^{JMP for Pharmaceutical Sciences}

Statistical Discovery for Professors, Researchers and Students

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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Success story: De Montfort University Prepares Students For Quality Careers In Pharma Using JMP®

“One thing that makes our Quality by Design program unique is the open collaboration across industry, academia, consultants and software vendors. As a result, we can provide a hands-on, pragmatic introduction to Quality by Design, using a motivational mix of theory, case studies, simulations and practice.”

Martin Owen | Lecturer at De Montfort University

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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.
Interactive Data Visualization

Explore and graph data dynamically, developing visualizations that tell the story of your data. Move effortlessly (point & click) from discovery to sharing with the data visualization capabilities in JMP.
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.
Basic Data Analysis

Use histograms, regression, distribution fitting and other analysis tools to launch data exploration.