Lean Six Sigma Book of Knowledge Table of Contents

This lean Six Sigma book of knowledge table of contents summarizes the topics for a 1100+ page book, which follows and provides the details for executing the enhanced Integrated Enterprise Excellence (IEE) business management’s lean Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) roadmap improvement projects (that benefit the big picture).  The book title is Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard

 

lean six sigma book of knowledge table of contents

 

This lean Six Sigma book of knowledge is useful for both teaching lean Six Sigma classes and students’ before and after training reference. An instructor guide for teaching from this book is available.

The book is divided into parts for each of the Lean Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) phases. The first part of the book describes a system for project selection using an Enterprise DMAIC (E-DMAIC) roadmap approach, which is described in more details in IEE Volume II of this series. Part II initiates the step-by-step details of the Project DMAIC (P-DMAIC) roadmap.

Lean Six Sigma Book of Knowledge Table of Contents

 

Part I Integrated Enterprise Excellence (IEE) Management System and E-DMAIC

1. Background

  • Messages in Volume 2 and Part I of this volume
  • Messages in Part II – Part VI of this volume
  • Volume layout
  • The IEE System
  • Six Sigma and Lean Six Sigma
  • Traditional performance metrics can stimulate the wrong behavior
  • Characteristics of a good metric
  • Traditional scorecards, dashboards, and performance metrics reporting
  • Strategic planning
  • The balanced scorecard
  • Red-yellow-green scorecards
  • Example 1.1: Tabular red-yellow-green scorecard reporting alternative

2. Creating an Integrated Enterprise Excellence (IEE) System

  • Overview of IEE
  • IEE as a business strategy
  • Applying IEE

3. Enterprise Define-Measure-Analyze-Improve-Control (E-DMAIC)

  • E-DMAIC – Roadmap
  • E-DMAIC – Define and measure phase: Enterprise process value chain
  • E-DMAIC – Technical aspects of satellite-level and 30,000-foot-level charting
  • E-DMAIC – Example 3.1: Satellite-level metrics
  • E-DMAIC – Example 3.2: 30,000-foot-level metric with specifications
  • E-DMAIC – Analyze phase: Enterprise process goal setting
  • E-DMAIC – Analyze phase: Strategic analysis and development
  • E-DMAIC – Analyze phase: Theory of constraints (TOC)
  • E-DMAIC – Example 3.3 Theory of Constraints
  • E-DMAIC – Analyze phase: Lean tools and assessments
  • E-DMAIC – Analyze phase: Identification of project opportunities
  • E-DMAIC – Improve phase
  • E-DMAIC – Control phase
  • E-DMAIC – Summary

 

Part II Improvement Project Roadmap: Define Phase

4. P-DMAIC – Define Phase

  • P-DMAIC roadmap component
  • Process and metrics
  • Supplier-input-process-output-customer (SIPOC)
  • Project valuation, cost of poor quality, and cost of doing nothing differently
  • Define phase objective
  • Primary project metric
  • Problem statement
  • Secondary project metrics
  • Project charter
  • Applying IEE
  • Exercises

5. P-DMAIC – Team Effectiveness

  • P-DMAIC roadmap component
  • Orming model
  • Interaction styles
  • Making a successful team
  • Team member feedback
  • Reacting to common team problems
  • Applying IEE
  • Exercises

 

Part III Improvement Project Roadmap: Measure Phase

6. P-DMAIC – Measure Phase (Plan Project and Metrics): Voice of the customer and in-process Six Sigma Metrics

  • P-DMAIC roadmap component
  • Project customer definition and information sources
  • Example 6.1: Project customer identification
  • Project Voice of the Customer (VOC)
  • In-process metrics: Overview
  • In-process metrics: Defects per million opportunities (DPMO)
  • In process metrics: Rolled throughput yield (RTY)
  • In-process metrics: Applications
  • Exercises

7. P-DMAIC – Measure Phase (Plan project and metrics): Project Plan

  • P-DMAIC roadmap component
  • Project management
  • Project management: Planning
  • Project management: Measures
  • Example 7.1:CPM/Pert
  • Applying IEE
  • Exercises

