Course Overview

Master Black Belts have a tough job.  First they have to work effectively with people, including senior management, project champions, project stakeholders, and teams.  They are expected to be experts in topics such as:  Six Sigma deployment strategy and project selection, cutting edge quality technology, and statistics so that they can help any team anytime with any problem.  And of course, they are expected to deliver breakthrough results.  Yet, the typical Master Black Belt is selected from Black Belts, who have received very little formal training in the topics they are expected to know. 

This course is designed to fill in the knowledge gaps, and provide Master Black Belts with the key knowledge they need to be more effective and efficient.

Highlights

• Discuss our collective experiences and share knowledge/insights collected over a thirty year period of time on special topics that are important to the practical success of Master Black Belts
• Review the past, present and future of quality/statistical methods in new product development and their application to Six Sigma and Design for Six Sigma (DFSS).   Understand why DFSS is so hard to implement, be exposed to some new ways of thinking, the power of peer reviews, a fractal approach used to both integrate DFSS tools and simplify DFSS concepts for easier implementation, and how to measure how well up-front engineering is being done.
• Leadership:
• Strategic development of Six Sigma and Design for Six Sigma
• Issues and answers in project selection
• Introduction to and use of the seven management and planning tools
• Mentoring effective project champions
• Working with teams and the five disciplines of a learning organization
• A review of cutting-edge topics and methods that make big differences in results.   These topics include:
• An introduction to and the roles of axiomatic design and TRIZ in a Six Sigma or a DFSS program
• Dr. Taguchi’s concepts of parameter and tolerance design
• Pugh concept selection
• Linkages between tools and effective mistake-proofing
• How to handle problem projects:
• Verification of assumptions in statistical analysis
• Dealing with non-normal data
• Issues with SPC and handling autocorrelation