The six sigma handbook / Thomas Pyzdek, Paul Keller.

Includes index.

Includes bibliographical references and index.

Preface -- Part 1: Six Sigma Implementation And Management: -- Building the responsive six sigma organization: -- What is six sigma?: -- Why six sigma? -- Six sigma philosophy -- Six sigma versus traditional three sigma performance -- Change imperative -- Implementing six sigma: -- Timetable -- Infrastructure -- Integrating six sigma and related initiatives -- Deployment to the supply chain -- Communications and awareness -- Recognizing and capitalizing on opportunity: -- Methods for collecting customer data: -- Surveys -- Focus groups -- Operational feedback systems -- Cost of poor quality: -- Cost of quality examples -- Quality cost bases -- Benchmarking: -- Benchmarking process -- Getting started with benchmarking -- Why benchmarking efforts fail -- Benefits of benchmarking -- Some dangers of benchmarking -- Innovation: -- Kano model -- Quality function deployment -- Translating customer demands -- Creative destruction -- Strategic planning: -- Organizational vision -- Strategy development -- Strategic styles -- Possibilities-based strategic decisions -- Strategic development using constraint theory: -- Systems approach -- Basic constraint management principles and concepts -- Tools of constraint management -- Constraint management measurements -- Summary and conclusion -- Data-driven management: -- Attributes of good metrics: -- Measuring causes and effects -- Balanced scorecard: -- Translating the vision -- Communicating and linking -- Business planning -- Feedback and learning -- Maximizing resources: -- Choosing the right projects: -- Types of projects -- Analyzing project candidates -- Using Pareto analysis to identify six sigma project candidates -- Throughput-based project selection -- Ongoing management support: -- Internal roadblocks -- External roadblocks -- Individual barriers to change -- Ineffective management support strategies -- Effective management support strategies -- Cross-functional collaboration -- Tracking six sigma project results: -- Financial results validation -- Team performance evaluation -- Team recognition and reward -- Lessons-learned capture and replication -- Part 2: Six Sigma Tools And Techniques: -- Project management using DMAIC and DMADV: -- DMAIC and DMADV deployment models: -- Project scheduling -- Project reporting -- Project budgets -- Project records -- Six sigma teams: -- Team membership -- Team dynamics management, including conflict resolution -- Stages in group development -- Member roles and responsibilities -- Management's role -- Facilitation techniques -- Define phase: -- Project charters -- Project decomposition: -- Work breakdown structures -- Pareto analysis -- Deliverables: -- Critical to quality metrics -- Critical to schedule metrics -- Critical to cost metrics -- Top-level process definition: -- Process maps -- Assembling the team -- Measure phase: -- Process definition: -- Flowcharts -- SIPOC -- Metric definition: -- Measurement scales -- Discrete and continuous data -- Process baseline estimates: -- Enumerative and analytic studies -- Principles of statistical process control -- Estimating process baselines using process capability analysis -- Process behavior charts: -- Distributions: -- Methods of enumeration -- Frequency and cumulative distributions -- Sampling distributions -- Binomial distribution -- Poisson distribution -- Hypergeometric distribution -- Normal distribution -- Lognormal distribution -- Exponential distribution -- Weibull distribution -- Control charts for variables data: -- Averages and ranges control charts -- Averages and standard deviation (sigma) control charts -- Control charts for individual measurements (X charts) -- Control charts for attributes data: -- Control charts for proportion defective (p charts) -- Control charts for count of defective (np charts) -- Control charts for average occurrences-per-unit (u charts) -- Control charts for counts of occurrences-per-unit (c charts) -- Control chart selection: -- Rational subgroup sampling -- Control chart interpretation: -- Run tests -- Short-run statistical process control techniques: -- Variables data -- Attribute SPC for small and short runs -- Summary of short-run SPC -- SPC techniques for automated manufacturing: -- Problems with traditional SPC techniques -- Special and common cause charts -- EWMA common cause charts -- EWMA control charts versus individuals charts -- Process capability indices: -- Example of non-normal capability analysis using Minitab -- Measurement systems evaluation: -- Definitions: -- Measurement system discrimination -- Stability -- Bias -- Repeatability -- Reproducibility -- Part-to-part variation -- Example of measurement system analysis summary: -- Gage Re-R analysis using Minitab -- Linearity: -- Linearity analysis using Minitab -- Attribute measurement error analysis: -- Operational definitions -- How to conduct attribute inspection studies -- Example of attribute inspection error analysis -- Minitab attribute gage Re-R example -- Analyze phase: -- Value stream analysis: -- Value stream mapping -- Spaghetti charts -- Analyzing the sources of variation: -- Cause and effect diagrams -- Boxplots -- Statistical inference -- Chi-Square, student's t, and f distributions -- Point and interval estimation -- Hypothesis testing -- Resampling (bootstrapping) -- Regression and correlation analysis: -- Linear models -- Least-squares fit -- Correlation analysis -- Designed experiments -- Traditional approach versus statistically designed experiments: -- Terminology -- Design characteristics -- Types of design -- One-factor ANOVA -- Two-way ANOVA with no replicates -- Two-way ANOVA with replicates -- Full and fractional factorial -- Power and sample size -- Testing common assumptions -- Analysis of categorical data: -- Making comparisons using chi-square tests -- Logistic regression -- Binary logistic regression -- Ordinal logistic regression -- Nominal logistic regression -- Non-parametric methods -- Improve/design phase: -- Using customer demands to make design and improvement decisions: -- Pugh concept selection method -- Lean techniques for optimizing flow: -- Tools to help improve flow -- Using empirical model building to optimize: -- Phase 0: Getting your bearings -- Phase 1: Screening experiment -- Phase 2: Steepest ascent (descent) -- Phase 3: Factorial experiment -- Phase 4: Composite design -- Phase 5: Robust product and process design -- Data mining, artificial neural networks, and virtual process mapping: -- Example of neural net models -- Optimization using simulation: -- Predicting CTQ performance -- Simulation tools -- Random number generators -- Model development -- Virtual DOE using simulation software -- Risk assessment tools: -- Design review -- Fault-tree analysis -- Safety analysis -- Failure mode and effect analysis -- Defining new performance standards using statistical tolerancing: -- Assumptions of formula -- Tolerance intervals -- Control/verify phase: -- Validating the new process or product design -- Business process control planning: -- Maintaining gains -- Tools and techniques useful for control planning -- Preparing the process control plan -- Process control planning for short and small runs -- Process audits -- Selecting process control elements -- Other elements of the process control plan -- Appendix 1: Glossary of basic statistical terms -- Appendix 2: Area under the standard normal curve -- Appendix 3: Critical values of the t-distribution -- Appendix 4: Chi-Square distribution -- Appendix 5: F Distribution (cx = 1%) -- Appendix 6: F Distribution (cx = 5%) -- Appendix 7: Poisson probability sums -- Appendix 8: Tolerance interval factors -- Appendix 9: Control chart constants -- Appendix 10: Control chart equations -- Appendix 11: Table of d2* values -- Appendix 12: Factors for short run control charts for individuals, X, and R charts -- Appendix 13: Sample customer survey -- Appendix 14: Process cx levels and equivalent PPM quality levels -- Appendix 15: Black belt effectiveness certification -- Appendix 16: Green belt effectiveness certification -- Appendix 17: AHP using Microsoft Excel -- References -- Index.