8. Response Statistics, Graphical Representations, and Data Analysis

  • Continuous versus attribute response
  • Time-series plot
  • Example 8.1: Time-series plot of gross revenue
  • Example 8.2: Culture firefighting or fire prevention?
  • Measurement scales
  • Variability and process improvements
  • Sampling
  • Simple graphic presentations
  • Example 8.3: Histogram and dot plot
  • Sample statistical (mean, range, standard deviation, and median)
  • Descriptive statistics
  • Pareto charts
  • Example 8.4: Improving a process that has defects
  • Population distribution: Continuous response
  • Normal distribution
  • Example 8.5: Normal distribution
  • Probability plotting
  • Interpretation of probability plots
  • Example 8.6: PDF, CDF, and then a probability plot
  • Probability plotting censored data
  • Weibull and exponential distribution
  • Lognormal distribution
  • Example 8.7: Comparing distributions
  • Distribution application and approximations
  • Applying IEE
  • Exercises

9. Attribute Response Statistics

  • Attribute versus continuous data response
  • Visual inspections
  • Binomial distribution
  • Example 9.1: Binomial distribution – Number of combinations and rolls of die
  • Example 9.2: Binomial – probability of failure
  • Hypergeometric distribution
  • Poisson distribution
  • Example 9.3: Poisson distribution
  • Population distributions: Applications, approximations, and normalizing transformations
  • Applying IEE
  • Exercises

10. Traditional Control Charting and IEE Implementation

  • Monitoring processes
  • Statistical process control charts
  • Interpretation of control chart patterns
  • x-bar and R and x-bar and s charts: Mean and variability measurements
  • Example 10.1 x-bar and R chart
  • XmR and individuals control chart: Individual measurements
  • Example 10.2:XmR charts
  • p chart: Proportion nonconforming measurements
  • Example 10.3: P chart
  • np chart: number of nonconforming items
  • c chart: Number of nonconformities
  • u chart: Nonconformities per unit
  • Notes on the Shewhart control chart
  • Rational subgroup sampling and IEE
  • Applying IEE
  • Exercises

11. Traditional Process Capability and Process Performance Metrics

  • Process capability indices for continuous data
  • Process capability indices: Cp and Cpk
  • Process capability/performance indices: Pp and Ppk
  • Process capability/performance misunderstandings
  • Confusion: Short-term versus long-term variability
  • Calculating standard deviation
  • Example 11.1: Process capability/performance indices
  • Process capability/performance for attribute data
  • Exercises

12. P-DMAIC – Measure Phase (Baseline Project): IEE Process Predictability and Process capability/performance metric Assessment (Continuous Response)

  • P-DMAIC roadmap component
  • Satellite-level view of the organization
  • 30,000-foot-level, 20,000-foot-level, and 50-foot-level operational and project metrics
  • IEE application examples: Process predictability and process capability/performance metric
  • Traditional versus 30,000-foot-level control charts and process capability/performance metric assessments
  • Traditional control charting problems
  • Discussion of process control charting at the satellite-level and 30,000-foot-level
  • IEE process predictability and process capability/performance metric: Individual samples with specifications
  • Example 12.1: IEE process predictability and process capability/performance metric: Individual samples with specifications
  • IEE process predictability and process capability/performance metric: Multiple samples in subgroups where there are specification requirements
  • Example 12.2: IEE process predictability and process capability/performance metric
  • Multiple samples in subgroups where there are specification requirements
  • Example 12.3: IEE individuals control chart of subgroup means and standard deviation as an alternative to traditional x-bar and R chart
  • Example 12.4: The implication of subgrouping period selection on process stability statements
  • Describing a predictable process output when no specification exists
  • Example 12.5: Describing a predictable process’ output when no specification exists
  • Non-normal distribution prediction plot and process capability/performance metric reporting
  • Example 12.6: IEE process predictability and process capability/performance metric – non-normal distribution using Box-Cox transformation
  • Example 12.7: IEE process predictability and process capability/performance metric – non-normal distribution with zero and/or negative values
  • Non-predictability charts and seasonality
  • Value chain satellite-level and 30,000-foot-level example metrics
  • Example 12.8: Value chain metric computations – Satellite-level metric reporting
  • Example 12.9: Value chain metric computations – 30,000-foot-level metric with specifications
  • Example 12.10: Value chain metric computations – 30,000-foot-level continuous response metric with no specifications
  • IEE difference
  • Additional control charting and process capability alternatives
  • Applying IEE
  • Exercises

