PHARMACEUTICAL RESEARCH & DEVELOPMENT
F-star Therapeutics: JMP dramatically reduces biopharmaceutical development time
Context and challenges
- Standard biological experimentation methods today fall short of scientists’ needs in the increasingly data-rich environment of early-phase biopharmaceutical development. Biologists need alternatives to deliver more precise outcomes, more reproducibly and on a shorter time scale.
- JMP at F-star is a “game-changer.” Capacity for multivariate experimentation has reduced the number of tests needed, leading also to reduced costs.
- Scientists say even failed experiments produce useful information leading to the next step; when every experiment is useful, resources and efforts are not wasted.
ROI
- Scientists say DOE in JMP has condensed what used to take one day into just one hour. Effective and powerful data analysis is saving time, and therefore also shortening drug development cycles.
- In one case, a problem that had been leading to dead ends for two months was solved in 8 minutes with JMP.
- Teams can achieve the same process knowledge with 30 experiments as would have previously required over 100.
Quotes from Jon Armer, Formulation Lead Scientist, F-star Therapeutics
- “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.”
- “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.”
- “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.”
- “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 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.”
Related Links
____________________________________________________________________________________________________________________
TECHNOLOGY
Microsoft: JMP shortens development cycles and is instrumental in building a culture of analytics at the enterprise level
Context and challenges
- Microsoft has long been an advocate of data-driven experimentation. The organization has in recent years developed a best-in-class analytics culture whereby robust, reproducible experiments are standard practice.
- The primary barrier to uptake has been changing mindsets and overcoming the limitations of previous software and IT resources.
- After seeing teams make demonstrable improvements with JMP, leadership blessed a conversion from Matlab. Resources for learning both statistics and JMP itself have helped ease the cultural transformation.
ROI
- JMP has categorically shortened development cycles. In one case, a chart that took 10 seconds to make in JMP solved a problem that the analytics department had spent weeks trying to solve.
- JMP has been instrumental in deploying multivariate experimentation methods that allowed teams to identify key phase transitions and begin developing measurement protocols based on models discovered in JMP; “Now we can make progress.”
Quotes from Vicky Svidenko, Partner, Quantum Data Sciences, Microsoft
- “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.”
- “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.”
- “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.”
Related links
____________________________________________________________________________________________________________________
HIGH-TECH MANUFACTURING
Coherent: In just 6 weeks, JMP helped resolve a costly 15-year problem
Context and challenges
- Management has embraced JMP as a vehicle for transforming Coherent’s analytics culture.
- An historical focus on subject matter expertise caused some reluctance among research scientists to embrace DOE. It soon became apparent, however, that DOE – rather than replacing expertise as some feared – actually deepened experts’ product and process knowledge.
ROI
- JMP has helped Coherent to achieve substantial time and cost savings and extend the lifetime of its products.
- In one case, a team using JMP in 6 weeks solved a costly problem that had been plaguing the company for 15 years.
Quotes from Melisa Buie, General Manager at Coherent
- “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.”
- “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.”
Related links
____________________________________________________________________________________________________________________
PHARMACEUTICAL RESEARCH & DEVELOPMENT
Ipsen: JSL add-ins reduce processing time from 20 hours to 30 minutes
Context and challenges
- New immunoassay profiling methods were producing data that were useful but complex, and researchers had to spend copious time processing data files.
- Ipsen’s bioinformatics department began working with oncology researchers to develop custom applications in JMP that streamline research processes.
- JMP and its custom add-ons are fully integrated with Ipsen’s other IT architecture, creating a central statistics hub optimized for visual exploration.
ROI
- Processing time for analyzing 10 biomarkers has been reduced from 20 hours to 30 minutes.
- With the automation of repetitive analyses via custom JMP add-ins, researchers now have time to concentrate on more value-adding tasks.
- JMP add-ins are making research more reproducible and less prone to human error.
- Biologists no longer need to call in third-party expertise for IT or statistical support.
Quotes from Jean-Luc Blachon, Head of Statistical Research at Ipsen
- "For any given experiment, with these three applications on hand, the time needed to process samples can be reduced from 20 or so hours to just 30 or even 15 minutes.”
- “Our aim in using JMP is to save researchers time, which they can devote instead to reflecting on the meaning of the statistical analyses they have requested, among other things. These applications free up time for thinking and carrying out more value-adding tasks. So there are gains in terms of quality too. Researchers can now explore avenues they wouldn’t have even considered in the past.”
Related links
____________________________________________________________________________________________________________________
PHARMACEUTICAL RESEARCH & DEVELOPMENT
Johnson Matthey: JMP doubles innovation capacity with no extra resources
Context and challenges
- Johnson Matthey’s primary challenge is changing the behavior of R&D scientists who find it difficult to embrace new methodologies.
