Vital tools for implementing Lean Six Sigma--what they are, how they work, and which to use

The Lean Six Sigma Pocket Toolbook is today's most complete and results-based reference to the tools and concepts needed to understand, implement, and leverage Lean Six Sigma. The only guide that groups tools by purpose and use, this hands-on reference provides:

• Analyses of nearly 100 tools and methodologies--from DMAIC and Pull Systems to Control Charts and Pareto Charts
• Detailed explanations of each tool to help you know how, when, and why to use it for maximum efficacy
• Sections for each tool explaining how to create it, how to interpret what you find, and expert tips

Lean Six Sigma is today's leading technique to maximize production efficiency and maintain control over each step in the managerial process. With The Lean Six Sigma Pocket Toolbook, you'll discover how to propel your organization to new levels of competitive success--one tool at a time.

McGraw-Hill authors represent the leading experts in their fields and are dedicated to improving the lives, careers, and interests of readers worldwide

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

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