Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements

Name: Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements
Brand: Wiley
Price: 162.99 EUR
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Mikel Harry Prem S. Mann Ofelia C. De Hodgins Richard L. Hulbert Christopher J. Lacke(Autor*in)

Wiley (Verlag)

Erschienen am 20. September 2011

832 Seiten

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Beschreibung

This hands-on book presents a complete understanding of SixSigma and Lean Six Sigma through data analysis and statisticalconcepts
In today's business world, Six Sigma, or Lean Six Sigma, is acrucial tool utilized by companies to improve customersatisfaction, increase profitability, and enhance productivity.Practitioner's Guide to Statistics and Lean Six Sigma forProcess Improvements provides a balanced approach toquantitative and qualitative statistics using Six Sigma and LeanSix Sigma methodologies.
Emphasizing applications and the implementation of data analysesas they relate to this strategy for business management, this bookintroduces readers to the concepts and techniques for solvingproblems and improving managerial processes using Six Sigma andLean Six Sigma. Written by knowledgeable professionals working inthe field today, the book offers thorough coverage of thestatistical topics related to effective Six Sigma and Lean SixSigma practices, including:
* Discrete random variables and continuous random variables
* Sampling distributions
* Estimation and hypothesis tests
* Chi-square tests
* Analysis of variance
* Linear and multiple regression
* Measurement analysis
* Survey methods and sampling techniques
The authors provide numerous opportunities for readers to testtheir understanding of the presented material, as the real datasets, which are incorporated into the treatment of each topic, canbe easily worked with using Microsoft Office Excel, Minitab,MindPro, or Oracle's Crystal Ball software packages. Examples ofsuccessful, complete Six Sigma and Lean Six Sigma projects aresupplied in many chapters along with extensive exercises that rangein level of complexity. The book is accompanied by an extensive FTPsite that features manuals for working with the discussed softwarepackages along with additional exercises and data sets. Inaddition, numerous screenshots and figures guide readers throughthe functional and visual methods of learning Six Sigma and LeanSix Sigma.
Practitioner's Guide to Statistics and Lean Six Sigma forProcess Improvements is an excellent book for courses on SixSigma and statistical quality control at the upper-undergraduateand graduate levels. It is also a valuable reference forprofessionals in the fields of engineering, business, physics,management, and finance.