- The most important factor in overcoming this resistance is good support for colleagues learning new approaches. Researchers have found learning resources available for free from JMP to be extremely helpful.
ROI
- DOE in JMP saves more than 50% in R&D time and resources. In some cases, this benefit is up to 70% in time and resource savings.
- Time and cost savings have been proven with several internal case studies, and DOE practitioners now have the full support of management.
Quotes from Pilar Gomez Jimenez, Principal Scientist at Johnson Matthey
- “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.”
- “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.”
- “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.”
- “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].”
Related links
____________________________________________________________________________________________________________________
PHARMACEUTICAL PROCESS DEVELOPMENT
Lonza: Process development scientists double overall yield and cut experimentation time in half with DOE in JMP
Context and challenges
- Scientists in Lonza’s R&D group use JMP widely for experimentation and process development.
- The processes involved in Lonza’s bio-manufacturing efforts have many unknown parameters; so experts say DOE is critical.
- STIPS, a free course from JMP, is helping Lonza to build in-house statistical expertise.
ROI
- A process development team was able to increase overall yield by nearly a factor of two. This was an especially impressive result given that the process in question was one in which scientists are often unable to make meaningful advances so quickly.
- Process experts estimate that JMP Custom Design cut the time needed to investigate relevant factors in half. Instead of 4 or more weeks, the team now only needs 2.
Quotes from Andreas Trautmann, Process Expert at Lonza
- “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.”
- “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.”
Related links
____________________________________________________________________________________________________________________
CHEMICAL MANUFACTURING / QUALITY ENGINEERING
Saint-Gobain NorPro: Predictive modeling in JMP solves a quality issue that had plagued production for 20 years
Context and challenges
- Engineering teams use JMP for quality monitoring and process optimization.
- Variations in suppliers’ raw materials had plagued production lines for two decades. Though engineers could observe a decline in quality from the company’s suppliers, they had no way of resolving the issue as they weren’t able to clearly identify the root cause.
ROI
- Predictive models in JMP resolved a production issue that had perplexed engineers for 20 years.
- The team identified the root cause of the problem as the interaction between two previously unknown quality parameters in a supplier’s raw material.
- A quality manager says this issue could not have been solved with any other software.
Quotes from Jim Lamar, Quality Manager at Saint-Gobain NorPro
- “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.’”
- “Through this process, we answered questions that in the past nobody knew how to even approach. Folks are excited about these results: OK, let’s do more. Let's build on this to achieve even bigger successes.”
Related links
____________________________________________________________________________________________________________________
RETAIL TECHNOLOGY / BUSINESS ANALYTICS
Diebold Nixdorf: With EDA and modeling in JMP, business analysts improve the company’s low Net Promoter Score
Context and challenges
- Exploratory data analysis and modeling in JMP help analysts better understand customer feedback, home in on process issues and identify opportunities for new customer-centric solutions.
- A new hire in the company’s Global Business Services group used JMP to resolve a key customer service problem in a matter of minutes during a meeting with executive leadership.
- Management has since begun encouraging more analysts to use JMP.
ROI
- Business analysts identified key correlations in process data that, when acted upon by the company’s line-of-business managers, brought quantifiable value through stronger customer relationships.
- In a matter of minutes, one analyst used JMP to identify an easily fixed invoicing problem as the root cause of the company’s low Net Promoter Score.
Quotes from Tyler Wise, Project Analyst at Diebold Nixdorf
- “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.”
Related links
____________________________________________________________________________________________________________________
PHARMACEUTICAL RESEARCH & DEVELOPMENT
Lundbeck: JMP for DOE increased yield by 25-30% and cut a development process from 9 months to 14 days
Context and challenges
- To achieve Lundbeck’s business goals, the R&D team pursues higher yields and lower cost of goods. DOE is key to increasing yield because it enables scientists to test multiple concentrations of multiple factors all at once.
- Scientists say that though cell culture processes may work very well every time in the lab, they must be scalable. Reproducibility is necessary in order for processes to be recreated at large scale.
- DOE helps scientists not only with reproducibility, but to also deepen their process knowledge with an understanding of which factors are important and those factors interact.
ROI
- DOE in JMP helped scientists to increase yield by 25-30%. Improvements in yield have lowered the overall cost of production by a similar factor.
- Processes that used to take 9 months now take 14 days, thanks to multifactor experimentation in JMP.
Quotes from Patti McNeill, Associate Director of Cell Culture Development at Lundbeck
- “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%.”
- “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.”
- “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.”
Related Links
____________________________________________________________________________________________________________________
PHARMACEUTICAL MANUFACTURING
Merck: JMP helped understand variability and cut the number of tests in half
Context and challenges
- Merck has spent years building a deep organizational culture of analytics. Leaders say the result is more productive synergies between subject matter experts and analytics experts.
