we can do all of this without having to do any coding… With JMP, we were able to quickly find and judge a highly predictive model. We used the Desirability feature of the Profiler, and we also had the advantage of communicating and setting directions quickly with our customers based on the results we got from those models.”
– Jong-Seok Lee, Professor, Sungkyunkwan University
Sungkyunkwan University success story
“JSL was useful for pulling in some custom functions that were not available in the standard JMP UI at that time. Also the map function made it easier to identify trends according to the environment by visualizing repair data on Korean and world maps.”
– Jong-Seok Lee, Professor, Sungkyunkwan University
Sungkyunkwan University success story
“The biggest secret is that 80% of data science is in pre-processing the data. So you've got to practice getting your data into a computer and visualizing it… These tools and this way of thinking has always been a part of what I do. And that gives me confidence. I'm never worried to make a decision when it's data-driven.”
– Teena Bonizzi, Staff Scientist, SABIC
Statistically Speaking: Solving science and engineering problems with machine learning
“There was a time when we had a team that was trying to understand why our product was not performing as well as it should at a customer's facility. They had gone out and collected some data to try and understand how different operating conditions were influencing, in this case, the flexibility of the product. When the data came back, the team was quite surprised. The results were not at all what they were expecting, and these flexibility scores were unusually lower. They decided to redo the testing – all 150 samples. Then when those results came back, what they ended up with was two data sets and had trouble reconciling the results while still having the situation where some of these operating conditions were giving results that they couldn't understand. But once we'd gotten the data into [JMP], it literally only took a few minutes to fit our model and have it up on the screen. As soon as we were able to visualize those relationships, we could see a very prominent interaction. So we were able to go back to the customer and we discovered that their line was actually operating a lot slower than what we typically expect, so we recommended they switch over in order to get optimal results. Without that regression model, without that technique and seeing that interaction, we wouldn't have been able to have the confidence or the understanding to make the right recommendation.”
– Teena Bonizzi, Staff Scientist, SABIC
Statistically Speaking: Solving science and engineering problems with machine learning
“If I hadn't discovered these advanced analytical methods, I would have obviously been behind the curve. I would not be as agile or flexible and I think I would have seen things taking much longer to do than I can do them today.”
– Steve King, retired Engineering Associate Fellow, Advanced Analytical Methods, Rolls-Royce
Statistically Speaking: Solving science and engineering problems with machine learning
“When you have a conversation with a subject matter expert, particularly somebody who's a stakeholder, you need to win them over. And one way you do that is by saying, look, I have solved the problem using data science. It's not just the precision of what you have done that is important. We all get great models using random forest, gradient posting, neural networks. It's the variable importance. So when I rank order the scores, and I say, look, I don't know your subject. I'm trying to learn here. But out of these variables, these are the ones that I think are really important. And he's nodding his head and saying, I get it. You're not teaching me anything new. But I'm glad that you're on the same side, that you're capturing the physics of the problem…. and JMP has the ability to do that.”
– Sameer Vittal, Chief Consulting Engineer for Analytics & Reliability, GE Gas Power
Statistically Speaking: The key elements to create and promote a data-driven culture
“Without JMP, I don’t know that I could have mastered this kind of complex survival analysis as quickly and easily as I did. Its visualization and graphing features really make it easier for us as biologists to answer our research questions.”
– J.P. Montagne, Research Coordinator, Applied Animal Ecology Division, San Diego Zoo
San Diego Zoo success story
“Before we had JMP, we didn’t have a good way to make accurate predictions. JMP has changed our perspective as to how we can better anticipate changes and fluctuations in water quality in the future.”
– Lei Wang, Engineer, Beijing Aquarium
Beijing Aquarium success story
“The text analysis platform in JMP has been incredibly valuable to us because it is flexible enough to allow defect categorization into pareto charts from comments made by people whose first language is not English. It handles all the variants almost magically.”
– David Seniuk, Senior Product Engineer, Ciena
Ciena success story
“Data quality is a big issue for public health. And that black box… that’s where JMP comes in. JMP has been absolutely indispensable with data cleaning, quality control, reporting and visualization. No matter how difficult or time consuming, I always find a way using JMP to link records from different data systems, work with dates and calculate time elapsed for intervention outcomes.”
– Elaine Daniloff, Epidemiologist, Colorado Department of Public Health and Environment
CDPHE success story
“We de-duped and merged the files and pulled it together. We started with about 2.2 million records, and after cleaning ended up with about 900,000 records, consolidated by individual consumer. We created a dashboard so that the client could see these records in a visually pleasing way. JMP Pro is the actual driver; there’s no point in having a nice dashboard if the data is corrupt.”
– Huw Davis, Data and Analytics Lead for Asia Pacific, J. Walter Thompson
Walter Thompson success story
“No process can be 100 percent error-free, but the more errors we can catch before they go out the door, the greater the benefit to our customers…. We have parameters [in JMP] that say, when these events take place, the customer gives us a poor NPS rating. Now, if we see a similar issue, even before we get customer feedback, we know to address it proactively. When you address customer needs proactively, you really help build a strong customer relationship.”
– Tyler Wise, Project Analyst in Global Business Services, Diebold Nixdorf
Diebold Nixdorf success story
“With JMP, it’s taking chaos and ordering it. It was a process of discovery — and I came up with standard deviation and interquartile range as being nice ways of characterizing the metrics and learning about the characteristics of the data.”
– Mike Paglione, Engineer, Federal Aviation Administration
FAA success story
“In this study, I was doing data discovery; I didn’t know how I wanted to characterize the data. But in using JMP, I was able to explore how others have done it in the past and was able to characterize it in lots of different ways.”
– Mike Paglione, Engineer, Federal Aviation Administration
FAA success story
“We have a tremendous amount of traffic data, from many, many measurements. I honestly don’t think I would have been able to do this analysis – certainly not at this scale, and maybe not at all. You need a tool like JMP to process this much data. JMP has become an essential component of the FAA’s analysis toolbox.”
– Mike Paglione, Engineer, Federal Aviation Administration
FAA success story
“Our goal is reducing or eliminating variations and text data provides an advantage. It allows us to do a deeper analysis and help decision makers make better decisions.”
