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Lean Six Sigma Black Belt Certification Training

Course Description

Lean Six Sigma Black Belt Certification Training

The Lean Six Sigma Black Belt certification training is the elite certification for Lean Six Sigma Quality Management professionals. This Lean Six Sigma Black Belt training validates your professional skill in handling complex projects and expertise in implementing Six Sigma methodologies.

Six Sigma Black Belt Course Overview

This Lean Six Sigma Black Belt training and certification course is designed to help you master the combined concepts of Lean and Six Sigma. This course will help you develop an in-depth understanding of the Six Sigma phases Define, Measure, Analyze, Improve and Control (DMAIC) and how to maximize customer value while minimizing waste.


This Black Belt Six Sigma certification course is suitable for people who are senior managers, team leaders, software professionals, project managers, quality assurance engineers, and management professionals.


There are no prerequisites required in order to sit for the IASSC Certified Lean Six Sigma Black Belt Exam. To be successful during the exam, formal Lean Six Sigma training is recommended from a verified Lean Six Sigma trainer or corporate program. It is also recommended that those appearing the exam have some degree of real-world Lean Six Sigma work experience.


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Course Syllabus

Section 00 – Six Sigma Black Belt

0.1 Welcome
0.2 Six Sigma Black Belt
0.3 Agenda
0.4 What is SSBB
0.5 Accreditation Institute
0.6 Target Audience
0.7 SSBB Exam Format
0.8 Simplilearn LSSBB Course Offer
0.9 Thank You

Section 01 – Overview

1.1 Welcome
1.2 Overview
1.3 Agenda
1.4 Lesson 1 About LSSBB
1.5 Agenda
1.6 What is Six Sigma
1.7 Six Sigma Roles and Responsibilities
1.8 About SSBB
1.9 LSSBB Roles and Responsibilities
1.10 Summary
1.11 Lesson 2 Organizational Roadblocks
1.12 Agenda
1.13 Traditional Organization versus Customer Driven Organization
1.14 Types of Organizational Roadblocks
1.15 Types of Organizational Roadblocks(Contd.)
1.16 Types of Organizational Roadblocks(Contd.)
1.17 Types of Organizational Roadblocks(Contd.)
1.18 Types of Organizational Roadblocks(Contd.)
1.19 Types of Organizational Roadblocks(Contd.)
1.20 Types of Organizational Roadblocks(Contd.)
1.21 Change Resistance Curve
1.22 Change Resistance Curve(Contd.)
1.23 Change Resistance Curve(Contd.)
1.24 Common Resistance Points
1.25 Common Resistance Points(Contd.)
1.26 Overcoming Resistance Points
1.27 Force Field Analysis
1.28 Force Field Analysis(Contd.)
1.29 Force Field Analysis(Contd.)
1.30 Force Field Analysis(Contd.)
1.31 Summary
1.32 Lesson 3 Role of Communication and Selection Criteria in Black Belt
1.33 Agenda
1.34 Black Belt Role Summary
1.35 Black Belt Communication Expectations
1.36 Black Belt Selection Criteria
1.37 Summary
1.38 Lesson 4 Overview of Continuous Improvement
1.39 Agenda
1.40 Continual Improvement Process
1.41 Continuous versus Continual Improvement
1.42 Kaizen Continual Improvement
1.43 Kaizen Continual Improvement(Contd.)
1.44 Summary
1.45 Lesson 5 Lean An Overview
1.46 Agenda
1.47 What is Lean
1.48 History of Lean
1.49 Principles of Lean
1.50 Key Benefits of Implementing Lean
1.51 Key Benefits of Implementing Lean(Contd.)
1.52 Why Lean before Six Sigma
1.53 Summary
1.54 Lesson 6 Lean Concepts Explained
1.55 Agenda
1.56 Warusa Kagen
1.57 Warusa Kagen(Contd.)
1.58 3Ms
1.59 8 Types of Waste (TIMWOODS)
1.60 Mottainai
1.61 Hoshin Kanri
1.62 Hoshin Kanri(Contd.)
1.63 Hoshin Kanri(Contd.)
1.64 Takt Time
1.65 Takt Time(Contd.)
1.66 Cycle Time
1.67 Lead Time
1.68 Lead Time Assignment
1.69 Lead Time Assignment(Contd.)
1.70 Production Cycle Efficiency
1.71 Batch Size
1.72 Every Part Every Interval(EPEI)
1.73 EPEI(Contd.)
1.74 EPEI(Contd.)
1.75 EPEI Calculation Spreadsheet
1.76 Batch Size Assignment
1.77 Batch Size Assignment(Contd.)
1.78 Batch Size Assignment(Contd.)
1.79 Batch Size Assignment(Contd.)
1.80 Batch Size Assignment(Contd.)
1.81 Crew Size
1.82 Crew Size Assignment
1.83 Crew Size Assignment(Contd.)
1.84 Crew Size Assignment(Contd)
1.85 Standardized Working Progress(SWIP)
1.86 Summary
1.87 Lesson 7 Lean Tools Explained
1.88 Agenda
1.89 5S
1.90 5S(Contd.)
1.91 5S Audit Worksheet
1.92 SMED
1.93 SMED(Contd.)
1.94 Heijunka
1.95 Heijunka An Example
1.96 Genchi Genbutsu
1.97 Value Stream Mapping(VSM)
1.98 VSM Symbols
1.99 Value Stream Mapping(Contd.)
1.100 Value Stream Mapping(Contd.)
1.101 Summary
1.102 Quiz
1.103 Thank You