13. P-DMAIC – Measure Phase (Baseline Project): IEE Process Predictability and Process Capability/Performance metric assessment (Attribute response)

  • P-DMAIC roadmap component
  • IEE process predictability and process capability/performance metric: Attribute pass/fail output
  • Example 13.1: IEE process predictability and process capability/performance metric
  • Attribute pass/fail output
  • Example 13.2: IEE individuals control chart as an alternative to traditional P chart
  • IEE process predictability and process capability/performance metric: Infrequent failures
  • Example 13.3: IEE process predictability and process capability/performance metric – Infrequent failure output
  • Example 13.4: IEE process predictability and process capability/performance metric – Rare spills
  • Direction for improving an attribute response
  • Example13.5: Value chain metric computation – 30,000-foot-level attribute assessment with Pareto chart
  • Applying IEE
  • Exercises

14. P-DMAIC – Measure Phase (Lean Assessment)

  • P-DMAIC roadmap component
  • Waste identification and prevention
  • Principles of Lean
  • Example 14.1: Takt time
  • Little’s law
  • Example 14.2 Little’s Law
  • Identification of process improvement focus areas for projects
  • Lean assessment
  • Workflow analysis: Observation worksheet
  • Workflow analysis: Standardized work chart
  • Workflow analysis: Combination work table
  • Workflow analysis: Logic flow diagram
  • Workflow analysis: Spaghetti diagram or physical process flow
  • Why-why or 5 whys diagram
  • Time-value diagram
  • Example 14.3: Development of a bowling ball
  • Value stream mapping
  • Value stream considerations
  • Additional enterprise process lean tools, concepts and examples
  • Applying IEE
  • Exercises

15. P-DMAIC – Measure Phase: Measurement Systems Analysis

  • IEE project execution roadmap
  • Data integrity and background
  • IEE application examples: MSA
  • Initial MSA considerations
  • Simple MSA assessment
  • Variability sources in a 30,000-foot-level metric
  • Three uses of measurement
  • Terminology
  • Gage R&R considerations
  • Gage R&R relationships
  • Preparation for a measurement system study
  • Measurement systems improvement needs and possible improvement sources
  • Example 15.1: Gage R&R
  • Linearity
  • Example 15.2 Linearity
  • Attribute agreement analysis
  • Example 15.3: Attribute agreement analysis
  • Gage study of destructive testing
  • Example 15.4: Gage study of destructive testing
  • 5-step measurement improvement process
  • Uncertainty due to data rounding
  • Example 15.5: 5-step measurement improvement process
  • Applying IEE
  • Exercises

16. P-DMAIC – Measure Phase (Wisdom of the Organization)

  • P-DMAIC roadmap component
  • Flowcharting
  • Process modeling and simulation
  • Benchmarking
  • Brainstorming
  • Cause-and-effect diagram
  • Cause-and effect matrix and analytical hierarchy process (AHP)
  • Affinity diagram
  • Nominal group technique (NGT)
  • Force field analysis
  • FMEA
  • IEE application examples: FMEA
  • FMEA implementation
  • Development of a process FMEA
  • Process FMEA tabular entries
  • Generating a FMEA
  • Exercises

 

Part IV Improvement Project Roadmap: Analyze Phase

17. P-DMAIC – Analyze Phase: Data Collection Plan (DCP) and Experimentation Traps

  • P-DMAIC roadmap component
  • Solutions determination process
  • Data collection plan (DCP) needs, source and types
  • Data collection tools
  • Sampling error sources
  • Experimentation traps
  • Example 17.1: Experimentation trap – Measurement error and other sources of variability
  • Example 17.2: Experimentation trap – Lack of randomization
  • Example 17.3: Experimentation trap – Confounded effects
  • Example 17.4: Experimentation trap – Independently designing and conducting an experiment
  • Sampling considerations
  • Example 17.5: Continuous response data collection
  • Example 17.6: Attribute response data collection;
  • Exercises

18. P-DMAIC – Analyze Phase: Visualization of Data

  • P-DMAIC roadmap component
  • IEE application example: Visualization of data
  • Box plot
  • Example 18.1: Plots of injection-molding data – Box plot, marginal plot, main effects plot, and interaction plot
  • Multi-vari charts
  • Example 18.2: Multi-vari chart of injection-molding data
  • Applying IEE
  • Exercises