– Gokhan Sarpkaya, Subsea Systems Western Region Planning System Manager, FMC Technologies
FMC Technologies success story
“Too many codes make it difficult for people putting in the data to enter it correctly. If I have five projects with 15,000 activities, that gives me 75,000 lines of activities. For one human being to be able to go in there and combine them where different texts are expressing the same thought and do an analysis, it may not be possible. But with text mining and a cluster analysis in JMP, it takes a while, but very few hours compared to practically impossible.”
– Dan Fortune, Global Business Excellence Manager, FMC Technologies
FMC Technologies success story
“We weren’t interested in the complexity of the scripting in every technical detail. We wanted a simple, lean tool that would allow for highly interactive data analysis. That’s something that JMP does very, very well."
– Laura Zambianchi, Senior Filtering Material Specialist, Fresenius
Fresenius success story
“The classical way of using statistics is to confirm an existing hypothesis, but when you’re in the shop, you’re looking for real-time answers to questions that arise each day. What you want is something that works as fast as possible, something that’s highly interactive and graphical. That’s JMP and Visual Six Sigma. Every user, after very minimal training, can access the data, analyze it and get answers to their questions.”
– Laura Zambianchi, Senior Filtering Material Specialist, Fresenius
Fresenius success story
“JMP unlocks huge potential. It enabled us to approach complex programming projects modularly. The feasibility of the end result then becomes only a matter of resources and time.”
– Laura Zambianchi, Senior Filtering Material Specialist, Fresenius
Fresenius success story
“Due to the high complexity in our processes, we needed a better tool to assess the impact of physical forces such as fluid and gas flow in addition to consideration of more conventional parameters, which would include feed rate, cell density, etc. Although there are a lot of complicated calculations and coding going on in the background, the very user-friendly platform in JMP makes it quite easy to use.”
– Somaieh Mohammadi, Staff Scientist, Fujifilm Diosynth Biotechnologies
Fujifilm Diosynth Biotechnologies success story
“JMP comes with so many things that are built in that are very nice to use, and if it doesn’t have what I need, I can easily write a program for it. JSL makes JMP extremely extensible.”
– Peter Mroz, Senior Statistical Programmer, Janssen
Janssen success story
“JMP provided confidence in the data, which is what I was really looking for. You can have the correlations, but how good are the fits to the data? I didn’t know what to expect there.”
– Rich Giannola, Atmospheric Scientist, Johns Hopkins University Applied Physics Laboratory
Johns Hopkins University Applied Physics Laboratory success story
“I use JMP for everything. It’s very easy to construct models. I like that you don’t have to enter code into the graphical user interface, like with some software. You simply click something and choose the columns of your tables, and you immediately see the results. That’s what makes the software so easy to use, even for colleagues who are not familiar with mathematical analyses and programming.”
– Katharina Lankers, Research Scientist, SCHOTT
SCHOTT success story
“I like having all these tools together in one software package – the data preparation and visualization tools, the design of experiments, SPC and various modeling. It’s very easy to handle, and so intuitive. You see what you’re doing by simply clicking on your data. We like the interactive component a lot.”
– Katharina Lankers, Research Scientist, SCHOTT
SCHOTT success story
“Day-to-day monitoring of the yield on the line is now managed by JMP. We have automated the production of reports that allow us to monitor changes in elements that have a negative impact on yield quality. JMP not only allows us to resolve the causes of quality-related problems on the line more quickly, but also to identify the causes of any discrepancies and quickly decorrelate them. We can now predict more effectively and adjust any factors that have an impact on the manufacturing of products that are in demand from our clients using the modeling module.”
– Annick Vuillermoz, Yield/Process Control System Senior Manager, Soitec
Soitec success story
“JMP encourages curiosity. When you look at the results of an analysis, you may see some terms that you’re not familiar with, and so you investigate and try to understand what they mean.”
– Matteo Patelmo, Engineer, STMicroelectronics
STMicroelectronics success story
"JMP allows us to gather our data much more quickly, and then presents it in a format that makes it so easy to explore… JMP is so accessible. Our people have been very satisfied with the user experience."
– Pius Dahinden, Analytical Science Manager, Tillotts Pharma
Tillotts success story
“[JMP] allows me to really explore the data, and if I’m curious about a certain pattern or question I can ask that question of myself pretty quickly. Things just become more immediately obvious.”
– Daniel Arneman, Environmental Specialist, University of North Carolina at Chapel Hill
University of North Carolina at Chapel Hill success story
"In many instances, we can use JMP Pro to meet our needs on the data and analysis side to provide insights and help in making decisions. Our investment in JMP Pro – up from the base version of JMP – has made this type of modeling much easier for our associates."
- John Szarka, Statistician, W.L. Gore
John Szarka on JMP Pro, reproducibility and why being active in the JMP Community and Early Adopter program helps users at W. L. Gore get more from their investment in the software
COMPARISON WITH OTHER SOFTWARE PACKAGES
JMP was the market leader. It utilized strong multivariate tools, which we needed, and I could see that every version release comes with new and improved bells and whistles. On top of that, the support community was very strong. So it just made sense to go with JMP. And it was quickly embraced as well.”
- Kieran O’Mahony, Data Science and Analytics Manager, Dairygold
Dairygold | JMP
"At the time when I was first introduced to JMP, I was a Minitab superuser and, to be honest, didn’t immediately see the value. Nevertheless, once I made the switch with help from @Jordan_Hiller’s onsite training and a few excellent on-demand webinars, I never looked back. Everyone who has made the transition feels the same way"
-Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
“As anyone who has used Minitab knows, if your columns aren’t exactly the same size, you’re in for a world of frustration. And when engineering managers hear ‘I was going to have this presentation done but I can’t figure out how to get it out of Minitab’ enough times, there’s a point where you go elsewhere.”
– Michael Anderson, Senior Statistical and Measurement Development Engineer, Corning
Corning success story
“We tried different platforms, but we ultimately chose JMP. It's a lot more user-friendly than the others. And there is continuous development – new tools are always being developed [in JMP]. And you can actually give feedback to JMP about things you would like to see [in future releases]. There is very good support, so every time we need something, we know who to contact at JMP to help us.”