Section 02 – DFSS, Pre-define and Define (DMAIC)

2.1 Welcome
2.2 Agenda
2.3 DFSS Pre Define and Define(DMAIC)
2.4 DFSS Design for Six Sigma and DMAIC versus DFSS
2.5 Agenda
2.6 Agenda(Contd.)
2.7 Design for Six Sigma(DFSS)
2.8 DFSS Approach to Problem Solving
2.9 DMAIC Approach to Problem Solving
2.10 DMAIC versus DFSS
2.11 DFSS Tools
2.12 Toll Gate Review
2.13 Benchmarking
2.14 MSA
2.15 VOC
2.16 Needs Vs Requirements
2.17 KJ Diagram
2.18 Quality Function Deployment(QFD)
2.19 Kano Model
2.20 Kano Model(Contd.)
2.21 Kano Model(Contd.)
2.22 HOQ
2.23 HOQ(Contd.)
2.24 HOQ(Contd.)
2.25 AHP
2.26 AHP(Contd.)
2.27 Pugh Matrix for Concept Selection
2.28 Pugh Matrix for Concept Selection(Contd.)
2.29 Sample Pugh Matrix
2.30 Monte Carlo Simulation
2.31 Design for X
2.32 Summary
2.33 Lesson 2 Pre Define Activities
2.34 Agenda
2.35 Prerequisites of a Six Sigma Project
2.36 Qualifications of a Six Sigma Project
2.37 Cornerstones of a Six Sigma Project
2.38 Six Sigma Deployment Cycle Plan
2.39 10 Point Ongoing Project Evaluation
2.40 Project Prioritization Matrix
2.41 Project Prioritization Matrix(Contd.)
2.42 Enterprise Wide versus LOB View
2.43 Enterprise Wide Roles and Responsibilities
2.44 NPV(Net Present Value)
2.45 Internal Rate of Return(IRR)
2.46 NPV and IRR An Example
2.47 NPV and IRR An Example(Contd.)
2.48 Summary
2.49 Lesson 3 Define
2.50 Agenda
2.51 Define Key Objectives
2.52 Voice of Customer
2.53 Voice of Business
2.54 Voice of Process
2.55 VOC VOB and VOP
2.56 Kano Model
2.57 Assignment
2.58 Translation to Project Y
2.59 Quality Function Deployment
2.60 Quality Function Deployment(Contd.)
2.61 Quality Function Deployment An Example
2.62 Process Map
2.63 Y Baseline Performance
2.64 Y Baseline Performance(Contd.)
2.65 SIPOC
2.66 Project Charter
2.67 The Problem Statement and the Goal Statement
2.68 RACI Matrix
2.69 Business Metrics
2.70 Business Metrics(Contd.)
2.71 Project Deliverables
2.72 Project Scheduling
2.73 Team Selection
2.74 Define Roles and Responsibilities
2.75 Define Tools Summary
2.76 Lesson 3 Summary
2.77 Quiz
2.78 Thank You

