Quality Tools for Process Improvement Methodologies

Mastery of the fundamentals of statistical design and analysis of experiments enables you to ensure effectiveness and efficiency in your empirical learning across a range of science and engineering situations – but you may soon have questions about how to solve more complex problems using Design of Experiments (DOE).

In this workshop, you will learn about designs that go beyond classical factorial and fractional factorial designs and explore how optimal designs can be tailored to the practical constraints of your process or system.

You will have the opportunity to discover unique methods for maximising your insight when time is a variable or when you have curve responses. You will also learn more about sequential approaches to experimentation, including newer approaches like Bayesian optimisation.


This three-part series is intended to expand existing knowledge of DOE concepts. Participants are encouraged to first learn the basics of DOE by registering for the on-demand workshop ‘Getting started with Design of Experiments’.


Key learning points:

  • You don’t need to force your problem into a standard design; optimal DOE gives you experiments that are bespoke to your requirements.
  • You can model curve or 'functional' responses including spectra and chromatograms.
  • Sequential experimentation ensures efficiency.


This three-part workshop has been designed for scientists and engineers who want to make their experimentation more efficient and effective. Follow along in your own time with JMP Statistical Discovery software and give your learning a valuable boost.


Presented by the Royal Society of Chemistry

Register now for this free webinar

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