– Pilar Gomez Jimenez, Principal Scientist, Johnson Matthey
Time to Innovate: Using a DOE mindset for successful experimentation (link coming soon)
“Statistical modeling is something that I tried to do numerous times with various software programs, like R and Minitab. And really what I felt with JMP was that under the hood there was just so much rigor there. But on the front end, it's very easy to use for a scientist. So there is just no way we would have -- for me, personally -- been able to get that kind of modeling capability up without that software. So the DOE and the modeling interacts seamlessly in the software, and it's probably something that I would have struggled massively with. Now, I think the benefits of that are that we can start to look at all these different factors and interactions at the same time. And I don't think I'd have been able to do that nearly as quickly without this piece of software.”
– David McClymont, Head of Automation, London DNA Foundry
Time to Innovate: How your organization can bring about a culture of analytic excellence: An example from synthetic biology (link coming soon)
“One of the big differences between JMP and the other advanced analytics tools is that JMP is a tool for scientists and engineers. JMP has really good resources on their website to start with and many examples that can be applied to not only advanced analytics but other branches of statistics and visual analytics. I think JMP is a good recommendation in general for every engineer or scientist in the room.”
– Francisco Navarro, Chemical Engineer, Solvay
Time to Innovate: Industrial data science for chemical process perfection (link coming soon)
"Excel was not able to solve the problems that I was dealing with, and I had heard of JMP previously. I was able to solve a problem the first night after taking a data exploration class – we’d gone over JMP data partitioning tools …. and I realized very quickly that I needed to stop using Excel for analytics and switch over to JMP. I've been using JMP on a daily basis ever since.”
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
“If you are spending more than five minutes per month using Excel for data visualization, you are wasting your time by not using JMP.”
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
"JMP dramatically reduced working hours. If I had used R, it would have taken me about a month to complete the work, but with JMP, it was done within a week."
– Jong-Seok Lee, Professor, Sungkyunkwan University
Sungkyunkwan University success story
“The JMP Graph Builder in particular is just fantastic for this. And everyone I've shown it to has stopped using Excel and Prism and they now use Graph Builder. They save so much time.”
– Jon Armer, Formulation Lead Senior Scientist, F-star Therapeutics
F-star Therapeutics success story
“Graph Builder is an extremely useful tool in my daily work. Being able to quickly import data and create insightful data visualizations with labels for outliers accelerates decision making. These are all very powerful tools that provide a distinct advantage over competing tools – in both timeliness and content.”
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
“I've seen so many engineers in Excel spreadsheets going number by number, spending 45 minutes, an hour and a half trying to get their data together when really, and especially when it's something that's repeatable, you can essentially put something into a script and do it with a click of a button. For example, a few years ago, I spent 10 minutes to create a script [in JMP] that basically went through a whole database just to make sure that the data values were correct. It took me 10 minutes. And there was no way in the world that the old way they were doing it by importing portions of the data into Excel and going number by number would they have been able to do that. So right there, if you're looking at, how to get more out of your organization, get them to do less of the data manipulation by scripting it. Doing things in a documentary, reproducible way saves the company so much time.”
– Sarah Kalicin, Data Scientist, Intel
Statistically Speaking: Lead your organization to analytic excellence
“Henry Ford said, if I asked the customer what they wanted, they would ask for a horse, but you know, I gave them a car. And I use that analogy when companies or organizations want to use Excel. Yes, it's free. You can get from point A to point B in a straight line. But there's an economy of scale when you can actually use a car on a road... It might take you 10 more miles to get there, but you can get there faster, you can get there with more stuff or more people, and it's much more reliable. It's the same thing with data and your infrastructure.”
– Sarah Kalicin, Data Scientist, Intel
Statistically Speaking: Lead your organization to analytic excellence
“I was new in this analytics department at the time and my tool of choice was Matlab. I was building a multi-layer variability chart in Matlab. And as you know, Matlab is a pretty powerful tool but it takes a little bit of labor to make anything in it. A couple of weeks later, we hired a consultant. He came in and he pulls up a chart in like 10 seconds. And I’m actually super happy because he’s making this chart I was working on… so I tell him. And he says… what did you learn from this chart? It was one of the harshest moments in my career because I hadn’t learned anything because I had spent the two weeks on just the mechanics of creating the chart – not making inferences and driving conclusions. JMP shortened that cycle, so I could make the chart in 10 seconds. That was the moment that got me into this.”
– Vicky Svidenko, Partner, Quantum Data Sciences, Microsoft
Statistically Speaking: The power of structured experimentation in science and engineering
"Column histograms are really cool, but as an engineer who has spent 20 years trying to make sense of simulation data, frankly, Graph Builder is probably the single most-useful piece of software I've ever seen. We used to use Excel, then gnuplot, skipped MathCAD and Mathematica and moved to Matlab, then python/Matplotlib, then considered R for a bit, but 3 years ago, after a 5-minute demo of Graph Builder we dumped all of those and switched to JMP and haven't looked back. How in the world do you get it to plot half-a-million data-points *while I'm dragging the mouse* over the drop zones?! It's some kind of crazy black-magic you've got going in there, and it's seriously amazing. And it works just as well on the Mac as on Windows. Wow. I've given demos to a bunch of folks at work and have probably converted 20 people over to JMP with it, and I'm just getting started."
– bharris in the JMP User Community
Read the thread in the Community
“We were using Minitab and then JMP, and the vast majority of engineers chose JMP in engineering analysis for its compatibility and ease of use. Now I think we’ll never give up JMP.”
– Corinne Bergès, Manager, Risk Assessment, Statistical and Safety Analysis and Six Sigma, NXP
NXP success story
“With Minitab, you have to have a certain knowledge of statistics to produce meaningful analytics. But because JMP is so intuitive, anyone can achieve useful analytics.”
– Corinne Bergès, Manager, Risk Assessment, Statistical and Safety Analysis and Six Sigma, NXP
NXP success story
“JMP combines a user-friendly interface with deep technology. People love to set up graphs in Excel. But once they’ve touched JMP and see how easy it is – like with Graph Builder or the variability gauge chart, for example – they see how easily you’re able to get a graph in just a second. JMP is so quick and self-explanatory, users don’t want to go back to Excel.”