Section 03 – Measure

3.1 Measure
3.2 Introduction to MEASURE
3.3 Agenda
3.4 Pre Measure Considerations and Tools
3.5 Agenda
3.6 Define Phase Toll Gate Review
3.8 DFMEA(Contd.)
3.9 DFMEA(Contd.)
3.10 DFMEA(Contd.)
3.11 DFMEA(Contd.)
3.12 Cause and Effect Matrix(CE Matrix)
3.13 Cause and Effect Matrix(Contd.)
3.14 Cause and Effect Matrix(CE Matrix)(Contd.)
3.15 Cause and Effect Matrix(CE Matrix)(Contd.)
3.16 Cause and Effect Matrix(CE Matrix)(Contd.)
3.17 Cause and Effect Matrix(Contd.)
3.18 Summary
3.19 Types of Data and Measurement Scales
3.20 Agenda
3.21 Objectives of Measure Phase
3.22 What is a Process
3.23 Flowcharts
3.24 SIPOC
3.25 SIPOC(Contd.)
3.26 SIPOC(Contd.)
3.27 Metrics
3.28 Measurement Scales
3.29 Measurement Scales(Contd.)
3.30 Measurement Scales(Contd.)
3.31 Types of Data
3.32 Types of Data(Contd.)
3.33 Summary
3.34 Central Tendency and Dispersion
3.35 Agenda
3.36 Central Tendency and Dispersion Introduction
3.37 Mean
3.38 Mean(Contd.)
3.39 Median
3.40 Mode
3.41 Range
3.42 Variance
3.43 Standard Deviation
3.44 Mean Deviation
3.45 Summary
3.46 Measurement System Analysis
3.47 Agenda
3.48 Purpose of Measurement System Analysis
3.49 Measurement System Errors
3.50 Measurement System Errors(Contd.)
3.51 Measurement System Errors(Contd.)
3.52 Properties of Good Measurement Systems
3.53 Measurement System Errors Illustrated
3.54 Measurement System Discrimination
3.55 Bias
3.56 Bias(Contd.)
3.57 Measurement System Analysis Process Flow
3.58 Part Variation
3.59 Measurement System Analysis Formulas
3.60 Measurement Systems Analysis Example
3.61 Measurement Systems Analysis Example(Contd.)
3.62 Measurement Systems Analysis Graphs
3.63 Measurement Systems Analysis Graphs(Contd.)
3.64 Measurement Systems Analysis Graphs(Contd.)
3.65 Assignment
3.66 Attribute RR
3.67 Attribute RR(Contd.)
3.68 Attribute RR(Contd.)
3.69 Attribute RR(Contd.)
3.70 Attribute RR(Contd.)
3.71 Attribute RR(Contd.)
3.72 Attribute RR(Contd.)
3.73 Attribute RR(Contd.)
3.74 Attribute RR(Contd.)
3.75 Attribute RR(Contd.)
3.76 When to Do Measurement System Analysis
3.77 Data Collection Plan
3.78 Data Collection Plan Template and Example
3.79 Summary
3.80 Stability Conditions
3.81 Agenda
3.82 Controlled Process and Variation
3.83 Special Causes of Variation
3.84 Common Causes of Variation
3.85 Common Causes of Variation(Contd.)
3.86 Stability Introduction and SPC
3.87 Stability Check with Minitab
3.88 Stability Check with Minitab(Contd.)
