Design of Experiments is a powerful tool that enables users to optimize and improve processes, products, or systems by identifying the key factors that influence the outcome and determining the optimal settings for those factors. This leads to greater efficiencies, improved end-products, and drastically lower costs.
This 6-part web series was designed to help you unlock the power of Design of Experiments (DOE).
For those new to DOE, the series will help you understand the benefits of using DOE over traditional methods, as well as how to get started. For those already running DOE, the series will help you extract even more information from the designs you are running.
Session 1: What is Design of Experiments - Click Here to Watch
Join us to learn about the fundamentals of statistical design of experiments (DOE). This session will show how this experimental method differs from traditional methods and how it enables scientists and engineers to maximize the return on investment in their experiment.
Session 2: Getting Started with Easy DOE - Click Here to Watch
New to DOE? No problem! Easy DOE is a guided platform to get you started with DOE. Learn how to design and analyze an experiment all in a single guided process. Once you are comfortable with the basics of DOE, learn how to seamlessly transition to standard DOE approaches.
Session 3: Designing Optimal Experiments with Modern DOE Methods - Click Here to Watch
Modern DOE methods allow you to create an experiment that fits your system. Learn how to overcome the limitations of traditional DOE by using the Custom Design platform. After creating the Design, use different tools to explore and compare different designs to make sure you are creating the best design to support your goals.
Session 4: Formulation Optimization with Mixture DOE - Click Here to Watch
Paint, salad dressing, soap. What do they have in common? They are all mixtures. In order to optimize a mixture, you must change the proportions of the ingredients or components while keeping the total mass or volume the same. If this sounds like a system you are working with, join us to learn how to create and analyze Mixture DOEs.
Session 5: Doing More with Less - Click Here to Watch
Are you already using a DOE approach in your experiments and are looking to continue extracting more information with fewer experiments? Join us to learn about advanced screening designs and how to Augment DOEs to build upon prior experiments. The Definitive Screening Design (DSD) is an efficient screening design that provides a platform for reducing the overall number of experiments AND gives additional information about the system. Augmenting a DOE is a methodology that helps you build upon prior experiments. See how you can start with a simple screening design and then build upon it to learn more without starting a new design.
Session 6: Advanced DOE Topics - Click Here to Watch
JMP continues to push the boundaries of what is possible with DOE in new capabilities and platforms. This session will focus on two of those capabilities: Functional DOE and self-validating ensemble models (SVEM). Functional data is two dimensional data like a kinetics curve, heat evolution over time, particle size distribution, or a chemical spectrum. A functional DOE allows you to optimize that entire response curve (instead of a single parameter). Self-validating ensemble models (SVEM) allow you to run even fewer experiments in your design through an advanced modeling algorithms.