– Maria Lanzerath, Global Head of Statistics, W.L. Gore
W.L. Gore success story
“The attractive part [about JMP] really is the approach to workflow. It's not a toolbox. Minitab is a statistical toolbox; JMP is not…. it gets people thinking, saying, ‘Hey, how about if we slice and dice this data a different way?’ It's this workload that we find to be incredibly valuable as opposed to the toolbox approach.”
– Steve Czupryna, Quality Process Engineer, Samson Rope
Samson Rope success story
“[Minitab] doesn’t offer the newer analytic techniques that JMP embraces. I think they’re falling behind. I really like that JMP is embracing some of the newer generalized regression techniques.”
– David Lee, Director of Quality and Reliability, ON Semiconductor
ON Semiconductor success story
“We were spending a lot of time with Excel just trying to get the right view of the data. Instead, we found that JMP can much more easily connect data and shift from one view to another, changing one parameter, slicing the data a certain way. There were productivity gains to be made just from the interface and the data-centric view that JMP has.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Siemens Healthineers success story
“To achieve visualization in Excel, we’d have to buy prewritten macros or do a lot of macro- or script-writing ourselves. With JMP, we can find the most effective way to slice up the data or show the results of a machine model without spending a lot of time making the program do something it wasn’t explicitly designed to do. I’ve definitely noticed less pushback from management when we use JMP visualizations.”
– Greg Mattiussi, Senior Director of Manufacturing, Siemens Healthineers
Siemens Healthineers success story
“Everyone started using JMP because you could script and pull in data directly. Once people saw it, they’d say, ‘Whoa, I just circle this and then this graph highlights – wow!’ Scripting, pulling in the data, visualizing, doing that over and over – you can’t really do that in Excel or Minitab.”
– Jim Gillard, Operations Master Black Belt, Polar Semiconductor
Polar Semiconductor success story
“When I first started using JMP, I found it was more powerful than Minitab because it was designed from an engineer's perspective. JMP belongs to a higher class than other programs because it is designed to solve all kinds of problems experienced by engineers – it’s really convenient for engineers to use.”
– SungSoo Lee, Innovation Group Manager, Daeduck Electronics
Daeduck Electronics success story
“What I’ve learned from using JMP is that the connectivity of analysis is completely different from Minitab. While Minitab has independent menus to be operated when changing between analyses, JMP itself can easily change a distribution analysis into that of process capability. JMP is a one-stop shop.”
– SungSoo Lee, Innovation Group Manager, Daeduck Electronics
Daeduck Electronics success story
“I could think about writing in Excel, but the time it would take would probably make it not worth doing in the first place.”
– Matt Brown, Process Engineer, US Synthetic
US Synthetic success story
“JMP is able to showcase big data that is not viewable in commonly used tools – and do so in a user-friendly interface. [We can therefore] present more insights for decision making and analysis than compared to other tools.”
– Kyoung-Yun Kim, NABOTA Research Group Manager, Daewoong Pharmaceutical
Daewoong Pharmaceutical success story
"Some programs might do experimental design well, but that’s all they do. JMP has the table manipulation, it has the modeling, it has the visualization. It has all these things. You can actually use one package to do everything and organize the output…. A problem endemic to both academia and industry is having your data scattered all over the place, but with JMP, you can very quickly bring it all together.”
– Martin Owen, Lecturer, De Montfort University, and former Chemical Development Manager, GSK
De Montfort University success story
“[Programming languages like R and Python] are definitely a direction a lot of companies are moving toward. But you need to trust that the things you use … have actually been developed in the right way, and that you can use them for validation purposes.”
– Kasper Munck, Principal Consultant, NNE
Ferring Pharmaceuticals & NNE success story
“JMP is like Excel on statistical steroids. There’s so much you can do with it without having to write a program – and then you can easily write one if you want to. You can go very far with JMP.”
– Peter Mroz, Senior Statistical Programmer, Janssen
Janssen success story
“Other analytics tools can be very powerful, but data visualization is typically static … Interactive visualization in JMP helps you understand and explore the data at a totally different level.”
– Serguei Roupassov, Data Science Group Leader, Valard Construction
Valard Construction success story
“We have very extensive process control data sets across our organization. JMP gives us the ability to look at that data and look for correlations across it. I’m not aware of any other process tool that would allow us to get the degree of statistical analysis that we can get with JMP across those large data sets. From a statistical-analysis point of view, it’s really the tool of choice for us – and I think it’s the only tool out there that gives us the flexibility to do what we need to do.”
– Geoff Beatham, Quality Director, Kodak
Kodak success story
“It used to take me two days, once a month, to do those calculations and then graph them in Excel. With JMP, I can do it in less than an hour. The data comes to me as a download in Excel, and I just throw it straight into JMP, press a few buttons, check to see if the data is tidied up and then launch my analysis in JMP.”
– Peter Blum, Quality and Technical Manager, Kodak
Kodak success story
“SPSS can also be very difficult to use. If you don’t know [how to run a] stats check at a professional level, you could end up at a dead end very quickly because you don’t know how to go to the next step…. With Graph Builder, you can change the X and Y axes at any time. There are so many special features that Excel or SPSS can’t do.”
– Kai Xu, Professor of Exercise Physiology, Nanjing Sports Institute
Nanjing Sports Institute success story
“I could do that in R, but it would take me forever! In JMP, I can get a PCA with just the press of a button. There’s such a learning curve with R – command line interfaces are so daunting if you’re not a coder. A PCA or a correlation matrix, for example, would take me at least three or four lines of code.”
– Arielle Parsons, Ecologist, North Carolina Museum of Natural Sciences
North Carolina Museum of Natural Sciences success story
“If you wanted to use Excel or something else to generate the report in multidimensional graphics, it would be too hard. But with Graph Builder, you can just drag and drop and then push a couple of buttons and the data appears for you. Those are some of the best features in JMP.”
– Ron Zhang, Senior Chief Engineer, Oshkosh Corporation
Oshkosh Corporation success story
“R can be really difficult to use. And then you have SPSS, which maybe isn’t as innovative and can be a bit cumbersome with all of your outputs in one area. JMP, on the other hand, is kind of the perfect middle ground. It has some really unique features but it’s also really easy to use. You can keep everything separate and organized.”
– Jordan Gillespie, Graduate student, Auburn University
Penn Vet Working Dog Center success story
“Everyone started using JMP because you could script and pull in data directly. Once people saw it, they’d say, ‘Whoa, I just circle this and then this graph highlights – wow!’ Scripting, pulling in the data, visualizing, doing that over and over – you can’t really do that in Excel or Minitab.”