3.89 Stability Check with Minitab(Contd.)
3.90 Stability Check with Minitab(Contd.)
3.91 Stability Check using Run Charts
3.92 Stability Conditions
3.93 Central Limit Theorem
3.94 Summary
3.95 Capability Metrics
3.96 Agenda
3.97 Process Capability Pre Considerations
3.98 Process Capability Pre Considerations(Contd.)
3.99 Process Capability Pre Considerations(Contd.)
3.100 Process Capability Pre Considerations(Contd.)
3.101 Process Capability Pre Considerations(Contd.)
3.102 Process Capability Pre Considerations(Contd.)
3.103 Process Capability Pre Considerations(Contd.)
3.104 Process Capability Pre Considerations(Contd.)
3.105 Process Capability Pre Considerations(Contd.)
3.106 Process Capability Indices for Continuous Data
3.107 Process Capability Indices for Continuous Data(Contd.)
3.108 Process Capability Indices for Continuous Data(Contd.)
3.109 Process Capability Indices Interpretation
3.110 Process Capability for Discrete Data
3.111 Process Capability for Discrete Data(Contd.)
3.112 Process Capability for Discrete Data(Contd.)
3.113 Non Normal Capability Analysis
3.114 Non Normal Capability Analysis(Contd.)
3.115 Non Normal Capability Analysis(Contd.)
3.116 Non Normal Capability Analysis(Contd.)
3.117 Non Normal Capability Analysis(Contd.)
3.118 Non Normal Capability Analysis(Contd.)
3.119 Assignment
3.120 Summary
3.121 Variations Variability Capability and Process Conditions
3.122 Agenda
3.123 Variations and Variability
3.124 Variations and Variability(Contd.)
3.125 Variations and Variability(Contd.)
3.126 Capability and Process Conditions
3.127 Summary
3.128 Data Distributions
3.129 Agenda
3.130 Permutations and Combinations
3.131 Permutations and Combinations(Contd.)
3.132 Frequency and Cumulative Distributions
3.133 Binomial Distribution
3.134 Binomial Distribution(Contd.)
3.135 Binomial Distribution(Contd.)
3.136 Binomial Distribution(Contd.)
3.137 Poisson Distribution
3.138 Poisson Distribution(Contd.)
3.139 Poisson Distribution(Contd.)
3.140 Normal Distribution
3.141 Normal Distribution(Contd.)
3.142 Exponential Distribution
3.143 Exponential Distribution
3.144 Summary
3.145 Sigma Shift Mean Shift and Reducing Variations
3.146 Agenda
3.147 Sigma Shift
3.148 Mean Shift or Reducing Variations
3.149 Mean Shift or Reducing Variations(Contd.)
3.150 Baseline Data
3.151 Summary
3.152 Measure Phase Summary
3.153 Measure Activity Summary
3.154 Measure Tools Summary
3.155 Quiz
3.156 Thank You

