Lean Six Sigma Black Belt

The original and most comprehensive Lean Six Sigma Training in the world

### Lean Six Sigma Black Belt Program Overview

Lean Six Sigma Black Belt

## The SSMI Lean Six Sigma Black Belt Training is one the most popular certification in the world.

As founders of Six Sigma, we have trained thousands of individuals and coached hundreds of companies by helping them reach breakthrough performance wether by reducing costs, improving cycle times, eliminating defects, variation or by increasing customer satisfaction.

This program Lean Six Sigma Black Belt is the culmination of more than 30 years of successful application of one the most successful improvement methodologies and there is no better assurance to learn it and get certified by the Institute that created it.

### Start your training with the founders of Six Sigma

#### International Certification

#### Self-Paced

Training

#### One-to-one

Coaching Sessions

#### Tools &

Resources

### Target Audience

## This program of study Lean Six Sigma Black Belt has been designed for individual contributors and managers seeking vertical mobility or pursuing horizontal opportunities within their respective fields of practice. The successful candidate enjoys working with data and solving problems, as well as working in a project-based, team-oriented environment. Basic arithmetic and computer skills are essential. In this context, a rudimentary understanding of Excel is highly recommended, but not essential. Furthermore, a most rudimentary understanding of algebra is a plus, but not required. Generally speaking, the successful completion of any undergraduate degree program will likely support the academic demands of this program.

### Program Goals

## Master the Six Sigma DMAIC methodology and the related set of analytical tools.

## Utilize the principles and practices of Lean Six Sigma to better frame and solve daily problems.

## Implement the DMAIC methodology and tools to accomplish Black Belt level projects.

## Improve business value for the customer and provider in a concurrent and synergistic way.

### Certification Path

### Lean Six Sigma Black Belt

#### Step 1

Online Training

#### Step 2

Online Exam

#### Step 3

Digital Project

#### International

Certification

### Program Modules

### Lean Six Sigma Black Belt

Breakthrough Vision provides the participant a general overview of the essential concepts that underpin the practice of Six Sigma. Through dynamic lectures, the participant will better understand the nature, purpose, and drivers of Six Sigma, as well as the core beliefs that form its foundation. The topics that constitute this module are:

- Content Overview
- Driving Need
- Customer Focus
- Core Beliefs
- Deterministic Reasoning
- Leverage Principle
- Tool Selection
- Performance Breakthrough

Business Principles provides the participant with a clear understanding of the term quality and how this idea interacts with the concept of value entitlement. Through dynamic lectures, the participant will gain a deep appreciation for the global use of performance metrics and how such measures of success can be effectively cascaded throughout an organization. The topics that constitute this module are:

- Quality Definition
- Value Proposition
- Metrics Reporting
- BOPI Goals
- Underpinning Economics
- Third Generation
- Success Factors

Process Management provides the participant with a clear understanding of what it takes to create and sustain world-class processes. Through dynamic lectures, the participant will learn how to effectively assess the performance of any type of process, the vital role of measurement, how to create a performance baseline and how to use such benchmarks to assess business performance. The topics that constitute this module are:

- Performance Yield
- Hidden Processes
- Measurement Power
- Establishing Baselines
- Performance Benchmarks
- Defect Opportunity
- Process Models
- Process Capability
- Design Complexity
- Product Reliability

The Installation Guidelines topic will provide the participant with the experience-based knowledge that is needed to better understand the nature and aims of each vital operational role that underpins a successful Six Sigma deployment. Of interest, the primary requirements and performance expectations of each key role are thoroughly discussed and illustrated. In addition, the participant will come to understand the crucial planning elements of a Six Sigma deployment and the prime milestones that must be met to ensure a successful roll-out. The topics that constitute this module are:

- Deployment Planning
- Deployment Timeline
- CXO Role
- Champion Role
- Black Belt Role
- Green Belt Role
- White Belt Role
- Application Projects
- DFSS Principles
- PFSS Principles
- MFSS Principles

The Application Projects topic will provide the participant with the experience-based knowledge and leadership insights necessary to reap the many benefits associated with a Six Sigma application project. Specifically, the participant will learn how to select, scope, define, plan, initiate, execute, validate, report, and close a results-oriented project that is designed to produce tangible financial benefits. Of interest, this topic will focus on the driving and restraining forces that govern the speed of execution and the resultant quality of a Black Belt, Green Belt, Yellow Belt, or White Belt application project. The topics that constitute this module are:

- Project Description
- Project Overview
- Project Guidelines
- Project Scope
- Project Leadership
- Project Teams
- Project Financials
- Project Management
- Project Payback
- Project Milestones
- Project Charters

The Value Focus topic will provide each participant with the application-based knowledge and experience-driven principles that are required to develop a meaningful understanding of the systematic way in which value is created, for any type or size of the enterprise. This powerful knowledge platform is framed by the time-tested Six Sigma DMAIC problem-solving strategy but focuses its energy on how to identify, extend and exploit opportunities to create value, to the benefit of the customer and provider concurrently. Furthermore, the participant will gain vital insights into how a value creation opportunity can be optimized and subsequently leveraged to produce quantum change in everything a business does or seeks to do, at any level of the enterprise. The topics that constitute this module are:

- Value Creation
- Recognize Needs
- Define Opportunities
- Measure Conditions
- Analyze Forces
- Improve Settings
- Control Variations
- Standardize Factors
- Integrate Lessons
- Application Example

Lean Practices provides the participant with the time-proven knowledge, methods and tools associated with the best-practices of a modern lean enterprise. Specifically, the participant will learn how to solve on-going operational problems and discover how to enhance or otherwise streamline daily operations. Of course, this goal is accomplished by the application of lean manufacturing principles such as mistake-proofing, pull systems and basic shop-floor improvement practices, just to mention a few. Of interest, the participant will learn how to apply such tools to a wide range of commercial as well as industrial applications. The topics that constitute this module are:

- Lean Thinking
- Constraint Theory
- Continuous Flow
- Pull Systems
- Visual Factory
- Kanban System
- PokaYoke System
- 6S System
- SMED System
- 7W Approach
- Kaizen
- Value Stream Mapping
- 6M Approach
- A3
- Overview of Flow
- Hiejunka
- TPM
- Jidoka
- Lean Wrap – Up

The Quality Tools topic will provide the participant with an array of classical quality improvement methods and diagnostic tools commonly associated with such initiatives as Six Sigma, Lean Sigma, TQM, 8D, and other fine process improvement programs. Specifically, the participant will learn how to establish basic cause-and-effect relationships, solve ongoing operational problems and discover how to enhance or otherwise improve daily operations. The topics that constitute this module are:

- Variable Classifications
- Measurement Scales
- Problem Definition
- Focused Brainstorming
- Process Mapping
- SIPOC Diagram
- Force – Field Analysis
- Matrix Analysis
- C&E Analysis
- Failure Mode Analysis
- Performance Sampling
- Check Sheets
- Analytical Charts
- Pareto Charts
- Run Charts
- Multi – Vari Charts
- Correlation Charts
- Frequency Tables
- Performance Histograms
- Basic Probability
- Pre – Control Charts
- Control Charts
- Score Cards
- Search Patterns
- Concept Integration
- Quality Simulation

The Basic Statistics topic will provide the participant with the knowledge and skills necessary to statistically characterize virtually any set or array of data. This topic represents the first step into the world of applied statistics, and therefore the underpinning principles and practices contained within are vital to realizing a higher level of analytical power. Students will organize a set of data for subsequent statistical analysis using descriptive statistics and match that data to a common distribution such as the normal curve. In addition, candidates will learn how to define the central value of that data distribution, characterize the inherent variability associated with that distribution and estimate the probability of any given value or point of interest. Participants will then be taught how to report the related statistics and descriptive findings in a simple and comprehensible manner. The topics that constitute this module are:

- Performance Variables
- Statistical Notation
- Performance Variation
- Normal Distribution
- Distribution Analysis
- Location Indices
- Dispersion Indices
- Quadratic Deviations
- Variation Coefficient
- Deviation Freedom
- Standard Transform
- Standard Z – Probability
- Central Limit
- Standard Error
- Student’s Distribution
- Standard T – Probability
- Statistics Simulation

The Continuous Capability topic will provide the participant with the knowledge and skills related to a wide array of continuous process capability metrics. Specifically, the participant will learn how to compute, interpret, interrelate and report the primary indices of capability that rely on continuous data, such as Cp, Cpk, Z.st, Z.lt, just to mention a few. Of interest, the participant will also learn how to create rational subgroups so as to constrain, or otherwise limit, the influence of nonrandom events during the course of sampling. This is an important element since such events might bias the short-term estimate of process capability, thereby providing a misleading indicator of capability. The topics that constitute this module are:

- Performance Specifications
- Rational Subgrouping
- Capability Study
- Instantaneous Capability
- Longitudinal Capability
- Cp Index
- Cpk Index
- Pp Index
- Ppk Index
- Process Shifting
- Process Qualification
- ConcaP Simulation

Discrete Capability will provide the participant with the knowledge and skills associated with a broad array of process capability metrics for discrete data. Specifically, the participant will learn how to compute, interpret, interrelate and report the primary indices of capability that rely on discrete data, such as Rolled Throughput Yield (Y.rt), Defects-Per-Million-Opportunities (DPMO), and Defects-Per-Unit (DPU). The topics that constitute this module are:

- Defect Metrics
- Defect Opportunities
- Binomial Distribution
- Poisson Distribution
- Throughput Yield
- Rolled Yield
- Metrics Conversion
- DiscaP Simulation

This Hypothesis Testing topic will provide the participant with the knowledge and skills necessary to translate practical problems into statistical questions that are suitable for analytical investigation. Naturally, the ability to formulate statistical questions is essential to the valid integration and investigation of data that results from random sampling. Whenever a representative sample is used to draw an inference about the corresponding population, certain statistical hypotheses must be established and tested. Of course, such analytical questions are referred to as statistical hypotheses. Hence, this topic represents the first step into the world of sampling statistics, also called inferential statistics. The topics that constitute this module are:

- Statistical Inferences
- Statistical Questions
- Statistical Problems
- Null Hypotheses
- Alternate Hypotheses
- Statistical Significance
- Alpha Risk
- Beta Risk
- Criterion Differences
- Decision Scenarios
- Sample Size

Confidence Intervals provide the participant with the knowledge and skills necessary to compute statistical confidence intervals for various measures of central tendency and variability (with known degrees of risk and confidence). Specifically, the participant will learn how to compute, interpret and report statistical confidence intervals for virtually any application involving the use of continuous or discrete data, such as the confidence intervals that would embody the true mean and variance of a continuous product performance characteristic or the confidence intervals around a given defect rate. The topics that constitute this module are:

- Mean Distribution
- Mean Interval
- Variance Distribution
- Variance Interval
- Proportion Distribution
- Proportion Interval
- Frequency Interval

This topic provides the participant with the knowledge and skills necessary to effectively deploy and benefit from the use of statistical process control charts, or SPC charts as they are commonly referred to. Specifically, the participant will learn how to identify, plan, construct, implement and interpret SPC charts for continuous and discrete performance characteristics. Naturally, such charts are related to industrial and commercial products, services, and transactions. The participant will learn the statistical logic that underpins SPC in terms of how a control chart works and how it can be employed to better center a process and limit the overall bandwidth of its operation (reduce spread). The topics that constitute this module are:

- Statistical Control
- Control Logic
- Control Limits
- Chart Selection
- Chart Interpretation
- Zone Testing
- Variables Chart
- Attribute Chart
- Individuals Chart
- IMR Chart
- Xbar Chart
- Range Chart
- Proportion Chart
- Defect Chart
- Other Charts
- Capability Studies
- Control Simulation

Parametric Methods will provide the participant with the knowledge and skills necessary to employ parametric tools and methods. Parametric methods represent a class of statistical tools that are mathematical procedures for testing statistical hypotheses, often related to the mean and variance. Of interest, this particular class of statistics assumes that the underlying distributions of the variables being assessed belong to a known family of probability distributions. An example is a statistical procedure called Analysis-of-Variance, or simply ANOVA. The use of this statistical method assumes that the underlying distributions are normally distributed and that the variances of the distributions being compared are similar. Parametric techniques are very powerful tools because they are quite robust to violations of the underlying assumptions. The topics that constitute this module are:

- Mean Differences
- Variance Differences
- Variation Total
- Variation Within
- Variation Between
- Variation Analysis
- One – Way Anova
- Two – Way Anova
- N – Way Anova
- ANOVA Graphs
- Linear Regression
- Multiple Regression
- Residual Analysis
- Parametric Simulation

The Chi-Square Methods topic will provide the participant with the knowledge and skills necessary to employ several key forms of the chi-square statistic. Of interest, the chi-square test is any statistical hypothesis test in which the test statistic has a chi-square distribution, given that the null hypothesis is true. This test statistic is often employed to determine the underlying distribution of a product or service performance variable or estimate the extent of association between one categorical variable and another. It is also a foundational statistic when conducting survey-based investigations and research, such as customer satisfaction analyses. The topics that constitute this module are:

- Statistical Definition
- Model Fitting
- Testing Independence
- Contingency Coefficients
- Yates Correction
- Testing Proportions

Survey Methods will provide the participant with the knowledge and skills necessary to effectively and efficiently design and deploy quantitative surveys, as well as how to analyze the resulting data, draw conclusions, and report findings. Specifically, statistical survey methods are used to collect numerical information about people’s opinions or otherwise assemble certain pieces of factual information (in quantitative form). But regardless of intent, all surveys involve administering questions to individuals. Examples of this are clearly seen in customer satisfaction surveys, employee morale studies, and so on. The topics that constitute this module are:

- Research Design
- Information Sources
- Questionnaire Construction
- Formulating Questions
- Question Quality
- Sampling Plans
- Data Analysis

The Nonparametric Methods topic will provide the participant with the knowledge and skills necessary to employ nonparametric tools and methods to analyze and report on non-normal data. Of interest, this branch of mathematical statistics is concerned with statistical models and tests that are not dependent upon the type or nature of the underlying distribution. Nonparametric models differ from parametric models in that the model is not specified before-the-fact, but is instead determined from data. Nonparametric models are also called distribution-free statistics. The topics that constitute this module are:

- Nonparametric Concepts
- Median Test
- Runs Test
- Other Tests

Experimental Methods provides the participant with the knowledge and skills necessary to effectively and efficiently design and execute statistically designed experiments. Experiment design is most often used to establish a rational set of testing conditions that, when executed, will provide the data necessary to analyze the primary effect of each independent variable, often including one or more variable interactions. The applications of designed experiments that are covered in this course range from learning to screen a large group of variables, so as to discover the vital few contributors, understand how to segregate sources of nonrandom error from random error, identify and analyze variable interactions, maximize the mean of a performance variable while concurrently reducing the variance and establish realistic performance specifications and apply tolerances for products and processes. Of course, these are just a few of the many applications in which statistically designed experiments can play a central role. The topics that constitute this module are:

- Design Principles
- Design Models
- Experimental Strategies
- Experimental Effects
- One – Factor Two Level
- One – Factor Multi Level
- Full Factorials
- Two – Factor Two Level
- Two – Factor Multi Level
- Three – Factor Two Level
- Planning Experiments
- Fractional Factorials
- Four – Factor Two Level
- Five – Factor Two Level
- Screening Designs
- Robust Designs
- Experiment Simulation

DFSS Methods will provide the participant with the knowledge and skills related to several of the key practices pertaining to the field of Design-For-Six-Sigma, or DFSS as it is often called. The participant will learn the basics of Quality Function Deployment (QFD) and the role this methodology plays in the design process or quality improvement initiative. Of interest, the participant will not only be exposed to the industrial applications of DFSS but the commercial applications as well. In addition, the participant will discover how to design and execute a hierarchical capability forecast, often referred to as capability flow-down and capability flow-up. As such, this methodology is founded upon the principles of forwarding and reverse error propagation. The topics that constitute this module are:

- QFD Method
- Capability Flow
- Capability Flow
- Tolerance Analysis
- Monte – Carlo Simulation

The Measurement Analysis topic will provide the participant with the knowledge and skills necessary to effectively and efficiently examine the capability and capacity of the measurement system. This field of study is often referred to as Measurement Systems Analysis, or MSA in short. The purpose of an MSA study is to establish the statistical potential of a measurement system, its short-term capability, and its long-term capability. In this sense, the aim of an MSA study is to define and improve the extent of statistical uncertainty inherent to a given measurement system and its related operators, equipment, tools, and procedures. The topics that constitute this module are:

- Measurement Uncertainty
- Measurement Components
- Measurement Studies

The Training Project topic is entirely focused on an extensive simulated case problem that fully emulates the application environment of a Six Sigma project. Through this topic, the participant is provided a grand opportunity to apply their process improvement knowledge and problem-solving skills in a hands-on fashion. Expressed differently, this course is based on a real-world problem-solving situation that can only be resolved through the progressive application of common process improvement methods and tools. Naturally, this type of hands-on training is intended to stretch each participant, regardless of their previous training. The topics that constitute this module are:

- Recognize Phase
- Risk Analysis
- Project Introduction
- Define Phase
- Measure Phase
- Analyze Phase
- Improve Phase
- Control Phase
- Survey Analysis