– Jim Gillard, Operations Master Black Belt, Polar Semiconductor
Polar Semiconductor success story
“First is the amount of data JMP can handle. Excel was too slow, or it crashed, or its application in a production environment was too limited. My productivity increased dramatically with JMP. I noticed this right away.”
– Raghavendran Swaminathan, Senior Process Development Engineer, Roche
Roche success story
“Students are convinced [of the utility of JMP] once they produce a more complex multivariate graph and say ‘it would have taken me I don’t know how many more minutes with R!’ Those tradeoffs are a big problem when you’re working on an analytics problem in real time. You need to learn the data as quickly as possible – before you estimate any models. And visualization in JMP is really fast – not to mention you can load a large data set so quickly. It was so cool to see students from more quantitative fields getting excited about doing this with JMP.”
– Christian Hildebrand, Assistant Professor of Marketing Analytics, University of Geneva
University of Geneva success story
“Had I not had JMP, I would have had to learn R. I’ve used R in the past and it’s also a great tool, but there’s a huge learning curve—having to master scripting, for one. Most of us here at the institute are not statisticians. We’re ecologists, biologists, reproductive physiologists. And we use statistics as a tool to help us answer the questions posed by our research.”
– J.P. Montagne, Research Coordinator, Applied Animal Ecology Division, San Diego Zoo
San Diego Zoo success story
“JMP is a statistical tool that takes advantage of computing in a powerful way. Unlike the other tools that are out there – which are basically the same as they were 20 years ago, just faster – JMP does things that other software doesn’t do.”
– Robert Stine, Professor of Statistics, The Wharton School of the University of Pennsylvania
University of Pennsylvania success story
TEXT ANALYTICS
“Recently I had the opportunity to work with Avon’s quality group to analyze some product reviews. There were voluntary comments offered by some of consumers on a line of lip and brow [cosmetic] products. In looking at the ratings, you might just see mediocre ratings. But you can see from diving deeper that actually the distribution was bimodal. You had people who were in love with the product – it was delivering on expectations. And then you had people who were extremely disappointed. So we completed a sentiment analysis to dive deeper into the text comments in JMP. It was clear that there was a quality issue. The color of the product was not matching the consumer expectations – and that was what was extremely disappointing to people. We were able to create a word cloud and using sentiment analysis, show the words in red for negative ratings and green for positive ratings. This analysis showed a vast difference between consumer attitudes towards this product, but it was also very helpful in presenting to Avon decision makers. [The visualization] made it very clear to decision makers when the company name is appearing in red in this word cloud that the consumer is really frustrated with the company. And thankfully as a result of this analysis, we were able to reformulate several products within that lip and brow line to better meet the consumer’s needs.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
“Keeping the questions simple is really crucial. For Avon studies, we get consumer data in a number of different ways – through surveys as well as focus groups. As a scientist working on developing and testing our products, I’ll work alongside consumer experts and marketing experts to craft a series of questions that are not only simple, but that make sense to the consumer. A lot of times, this is sequential questioning that guides the thought process of the consumer in multiple choice or multiple response surveys. We start with simple things reflecting on their [makeup] routine. And eventually we get to the questions that are at the crux of our analysis, getting at their true opinions in open text questions.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
“There’s so much emotion behind the field of cosmetic science and personal care. People have clear preferences and an emotional draw to specific types of products, fragrances, colors. We really work to merge the product performance data that we’re collecting in the lab with these emotions and preferences that we collect. And JMP has been a really useful tool in getting all that data into one place.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
“As part of the upstream team, I think visualizations can help move decisions along and get all our stakeholders on the same page. I’ve shared an example of using sentiment analysis [in JMP] to resolve a quality issue, but in the upstream world I’ve been focused on working with Avon’s exclusive active ingredient, Protinol. Looking at leveraging that technology over the next 10 years, we’re really trying to find out where we can place it within Avon’s business priorities – and also what makes sense to the consumer. So these responses from consumers present a word cloud representing what is critical to driving this ingredient forward and really maximizing the benefits it can have in our portfolio... Data like this that we get from consumers really highlight where we want to go as a direction for Avon and are crucial to helping make key decisions in positioning our products.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
“I’ve been using JMP over the past four years or so and my skills have developed over time through interfacing with these different survey tools that we use – and design of experiments – even just the simple Graph Builder. There’s a tremendous amount of documentation available that’s really helpful. I also can’t say enough about the customer representatives as well because at Avon, we work with our rep consistently. We’re able to go back and forth and really get to the best place with our data and finding the right tools within JMP.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
“Consumers are compelled to buy our products because of the combination of strong science, desirable aesthetics and the compelling language that’s behind them. And really, for us, this is an iterative process. We’re looking at laboratory instrumentation data, consumer perception data, and combining all of these things together [with analytics] to really provide insight into these emotional responses from our consumers. And that’s what creates success.”
– Elizabeth Knapp, Senior Chemist, Avon Products
Statistically Speaking: Answer crucial market research questions
JMP CLINICAL
“We felt that JMP Clinical would provide Almirall the data management possibilities [we needed] without requiring additional data transformation from the CROs to deliver integrated patient data access to clinical teams efficiently not only during the study but also when exploring data retrospectively.”
– Diego Herrera, Head of Global Data Management and Project Information, Almirall
Almirall success story
“JMP Clinical helps save on cost and time in the data access process and extend patient data re-use along the clinical development life cycle.”
– Diego Herrera, Head of Global Data Management and Project Information, Almirall
Almirall success story
“We were looking for options to visualize the data in an efficient, integrated way that was interactive and allowed people to explore, visualize and get more sensitive data... We found JMP Clinical to be the best.”
– Diego Herrera, Head of Global Data Management and Project Information, Almirall
Almirall success story
“JMP Clinical allows us to analyze a large volume of data. In a risk-based approach, where do you have the risk? In the outliers. [JMP Clinical allows us to] see why they are outliers and how you can solve issues and which actions we need to perform.”
– Merce de Frias, International Clinical Trial Manager, Almirall
Almirall success story
“While everybody has access to the same trial data, JMP Clinical offers a user-role concept that provides each role with a specific set of analysis functions.”