- The primary barrier to cultural transformation has been changing peoples’ mindset. But, guided by the belief that more analytics capability is a direct path to more productivity and innovation, Merck’s leadership says that “it’s not extra work – it’s extra value.”
- DOE is seen as central to simultaneously reducing variability and streamlining experimentation, thereby cutting R&D costs.
- In the case of its portfolio companies, Merck’s venture capital arm M Ventures values a mature data strategy as a key indicator of commercial scalability.
ROI
- In one case, former engineering director Julia O’Neill’s team was able, in 3 weeks, to solve a problem that had been plaguing production for 2 years.
- Executives say DOE has made development labs roughly 35% more efficient.
Quotes from Julia O’Neill, former Director of Engineering, Merck
- “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.”
- “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.”
- “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.”
- “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.”
- “Once we made analytics more a part of the problem-solving culture, we actually did see problems beginning to subside.”
Quotes from Owen Lozman, Vice President, M Ventures and former Global New Platform R&D Director, Merck
- “In the industry we’re in, we’re effectively financing companies to do experiments to show an output that makes people in the financial industry [see] they’re worth more money than when they started. That means doing the fewest number of experiments, doing the most cost-effective experiments, doing the experiments you learn the most from, and exploring as much of the parameter space as you can by making sure you’re solving problems that are going to be relevant in the future. I think it’s our responsibility as scientists to make sure that we’re doing science in the most scientifically robust way possible.”
- “The [success of the venture] is being heavily driven by DOEs. The sooner entrepreneurs can get into the mindset of how their customers are going to think, the better it is for them. It’s also important for them to communicate how they’re developing the product in line with what the customer’s going to need.”
- “We were making critical decisions based on data that were not reproducible. [But after adopting a better data management strategy via JMP], we were also able to increase the throughput in our lab. After a year, we were something like 35% more efficient. And we were able to demonstrably show that our data was more reliable and more reproducible.”
- “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.”
- “Part of our responsibility [as venture capitalists] is to help install best practices. It’s not extra work – it’s extra value.”
Related links
____________________________________________________________________________________________________________________
RESEARCH & DEVELOPMENT / LIFE SCIENCES CONSULTING
Riffyn: JMP cuts the time to market 4-fold and boosts manufacturing plant productivity by 5-10%
Context and challenges
- DOE has changed the course of Riffyn CEO Timothy Gardner’s career, he says, as well as the companies he has worked for, including Amyris and Cellicon Biotechnologies. It is now a strategy for which he advocates among clients in the life sciences.
ROI
- With JMP, Riffyn consultants cut the time to market 4-fold across a variety of projects from 13-15 months down to 3-4 months.
- Gardner says DOE in JMP boosts the overall productivity of production plants by 5-10%, just by making what are essentially cost-free changes.
Quotes from Timothy Gardner, Founder & CEO, Riffyn
- “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.”
- “[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.”
- “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.”
- “JMP has amazing tutorials that handhold you through this. You can just click your way through to new levels of learning.”
Related links
____________________________________________________________________________________________________________________
RESEARCH & DEVELOPMENT / QUALITY ENGINEERING
University of Heidelberg: JMP Genomics adoption resolves analysis bottleneck for the medical research team
Context and challenges
- PKD is genetically transmitted and afflicts 1 in 1,000 people in the general population. Left untreated, it progresses to end-stage renal failure in 50 percent of patients afflicted by the most severe forms of the disease.
- Medical researchers seek fast and robust validation of results as they investigate the link between genetic factors and polycystic kidney disease (PKD).
- In addition to robust statistics, the ZMF team also sought a platform flexible enough to accommodate diverse data sets from emerging technologies.
ROI
- Using JMP Genomics, thorough microarray analyses can now be completed within a few hours, and results delivered quickly to researchers’ desktops.
- Microarray analysts at ZMF have provided important feedback to the JMP Genomics team, resulting in the addition of new quality-control functions in the software.
- JMP Genomics developers added a new annotation process to link gene identifiers to global bioinformatics databases like UniGene, Gene Ontology, and Entrez Gene. These tools make it possible for scientists to verify the relevance of research results and to formulate new hypotheses.
Quotes from Professor Norbert Gretz, Director of the Medical Research Center in Mannheim, Germany
- “Formerly, the validation of results took several weeks to achieve, but today we get the results in just a few hours. We can very easily implement additional analysis processes, giving us a decisive advantage in our daily research.”
- “The output is easier to handle, the graphs are more convenient, and you still have the functionality of SAS...it’s really a complete workflow.”
- “Through targeted progress and the possibility of analyzing large data sets quickly, we learn more and more about the gene mutation which is responsible for the diseased kidney...JMP Genomics from SAS is the right tool for our purposes.”
