Lean Six Sigma Black Belt Training

Duration: Four – one week training sessions (each three weeks apart)

For: Those identified to be future leaders and statistical experts

Objectives:

• Understand the Fundamentals of a Lean Six  Sigma Quality System

• Understand principals and concepts of quality  management

• Understand and apply the Advanced Statistical Tools

• Lean Six Sigma, History of Quality, Strategy and Principles

• Roles & responsibilities

• DMAIC and DMADV, Lean Process Overview

• Available Lean Six Sigma Training

• Deployment Plan

• Policy Deployment

• Strategic Planning-Deploying Projects

• System of Indicators– Key Performance Indicators, Balanced Scorecard, Flag System of Indicators

• Define COPQ– Hidden Cost, Internal Failure Cost, Cost of Field Failures, Cost of Inspection, Cost of Prevention

• Introduction to Lean Six Sigma Tools- Value Stream Mapping , Types of Data, Data Collection, Measurement System Analysis, Process Capability, Descriptive Statistics, Hypothesis Testing, ANOVA, Design of Experiments, Lean Enterprise, SPC, FMEA, Weibull Analysis

• Voice of the Customer – Customer CTQ’s, Sources of VOC, Dimensions of Quality the Kano Model, VOC Process Planning

• Project Selection  

• Management Reviews – Definition and Purpose, How to Conduct a review and Guidelines for Management

• Define– Indicators, Problem Definition, Project Charter, Project Planning Worksheet

• Process Management – Definition of Core, Macro Micro/Enabling, Select Top Priority Process, Process Flowcharting – construct flowchart of identified process, selecting Process Indicators

• Measure – Purpose of Data, Types of Data

•  Basic Sampling - Simple Random Sampling, Systematic Sampling

•  Data Collection – Principles of Data Collection, Sources of Data, How to design a Checksheet
  

• Displaying Data – Histograms, Frequency Charts, Line Graphs, Run Charts, Bar Charts, Radar Charts
  

• Project Planning – Status, Action Planning  
  
  

• Analyze – Logic of Root Cause Analysis

• Basic Probability & Statistics – Descriptive vs. Inferential Statistics, Measures of Central Tendency, Measures of Dispersion, Basic Probability

• Stratification – Pareto Charts

• Cause/Effect Analysis – Potential Root Causes, Cause & Effect Diagram, Root Cause Verification

• Improve and Implement – Identifying Countermeasures, Cost/Benefit Analysis, Force Field Analysis, Develop Implementation Plan, Confirm Results

• DFSS / DMADV – Define-Measure-Analyze-Design – Validate

• Control – Control of Processes/Products –Standardize – Standard Operating Procedures, Eliminate Variation , Control Charts

• Introduction to Statistical Process Control – Understanding Variation, Types of Variation, Sources of Variation, Control Chart Definitions, Control Chart Components, How to use Control Charts, Interpreting Control Charts

• Variables Control Charts – X bar R Chart, X, mR Chart

• Attribute Control Charts – np Chart, p Chart, c Chart, u Chart, How to select the proper control chart

• Lean Enterprise – Principles for a Lean Enterprise

• Components of Lean – Operations Layout and Balancing, Design for Production, Mistake Proofing, Batch Size Reduction, Customer Demand Pull The Value Stream Map

• Waste Elimination

• 8 Sources of Waste

• Continuous One-Piece Workflow

• Demand Pull Processing

• Kan Ban

• 5 S’s  - Sort, Store, Shine, Standardize, Sustain

• Mistake Proofing  

• Failure Modes and Effects Analysis (FMEA) – Identify Failure Modes and Effects, Assess Seriousness, Identify Potential Causes, Estimate probability, Identify Detect ability, Measure Controls – Calculate Risk Prioritization Number

• Plan to Mitigate Top Priority Risks – Identify actions to attack risks – develop action plan, estimate post seriousness after mitigation, estimate Post-Probability of Occurrence – Calculate Post-Risk Prioritization Number.

• Advanced Probability and Statistics – Review of Descriptive Statistics

• Introduction to Minitab – Worksheet and Project File Structure, Session Window, Using Minitab Worksheets, Manipulating Data with Minitab

• Characteristics of the Normal Curve – Chebyshev’s Theorem, Calculating Percentiles.

• Standard Normal Transformation

• Probability Definitions- Permutations and Combinations, Laws of Probability

• Advanced Sampling –Sampling Rules of Thumb, Sampling Definitions, Sample Size Formulas, Sampling Error, Significance Level.

• Other Sampling Techniques-Two-Stage Sampling, Stratified Sampling, Cluster Sampling, Confidence Intervals.

• Measurement Systems Analysis - Measurement System Components, Sources of Observed Variation, How to Conduct MSA Studies, Attribute MSA, Variables MSA.

• Process Capability- Control Limits vs. Customer Specifications, Loss Function Concept, Process Capability Indices, Shifts and Drifts (1.5 Sigma Shift).

• Calculating Process Yield

• Comparing Stability and Capability

• Hypothesis Testing- Detecting Differences, The Null Hypothesis, The Alternate Hypothesis, Visualizing Risks, Hypothesis Testing Procedure, Hypothesis Testing Decision Tree, Test of Means-Variance Known, Test of Means- Variance Unknown, Test of Variances vs. Standard, Test of Two Population Variances, Test of Proportions vs. Standard, Test of Two Population Proportions

• Non Parametric Tests of Hypothesis -Test for Normality, Tests of Medians, Test of Two Population Medians, Test of Multiple Medians

• Correlation/Regression Analysis

• ANOVA – Test of Multiple Means, Analysis of Means

• Introduction to Design of Experiments – One factor at a time experiments, DOE Process, DOE Planning, DOE Applications

• DOE Activity Instructions

• Build DOE Model – Test and Modify DOE Model, DOE Catapult Competition

• Benchmarking -   Reasons for Benchmarking , Types of Benchmarking, Basic Benchmarking Methodology, Preparing for Benchmarking, Benchmarking Ethics

• Reliability – Basic Reliability Methods,  Reliability Management

• Fault Tree Analysis – Causes of Undesirable Events, Fault Tree Terms and Symbols, Fault Tree Diagram, Quantifying the Fault Tree

• Quantifying Reliability - Measures of Reliability, Data Components, Goals of Reliability Data Analysis, Censored Data, Life Data Models, Calculating Mean Time Between Failures

• Weibull Analysis – Calculating Failure Rate, The Bathtub Curve, Weibull Distribution, Failure Data Analysis

• Failure Modes and Effects Analysis (FMEA) – Identify Failure Modes and Effects, Assess Seriousness, Identify Potential Causes, Estimate probability, Identify Detect ability, Measure Controls – Calculate Risk Prioritization Number  

• Management and Planning Tools – Seven New Management & Planning Tools – Relationship Diagram, Affinity Diagram, Systematic Diagram, Matrix Diagram, Matrix Data Analysis, Arrow Diagram, Process Decision Program Chart