The Lean Six Sigma Pocket Toolbook - David T. Rowlands

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Résumé :
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

Biographie:
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

Sommaire:
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