Related links
____________________________________________________________________________________________________________________
QUALITY ENGINEERING
U.S Department of Energy’s National Renewable Energy Laboratory: A week's work in 10 minutes with JMP
Context and challenges
- There’s a tremendous amount of information available for this research – scads of data from hundreds of variously manufactured and maintained solar energy systems deployed in a range of climates on vastly different terrains. The task is to harness that data to better understand how well these systems operate.
- Research the degradation rates of photovoltaic systems. The results of that research will be used to help develop a standard sanctioned by the International Electrotechnical Commission. This standard will define how to filter data and calculate degradation rates in an accurate and reproducible way. In what is a $77 billion global industry, minute variations from the norm add up to a great deal of money.
- Data on a single solar energy system can be analyzed in a couple of thousand different ways.
ROI
- JMP time-series analysis came in handy in examining the many and varied factors.
- Using JMP, researchers are now generating in a matter of minutes reports that previously took a week or more to produce.
- JMP Scripting Language (JSL) and the software’s versatile data mining capabilities have been useful in research into extremely large, richly nuanced data sets. The ability to mine so deeply into data has led to a greater appreciation of the fact that even very subtle disturbances can have significant effects on photovoltaic systems.
Quotes from Dirk Jordan, Senior Reliability Engineer
- “[Analyzing the data] would have taken, probably, weeks, if not months, if I’d had to do it all manually...but with an automated process, it ran in a few hours.”
- “We’re interested in this really tiny rate at which these systems are degrading...and to detect this, with all these variables, is very challenging. Time-series modeling has really helped with this.”
- “I went from at least a week of work to maybe 10 minutes...it was pretty considerable.”
Related links
____________________________________________________________________________________________________________________
CLINICAL RESEARCH & DEVELOPMENT
Almirall: JMP Clinical enables the support of Almirall strategies to expand into other markets
Context and challenges
- Rapid growth in the clinical research industry requires innovative approaches to data analysis.
- With improvements in technology and clinical research interest in new tools for managing clinical trials, researchers have begun to tap into the wealth of data that continues to become available - the sheer quantity of data can present challenges for clinical researchers.
- In parallel with increases in data volume, regulatory demands have likewise increased, resulting in growing costs and extended timelines.
ROI
- JMP's dynamic and interactive visualization features give clinical teams the resources to conduct internal analyses on patient data during the review, respond to regulatory questions, and reduce programming costs throughout the process.
- Since implementing JMP, the Almirall team has transformed how they review data, allowing them to explore risk-based monitoring and collaborate effectively.
- The alerts and documentation afforded by JMP Clinical’s Notes feature have relieved the regulatory burden on Almirall.
Quotes from Diego Herrera, Head of Global Data Management and Project Information
- “We were looking for options to visualize the data in an efficient way...an integrated way [that is] interactive and allows the people to explore, visualize and get more sensitive data... We found JMP Clinical to be the best.”
- “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... [JMP Clinical would help them] save on cost and time in the data access process and extend patient data re-use along the clinical development life cycle.”
Related links
____________________________________________________________________________________________________________________
CONSUMER & MARKET RESEARCH
Kraft: Evaluation tool developed with JMP cuts analysis time in half
Context and challenges
- A consumer test evaluation tool for Kraft Foods, built on a JMP statistical software platform in collaboration with SAS Professional Services.
- Kraft Foods’ researchers must analyze data from taste tests involving professional tasters and hundreds of consumers.
- Nonstatisticains sometimes struggle with the tools used. They needed something a little easier to use.
- Experts are bogged down in analysis and don’t have time to work on new applications and method development.
ROI
- It quickly began bringing efficiency to the testing process – showing an impressive 50 percent savings in analysis time soon after its deployment.
- It replaces a set of data analysis macros developed by Kraft Foods and written in various software packages. The JMP scripts create a workflow that combines data handling and calculation with novel high-quality graphical output tailored for import into PowerPoint presentations.
- Kraft Foods has developed its best-practice approach for identifying differentiated consumer preference groups and coded it within this application. The experience of experts is now more easily passed on to others.
- JMP's 3-D graphics help the user find the best angle from which to look at the data. Principal components analysis in JMP helps the user discover patterns within the data that would otherwise be obscured.
- More efficient and effective process to arrive at the right business decision. More trained sensory/consumer professionals can now perform analyses efficiently and ensure that local product knowledge is incorporated into the interpretation process. Experts have more time to work on innovative new applications and method development.
Quotes from Jeff Stagg, Principal Scientist in Research and Development for Kraft Foods UK
- “It's very user-friendly and has wonderful graphics. I recognized that with JMP’s ease of use, I’d be able to give this tool to sensory/consumer professionals around the globe to do their own analysis, significantly reducing reliance on experts in the field.”