Section 04 – Analyze

4.1 Welcome
4.2 Analyze
4.3 Agenda
4.4 Lesson 1 Pre Analyze Considerations
4.5 Agenda
4.6 Analyze Phase Introduction
4.7 Pre Analyze Considerations
4.8 Pre Analyze Considerations(Contd.)
4.9 Objectives of Analyze
4.10 Visually Displaying Data
4.11 Summary
4.12 Lesson 2 Value Stream Analysis
4.13 Agenda
4.14 Value Waste and NVA Activities
4.15 What is a Value Stream
4.16 Value Stream Example
4.17 Value Stream Analysis Muda
4.18 Value Stream Analysis Muda(Contd.)
4.19 Value Stream Map
4.20 Value Stream Map(Contd.)
4.21 Spaghetti Charts
4.22 Spaghetti Chart As Is
4.23 Spaghetti Chart Should Be
4.24 Spaghetti Charts(Contd.)
4.25 Summary
4.26 Lesson 3 Sources of Variation
4.27 Agenda
4.28 Sources of Variation
4.29 Sources of Variation(Contd.)
4.30 Cause and Effect Diagram
4.31 Cause and Effect Diagram(Contd.)
4.32 Cause and Effect Diagram(Contd.)
4.33 Cause and Effect Diagram(Contd.)
4.34 Affinity Diagram
4.35 Box Plot
4.36 Box Plot (Contd.)
4.37 Box Plot(Contd.)
4.38 Box Plot(Contd.)
4.39 Box Plot(Contd.)
4.40 Summary
4.41 Lesson 4 Regression
4.42 Agenda
4.43 Objectives of Regression Analysis
4.44 Concepts of Regression Analysis
4.45 Concepts of Regression Analysis(Contd.)
4.46 Simple Linear Regression
4.47 Simple Linear Regression(Contd.)
4.48 Simple Linear Regression(Contd.)
4.49 Simple Linear Regression(Contd.)
4.50 Simple Linear Regression(Contd.)
4.51 Simple Linear Regression(Contd.)
4.52 Multiple Linear Regression
4.53 Multiple Linear Regression(Contd.)
4.54 Multiple Linear Regression(Contd.)
4.55 Multiple Linear Regression(Contd.)
4.56 Multiple Linear Regression(Contd.)
4.57 Multiple Linear Regression(Contd.)
4.58 Multiple Linear Regression(Contd.)
4.59 Multiple Linear Regression(Contd.).mp4
4.60 Multiple Linear Regression(Contd.)
4.61 Best Subsets Regression and Stepwise Regression
4.62 Summary
4.63 Lesson 5 Confidence Intervals
4.64 Agenda
4.65 Concepts of Confidence Intervals and Confidence Intervals Testing
4.66 Concepts of Confidence Intervals and Confidence Intervals Testing(Contd.)
4.67 Concepts of Confidence Intervals and Confidence Intervals Testing(Contd.)
4.68 Concepts of Confidence Intervals and Confidence Intervals Testing(Contd.)
4.69 Confidence Intervals for Difference between Two Means
4.70 Confidence Intervals Working
4.71 Confidence Intervals Working(Contd.)
4.72 Confidence Intervals Impactors
4.73 Chi Square Confidence Intervals for Variances
4.74 Chi-Square Confidence Intervals for Variances(Contd.)
4.75 Z Confidence Intervals for Proportions
4.76 Chi Square and Probability
4.77 T Distribution Confidence Intervals
4.78 Summary
4.79 Lesson 5 Parametric Hypothesis Testing
4.80 Agenda
4.81 Agenda(Contd.)
4.82 Hypothesis Testing Objective
4.83 Hypothesis Testing Concepts
4.84 Null and Alternate Hypothesis
4.85 Type 1 Error
4.86 Type II Error
4.87 Significance Level (α)
4.88 Significance Level (α) (Contd.)
4.89 Type II Error (Contd.)
4.90 β and Power
4.91 P Value, and Acceptance and Rejection Conditions
4.92 Sample Size Determination for Tests
4.93 1 Sample z Test
4.94 1 Sample z Test(Contd.)
4.95 1 Sample z Test(Contd.)
4.96 2 Sample z test
4.97 f Test of Equality of Variances
4.98 1 Sample t Test
4.99 1 Sample t Test(Contd.)
4.100 2 Sample t Test
4.101 2 Sample t Test
4.102 2 Sample t Test
4.103 Paired t Test
4.104 Paired t Test(Contd.)
4.105 Paired t Test Interpretation
4.106 Paired t Test(Contd.)
4.107 Paired t Test(Contd.)
4.108 ANOVA
4.109 One Way ANOVA
4.110 Two Way ANOVA with Replication
4.111 Two Way ANOVA with Replication(Contd.)
4.112 Two Way ANOVA with Replication(Contd.)
4.113 Two Way ANOVA with Replication(Contd.)
4.114 Summary
4.115 Lesson 7 Nonparametric Hypothesis Testing
4.116 Agenda
4.117 Nonparametric Testing Conditions
4.118 Mann Whitney Test
4.119 Mann Whitney Test(Contd.)
4.120 1 Sample Sign
4.121 Wilcoxon Sign Rank Test
4.122 Kruskal Wallis
4.123 Mood’s Median
4.124 Friedman ANOVA
4.125 Friedman ANOVA(Contd.)
4.126 Summary
4.127 Lesson 8 Analyze Additionals Categorical Data and Current Reality Tree
4.128 Agenda
4.129 Categorical Data Analysis
4.130 Categorical Data Analysis(Contd.)
4.131 Categorical Data Analysis(Contd.)
4.132 Categorical Data Analysis(Contd.)
4.133 Current Reality Tree
4.134 Current Reality Tree(Contd.)
4.135 Summary
4.136 Activity Summary Analyze
4.137 Tools Summary Analyze
4.138 Quiz
4.139 Thank You

