Data Analysis Software for Pharmaceutical Process Engineers and Scientists | JMP

Data Analysis Software

For Pharmaceutical Process Engineers and Scientists

Pharmaceutical engineers and scientists are depended upon to derive insights from data, solve complex problems and design better processes. JMP data analysis software from SAS enables you to explore your process and lab data so you can understand sources of process variation, learn more from root-cause investigations, and optimize process and experimental designs, all without having to learn complicated coding.   

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Johnson Matthey Biologics

“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

JMP Capabilities for Pharmaceutical Engineers and Scientists


  • Design of Experiments

    Actively manipulate factors according to a pre-specified design to quickly and easily gain useful, new understanding.

  • Statistical Process Control

    Separate common and special causes to assist your process analysis efforts, including problem investigation, out-of-control conditions and ongoing monitoring for stability.

  • Stability and Shelf Life Analysis

    Assess poolability of batches, establish expiration dating, and easily calculate confidence limits and crossing times -- all in adherence to ICH guidelines.

  • Quality by Design (QbD)

    Identify and evaluate all sources of variability with respect to quality parameters of the finished product. 

  • Dose Response

    Analyze precision, accuracy, linearity, bias and reproducibility. Fit curves and compare models for a wide range of sigmoidal responses (4p, 5p). Efficiently assess parallelism for relative potency and apply streamlined methods for cut point determination.

  • Robust Process Optimization

    Find the sweet spot in the design where performance is minimally sensitive to variation for all critical quality attribute (CQA) goals in your process, following ICH Q11 guidelines.


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