Design of Experiments training
Advanced Design of Experiments Topics
D107 - 1 day
This hands-on workshop for more experienced Design of Experiments practitioners will further develop the key DoE themes and will equip you to more confidently identify critical process, assay and analytical parameters and to deliver an effective robust risk-based design space and control strategy.
You’ll understand how to construct robust optimums for increased control, throughput, quality and production – and be able to construct a map of your process, assay, method or product. Discover how mixture designs and analyses can help you find the ideal mix of ingredients to meet your performance criteria. Learn about design regions, where constraints are imposed by working with proportions of ingredients.
Topics covered
- Refining your process, assay and analytical method understanding
- Augmenting previous designs
- Effective resource management
- Definitive Screening Designs
- Custom / Optimal designs and augmentation
- Response surface modelling, mapping and choices of optimization designs
- Parameter/process understanding, control and capability
- Handling formulation components as process factors
- Setting up and analysing simplex mixture designs
- Models and graphs to analyse and visualize your mixture data
Software Tools Used
We also use our own suite of interactive training tools in this workshop, including our simulation tool, ProSim.
Who should attend?
- Formulators, scientists or engineers who are keen to move on to a more advanced application of their DoE knowledge.
Recommended Preparation
Previous attendance on our 2-day Effective DoE Implementation course (or equivalent practical Design of Experiments experience).
Comments from previous Advanced Design of Experiments Topics delegates...
The trainer was great and I appreciated his skills explaining details and delivering the course. Thank you.
Very well explained, great to have a trainer who can answer questions from real data, with great knowledge of the subject.
I particularly enjoyed comparing the different designs on the same problem and looking at the efficiency to identify the critical factors.