19. Confidence Intervals and Hypothesis Tests

  • Sampling distributions
  • Confidence interval statements
  • Central limit theorem
  • Hypothesis testing
  • Example 19.1: Hypothesis testing
  • Example 19.2 Probability plot hypothesis test
  • Choosing alpha
  • Nonparametric estimates: Runs test for randomization
  • Example 19.3: Nonparametric runs test for randomization
  • Applying IEE
  • Exercises

20. Inferences: Continuous Response

  • Summarizing sampled data
  • Sample size: Hypothesis test of a mean criterion for continuous data response
  • Example 20.1: Sample size determination for a mean criterion test
  • Confidence intervals on the mean and hypothesis test criteria alternatives
  • Example 20.2 Confidence intervals on the mean
  • Example 20.3: Sample size – an alternative approach
  • Standard deviation confidence interval
  • Example 20.4: Standard deviation confidence statement
  • Percentage of the population assessment
  • Example 20.5: Percentage of the population statements
  • Example 20.6: Base-lining a 30,000-foot-level continuous-response metric and determining process confidence interval statements
  • Applying IEE
  • Exercises

21. Inferences: Attribute (Pass/fail) Response

  • Attribute response situations
  • Sample size: Hypothesis test of an attribute criterion
  • Example 21.1: Sample size – A hypothesis test of an attribute criterion
  • Confidence intervals for attribute evaluations and alternative sample size considerations
  • Reduced sample size testing for attribute situations
  • Example 21.2: Reduced sample size testing – Attribute response situations
  • Example 21.3: Sampling does not fix common-cause problems
  • Example 21.4: Base-lining a 30,000-foot-level attribute-response metric and determining process confidence interval statement
  • Attribute sample plan alternatives
  • ASQ (Acceptable Quality Level) sampling can be deceptive
  • Example 21.5: Acceptable quality level
  • Applying IEE
  • Exercises

22. P-DMAIC – Analyze Phase: Continuous Response Comparison Tests

  • P-DMAIC roadmap component
  • IEE application examples: Comparison tests
  • Comparing continuous data responses
  • Sample size: Comparing means
  • Comparing two means
  • Example 22.1: Comparing the means of two samples
  • Comparing variances of two samples
  • Example 22.2: Comparing the variance of two samples
  • Comparing populations using a probability plot
  • Example 22.3 Comparing responses using a probability plot
  • Example 22.4: IEE demonstration of process improvement for a continuous response
  • Paired comparison testing
  • Example 22.5: Paired comparison testing for a new design
  • Example 22.6: Paired comparison testing for improved gas mileage
  • Comparing more than two samples
  • Example 22.7: Comparison means to determine if process improved
  • Applying IEE
  • Exercises

23. P-DMAIC – Analyze Phase: Comparison Tests for Attribute Pass/Fail Response

  • P-DMAIC roadmap component
  • IEE application examples: Attribute comparison tests
  • IEE application examples: Attribute comparison tests
  • Comparing attribute data
  • Sample size comparing proportions
  • Comparing proportions
  • Example 23.1: Comparing proportions
  • Comparing nonconformance proportions and count frequencies
  • Example 23.2: Comparing nonconformance proportions
  • Example 23.3: Comparing counts
  • Example 23.4: Difference in two proportions
  • Example 23.5: IEE demonstration of process improvement for an attribute response
  • Applying IEE
  • Exercises

24. P-DMAIC – Analyze Phase: Variance Components

  • P-DMAIC roadmap component
  • IEE application examples: Variance components
  • Description
  • Example 24.1: Variance components of pigment paste
  • Example 24.2: Variance components of a manufactured door including measurement system components
  • Example 24.3: Determining process capability/performance metric using variance components
  • Example 24.4: Variance components analysis of injection-molding data
  • Example 24.5: Project analysis for variance components of an hourly response that had an unsatisfactory process capability/performance metric
  • Applying IEE
  • Exercises