Section 05 – Improve

5.1 Welcome
5.2 Section V Improve
5.3 Agenda
5.4 Section V Lesson 1
5.5 Agenda
5.6 Pre Improve Considerations
5.7 Model Adequacy Checking
5.8 Model Adequacy Checking(Contd.)
5.9 Model Adequacy Checking(Contd.)
5.10 Multi Vari Charts
5.11 7M Tools
5.12 Activity Network Diagram
5.13 Point and Interval Estimation
5.14 Porter s Five Forces
5.15 Porter s Five Forces (Contd.)
5.16 Pugh Analysis
5.17 Lean 5S
5.18 Summary
5.19 Section V Lesson 2 Design of Experiments Theory
5.20 Agenda
5.21 Introduction to DOE
5.22 Introduction to DOE(Contd.)
5.23 Introduction to DOE(Contd.)
5.24 Introduction to DOE(Contd.)
5.25 Introduction to DOE(Contd.)
5.26 Introduction to DOE(Contd.)
5.27 Types of Designed Experiments
5.28 Main and Interaction Effects
5.29 Main and Interaction Effects(Contd.)
5.30 Main and Interaction Effects(Contd.)
5.31 Main and Interaction Effects(Contd.)
5.32 Replication
5.33 Randomization
5.34 Blocking
5.35 Confounding
5.36 Coding and other DOE Terms
5.37 Sum of Squares Analysis
5.38 Sum of Squares Analysis(Contd.)
5.39 Sum of Squares Analysis(Contd.)
5.40 Sum of Squares Analysis(Contd.)
5.41 Sum of Squares Analysis(Contd.)
5.42 Sum of Squares Analysis(Contd.)
5.43 Sum of Squares Analysis(Contd.)
5.44 Summary
5.45 Section V Lesson 3 Design of Experiments Practice
5.46 Agenda
5.47 Introduction to 2 Factor Factorial Design
5.48 Introduction to 2 Factor Factorial Design(Contd.)
5.49 Introduction to 2 Factor Factorial Design(Contd.)
5.50 2&sup2 Design
5.51 2&sup2 Design(Contd.)
5.52 2&sup2 Design(Contd.)
5.53 2&sup2 Design(Contd.)
5.54 2&sup2 Design(Contd.)
5.55 2&sup2 Design(Contd.)
5.56 2&sup2 Design(Contd.)
5.57 2&sup2 Design(Contd.)
5.58 2&sup2 Design(Contd.)
5.59 2&sup2 Design(Contd.)
5.60 2&sup2 Design(Contd.)
5.61 2&sup2 Design(Contd.)
5.62 2&sup2 Design(Contd.)
5.63 2&sup2 Design(Contd.)
5.64 2&sup2 Design(Contd.)
5.65 2&sup2 Design(Contd.)
5.66 2&sup2 Design Summary
5.67 General 2k Design
5.68 General 2k Design(Contd.)
5.69 General 2k Design(Contd.)
5.70 General 2k Design(Contd.)
5.71 General 2k Design(Contd.)
5.72 General 2k Design(Contd.)
5.73 General 2k Design(Contd.)
5.74 General 2k Design(Contd.)
5.75 General 2k Design(Contd.)
5.76 General 2k Design(Contd.)
5.77 Single Replicate of 2k Design
5.78 Half Fractional 2k-1 Design
5.79 Half Fractional 2k-1 Design(Contd.)
5.80 Half Fractional 2k-1 Design(Contd.)
