In today’s increasingly competitive business environment, the most successful organizations are those that identify opportunities to trim costs and maximize efficiency. To do so, they’re looking at data. By combining predictive modeling techniques with a strategic approach to experimentation known as design of experiments (DOE), we can now do so much more to improve understanding, optimize parameters and make better predictions.
Doug Montgomery, PhD, of Arizona State University will share new perspectives of applying predictive modelling techniques to build more efficient multi-factor experiments. A long-time proponent of DOE approach, Montgomery will demonstrate how predictive modelling techniques can help identify relationships among experimental factors. Using this input, you can construct more strategic experiments to look at only those factors that warrant further testing and exploration.
You will learn how to:
- Get the most from large and messy data, even with missing information.
- Use modern screening approaches to find the right potential drivers of performance.
- Compare the potential drivers of performance to pick the best ones for further experimentation.
- Select the right types of experiments to optimize results at the lowest cost.