Successful companies in chemical, biotech, life-sciences process industries apply statistical methods to accelerate innovation and reduce production cost. Explore this series of industry led papers and events to learn how to discover insights in your data, reduce time to market, uncover and solve blind spots in processes.

Featured Resources


On-Demand Webinar

Symrise - Mastering complex processes with Design of Experiments

It’s important for new scents and flavors to be developed and tested quickly to ensure they reach the market swiftly. It’s equally important for experiments to be easily replicable, both in scale and in formulation. In this webinar, we share how Symrise uses Design of Experiments (DoE) and visualization in JMP to improve process understanding and to arrive at the right product formulation earlier.



CHEManager International Webinar

Accelerating Drug Commercialization 

Julia O'Neill shows how modern, data-driven experimentation can accelerate the development and supply chain for drugs and immunizations - to make sure that these products get to the patients who need them as quickly as possible. A webinar by JMP and CHEManager International



The Chemical Engineer Webinar Recording

Tata Steel: Enhanced product development through experimental design

In this webinar Tata Steel researcher Bernard Ennis explains how the use of design of experiments (DoE) has helped the company optimise the development cycle of new products.



White Paper

Optimizing Processes with Design of Experiments

This whitepaper shows how to collect and analyze new, relevant data simply and quickly using Design of Experiments (DOE) techniques.

 



White Paper

Big Data, Pharma 4.0 and Process Modeling

How can you prepare for industry 4.0? This paper outlines how to improve process knowledge, close the gaps for legacy products and set the foundation for big data use.



Customer Story

Statistical solutions for high-throughput science

Scientists at Fujifilm Diosynth Biotechnologies build process knowledge by optimizing experimentation at the development stage.

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