25. P-DMAIC – Analyze phase: Correlation and Simple Linear Regression

  • P-DMAIC roadmap component
  • IEE application examples: Regression
  • Scatter plot (dispersion graph) Correlation
  • Example 25.1: Correlation
  • Simple linear regression
  • Analysis of residuals
  • Analysis of residuals: Normality assessment
  • Analysis of residuals: Time sequence
  • Analysis of residuals: Fitted values
  • Example 25.2: Simple linear regression
  • Applying IEE
  • Exercises

26. P-DMAIC – Analyze Phase: Single-Factor (One-way) Analysis of Variance (ANOVA) and Analysis of Means (ANOM)

  • P-DMAIC roadmap component
  • IEE application examples: ANOVA and ANOM
  • Application steps
  • Single-factor analysis of variance hypothesis test
  • Single-factor analysis of variance table calculations
  • Estimation of model parameters
  • Unbalanced data
  • Model adequacy
  • Analysis of residuals: Fitted value plots and data normalizing transformations
  • Comparing pairs of treatment means
  • Example 26.1: Single-factor analysis of variance
  • Analysis of means (ANOM)
  • Example 26.2 Analysis of means
  • Example 26.3: Analysis of means of injection-molding data
  • General linear modeling (GLM)
  • Nonparametric estimate: Kruskal-Wallis test
  • Example 26.4: Nonparametric Kruskal-Wallis test
  • Nonparametric estimate: Mood’s median test
  • Example 26.5: Nonparametric Mood’s median test
  • Other considerations
  • Applying IEE
  • Exercises

27. P-DMAIC – Two-factor (Two-way) Analysis of Variance

  • P-DMAIC roadmap component
  • Two-factor factorial design
  • Example 27.1: Two-Factor factorial design
  • Nonparametric estimate: Friedman test
  • Example 27.2: Nonparametric Friedman test
  • Applying IEE
  • Exercises

28. P-DMAIC – Analyze Phase: Multiple Regression, Logistic Regression, and Indictor Variables

  • P-DMAIC roadmap component
  • IEE application examples: Multiple regression
  • Description
  • Example 28.1: Multiple regression
  • Other considerations
  • Example 28.2: Multiple regression best subset analysis
  • Indicator variables (dummy variables) to analyze categorical data
  • Example 28.3: Indicator variables
  • Example 28.4: Indicator variables with covariate
  • Binary logistic regression
  • Example 28.5: Binary logistic regression for ingot preparation
  • Example 28.6: Binary logistic regression for coating test
  • Other logistic regression methods
  • Exercises

 

Part V Improvement Project Roadmap: Improve Phase

29. Benefiting from Design of Experiments (DOE)

  • Terminology and benefits
  • Example 29.1: Traditional experimentation
  • Need for DOE
  • Common excuses for not using DOE
  • DOE application examples
  • Exercises

30. Understanding the Creation of Full and Fractional Factorial 2k DOEs

  • IEE application examples: DOE
  • Conceptual explanation: Two-level full factorial experiments and two-factor interactions
  • Conceptual explanation: saturated two-level DOE
  • Example 30.1: Applying DOE techniques to a non-manufacturing process
  • Exercises

31. P-DMAIC – Improve Phase: Planning 2k DOEs

  • P-DMAIC roadmap component
  • Initial thoughts when setting up a DOE
  • Experiment design considerations
  • Sample size considerations for a continuous response output DOE
  • Experiment design considerations: Choosing factors and levels
  • Experiment deign considerations: Choosing factors and levels
  • Experiment design considerations: Factor statistical significance
  • Experiment design considerations: Experiment resolutions
  • Blocking and randomization
  • Curvature check
  • Applying IEE
  • Exercises

32. P-DMAIC – Improve Phase: Design and Analysis of 2k DOEs

  • P-DMAIC roadmap component
  • Two-level DOE design alternatives
  • Designing a two-level fractional experiment using Tables M and N
  • Determine statistically-significant effects and probability plotting procedure
  • Modeling equation format for a two-level DOE
  • Example 32.1: A resolution V DOE
  • DOE alternatives
  • Example 32.2: A DOE development test
  • Fold-over designs
  • Applying IEE
  • Exercises

33. P-DMAIC – Improve Phase: Robust DOE

  • P-DMAIC roadmap component
  • IEE application examples: Robust DOE
  • Test strategies
  • Loss function
  • Example 33.1: Loss function
  • Analyzing 2k residuals for sources of variability reduction
  • Example 33.2: Analyzing 2k-residuals for sources of variability reduction
  • Robust DOE strategy
  • Example 33.3: Robust inner/outer array DOE to reduce scrap and downtime
  • Applying IEE
  • Exercises