- “A standard approach to the analysis can be done, and the reports/graphics are all customized in terms of what management expects to see as an output...The managers receiving the information will already understand the format of the chart – you don’t lose time explaining again the x-axis and the y-axis and all that sort of thing.”
- “I now save considerable time because JMP can assign properties to the products that transport across all the graphical routines...Not only that, it allows any number of product color profiles to be created that reflect, for example, the sequence of consumer product preferences, the origin of products, an ingredient level, etc. More important still is the seamless ability to switch between them in customizing the graphical output. It’s all about speed and user-friendliness.”
Related links
____________________________________________________________________________________________________________________
PROCESS DEVELOPMENT / QUALITY ENGINEERING
Kodak: JMP enables team in driving zero-defect manufacturing across the organization
Context and challenges
- Locate the source of a very low level of contamination generated in Kodak’s clean manufacturing facility in the production of motion picture film.
- Precise origin was elusive. The size and frequency of the particles were such that they fell beneath the detection capabilities of the inline film inspection process. Compounding the difficulties in detecting the problem was that it was occurring only intermittently.
- The team was responsible for driving zero-defect manufacturing across the organization.
ROI
- JMP allowed them to analyze 20 million records and a JMP phase control chart enabled examination of changes made at given points in the process; the team made process upgrades to the coating technology and enhanced edge controls resulted in improved process performance.
- Kodak reduced occurrences of the contaminant from one in every 10 million linear feet of this motion picture film to only one in every 300 million linear feet – a 30-fold improvement. And in 12 months, there have been no occurrences at all.
- After senior managers saw the data in JMP visualizations, they easily understood the need for remedies with almost no hesitation.
Quotes from Geoff Beatham, Quality Director for Kodak’s Film Capture
- “JMP has been significant in allowing us to unravel the key facts from all the noise in our complex manufacturing process.”
- “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.”
- “JMP is a key tool for our technical and engineering experts as we continue to focus on zero-defect manufacturing...we had just stellar success on this project. It really has been exciting, and it’s nice to be able to share it.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / RESEARCH & DEVELOPMENT
DOW: JMP played an "integral role" in over 75,000 six sigma projects
Context and challenges
- Dow is a global giant, developing not just chemicals, but an array of products that have become indispensable in our homes, our workplaces, and almost everywhere else.
- To turn data into knowledge that ensures the reliability of products fosters innovation and informs decisions.
- As Dow has evolved from a chemical manufacturer into a system supplier, product reliability has become an area of increasing focus. The challenge is to pinpoint defects in materials or processes, locate design issues and determine how to correct them.
ROI
- JMP helped uncover an inefficiency in a production process that, when corrected, resulted in millions of dollars of savings, a more environmentally sound process, and an improved product.
- Dow has saved or generated billions and billions of dollars in value through Six Sigma.
- JMP enables subject-matter experts to explore their data interactively and then collaborate more effectively with other experts who are using other technologies.
- JMP has also been used to determine where to build a new plant, how it should be designed, and what the working capital should be.
Quotes from Kevin McCarron, Six Sigma Service Leader
- “[JMP] is fundamental to what we do in our Six Sigma program...trainees are introduced to the basics of JMP and then graduate to proficiency in the use of design of experiments and the reliability platform. It’s woven into the fabric of the training program.”
- “When you’ve got 10 minutes to sit down and talk to somebody about their data set – bang! There it is...that really launches so much other activity that you can get into.”
- “JMP is so ubiquitous...We use it in our continuous improvement and innovation space.”
Quotes from Jeff Sweeney, Senior Research Statistician
- “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.”
- “It’s more what I’ll call objective-based in how it’s organized, as opposed to tool-based...It’s easier for someone who doesn’t have a statistics background.”
Related links
____________________________________________________________________________________________________________________
CONSUMER & MARKET RESEARCH
General Mills: Bioscientists save a tremendous amount of time with JMP Genomics
Context and challenges
- The challenge is to produce healthier varieties of oats, sweet corn, broccoli, and other crops while gaining higher yields.
- Bioscientists must tackle large data sets every day.
- JMP Genomics has helped General Mills bioscientists improve the accuracy and intensity of gene selection.
ROI
- JMP Genomics allows scientists to tackle large data sets quickly and effectively by combining point-and-click access, dynamically interactive JMP graphics, and robust SAS Analytics.
- Jackson uses JMP Genomics for predictive modeling and linkage mapping – the latter of which he’s been instrumental in developing.
- The JMP Genomics team recognized an opportunity to integrate SAS algorithms for operations research into JMP Genomics, producing a suite of tools in the software that enables biologists to quickly and effectively build genetic maps in the dynamic, visual environment.
Quotes from Eric Jackson, Bioscientist
- “I was attracted to JMP because of its ability to provide accurate and meaningful models that could be run easily on a GUI interface on a desktop.”
