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The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to Nearly 100 Tools for Improving Process Quality, Speed, and Complexity

Book Description


Bestselling Lean Six Sigma author Michael George provides the first pocket guide for deployers of Lean Six Sigma

The Lean Six Sigma Pocket Toolbook blends Lean and Six Sigma tools and concepts, providing expert advice on how to determine which tool within a "family" is best for different purposes. Packed with detailed examples and step-by-step instructions, it's the ideal handy reference guide to help Green and Black Belts make the transition from the classroom to the field.

• Features brief summaries and examples of the 70 most important tools in Lean Six Sigma, such as "Pull," "Heijunka," and "Control Charts"

• Groups tools by purpose and usage

• Offers a quick, easy reference on using the DMAIC improvement cycle

• Provides comprehensive coverage in a compact, portable format

Table of Contents

  1. Cover Page
  2. The Lean Six Sigma Pocket Toolbook
  3. Copyright Page
  4. Contents
  5. Chapter 1: Using DMAIC to Improve Speed, Quality, and Cost
    1. Define
    2. Measure
    3. Analyze
    4. Improve
    5. Control
    6. Kaizen DMAIC
    7. Project selection
  6. Chapter 2: Working With Ideas
    1. Brainstorming
    2. Affinity diagrams
    3. Multivoting
  7. Chapter 3: Value Stream Mapping and Process Flow Tools
    1. Process mapping
    2. Process observation
    3. SIPOC
    4. Process mapping steps
    5. Transportation and spaghetti (workflow) diagrams
    6. Swim-lane (deployment) flowcharts
    7. Value stream maps (basic)
    8. Flowchart and value stream symbols
    9. Value-add (VA) vs. non-value-add (NVA) analysis
    10. Time value maps
    11. Value-add chart (task time or takt time chart)
  8. Chapter 4: Voice of the Customer (VOC)
    1. Customer segmentation
    2. Sources of customer data
    3. Collecting VOC: Interviews
    4. Collecting VOC: Point-of-use observation
    5. Collecting VOC: Focus groups
    6. Collecting VOC: Surveys
    7. Kano analysis
    8. Developing critical-to-quality requirements
  9. Chapter 5: Data Collection
    1. Types of data
    2. Input vs. output data
    3. Data collection planning
    4. Measurement selection matrix
    5. Stratification factors
    6. Operational definitions
    7. Cautions on using existing data
    8. Making a checksheet
    9. Basic checksheets
    10. Frequency plot checksheet
    11. Traveler checksheet
    12. Location checksheet
    13. Sampling basics
    14. Factors in sample selection
    15. Stable process (and population) sampling
    16. Formulas for determining minimum sample size (population or stable process)
    17. Measurement System Analysis (MSA) and Gage R&R Overview
    18. Gage R&R: Collecting the data
    19. Interpreting Gage R&R Results
    20. MSA: Evaluating bias
    21. MSA: Evaluating stability
    22. MSA: Evaluating discrimination
    23. MSA for attribute/discrete data
  10. Chapter 6: Descriptive Statistics and Data Displays
    1. Statistical term conventions
    2. Measures of central tendency (mean, median, mode)
    3. Measures of spread (range, variance, standard deviation)
    4. Boxplots
    5. Frequency plot (histogram
    6. Normal distribution
    7. Non-normal distributions and the Central Limit Theorem
  11. Chapter 7: Variation Analysis
    1. Review of variation concepts
    2. Time series plots (Run charts)
    3. Run chart table
    4. Control chart basics
    5. Selecting a control chart
    6. Control charts for continuous data
    7. Subgrouping for continuous data
    8. Control limit formulas for continuous data
    9. Factors for Control Chart Formulas
    10. Creating an ImR Chart
    11. Creating X,R charts or X,S charts
    12. Control charts for attribute data
    13. Creating p-, np-, c-, and u-charts
    14. Control limit formulas for attribute data
    15. Assumptions for interpreting control charts
    16. Interpreting control charts (Tests for Special Cause Variation)
    17. Background on process capability calculations
    18. Confusion in short-term vs. long-term process capability calculations
    19. Calculating process capability
  12. Chapter 8: Identifying and Verifying Causes
    1. PART A: Identifying potential causes
    2. Pareto charts
    3. 5 Whys
    4. Cause-and-effect diagrams (fishbone or Ishikawa diagrams)
    5. C&E Matrix
    6. PART B: Tools for confirming causal effects
    7. Stratified data charts
    8. Testing quick fixes or obvious solutions
    9. Scatter plots
    10. Hypothesis testing overview
    11. Confidence intervals
    12. Type I and Type II errors, Confidence, Power, and p-values
    13. Confidence intervals and sample size
    14. t–test Overview
    15. 1-Sample t-test
    16. 2-Sample t-test
    17. Overview of correlation
    18. Correlation statistics (coefficients)
    19. Regression overview
    20. Simple linear regression
    21. Multiple regression
    22. ANOVA (ANalysis Of VAriance)
    23. One-way ANOVA
    24. Degrees of Freedom
    25. ANOVA assumptions
    26. Two-way ANOVA
    27. Chi-Square test
    28. Design of Experiments (DOE) notation and terms
    29. Planning a designed experiment
    30. DOE: Full-factorial vs.
    31. Fractional-factorials (and notations)
    32. Interpreting DOE results
  13. Chapter 9: Reducing Lead Time and Non-Value-Add Cost
    1. Basic Lean concepts
    2. Metrics of time efficiency
    3. Time Traps vs. Capacity Constraints
    4. Identifying Time Traps and Capacity Constraints
    5. 5S Overview
    6. Implementing 5S
    7. Generic Pull System
    8. Replenishment Pull Systems
    9. Two-Bin Replenishment System
    10. Computing minimum safe batch sizes
    11. Four Step Rapid Setup Method
    12. Adapting Four Step Rapid Setup for service processes
    13. Total Productive Maintenance (TPM)
    14. Mistake proofing & prevention (Poka-yoke)
    15. Process balancing design principles
    16. Work cell optimization
    17. Visual Process Controls
  14. Chapter 10: Complexity Value Stream Mapping and Complexity Analysis
    1. Product/service family grid
    2. Complexity Value Stream Map (CVSM)
    3. Process Cycle Efficiency (PCE)
    4. The Complexity Equation
    5. Complexity matrix
    6. PCE destruction calculations (for a Complexity Matrix)
    7. Substructure analysis
    8. “What-if” analyses with Complexity Matrix data
  15. Chapter 11: Selecting and Testing Solutions
    1. Sources of solution ideas
    2. Benchmarking
    3. Tips on solution selection
    4. Developing and using evaluation criteria
    5. Solution selection matrix
    6. Pairwise ranking
    7. Cost evaluation
    8. Impact/effort matrix
    9. Pugh matrix
    10. Other evaluation techniques
    11. Controls assessment matrix
    12. Failure Modes and Effects Analysis (FMEA)
    13. Pilot testing
  16. Index