Book Description: The Six Sigma approach is being used to vastly improve processes, profitability, sustainability, and long-term growth at global organizations of all sizes. Fully revised for the latest developments in the field, The Six Sigma Handbook, Fourth Edition, reveals how to successfully implement this improvement strategy in your company. The book explains how to define and deploy Six Sigma projects focused on key stakeholder requirements and carry out data-driven management. This comprehensive resource walks you through the phases of DMAIC and DMADV and demonstrates how to use the statistical tools and problem-solving techniques of Six Sigma with screenshots of Minitab and Excel applications. The new edition has been updated to include: Two online quizzes for Six Sigma certification, one for Green Belt candidates and one for Black Belt candidates; Links to five videos that walk you through specific processes, such as Minitab functions, statistical process control, and how to read a Pareto chart; Fully incorporated coverage of Lean methodologies. Find out how to select the right personnel to train, achieve technical proficiency, build the best teams, and foster effective leadership. Improve the quality of processes and products in your organization, increase customer satisfaction, and boost profits with help from this definitive guide to Six Sigma. Written by two of the foremost authorities on the subject, this authoritative resource delivers all of the guidance you need to successfully implement Six Sigma. Comprehensive coverage includes: Building the responsive Six Sigma organization; Recognizing and capitalizing on opportunity; Data-driven management; Maximizing resources; Project management using DMAIC and DMADV; The define phase; The measure phase; Process behavior charts; Measurement systems evaluation; The analyze phase; The improve/design phase; The control/verify phase.