- “It took everything that was arduous and that would give me problems in TASSEL and Structure and allowed me to do those analyses more quickly because of the power of JMP with SAS in the background...And I still could do it all on the desktop.”
- “I can’t tell you what a huge timesaver JMP has been.”
- “When I began using JMP Genomics, I was really impressed with how easily I could maneuver through and drill down into this data...Now that we’re into predictive modeling, it’s even more exciting.
Related links
____________________________________________________________________________________________________________________
TECHNOLOGY / RESEARCH & DEVELOPMENT
North Dakota State University: From days and weeks to minutes and hours
Context and challenges
- Accelerate query and analysis processes needed by plant breeders to assess and compare crop varieties as data is collected, rather than after weeks of manual work fraught with the potential for mistakes and missteps.
- Better manage accelerating flows of phenotypic, genomic, drone, and sensor data, data and pipeline managers in the Department of Plant Sciences at North Dakota State University sought a customizable statistical tool capable of extracting and running mixed model analysis on unbalanced data from trials across a range of locations and time periods.
- Used JMP to create the Ag Query Hub, or Ag.Q.Hub, an interactive add-in developed in JMP Scripting Language.
ROI
- Ag.Q.Hub in JMP has made standard queries more reproducible and less prone to user error or variability. Streamlines computational components.
- Breeders say the tool dramatically accelerates selection processes through automation of standardized tasks, guiding researchers in a user-friendly graphical interface and offering built-in analysis functionality.
- The system, unlike even the best prefab analytics tools on the market, allows users to extract and run mixed model analysis on unbalanced data from multi-environment trials – and do so across years of data taken from different locations. This wide-ranging functionality is possible, Walk says, because Ag.Q.Hub was built in JMP.
- The team is now also testing ways to incorporate JMP into their genomic selection work.
Quotes from Tom Walk, Ph.D., NDSU Breeding Pipeline Database Manager
- “Tasks like compiling data over years were practically impossible in the past.”
- “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.”
- “There are a number of modeling methods in JMP, and we have other algorithms with machine learning and artificial intelligence...save researchers a lot of time by automating parts of the process.”
- “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.”
- “As a university, we have the added challenge of training researchers and students for the jobs of today and tomorrow, and we have to stay relevant in the technology we use.”
Related links
____________________________________________________________________________________________________________________
RESEARCH & DEVELOPMENT / NATURAL SCIENCES CONSULTING
Brazca: After only a week JMP more than paid for itself
Context and challenges
- Evaluate complex geospatial information to determine opportunities for mineral extraction.
- With the advent of new digital technology, mining industry expectations shifted. Exploration geologists like can now more readily identify deposits but accessing these often complex bodies presents far greater challenges.
- The company needed to go into production within the year.
- Possible fields of application include virtually all disciplines of the natural sciences and every other field in which the spatial position of data matters such as image processing, logistics, marketing, and pattern recognition.
ROI
- JMP application developed with JMP Scripting Language (JSL) is invaluable in the face of challenging data sets – like the complex bimodal data.
- It is computationally inexpensive.
- It can be completed without expert assistance and is thus suitable for universal and routine everyday use.
Quotes from Steffen Brammer, Senior Resource Geologist
- “I’ve become a diehard fan when it comes to JMP...I’m an exploration geologist – we’re curious about discovery, and so is JMP.”
- “After only a week or so, JMP more than paid for itself because it saved me so much time. I don’t even know how my peers are working without JMP. I have an unfair advantage.”
- “When I first came across JMP, I bought it on my own dime. I began using it to run charts and plots – but soon realized that even the table options and summaries were a huge help. Today, I use JMP along with a geology software package like AutoCAD. I’ve written scripts in JMP to get beautiful reports, charts, graphs, and analysis.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / RESEARCH & DEVELOPMENT
Vishay: JMP significantly decreases costs with an 83% reduction in data processing time
Context and challenges
- With the imminent threat of climate change, electronics manufacturers race to innovate more energy-efficient devices.
- Requirements for semiconductor components are becoming more demanding: superior performance, lower costs, negligible failure rates, and quicker time to market.
- Most companies have vast data streams from which to derive improvements, but data management is often time-consuming and costly.
- JMP streamlines data management and analysis into one self-service workflow.
ROI
- JMP has allowed engineering talent to be more fully utilized, with individuals empowered to actively seek improvement opportunities in their data.
- JMP has significantly decreased costs as a result of an 83% reduction in data processing time.
- Implementing JMP has boosted morale within the R&D lab. An internal survey showed an increase in satisfaction among employees from 3.7 out of 10 to 9.7 out of 10.
- JMP offers an all-in-one workflow, it overcomes data silos, streamlining the entire data preparation process.
