Six Sigma Black Belt

Course Summary

This course is designed for process, product, and quality engineers who will lead project teams to achieve quality, yield, and productivity improvements.

Event Details

160 hours
Instructor-led class training, with opportunities to practice learned skills using prepared data, live demonstrations, and data collected real time in class
Minitab or JMP Statistical Software

Description

The training is organized around the five main phases of the Six Sigma Process Improvement Roadmap: Define, Measure, Analyze, Improve, and Control (DMAIC). Candidates participate in four training sessions, with at least three weeks in between, allowing them to apply the material learned to their project.

For project-based training, project reviews during each training session maintain project focus. In this case, it is the responsibility of the Champion to ensure that their Belt candidates arrive at class with a proper project charter.

Optional: In between training, QSG can provide coaching and guidance to ensure that the methodology and roadmap are used appropriately. This is a separate service provided apart from this 160-hour training program.

Who Should Attend

This course is designed for process, product, and quality engineers who will lead project teams to achieve quality, yield, and productivity improvements. The Black Belts will normally lead complex production process related problems which may transcend across several processes and/or departments within an organization.

Learning Objectives

Through training, participants will:

  • Know the process improvement roadmap DMAIC to systematically define, measure, analyze, improve, and finally control the process
  • Know how to identify projects and define the project charter
  • Achieve an in-depth understanding of the knowledge based and data based (statistical) methods and tools to characterize and baseline the process
  • Be able to perform Multi-Vari studies to determine the various sources of noise in the process
  • Be able to design experiments to screen, characterize, and optimize the process with respect to controllable process factors
  • Know about various control methods to ensure that the improvement achieved is sustained

Course Outline

Week/PhaseProject ObjectiveTopics
Week 1

(40 hrs)

Define/
Measure

  • Overview of Six Sigma
  • Document key information about project
  • Describing a process and its characteristics
  • Identify potential inputs (X’s) and outputs (Y’s)
  • Focusing and Prioritizing
  • Collecting data and ensuring accuracy
  • Understand process stability and capability of meeting customer’s requirements
Introduction to Six Sigma

DMAIC/DMADV Roadmaps

Project Scoping and Chartering

SIPOC/Process Mapping

Cause and Effect Matrix

Introduction to Minitab or JMP

Basic Statistics

Basic Quality Tools

Central Limit Theorem

Introduction to SPC

Variables & Attribute Measurement System Analysis (MSA)

Capability Study

Initial Control Plan

Project Review

 

Week/PhaseObjectivesTopics
Week 2

(40 hrs)

Analyze

  • Determine the effect of uncontrollable (noise) and controllable variables on the key output variables through passive data observations.
  • Obtain clues for the Improve phase.
  • Identify key process input variables to be used in the Improve phase.
  • Techniques to handle and analyze  multiple input variables
  • Perform Risk Analysis (FMEA) on key input variables
Review of Week 1

Introduction to Multi-Vari Study

Hypothesis Testing

Confidence Intervals

One and Two Sample Comparisons

  • t-tests
  • Assumptions underlying t-tests

Three or more Sample Comparison

  • One Way Analysis of Variance
  • Blocking
  • Residual Analysis

Simple Regression

Multiple Linear Regression

Chi Square Test

Non-Parametric Tests for Medians

Sample Size Determination

Failure Mode and Effects Analysis

Project Review

 

Week/PhaseObjectivesTopics
Week 3
(40 hrs)Analyze/
Improve
  • Continuation of Multi-Vari Study
  • Effectively perform variable screening for KPIV via experimentation
  • Effectively perform process characterization to understand system behavior including main effects and interactions.
Review of Week 2

Logistic Regression for Attribute Response

Model Building

  • Stepwise Regression
  • Subset Regression

Introduction to DOE and Attributes of a well-designed experiment

General Factorial Designs

  • Main Effects and Interaction Effects
  • Continuous and Categorical factors

2k Designs

  • Replicated Designs
  • Blocked Designs
  • Un-replicated Designs

Fractional Factorial Screening Designs

  • Resolution V, IV and III designs

Live Experimentation

Project Review

 

Week/PhaseObjectivesTopics
Week 4

(40 hrs)

Improve/
Control

  • Effectively perform process optimization with respect to controllable factors
  • How to plan DOE’s to handle a variety of industrial situations
  • How to build robust processes that are insensitive to uncontrollable noise variables.
  • More Effective Methods for SPC and Process Control to ensure process performance is maintained.
Review of Week 3

Designs with Center points

Introduction to Response Surface Methods

  • Central Composite Designs
  • Box Behnken Design

Multiple Response Optimization

  • Desirability Function

Sequential Experimentation

DOE Planning

Live Experimentation

Evolutionary Methods

Introduction to Taguchi Methods

Intermediate SPC

Total Control Methodology

Poka-Yoke (Mistake-Proofing)

Final Control Plan

Closing Projects

Project Reviews

Prerequisites

Six Sigma Green Belt

Instructors

Upcoming Events

Customer Reviews

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