– Peter Bewerunge, Head of Life Sciences, Accantec
Accantec success story
“JMP Clinical users who aren’t programmers love that they get all relevant information with just a few clicks. The interactivity allows them to do progressive clinical data exploration – to go deeper and deeper into their data.”
– Peter Bewerunge, Head of Life Sciences, Accantec
Accantec success story
“The fact that JMP is preferred by regulatory agencies gives our customers confidence.”
– Peter Bewerunge, Head of Life Sciences, Accantec
Accantec success story
“Our customers tell us that JMP Clinical helps them to bring greater efficiency and accuracy to their studies. Now they have one integrated solution. They’re impressed with how easy and fast JMP Clinical is. It’s so easy to import clinical trial data, and then you’re ready to do meaningful analysis.”
– Peter Bewerunge, Head of Life Sciences, Accantec
Accantec success story
“[By instituting a risk-based approach,] we can free up so much time, enabling people to actually perform real tasks instead of just looking at data. That’s so beneficial for the company. It’s a question of optimizing time, and at the end of the day the employees can focus on the fun part of their work instead of tedious tasks…. JMP Clinical has definitely been the easiest tool to both teach and learn. That is why we tell our customers that if you don’t already have another preference, you should use JMP.”
– Kasper Munck, Principal Consultant, NNE
Ferring Pharmaceuticals & NNE success story
“We jointly decided to develop interactively with writing and modifying the specs, only starting from two basic requirements: first, being able to perform site performance using statistical inference and clinically relevant endpoint metrics (based on volcano plots). And second, recruitment quality as opposed to recruitment speed (the latter being only too often the limited short-term focus). All of course based on a dynamic click-and-play, slice-and-dice JMP Clinical interface tailored for central statistical monitoring.”
– Egbert A. Van Der Meulen, Senior Director of Biostatistics, Ferring Pharmaceuticals
Ferring Pharmaceuticals & NNE success story
“JMP Clinical is fully based on CDISC ADaM and SDTM data, which Ferring has had in place for over a decade. As such, it has the potential to, frankly, do anything needed for conducting and reporting clinical trials, whether it is central statistical monitoring, signal detection, narrative creation, building clinical data warehouses or, indeed, full-blown clinical trial reporting with close to a single push on a button. This great potential is not fully realized yet, but it will make room for statistical programmers to move on to the next, likely more challenging level of adding value to clinical trial reporting and data explorations.”
– Egbert A. Van Der Meulen, Senior Director of Biostatistics, Ferring Pharmaceuticals
Ferring Pharmaceuticals & NNE success story
“We are presently exploring with NNE – keeping the JMP Clinical business unit in the loop – the possibility of auto-generating statistical reports and further down the line, building clinical data warehouses using JMP Clinical and our AdaM-based repository databases. We also plan to explore how JMP Clinical can be put to use in the design of experiments applications of more complex adaptive and possibly Bayesian Seamless Phase IIa/IIb or II/III trial designs. JMP Clinical would be preferred over a stand-alone special purpose software package – which would likely be rather more expensive. Having a wonderful tool in place for central statistical monitoring is not the end of our collaboration with NNE – far from it.”
– Egbert A. Van Der Meulen, Senior Director of Biostatistics, Ferring Pharmaceuticals
Ferring Pharmaceuticals & NNE success story
“JMP Clinical is a product that stands out in its focus on clinical trials. Once data is standardized from the data collection stage, JMP Clinical helps with analysis and interpretation.”
– Joo Hwan Lee, Head of Clinical Development, Medytox
Medytox success story
“JMP Clinical allows you to easily transform the format of the data you need to submit to the regulatory body. If you have a standardized data set, you can easily get multiple useful clinical trial reports without any special manipulation.”
– Joo Hwan Lee, Head of Clinical Development, Medytox
Medytox success story
“It’s easy to generate reviews by simply linking the data set – something that, with other analysis tools, can only be obtained through a series of operations. The Safety Report feature in JMP Clinical is a good example of an innovative feature – it can handle more data than you might think, so it can be very useful for clinical investigators. In addition, JMP Clinical safety reports show the risk by country and institution using the risk assessment model of clinical trials. This provides great convenience to those who run clinical trials.”
– Joo Hwan Lee, Head of Clinical Development, Medytox
Medytox success story
“JMP Clinical puts the added value of JMP graphics together with SAS. It’s a huge and powerful tool – and I still have a lot of learning to do.”
– Chris Wells, Team Lead, Centralized Statistics Monitoring Group, Roche
Roche success story
STIPS
"I have found STIPS to be the best online teaching course I have ever attended, no matter the subject. I am very grateful for receiving the teaching material, and I will hopefully use it for upcoming internal training in my organization."
– Steffen Bugge, Scientist, Bayer AS
"The speed at which JMP allowed us to get to actionable items and the flexibility in distilling down complex issues with Graph Builder and the Profiler allowed PCC’s upper management to see the value of JMP for our company. Just this year, we are now training all our technical rotation personnel with the STIPS course to ingrain this thinking and the ability to use JMP from the beginning of their careers."
– Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
“[STIPS] is very good. It has a series of modules. I started off just doing the design of experiments module and then got hooked and went and did all the other modules. So that will give you a nice understanding all the way from basic thinking about how you should solve a problem to doing all the analysis, machine learning, all this kind of stuff. It gives you a nice introductory flavor.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“In 3 or 4 hours you could go through the [STIPS module] and have enough knowledge to do a design for a
simple experiment. Then it's up to you whether you go and run that experiment to test it. There are also a lot of data sets within JMP. That's the nice thing about [STIPS] – it gives you data to practice on.”
– Ben Deadman, Facility Manager, Centre for Rapid Online Analysis of Reactions, Imperial College London
Time to Innovate: Teaching design of experiments to chemists: lessons from Imperial College
London (link coming soon)
“I knew that STIPS was an outstanding opportunity to further promote statistical thinking within NVIDIA. STIPS is excellent in balancing statistical theory and the practical hands-on use of JMP to solve common problems that many organizations deal with on a routine basis. It was positively received by everyone I spoke with, and we are in discussions on rolling it out again this year. The best testament to the value of this training is when I go to meetings and see the methods being put to use.”
