LEAN SIX SIGMA TRAINING
Build DMAIC capability with GenAI,independent JMP validation and a consistentmethod for quality and productivity improvement.
AI LEAN SIX SIGMA
Complete DMAIC Practitioner Training
Green Belt training combining DMAIC with AI-assisted JMP analysis, root cause investigation, statistical validation, project documentation, and management-ready reporting.
Quality • Operations • Engineering • Improvement Professionals
Program Details →AI LEAN SIX SIGMA
Advanced Analysis & Improvement Leadership
Advanced training for professionals leading complex process, quality, operational, engineering, and cross-functional improvement projects using Lean Six Sigma, JMP, decision validation, and responsible AI governance.
Experienced Practitioners • Managers • Improvement Leaders
Black Belt Certification Pathway: Includes at least 95 student contact hours in the Lean Six Sigma Black Belt Body of Knowledge, plus AI-focused instruction, assessment, and project review.
Program Details →AI LEAN SIX SIGMA
Advanced Training for Green Belt Graduates
Build advanced analytical skills with GenAI, independent JMP validation and practical improvement cases. Day one covers JMP and the AI6Sigma workflow.
Prerequisite: completed AI6Sigma Green Belt or traditional Lean Six Sigma Green Belt training from another provider.
Program Details →HOW YOU WILL WORK MORE PRODUCTIVELY AFTER AI6SIGMA TRAINING
Students get an AI answer, run the appropriate analysis independently in JMP, compare the evidence and assumptions, correct errors, and explain the decision.
Use GenAI to support investigation, interpretation and communication.
Evaluate statistical claims with independent JMP analysis and engineering judgment. Agreement alone does not establish validity.
Reduce unsupported reasoning and analytical rework with a repeatable workplace practice.
Frame the problem, establish statistical evidence, evaluate AI-generated reasoning and explain the resulting improvement decision. Course depth follows the Green Belt or Black Belt syllabus.
Define the process problem, CTQs, scope, performance gap, and decision criteria using a structured problem-framing workflow.
Use Lean Six Sigma methods, calculations, JMP analysis, and practical case studies to establish what the data actually supports and develop defensible conclusions.
Accelerate investigation, analysis planning, interpretation, and reporting while validating critical conclusions against JMP statistical evidence.
Turn validated recurring analysis into controlled, repeatable JMP/JSL workflows that can remain inside the company's secured environment while professionals retain decision ownership.
ADVANCED JMP AUTOMATION
Advanced Engineering & Operations Workflow Automation
Uses a repeatable 11-step automation framework to build validated JMP/JSL automation engines for recurring analysis and process-improvement workflows.
Build validated AI-assisted analytical and process-improvement workflows using JMP and JSL for recurring engineering, manufacturing, quality, and operations work.
Engineering • Manufacturing • Quality • Operations • JMP-Enabled Teams
FROM CLASSROOM TO WORKPLACE
Use a condensed course PDF and a workplace application packet: a problem statement template, reusable prompts, a validation checklist, a decision summary template and course-specific practice materials.
Practice independently with course datasets and guidance. Additional project coaching and implementation support can be discussed separately.
Explore Product & Hardware Engineering; Quality, Manufacturing & Process Engineering; and Reliability Engineering problem-solving courses.
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Discuss your team’s capability gaps, delivery options and corporate pricing. Establish a consistent approach to AI-assisted analysis and decision review.
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