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” – Third Volume in a Three Book, Volume Series

Foreword Bill Wiggenhorn (Former Motorola University President) and Keith Moe (Former 3M Group VP): “This book deserves to become a standard desk reference for everyone in the organization responsible for meeting goals. It makes valuable reading for organization leaders and Six Sigma practitioners – it is the most thorough and detailed reference guide for Integrated Enterprise Excellence.”

Volume 2 of this book volume series described using an E-DMAIC (Enterprise process Define Measure Analyze Improve Control) system, which provided a roadmap for a unique and powerful system for goal setting, scorecard creation/execution, strategic analysis/building, enterprise improvement, and enterprise control. This book volume included the integration of Theory of Constraints (TOC) in the overall roadmap for the purpose of identification of constraint identification for process improvement focus efforts. In this Enterprise Process DMAIC (E-DMAIC) system, the roadmap included the use of Lean tools and their assessments in the analyze phase, along with process improvement project identification and enterprise control. Process improvement projects identified in the E-DMAIC analyze phase efforts, which blends innovation with analytics, can then be executed using the project execution DMAIC roadmap or P-DMAIC.

This volume of the 4 book, volume series (i.e., Volume 3) provides a detailed step-by-step project define-measure-analyze-improve-control (P-DMAIC) roadmap. This project execution DMAIC roadmap provides a true step-by-step integration of Six Sigma and Lean tools.

This volume, book (i.e., Volume 3) is available through Smarter Solutions, Amazon.com, local book stores, and other book retailers.

Contents of Volume 3 (1200+ pages) of this three volume book series is (Parts, chapters and sections titles):

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

Forrest Breyfogle
Forrest@SmarterSolutions.com
www.SmarterSolutions.com