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Machine Learning to Improve Lean Six Sigma Training Efficacy

Xander Hathaway, MoreSteam
Nick LaRosa, MoreSteam

If we intend to teach continuous improvement, then it follows that we need to continually improve the way we teach. We have to think creatively about new approaches to training, driven by data on our current performance. Drawing inspiration from machine learning, we applied various algorithms to tease out which inputs ("features") of training best predict learner success. Join us as we explore our findings and discuss the levers you can pull to increase training effectiveness.

This session focuses on two main takeaways. The first is to learn about machine learning and to see how it can apply to a project roadmap similar to the Lean Six Sigma DMAIC roadmap. The second is to gain insight into improving Lean Six Sigma training and the management of Lean Six Sigma deployments.

In this session, the following key points will be covered:

Xander Hathaway, MoreSteam

Xander Hathaway is an experienced lead software engineer with a demonstrated history of working in the information technology and services industry. He is skilled in modern web frameworks, back-end services, machine learning, and cloud architecture. Xander has a B.S. in Computer Science from the University of Notre Dame and a Master of Information and Data Science from the University of California, Berkeley.

Nick LaRosa, MoreSteam

Nick LaRosa began working for MoreSteam in 2014, lending his talents to both the eLearning and EngineRoom development teams. He currently focuses his efforts on the back-end development of EngineRoom. In addition to his role with MoreSteam, Nick also serves in the United States Marine Corps Reserve as a 2nd Lieutenant with the 14th Marine Regiment. Nick earned his B.S. in Computer Science from the University of Notre Dame.