5.81 Half Fractional 2k-1 Design(Contd.)
5.82 Half Fractional 2k-1 Design(Contd.)
5.83 Half Fractional 2k-1 Design(Contd.)
5.84 Half Fractional 2k-1 Design(Contd.)
5.85 Quarter Fractional 2k-2 Design
5.86 Quarter Fractional 2k-2 Design(Contd.)
5.87 Quarter Fractional 2k-2 Design(Contd.)
5.88 Quarter Fractional 2k-2 Design(Contd.)
5.89 3k Factorial Design
5.90 3k Factorial Design (Contd.)
5.91 Response Surface Designs
5.92 Response Surface Designs(Contd.)
5.93 Response Surface Designs(Contd.)
5.94 Response Surface Designs(Contd.)
5.95 Response Surface Designs(Contd.)
5.96 Response Surface Designs(Contd.)
5.97 Response Surface Designs(Contd.)
5.98 Response Surface Designs(Contd.)
5.99 Response Surface Designs(Contd.)
5.100 Nested Designs
5.101 Split Plot Designs Introduction
5.102 Taguchi’s Designs
5.103 Taguchi’s Designs (Contd.)
5.104 Taguchi’s L4 Design
5.105 Taguchi’s L4 Design Graphs
5.106 Taguchi’s L8 Design
5.107 Taguchi’s L8 Design(Contd.)
5.108 Taguchi’s L8 Design(Contd.)
5.109 Taguchi’s L8 Design(Contd.)
5.110 Plackett Burman’s Design
5.111 Plackett Burman’s Designs(Contd.)
5.112 Quality Function Deployment(House of Quality)
5.113 Summary
5.114 Section V Lesson 4 Brainstorming Solutions Prioritization and Cost Benefit Analysis
5.115 Agenda
5.116 Brainstorming
5.117 Multi Voting
5.118 Brainstorming Prioritization and Cost Benefit Analysis
5.119 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.120 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.121 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.122 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.123 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.124 Brainstorming Prioritization and Cost Benefit Analysis(Contd.)
5.125 Poka Yoke
5.126 Summary
5.127 Section V Lesson 5 Piloting Validating and FMEA
5.128 Agenda
5.129 Pilot Solutions
5.130 Piloting Tools
5.131 Piloting Tools (Contd.)
5.132 Paired t Test
5.133 Paired t Test(Contd.)
5.134 Paired t Test Interpretations
5.135 Paired t Test(Contd.)
5.136 Paired t Test(Contd.)
5.137 Improve Next Steps
5.138 Failure Mode Effects Analysis
5.139 Failure Mode Effects Analysis(Contd.)
5.140 Failure Mode Effects Analysis(Contd.)
5.141 Failure Mode Effects Analysis(Contd.)
5.142 Failure Mode Effects Analysis(Contd.)
5.143 Summary
5.144 Improve Activity Summary
5.145 Quiz
5.146 Thank You