Quotes from Fabrizio Ruo Redda, Senior Manager of R&D
- “[Without JMP] I believe we would have continued to make incremental improvements, but not realized the same level of business impact.”
- “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.
- “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.”
- “[JMP] was the right tool to speed up data analysis and share results through visual information.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / RESEARCH & DEVELOPMENT
PPG Industries: JMP license paying for itself in cost and time savings many times over
Context and challenges
- With the trend toward increasing automation, change agents at PPG recognized an opportunity to evolve the organization’s long-standing Six Sigma tradition in line with the new realities of the digital era.
- PPG employs just shy of 50,000 people at more than 150 facilities around the world. Ensuring quality for such a large global industry requires attention to detail, cross-company standardization, and common, easily accessible, modifiable platforms.
- Democratize the principles of Six Sigma by providing JMP to anyone who wants it.
ROI
- JMP® has, since its rollout at PPG, been a big factor in the company’s ability to democratize the spirit of Six Sigma to all departments and geographies.
- New hires are seeking out upskilling upon their arrival at PPG. JMP is a professional development opportunity that the company not only offers but encourages.
- These upskilling opportunities have knock-on implications for PPG’s ability to recruit and retain the industry’s brightest new talent.
- The ability to use JMP out of the box as well as to script customizations makes JMP an all-in-one tool that everyone can utilize at their own comfort level.
- JMP is a way of standardizing around best practices and boosting the reproducibility that is so critical to technology transfer.
Quotes from Kevin Gallagher, Data Scientist
- “When we're doing these classes for our science and technology audience, we tend to standardize it around JMP because of the visual connection between an analysis and graphics – and the dynamic nature of JMP that allows users to explore.”
- “We leverage JMP as much as we can because it's kind of like the Swiss Army knife of statistical tools – it has a lot of capabilities.”
- “It is JMP (software)’s application of the statistics and graphs and mathematics that cut across all the different languages...Ultimately, that improves collaboration.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / HIGH-TECH MANUFACTURING
Polar Semiconductor: JMP improves SPC charts by 35% and Cpk by 18%
Context and challenges
- Polar Semiconductor designs and manufactures advanced wafers for automotive computer chips
- Apply JMP to automate and optimize the collection, preparation, analysis, reporting, and workflows of engineering and process data.
- One wafer sees a couple of hundred tools in its lifetime and a couple of hundred parameters per tool. If you have 25 wafers in a lot, that’s a lot of data to analyze and correlate.
ROI
- Uses JMP scripts to collect, prep, analyze and report data for CTQ, SPC and Cpk – in the process improved statistical process control by 35% and process capability index by 18%.
- Embraced JMP for work with larger data sets, JMP can handle the tables that can be several thousand columns by several million rows.
- Scripted parametric model functions for electrical tests and inline functions for the actual process, previously looked at one chart at a time, which wasn’t efficient.
Quotes from Evan Ngo, Senior Electrical Test Engineer, and Jim Gillard, Operations Master Black Belt
- “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.”
- “JMP can handle tables with millions of rows easily. That, along with quick table joins and scripting, makes it useful for doing repeat correlations and analyses...Excel can handle only about 1 million rows and VLOOKUPs in Excel are pretty painful when you’re doing 10 or 20 of them.”
- “Having a dashboard that consistently communicates data is very valuable. Rather than having to perform an analysis repeatedly, you can just check your dashboard. Or if you have an action list, you can just publish that on the dashboard for various people to use. Those dashboards help us immensely, and we create them in JMP.”
- “People are talking about sensor data and machine learning and all this data that will be captured from machines every second. That involves a tremendous amount of data. It will take tools like JMP to dig through it and figure out what’s important.”
Related links
____________________________________________________________________________________________________________________
QUALITY ENGINEERING / CONSERVATION
The Beijing Aquarium: JMP helps cut energy use by two-thirds with tremendous cost savings
Context and challenges
- Inland aquariums rely on advanced technology for life support and water treatment in order to sustain the sensitive aquatic conditions that will enable marine species to thrive even thousands of miles from the ocean.
- With so large an aquarium at such great a distance to the sea’s natural systems, water management presents some especially tough challenges.
- Use JMP to run a series of experiments that will help guide water quality management decisions.
ROI
- The aquarium now uses only one-third the amount of energy they were using before, with savings of around $5,000 a month for the beluga whale tank alone – and that is one of their smaller tanks.
- Before the aquarium started using JMP, it would take Wang’s entire six-person team a full week to produce the annual report. With JMP, it only takes one person two days.
- Enhanced the visitor experience with improved tank visibility during peak visitor hours
Quotes from Wang Lei, Water Quality Engineer
- “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.”