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
“I highly recommend STIPS for any organization that wants to enhance and expand their use of statistical tools in solving problems."
– Pete Cannon, Senior Director, Product Quality Engineering and Quality Management Systems, NVIDIA
NVIDIA success story
“STIPS is a very good course. I was happy to have one online course concentrating a lot of important information in an easy, understandable level.”
– Andreas Trautmann, Process Expert for Upstream Processing, Manufacturing Science & Technology, Lonza
Lonza success story
“STIPS is expanding how much students learn and how they learn it. It’s filled with realistic industry case studies. And it’s self-contained, self-paced… The structure of STIPS is flexible, so it’s possible to order much of the content to suit the course one is teaching, which may not align perfectly with any one STIPS module. It’s all fully integrated – students can just move through the course … STIPS actually streamlines this and makes it better.”
– Phil Ramsey, Principal Lecturer, University of New Hampshire
University of New Hampshire success story
“I’m getting feedback from my students that they really like STIPS. It’s multimedia. It’s lively, things are well explained and they can complete it at their own pace. Just last week, one student told me ‘I think I finally understand statistics.’ She said she really liked design of experiments and was excited about the fact that she’s getting a better understanding of statistics along the way.”
– Phil Ramsey, Principal Lecturer, University of New Hampshire
University of New Hampshire success story
"Utilizing STIPS has been great for us because it gives rich context in the type of problems our technical associates are trying to solve. The content is framed with the user in mind – how they can answer their questions using statistical techniques."
- John Szarka, Statistician, W.L. Gore
John Szarka on JMP Pro, reproducibility and why being active in the JMP Community and Early Adopter program helps users at W. L. Gore get more from their investment in the software
SAS EDUCATION JMP TRAINING
"Learning JMP is easy, but it is not free. You need to invest time by reading manuals and attending JMP webinars. If you learn day by day by doing these things and learning in the field, you will experience a return on invested time very quickly"
Fabrizio Ruo Redda, Vishay
JMP DS Americas 2021- Recording Producers - Data Explorers Spotlight gallery.mp4 - All Documents (sharepoint.com)
“We – along with our management – looked at it like this: if we’re going to invest in JMP, then we have to invest [in training]. We realized we didn’t have the competency in-house and weren’t going to develop it out of thin air… The Education team really tailored the training for what we needed. I’ve heard from many of the team members who attended that this was one of those trainings they actually found useful. We were able to really leverage SAS’s knowledge and experience interacting with different customers to help identify what we needed and how best to approach it…. It’s been a good partnership.”
– Tim Holtz, Process Engineering Manager, Parker LORD
Parker LORD success story
“JMP is only as powerful as the person who's driving it... I realized in order to get the actual value from [JMP], some additional investment was needed. That’s when we decided to engage SAS Education…. SAS helped us fill in the blanks: we told them what we were trying to achieve, and they told us the tools we needed to get there.”
– Bernard Goguelet, Quality Leader, Parker LORD
Parker LORD success story
"The JMP organization is top notch, even back to the 1990s when I started using the software and did training at SAS headquarters in Cary. And expanding JMP to other companies that I have worked for has always been with great support from the commercial and technical teams."
- Craig DePorter, Head of Advanced Mineral Development, Omya
Self-described “analytics evangelist” Craig DePorter of Omya discusses why analytics enablement means learning more with less (jmp.com)
DISCOVERY SUMMITS
"For those who have never attended a Discovery Summit – I just can't recommend it enough! The JMP team actually feels like a family, and they bring you into that family, which is a pretty amazing experience. This is all on full display at Discovery. Every time I’ve attended the Summit, I’ve taken home not one thing but many things, and I consider it a key conference. The access to tutorials, trainings and presentations is amazing."
- Don Kent, manger of full-stack data science team, Intel
Intel’s Don Kent on what makes a mature analytics organization, the importance of mentorship and joining the “JMP family” at Discovery Summit
"I’ve been using JMP now for about six years (and JMP Pro for two), but it wasn’t until a couple of years ago that I really got involved in the JMP Community. I have been pretty active on the JMP Community discussion boards (mostly asking for scripting help), attended the 2019 Discovery Summit (which was awesome!) and was lucky enough to get selected to present at least year’s Discovery Summit."
"I've gotten more practical improvement ideas from this conference than any of my many years of company technical conferences."
-Steve Hampton, Process Control Manager, PCC Structurals
Steve Hampton of PCC Structurals on transitioning from Minitab and why it’s so important to take advantage of the JMP resources for engagement and learning he calls an “immense value-add”
"Do it. Well worth our time."
– Ted Ellefson, Seagate
"At Discovery Summits, you can always learn new things – whether it's a new method, new tool, new application. Also, you can easily connect to other users and the JMP software developers. By talking to the architects of the software, you can develop an even better understanding of the methods you have access to in JMP. And you can suggest they add new features for a specific model in upcoming version releases."
– Zhiwu Liang, Principal Statistician, Procter & Gamble
Procter & Gamble's Zhiwu Liang explains how nurturing a dynamic relationship with JMP Development helps the company get more from its investment in a software that is constantly being improved
"Go and learn and meet people and have fun!"
– AJ Kaur, Perrigo
"Discovery Summits are the best conferences I've ever attended! I learned how I could link up my R/MATLAB/Python machine learning models to JMP. I obtained a much better feel for the Functional Data Explorer and I found some new tricks for graphing."
– Anonymous
"Discovery Summit is the best conference in the world for data, analytics, and statistical problem solving."
– Jed Campbell, US Synthetic
"Great opportunity to learn aspects of JMP from different industries and use cases that are not part of your current tool set but may provide new insight into your data."
– Mark Dayton, MACOM Technology Solutions
"Discovery Summit is a great conference. You can pick up some great tidbits."
– Peter Mroz, Janssen Pharmaceutical
"Discovery Summit showcased a very wide array of topics. Lots of good insight."
– Craig Minor, Lexmark International
"If you want to deploy the full potential of JMP, it is a must to attend to the Discovery Summit."
– Fabio D'Ottaviano, Dow Chemical
"Regardless of whether or not you think a plenary, paper, or poster topic directly applies to you and/or your work, you will ALWAYS learn something either useful, interesting, or both... There are always new things to learn about JMP, regardless of how long you have been a user."