Section 06 – Control

6.1 Welcome
6.2 Section VI Control
6.3 Agenda
6.4 SectionVI Lesson 1 Pre Control Considerations
6.5 Agenda
6.6 Pre Control Considerations
6.7 Assessing the Results of Process Improvement
6.8 Rational Subgrouping
6.9 Summary
6.10 Section VI Lesson 2 Variables and Attributes Control Charts
6.11 Agenda
6.12 Concepts of Variables Control Charts
6.13 Concepts of Variables Control Charts(Contd.)
6.14 Concepts of Variables Control Charts (Contd.)
6.15 Concepts of Variables Control Charts (Contd.)
6.16 Concepts of Variables Control Charts (Contd.)
6.17 Variables Control Charts
6.18 Variables Control Charts(Contd.)
6.19 Variables Control Charts(Contd.)
6.20 Variables Control Charts (Contd.)
6.21 Variables Control Charts (Contd.)
6.22 Variables Control Charts(Contd.)
6.23 Variables Control Charts(Contd.)
6.24 Variables Control Charts(Contd.)
6.25 Variables Control Charts(Contd.)
6.26 Variables Control Charts(Contd.)
6.27 Variables Control Charts(Contd.)
6.28 Variables Control Charts(Contd.)
6.29 Variables Control Charts(Contd.)
6.30 Variables Control Charts(Contd.)
6.31 Variables Control Charts (Contd.)
6.32 EWMA Charts
6.33 EWMA Charts(Contd.)
6.34 Cusum Charts
6.35 Attribute Control Charts
6.36 Attribute Control Charts(Contd.)
6.37 Attribute Control Charts(Contd.)
6.38 Attribute Control Charts (Contd.)
6.39 Attribute Control Charts(Contd.)
6.40 Attribute Control Charts(Contd.)
6.41 Attribute Control Charts(Contd.)
6.42 Summary
6.43 VI Lesson 3 Measurement System Analysis Control Plan and Project Closure
6.44 Agenda
6.45 Measurement System Analysis
6.46 Control Plan
6.47 Control Plan(Contd.)
6.48 Control Plan(Contd.)
6.49 Control Plan(Contd.)
6.50 Project Closure
6.51 Summary
6.52 Section VI Lesson 4 Introduction to Total Productive Maintenance
6.53 Agenda
6.54 Total Productive Maintenance(TPM)
6.55 Total Productive Maintenance(TPM)(Contd.)
6.56 Total Productive Maintenance(TPM)(Contd.)
6.57 Total Productive Maintenance(TPM)(Contd.)
6.58 Total Productive Maintenance(TPM)(Contd.)
6.59 Total Productive Maintenance(TPM)(Contd.)
6.60 Total Productive Maintenance(TPM)(Contd.)
6.61 Summary
6.62 Tools to Refer
6.63 Quiz
6.64 Thank You

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Instructor Led Lectures
All IT Tutor Pro Formerly It Nuggets Courses replicate a live class experience with an instructor on screen delivering the course’s theories and concepts.These lectures are pre-recorded and available to the user 24/7. They can be repeated, rewound, fast forwarded.
Visual Demonstrations, Educational Games & Flashcards
IT Tutor Pro Formerly It Nuggets recognizes that all students do not learn alike and different delivery mediums are needed in order to achieve success for a large student base. With that in mind, we delivery our content in a variety of different ways to ensure that students stay engaged and productive throughout their courses.
Mobile Optimization & Progress Tracking
Our courses are optimized for all mobile devices allowing students to learn on the go whenever they have free time. Students can access their courses from anywhere and their progress is completely tracked and recorded.
Practice Quizzes And Exams
IT Tutor Pro Formerly It Nuggets Online’s custom practice exams prepare you for your exams differently and more effectively than the traditional exam preps on the market. Students will have practice quizzes after each module to ensure you are confident on the topic you are learning.
World Class Learning Management System
IT Tutor Pro Formerly It Nuggets provides the next generation learning management system (LMS). An experience that combines the feature set of traditional Learning Management Systems with advanced functionality designed to make learning management easy and online learning engaging from the user’s perspective.

Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.