- “Even after just a year and a half of using JMP, we made several meaningful discoveries with our data. And we learned that JMP was easy to use – even for people who don’t know much about statistics.”
- “JMP provides an intuitive, interactive way to view outputs. The aquarium’s high-level management doesn’t have to possess much statistical knowledge in order to understand the implications of our work.”
Related links
____________________________________________________________________________________________________________________
ACADEMIC / SPORTS
Davidson College: JMP enables coaches to focus and saves time during game prep
Context and challenges
- Despite the large quantity of sports data now available, it’s difficult to understand which statistics can help teams improve.
- Students involved in Cats Stats, Davidson’s one-of-a-kind sports analytics group, sift through the numbers to offer coaches important insight – from lineup efficiencies and shot prediction to personnel scouting.
- To help Davidson’s students hone their analytical skills while simultaneously providing real-world advice to the college’s athletics program.
ROI
- Using mathematical models, the research group has submitted brackets to ESPN and done well, with one bracket beating 99.9% of all other brackets in the country.
- Their statistics helped Davidson’s coaching staff identify key factors affecting gameplay, letting coaches focus and save time during game prep.
- JMP has allowed them to move forward with analytical methods without having to slow down to learn more complicated statistical programs or coding.
- The visual capabilities in JMP allowed them to present their data in a way that the coaching staff could easily understand and interpret.
Quotes from Tim Chartier, Ph.D., Professor of Mathematics
- “JMP just opened things up. I don’t really know any other way we would have noticed [an important correlation between fouling and shooting average].”
- “As I listened to what she wanted I said, okay, you need to learn JMP because it’s the only shot you have of figuring this out.”
- “What was neat with Charlotte’s work was also the way that she could use JMP to present what she saw.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / INDUSTRIAL MANUFACTURING
Dupont: JMP helps achieve twice the result in half the time
Context and challenges
- Modern manufacturing environments are subject to constant mechanical and process evolution – a result of increasing digitization, the advent of new technologies, and the collection of ever more data.
- A compulsory in-house Six Sigma training program based on JMP® statistical discovery software has accelerated the rollout of advanced analytics at the enterprise level.
- The company’s dual investment in both technology and upskilling has become a cornerstone of DuPont’s competitive edge.
ROI
- Leadership says that the capability of the whole organization has advanced significantly. They attribute that improvement in no small part to more widespread use of JMP.
- It is early proofs of concept there was quantifiable value in JMP – both in terms of time savings and cost reduction – and overcome initial skepticism among his colleagues.
- Efficient communication with customers has enabled DuPont to respond more nimbly to the evolution in customer needs that is a defining feature of high-tech manufacturing.
- JMP enabled the DuPont team to quickly identify and resolve variability issues – a challenge that would have required numerous experimental iterations with any other software. Had they not used JMP the yield rate would have been much lower, potentially leading to customer satisfaction problems.
Quotes from Jerry Huang, Analytical Lab Senior Engineer
- “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.”
- “JMP outputs are easy to understand, concise and visually explain the project clearly and easily.”
- “JMP is a sharp tool that empowers engineers to achieve twice the result in half the time.”
Related links
____________________________________________________________________________________________________________________
SIX SIGMA CONSULTING / INDUSTRIAL MANUFACTURING
Amperex Technology Ltd: JMP helped reduce variation by 50 percent
Context and challenges
- In the face of fast-growing demand, China has become a global leader in the lithium-ion battery market. At the center of this growth is Hong Kong-based Amperex Technology Ltd. (ATL).
- ATL’s embrace of robust analytics and Six Sigma at the enterprise level lays the foundation for increasingly reliable lithium-ion batteries
- Analytics integration via JMP enables engineers to easily apply statistical methods like reliability analysis to optimize all stages of the product life cycle from design and development to production and usage maintenance.
ROI
- Specialty applications like deterministic and probabilistic modeling in JMP have helped the company to reduce process variation on a broad scale.
- JMP now enables the team to perform tasks three times faster than they could with Excel.
- Thanks to the combination of its user-friendly interface and robust underlying statistical capability, most of ATL’s major clients have similarly adopted JMP as an enterprise-wide tool.
Quotes from Wang Guifu, Principal Engineer of Reliability Engineering
- “In the past, when we used Excel for the job, we spent a lot of time and energy. It was a tedious and error-prone process...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.”
- “JMP (software)’s interactive graphics are very easy to understand. The entire representation is very intuitive and clear. All my colleagues agree."
Quotes from Henry Qiao, Six Sigma Deployment Manager
- “We’ve reduced variance by around 50 percent. Our bosses and customers were very pleased with the result.”
- “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 hope to enhance everyone’s engineering capabilities through the use of Six Sigma – with the application of JMP of course. We regard JMP as a critical tool for the implementation of Six Sigma, a tool that can support our strategic implementations.”
Related links