34. P-DMAIC – Improve Phase: Response Surface Methodology (RSM) and Evolutionary Operation (EVOP), and Path of Steepest Ascent

  • P-DMAIC roadmap component
  • Modeling equations
  • Central composite design
  • Example 34.1: Response surface design
  • Box-Behnken designs
  • Additional response surface design considerations
  • Evolutionary operations (EVOP)
  • Example 34.2: EVOP
  • Applying IEE
  • Exercises

35. P-DMAIC – Improve Phase: Innovation and Creativity

  • P-DMAIC roadmap component
  • Alignment of creativity with IEE
  • Creative problem solving
  • Inventive thinking as a process
  • TRIZ
  • Six thinking hats
  • Creative problem solving process (CPS)
  • Exercises

36. P-DMAIC – Improve Phase: Lean Tools and the PDCA Cycle

  • P-DMAIC roadmap component
  • Learning by doing
  • Plan-do-check-act (PDCA)
  • Standard work and standard operating procedures
  • One-piece flow
  • Poka-yoke (Mistake proofing)
  • Visual management
  • 5S method
  • Kaizen event
  • Kanban
  • Demand management
  • Heijunka
  • Continuous flow and cell design
  • Changeover reduction
  • Total productive maintenance (TPM)
  • Applying IEE
  • Exercises

37. P-DMAIC – Improve Phase: Selecting, Implementing, and Demonstrating Project Improvements

  • P-DMAIC roadmap component
  • Process modeling and simulation in the improve phase
  • Solution selection and Pugh matrices
  • Walking the new process and value chain documentation
  • Pilot testing
  • Process change implementation training and project validation
  • Example 37.1: Sales quote process
  • Example 37.2: Sales quote project
  • Example 37.3: Sales personnel scorecard/dashboard and data analyses
  • Exercises

 

Part VI Improvement Project Roadmap: Control Phase

38. P-DMAIC – Control Phase: Active Process Control

  • P-DMAIC roadmap component
  • Process improvements and adjustments
  • IEE application examples: Engineering process control
  • Control of process input variables
  • Realistic tolerances
  • Exponentially weighted moving average (EWMA) and engineering process control (EPC)
  • Pre-control charts
  • Pre-control setup (Qualification procedure)
  • Classical pre-control charts
  • Two-stage pre-control chart
  • Example 38.1: Engineering process control during store checkout
  • Exercises

39. P-DMAIC – Control Phase: Control Plan and Project Completion

  • P-DMAIC roadmap component
  • Control plan: Is and is nots
  • Controlling and error-proofing processes
  • Control plan creation
  • AIAG control plan: Entries
  • Project completion
  • Applying IEE
  • P-DMAIC summary
  • Exercises

 

Part VII Appendix

Appendix A: Infrastructure

  • Roles and responsibilities
  • Reward and recognition

Appendix B: Six Sigma Metric and Article

  • Sigma quality level
  • Article: Individuals control chart and data normality

Appendix C: Creating Effective Presentations

  • Be in earnest
  • Employ vocal variety
  • Make it persuasive
  • Inspire your audience

Appendix D: P-DMAIC Execution Roadmap and Selected Drill Downs

  • P-DMAIC execution roadmap
  • P-DMAIC execution roadmap drill down: In-process metrics decision tree
  • P-DMAIC execution roadmap drill down: Baseline project
  • P-DMAIC execution roadmap drill down: Visualization of data and hypothesis decision tree

Appendix E: P-DMAIC Execution Tollgate Check Sheets
Appendix F: “Implementing Six Sigma” supplemental material
Appendix G: Reference Tables

List of Acronyms and Symbols

Glossary

More information about the book, Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Forrest W. Breyfogle III, Bridgeway Books/Citius Publishing, Austin, TX, 2008.

For additional information about Integrated Enterprise Excellence (IEE) see: Business Management Implementation: IEE Articles, Videos, Books

 

Contact Us to set up a time to discuss with Forrest Breyfogle how your organization might gain much from this lean Six Sigma body of knowledge book for a training guide and practitioner’s reference.

 

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