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CITATION

George, Michael; Maxey, John; Rowlands, David; and Upton, Malcolm. The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to 70 Tools for Improving Quality and Speed. US: McGraw-Hill, 2004.

The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to 70 Tools for Improving Quality and Speed

Authors: Michael George, John Maxey, David Rowlands and Malcolm Upton

Published:  September 2004

eISBN: 9780071505734 0071505733 | ISBN: 9780071441193
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  • Contents
  • Chapter 1: Using DMAIC to Improve Speed, Quality, and Cost
  • Define
  • Measure
  • Analyze
  • Improve
  • Control
  • Kaizen DMAIC
  • Project selection
  • Chapter 2: Working With Ideas
  • Brainstorming
  • Affinity diagrams
  • Multivoting
  • Chapter 3: Value Stream Mapping and Process Flow Tools
  • Process mapping
  • Process observation
  • SIPOC
  • Process mapping steps
  • Transportation and spaghetti (workflow) diagrams
  • Swim-lane (deployment) flowcharts
  • Value stream maps (basic)
  • Flowchart and value stream symbols
  • Value-add (VA) vs. non-value-add (NVA) analysis
  • Time value maps
  • Value-add chart (task time or takt time chart)
  • Chapter 4: Voice of the Customer (VOC)
  • Customer segmentation
  • Sources of customer data
  • Collecting VOC: Interviews
  • Collecting VOC: Point-of-use observation
  • Collecting VOC: Focus groups
  • Collecting VOC: Surveys
  • Kano analysis
  • Developing critical-to-quality requirements
  • Chapter 5: Data Collection
  • Types of data
  • Input vs. output data
  • Data collection planning
  • Measurement selection matrix
  • Stratification factors
  • Operational definitions
  • Cautions on using existing data
  • Making a checksheet
  • Basic checksheets
  • Frequency plot checksheet
  • Traveler checksheet
  • Location checksheet
  • Sampling basics
  • Factors in sample selection
  • Stable process (and population) sampling
  • Formulas for determining minimum sample size (population or stable process)
  • Measurement System Analysis (MSA) and Gage R&R Overview
  • Gage R&R: Collecting the data
  • Interpreting Gage R&R Results
  • MSA: Evaluating bias
  • MSA: Evaluating stability
  • MSA: Evaluating discrimination
  • MSA for attribute/discrete data
  • Chapter 6: Descriptive Statistics and Data Displays
  • Statistical term conventions
  • Measures of central tendency (mean, median, mode)
  • Measures of spread (range, variance, standard deviation)
  • Boxplots
  • Frequency plot (histogram)
  • Normal distribution
  • Non-normal distributions and the Central Limit Theorem
  • Chapter 7: Variation Analysis
  • Review of variation concepts
  • Time series plots (Run charts)
  • Run chart table
  • Control chart basics
  • Selecting a control chart
  • Control charts for continuous data
  • Subgrouping for continuous data
  • Control limit formulas for continuous data
  • Factors for Control Chart Formulas
  • Creating an ImR Chart
  • Creating X,R charts or X,S charts
  • Control charts for attribute data
  • Creating p-, np-, c-, and u-charts
  • Control limit formulas for attribute data
  • Assumptions for interpreting control charts
  • Interpreting control charts (Tests for Special Cause Variation)
  • Background on process capability calculations
  • Confusion in short-term vs. long-term process capability calculations
  • Calculating process capability
  • Chapter 8: Identifying and Verifying Causes
  • Part A: Identifying potential causes
  • Pareto charts
  • 5 Whys
  • Cause-and-effect diagrams (fishbone or Ishikawa diagrams)
  • C&E Matrix
  • Part B: Tools for confirming causal effects
  • Stratified data charts
  • Testing quick fixes or obvious solutions
  • Scatter plots
  • Hypothesis testing overview
  • Confidence intervals
  • Type I and Type II errors, Confidence, Power, and p-values
  • Confidence intervals and sample size
  • t–test Overview
  • 1-Sample t-test
  • 2-Sample t-test
  • Overview of correlation
  • Correlation statistics (coefficients)
  • Regression overview
  • Simple linear regression
  • Multiple regression
  • ANOVA (ANalysis Of VAriance)
  • One-way ANOVA
  • Degrees of Freedom
  • ANOVA assumptions
  • Two-way ANOVA
  • Chi-Square test
  • Design of Experiments (DOE) notation and terms
  • Planning a designed experiment
  • DOE: Full-factorial vs. Fractional-factorials (and notations)
  • Interpreting DOE results
  • Residual analysis in hypothesis testing
  • Chapter 9: Reducing Lead Time and Non-Value-Add Cost
  • Basic Lean concepts
  • Metrics of time efficiency
  • Time Traps vs. Capacity Constraints
  • Identifying Time Traps and Capacity Constraints
  • 5S Overview
  • Implementing 5S
  • Generic Pull System
  • Replenishment Pull Systems
  • Two-Bin Replenishment System
  • Computing minimum safe batch sizes
  • Four Step Rapid Setup Method
  • Adapting Four Step Rapid Setup for service processes
  • Total Productive Maintenance (TPM)
  • Mistake proofing & prevention (Poka-yoke)
  • Process balancing design principles
  • Work cell optimization
  • Visual Process Controls
  • Chapter 10: Complexity Value Stream Mapping and Complexity Analysis
  • Product/service family grid
  • Complexity Value Stream Map (CVSM)
  • Process Cycle Efficiency (PCE)
  • The Complexity Equation
  • Complexity matrix
  • PCE destruction calculations (for a Complexity Matrix)
  • Substructure analysis
  • “What-if” analyses with Complexity Matrix data
  • Chapter 11: Selecting and Testing Solutions
  • Sources of solution ideas
  • Benchmarking
  • Tips on solution selection
  • Developing and using evaluation criteria
  • Solution selection matrix
  • Pairwise ranking
  • Cost evaluation
  • Impact/effort matrix
  • Pugh matrix
  • Other evaluation techniques
  • Controls assessment matrix
  • Failure Modes and Effects Analysis (FMEA)
  • Pilot testing
  • Index