Track: Lean Six Sigma Competition

Abstract

Traditional Six Sigma curriculums including DMAIC, DFSS, DFR, Lean are not developed specifically and effectively for today’s AI Data Science fields. This project will demonstrate an innovative Six Sigma training curriculum for Data Scientists. There are several objectives in this modern Six Sigma training curriculum: (1) adopt modern Text Mining and Data Mining techniques on Root Cause and Problem Solving Analyses such as DFMECA, C&E, QFD, SIPOC, VSM, (2) integrate various JMP statistical platforms holistically to analyze pattern recognition and discover the insights, (3) enhance predictive modeling capability through Neural, Partition, Principal Component algorithms, (4) utilize modern Quality and Process platforms such as Goal Plot, Model-Driven Multivariate SPC, Process History Process and Screening to enhance production quality control, (5) map these modern JMP Platforms into the Six Sigma DMAIC/DFSS/Lean framework for facilitating the Six Sigma Project execution, and (6) integrate database through Query Builder to monitor the production status in real-time. This curriculum is not just designed for a Data Scientist but is also powerful for those working in process, quality, reliability, supply chin, business, statistics, and marketing fields who want to become a reliable decision maker and true project leader. This Six Sigma DMAIC for Data Scientist Approach can be commonly applied to other Quality Engineering, Project Management and Business Excellence methodology such as DFSS, DFR, Lean, 8D, PMP.