– Leslie Fuquay, Syngenta
"Discovery Summit provides a great opportunity to expand your talent stack."
– Dave Sandell, ExxonMobil
"Discovery Summit was a great experience: learning opportunities, a wide variety of topics, detailed information, fantastic opportunity to meet the developers and ask specific questions."
– Monique Lander, Ecolab
"Discovery Summit is a place to learn the power of JMP, learn features that you did not know existed, meet new people who are JMP users and have a really fun time. The plenary sessions are great examples of the power of data and always have motivational stories that are encouraging to make continuous improvements and never stop learning."
– Lori Montano-Martinez, Sandia National Laboratories
"Stop thinking about it and do it!"
– Kevin Vedvik, Exact Sciences
"These summits provide helpful examples of how JMP can enhance data review and presentation."
– Bruce Nielson, Xcel Energy
"Make a list of JMP questions before you attend the conference. Seek out developers and experts in those topics. You can get many questions answered and get a new and fresh perspective on topics you thought you knew."
– Anonymous
"No matter how much you think you know, you will learn something new at JMP Discovery Summit."
– Annie Dodson, Ascend Performance Materials
"Discovery Summit is a great meeting to hear about current data analytics trends and topics from plenary talks, meet and chat with fellow JMP users and to discuss JMP functions with the developers."
– Ed Tom, Prime Genomics
"Discovery Summit: Excellent presentations. Relevant topics and discussions. Invaluable opportunities for one-on-one interactions with the developers."
– Russell Hudson, Lumileds
"Discovery Summit is very useful re: stat methods, examples and JMP add-ins and scripting. Read as many presentation downloads as possible before the meeting and ask questions of poster presenters, in Unsessions, any time. Then check the JMP Community afterward and watch any part you missed."
– Richard Hertzberg, Emory University
"Discovery Summit is the best conference you will ever attend in terms of how one is treated and the opportunities for professional growth including valuable networking."
– Philip Ramsey, University of New Hampshire
"Discovery Summit: Best of the conferences. Much to see and learn. Great papers. Amazing hospitality. Easy to navigate, easy to connect with developers and ask questions."
– Leighton Spadone, DAAS Inc. Consulting
"Definitely attend Discovery Summit if you have the opportunity. I always meet new people and learn new things. It is truly an overall enjoyable and educational Summit."
– Michelle St. Peter, Grifols Therapeutics
"Come with big-picture questions that could be posed to developers. Keep a open mind and attend talks/events that might be out of your comfort zone."
– Anderson Mayfield, National Oceanic and Atmospheric Administration (NOAA) and the University of Miami
"Seeing how certain challenges are handled elsewhere can help a great deal when I’m looking to find a way to master my own similar challenges. Discovery Summits extend this to the international stage, but most importantly, these conferences present an opportunity to meet JMP developers. They are an excellent source to explain what happens in JMP platforms behind the scenes, and they’re always interested in users’ ideas for new functionality in JMP."
– Gerd Kopp, Quality Management and Reliability Statistician, Biotronik
Gerd Kopp talks advocating for analytics overcoming internal hurdles and building a relationship with the JMP organization
"Discovery Summits are superb at growing the community of statistically-minded people. No hard-sell of JMP. It certainly does not hurt that JMP is the best overall tool I have found for the sorts of things that I do, but the feel of the Summit is about espousing the concept of using statistics in your day-to-day work"
- Craig DePorter, Head of Advanced Mineral Development, Omya
Self-described “analytics evangelist” Craig DePorter of Omya discusses why analytics enablement means learning more with less (jmp.com)
JMP 16
“Having used model screening on JMP Pro 16 beta these past few months, it has already become an indispensable part of my coral reef predictive modeling research. Although models could be compared rather easily in earlier versions of JMP Pro (e.g., JMP Pro 15’s “model comparison” platform), this new tool makes it even easier for users to simultaneously test many different models (e.g., gen-reg, PLS, discriminant, neural networks, etc.) with the same data set. Something that once took me hours or even days can now be done in minutes (or even seconds). Wow, what a great feature! This surely must be the biggest improvement from version 15 to 16, and I’m sure many others will feel the same.“
- Anderson Mayfield, National Oceanic and Atmospheric Administration (NOAA) and the University of Miami
"I’m happy to see the addition of Limit of Detection Analysis in JMP Pro; it fills a real need. I’ve had many instances in the past with a limit of detection, but didn’t know how to treat it in the analysis. Now we have straight-forward options to incorporate detection limits into the analyses so we get more accurate models and can detect important effects we may otherwise have missed."
- Stan Siranovich, Crucial Connection LLC
“JMP 16 now brings you to the first newly-added row. It is one of those small things that has had an immediate impact when you are adding ~1,725 rows to an existing 375,000 row table!”
- Michael Anderson, Senior Statistical & Measurement Development Engineer, Corning
"JMP 16 brings two very exciting additions to the Time Series platform: the inclusion of State Space Smoothing models and the "Forecast on Holdback" option. Both the state space models and the performance criteria used for model selection based on the holdback sample are used extensively in supply chain applications.
- State Space Smoothing models are the models popularized as ETS (error, trend, seasonality) models by Rob Hyndman. This class of models is flexible and interpretable. The State Space Smoothing report provides a Component States report to help you understand the level, trend and seasonality components.
- The "Forecast on Holdback" option in the Time Series dialog allows you to fit State Space Smoothing and ARIMA models that optimize a performance criterion on a holdback set. The performance criteria include RMSE, MSE, MAPE and MAE.
An addition to the Time Series Forecast platform also allows the user to fit models using one of three widely-used performance metrics (RMSE, MSE and MAE)."
- Marie Gaudard, Statistical Consultant
"I like many of the new features in JMP 16. Machine learning, sentiment analysis, model screening and SEM are all very popular techniques among the technique-inclined or analysis addicts. Understanding sentiments and perceptions is essential these days, where the SEM path diagram can immediately picture the tangible and not so tangible associations between them. Be surprised on how much information and interactivity you will get!"
- Roselinde Kessels, Assistant Professor, Maastricht University
"I love the structural equation modeling platform in JMP, and the new features in version 16 make it even more powerful."
- Vic Strecher, CEO, Kumanu
