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One of the unique challenges of new products and processes is that there is often no historical data to serve as a guide. Research chemists must therefore supplement their subject matter knowledge with data analytics to drive faster, better-informed decisions from the existing data and ultimately solve problems correctly the first time around.
Missed milestones add unnecessary stress and may lead to late product launches, high product costs or low margins when on the market. But with its end-to-end workflow, JMP enables users to more actively manage project dependencies and timelines.
Chemists investigate manufacturing processes with an emphasis on understanding how the unit process works and determining whether the underlying chemistry behaves while managing trade-offs in quality, speed and cost.
Analytical chemists collect and assess data from wet chemistry methods, including highly sophisticated instruments that output large amounts of data. In fact, there are measurements all along the R&D and process development pipeline that can be used to determine product quality, yield and process capability. Extracting insight these insights from complex data lakes, however, can be a challenge.
The big advantage of JMP [with its custom scripting language applications] is that users don’t need any additional software to solve even the most complex problems. Learn More
Utilize mixture, classical or custom designs and analysis to learn better and faster, getting more information out of each experimental run.
Monitor the stability of your process and look for changes in variability. Determine how capable your process is at meeting spec limits.
Easily explore data with drag-and-drop visualizations and update graphs utilizing filters and a column switcher.
Look through historical data to better understand key drivers of your response using statistical techniques such as regression, partial least squares, principal components analysis and decision tree analysis.
Find the ideal process settings in a design to get ideal results.
Determine how robust your process is to variation by utilizing a statistical model with simulation and finding a region with minimal sensitivity to variation.
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