Upgrade your problem-solving and research capabilities with JMP
Rating: 10 out of 10
Use Cases and Deployment Scope
We assist our clients in accelerating research, both R&D, routine improvement initiatives as well as root cause analysis. JMP is central to this effort in two ways. First, we help them exploit the tool and secondly, we build and integrate JMP with databases and server-based software to create analytical systems that imbed analytics into standard operating procedures.
Pros
- JMP is designed from the ground-up to be a tool for analysts who do not have PhDs in Statistics without in anyway "dumbing down" the level of statistical analysis applied. In fact, JMP operationalizes the most advanced statistical methods. JMP's design is centred on the JMP data table and dialog boxes. It is data focused not jargon-focussed. So, unlike other software where you must choose the correct statistical method (eg. contingency, ANOVA, linear regression, etc.), with JMP you simply assign the columns in a dialog into roles in the analysis and it chooses the correct statistical method. It's a small thing but it reflects the thinking of the developers: analysts know their data and should only have to think about their data. Analyses should flow from there.
- JMP makes most things interactive and visual. This makes analyses dynamic and engaging and obviates the complete dependence on understanding p-values and other statistical concepts(though they are all there) that are often found to be foreign or intimidating.
- One of the best examples of this is JMP's profiler. Rather than looking at static figures in a spreadsheet, or a series of formulas, JMP profiles the formulas interactively. You can monitor the effect of changing factors (Xs) and see how they interact with other factors and the responses. You can also specify desirability (maximize, maximize, match-target) and their relative importances to find factor settings that are optimal. I have spent many lengthy meetings working with the profiler to review design and process options with never a dull moment.
- The design of experiments (DOE) platform is simply outstanding and, in fact, the principal developers of it have won several awards. Over the last 15 years, using methods broadly known as an "exchange algorithm," JMP can create designs that are far more flexible than conventional designs. This means, for example, that you can create a design with just the interactions that are of interest; you can selectively choose those interactions that are not of interest and drop collecting their associated combinations.
- Classical designs are rigid. For example, a Box-Benhken or other response surface design can have only continuous factors. What if you want to investigate these continuous factors along with other categorical factors such as different categorical variables such as materials or different furnace designs and look at the interaction among all factors? This common scenario cannot be handled with conventional designs but are easily accommodated with JMP's Custom DOE platform.
- The whole point of DOE is to be able to look at multiple effects comprehensively but determine each one's influence in near or complete isolation. The custom design platform, because it produces uniques designs, provides the means to evaluate just how isolated the effects are. This can be done before collecting data because this important property of the DOE is a function of the design, not the data. By evaluating these graphical reports of the quality of the design, the analyst can make adjustments, adding or reducing runs, to optimize cost, effort and expected learnings.
- Over the last number of releases of JMP, which appear about every 18 months now, they have skipped the dialog boxes to direct, drag-and-drop analyses for building graphs and tables as well as Statistical Process Control Charts. Interactivity such as this allows analysts to "be in the moment." As with all aspects of JMP, they are thinking of their subject matter without the cumbersomeness associated with having to think about statistical methods. It's rather like a CEO thinking about growing the business without having to think about every nuance and intricacy of accounting. The statistical thinking is burned into the design of JMP.
- Without data analysis is not possible. Getting data into a situation where it can be analyzed can be a major hassle. JMP can pull data from a variety of sources including Excel spreadsheets, CSV, direct data feeds and databases via ODBC. Once the data is in JMP it has all the expected data manipulation capabilities to form it for analysis.
- Back in 2000 JMP added a scripting language (JMP Scripting Language or JSL for short) to JMP. With JSL you can automate routine analyses without any coding, you can add specific analyses that JMP does not do out of the box and you can create entire analytical systems and workflows. We have done all three. For example, one consumer products company we are working with now has a need for a variant of a popular non-parametric analysis that they have employed for years. This method will be found in one of the menus and appear as if it were part of JMP to begin with. As for large systems, we have written some that are tens of thousands of lines that take the form of virtual labs and process control systems among others.
- JSL applications can be bundled and distributed as JMP Add-ins which make it really easy for users to add to their JMP installation. All they need to do is double-click on the add-in file and it's installed. Pharmaceutical companies and others who are regulated or simply want to control the JMP environment can lock-down JMP's installation and prevent users from adding or changing functionality. Here, add-ins can be distributed from a central location that is authorized and protected to users world-wide.
- JMP's technical support is second to none. They take questions by phone and email. I usually send email knowing that I'll get an informed response within 24 hours and if they cannot resolve a problem they proactively keep you informed about what is being done to resolve the issue or answer your question.
Cons
- JMP does a lot and can be intimidating for new users. New users and their managers need to understand that it’s unlikely that anyone will use all of JMP's capabilities in their work. Some uses are very limited. But it’s not important how much of the whole JMP product and capabilities you use but rather what use of the product contributes.
- We have seen time and again where organizations up their game analytically because they are using JMP. Though JMP makes these methods accessible by way of visualization and interactivity, there is still a learning curve involved. For example, JMP does a great job with time series analyses allowing manufacturers to find cyclical patterns that lead to yield hits. Using it in JMP is easy but engineers need to understand the concepts behind it to exploit it.
- JMP data tables are proprietary and I'm not sure that any other software can open native JMP files. Perhaps some competing products can but I would have to bet that some aspects of the data, particularly saved analyses, table variables and formulas would not come across.
- JMP Scripting Language (JSL) is incredibly powerful. With it you are actually working with JMP's building blocks in terms of analytics and in terms of how reports and dialogs are put together. I personally think that every JMP user should have some active expertise with JSl but building integrated analytical systems will have to be left to those who have the time and talent to focus on it daily.
- JMP forces you to change the way you approach analysis and that can be a difficult transition for some but it leads to some powerful capabilities once you make it through. Most analytic tools are focused on the analytic techniques and terms and use those names in their menus. JMP on the other hand, focuses on the data and the questions you’re asking: What is my Y and what’s my X? What’s the relationship between them? This way the emphasis is on the problem at hand, not deciding on a technique for analysis.
Likelihood to Recommend
Many organizations have seen their analytical capabilities, and the results from them, plateau. Of these, we've observed, that most of them didn't appreciate that they could do (even) better. These companies should definitely consider JMP. Any company that is research-based can benefit from accelerating their research, learning more in less time, effort and cost, with JMP's tools. Basically, any organization that is hungry enough for improvement to seek out better ways is suitable for JMP. Those who are happy with their current performance are not likely to consider the changes, though they were not major impediments by our clients, required.
WL
Wayne Levin
President in Engineering at Predictum Inc. (Information Technology and Services, 11-50 employees)
Vetted Review
19 years of experience