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:

• Become familiar with the basic machine learning workflow
• Compare the similarities between the DMAIC roadmap and the machine learning workflow
• See how machine learning allows for the inclusion of non-linear and non-obvious data points to improve model quality
• Recognize opportunities to use machine learning to improve processes, including Lean and Six Sigma initiatives
• Levers that you can move to improve your training outcomes