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Data Analysis Software for Chemists and Chemical Engineers

How JMP® Empowers the Chemical Industry

JMP data analysis software empowers some of the world's largest and most innovative chemical companies to accelerate their development timeline, more predictably and at a lower cost – all without having to write a line of code.

For research and development

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.

With JMP, you can:
  • Define the most cost-effective new data collection method and analysis plan needed to get started.
  • Achieve better outcomes by finding robust solutions to problems faster.
  • Meet project milestones more predictably.
  • Improve efficiency across teams by quickly sharing your discoveries.

For process and product development

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. 

With JMP, you can:
  • Modify prototypes through various design stages, refining a final product definition that customers will want to buy.
  • Determine whether a new product has the desired "wow factor."
  • Achieve development milestones more predictably, thereby enabling your organization to increase R&D output and improve speed to market.
  • Increase the speed and quality of analysis by automating routine, manual tasks.

For manufacturing

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.

With JMP, you can:
  • More effectively monitor processes to ensure consistent quality and identify and troubleshoot possible batch failures faster.
  • Continually improve processes and products when in production to increase quality and reduce waste.
  • Develop effective analysis plans to drive out high-failure or high-cost aspects of a process.
  • Save time by easily accessing and cleansing production data in a single self-service platform.

For analytical chemists

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.

With JMP, you can:
  • Quickly collect and aggregate data from multiple sources.
  • Assess data quality to determine whether any curation is needed before further analysis takes place.
  • Save time by consolidating the complete analytic workflow into one self-service platform.
  • Effectively communicate across functions when the data indicates an issue with a product or process.

Results achieved by JMP customers


reduction in design time


“Twice the result in half the time”



reduction in contaminant occurrence


JMP Capabilities for the Chemical Industry

  • Design of Experiments

    Utilize mixture, classical or custom designs and analysis to learn better and faster, getting more information out of each experimental run.

  • Statistical Process Control and Process Capability

    Monitor the stability of your process and look for changes in variability. Determine how capable your process is at meeting spec limits. 

  • Data Visualization and Cleaning

    Easily explore data with drag-and-drop visualizations and update graphs utilizing filters and a column switcher.

  • Data Mining and Predictive Modeling 

    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.

  • Process Optimization

    Find the ideal process settings in a design to get ideal results.

  • Monte Carlo Simulation

    Determine how robust your process is to variation by utilizing a statistical model with simulation and finding a region with minimal sensitivity to variation.