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Inhalt

Cover
Title
copyright
Preface
1: Principles of Six Sigma
1.1 OVERVIEW
1.2 SIX SIGMA ESSENTIALS
1.3 QUALITY DEFINITION
1.4 VALUE CREATION
1.5 BUSINESS, OPERATIONS, PROCESS, AND INDIVIDUAL (BOPI) GOALS
1.6 UNDERPINNING ECONOMICS
1.7 PERFORMANCE METRICS
1.8 PROCESS
1.9 DESIGN COMPLEXITY
1.10 NATURE AND PURPOSE OF SIX SIGMA
1.11 NEEDS THAT UNDERLIE SIX SIGMA
1.12 WHY FOCUSING ON THE CUSTOMER IS ESSENTIAL TO SIX SIGMA
1.13 SUCCESS FACTORS
1.14 SOFTWARE APPLICATIONS
GLOSSARY
REFERENCES
2: Six Sigma Installation
2.1 OVERVIEW
2.2 SIX SIGMA LEADERSHIP-THE FUEL OF SIX SIGMA
2.3 DEPLOYMENT PLANNING
2.4 APPLICATION PROJECTS
2.5 DEPLOYMENT TIMELINE
2.6 DESIGN FOR SIX SIGMA (DFSS) PRINCIPLES
2.7 PROCESSING FOR SIX SIGMA (PFSS) PRINCIPLES
2.8 MANAGING FOR SIX SIGMA (MFSS) PRINCIPLES
2.9 PROJECT REVIEW
2.10 SUMMARY
GLOSSARY
REFERENCES AND NOTES
3: Lean Sigma Projects
3.1 OVERVIEW
3.2 INTRODUCTION
3.3 PROJECT DESCRIPTION
3.4 PROJECT GUIDELINES
3.5 PROJECT SELECTION
3.6 PROJECT SCOPE
3.7 PROJECT LEADERSHIP
3.8 PROJECT TEAMS
3.9 PROJECT FINANCIALS
3.10 PROJECT MANAGEMENT
3.11 PROJECT PAYBACK
3.12 PROJECT MILESTONES
3.13 PROJECT ROADMAP
3.14 PROJECT CHARTERS (GENERAL)
3.15 SIX SIGMA PROJECTS
Summary
3.16 PROJECT SUMMARY
GLOSSARY
REFERENCES
4: Lean Practices
4.1 OVERVIEW
4.2 INTRODUCTION
4.3 THE IDEA OF LEAN THINKING
4.4 THEORY OF CONSTRAINTS (TOC)
4.5 LEAN CONCEPT
4.6 VALUE-ADDED VERSUS NON-VALUE-ADDED ACTIVITIES
4.7 WHY COMPANIES THINK LEAN
4.8 VISUAL CONTROLS-VISUAL FACTORY
4.9 THE IDEA OF PULL (KANBAN)
4.10 5S-6S APPROACH
4.11 THE IDEA OF PERFECTION (KAIZEN)
4.12 REPLICATION-TRANSLATE
4.13 POKA-YOKE SYSTEM-MISTAKEPROOFING
4.14 SMED SYSTEM
4.15 7W þ 1 APPROACH-SEVEN PLUS ONE DEADLY WASTE(S)
4.16 6M APPROACH
4.17 SUMMARY
GLOSSARY
REFERENCES AND NOTES
5: Value Stream Mapping
5.1 OVERVIEW
5.2 INTRODUCTION
5.3 VALUE STREAM MAPPING
5.4 FOCUSED BRAINSTORMING
5.5 GRAPHICAL REPRESENTATION OF A PROCESS IN A VALUE STREAM MAP
5.6 EFFECTIVE WORKING TIME
5.7 CUSTOMER DEMAND
5.8 TAKT TIME
5.9 PITCH TIME
5.10 QUEUING TIME
5.11 CYCLE TIME
5.12 TOTAL CYCLE TIME
5.13 CALCULATION OF TOTAL LEAD TIME(S)
5.14 VALUE-ADDED PERCENTAGE AND SIX SIGMA LEVEL
5.15 DRAWING THE CURRENT-VALUE-STREAM MAP
5.16 DRAWING THE VALUE STREAM MAP
5.17 WHAT MAKES A VALUE STREAM LEAN
5.18 THE FUTURE VALUE STREAM MAP
5.19 SUMMARY
GLOSSARY
REFERENCES AND NOTES
6: Introductory Statistics and Data
6.1 OVERVIEW
6.2 INTRODUCTION
6.3 GENETIC CODE OF STATISTICS
6.4 POPULATIONS AND SAMPLES
6.5 THE IDEA OF DATA
6.6 NATURE OF DATA
6.7 DATA COLLECTION
6.8 THE IMPORTANCE OF DATA COLLECTION
6.9 SAMPLING IN SIX SIGMA
6.10 SOURCES OF DATA
6.11 DATABASE
6.12 SUMMARY
GLOSSARY
REFERENCES
7: Quality Tools
7.1 OVERVIEW
7.2 INTRODUCTION
7.3 NATURE OF SIX SIGMA VARIABLES
7.4 QUALITY FUNCTION DEPLOYMENT (QFD)
7.5 SCALES OF MEASUREMENT
7.6 DIAGNOSTIC TOOLS
7.7 ANALYTICAL METHODS
7.8 GRAPHICAL TOOLS
7.9 GRAPHICAL REPRESENTATION OF A PROCESS
7.10 SIPOC DIAGRAM
7.11 IPO DIAGRAM-GENERAL MODEL OF A PROCESS SYSTEM
7.12 FORCE-FIELD ANALYSIS
7.13 MATRIX ANALYSIS-THE IMPORTANCE OF STATISTICAL THINKING
7.14 CHECKSHEETS
7.15 SCORECARDS
7.16 AFFINITY DIAGRAM
7.17 CONCEPT INTEGRATION
GLOSSARY
REFERENCES
8: Making Sense of Data in Six Sigma and Lean
8.1 OVERVIEW
8.2 SUMMARIZING QUANTITATIVE DATA: GRAPHICAL METHODS
8.3 SUMMARIZING QUANTITATIVE DATA: NUMERICAL METHODS
8.4 ORGANIZING AND GRAPHING QUALITATIVE DATA
8.5 SUMMARIZING BIVARIATE DATA
8.6 MULTI-VARI CHARTS
GLOSSARY
EXERCISES
9: Fundamentals of Capability and Rolled Throughput Yield
9.1 OVERVIEW
9.2 INTRODUCTION
9.3 WHY CAPABILITY
9.4 SIX SIGMA CAPABILITY METRIC
9.5 DISCRETE CAPABILITY
9.6 CONTINUOUS CAPABILITY-EXAMPLE
9.7 FUNDAMENTALS OF CAPABILITY
9.8 SHORT- VERSUS LONG-TERM CAPABILITY
9.9 CAPABILITY AND PERFORMANCE
9.10 INDICES OF CAPABILITY
9.11 CALIBRATING THE SHIFT
9.12 APPLYING THE 1.5s SHIFT CONCEPT
9.13 YIELD
9.14 HIDDEN FACTORY
GLOSSARY
REFERENCES
10: Probability
10.1 OVERVIEW
10.2 EXPERIMENTS, OUTCOMES, AND SAMPLE SPACE
10.3 CALCULATING PROBABILITY
10.4 COMBINATORIAL PROBABILITY
10.5 MARGINAL AND CONDITIONAL PROBABILITIES
10.6 UNION OF EVENTS
10.7 INTERSECTION OF EVENTS
GLOSSARY
EXERCISES
11: Discrete Random Variables and their Probability Distributions
11.1 OVERVIEW
11.2 SIX SIGMA PERFORMANCE VARIABLES
11.3 SIX SIGMA LEVERAGE VARIABLES
11.4 RANDOM VARIABLES
11.5 PROBABILITY DISTRIBUTIONS OF A DISCRETE RANDOM VARIABLE
11.6 MEAN OF A DISCRETE RANDOM VARIABLE
11.7 STANDARD DEVIATION OF A DISCRETE RANDOM VARIABLE
11.8 THE BINOMIAL DISTRIBUTION
11.9 THE POISSON PROBABILITY DISTRIBUTION
11.10 THE GEOMETRIC DISTRIBUTION
11.11 THE HYPERGEOMETRIC PROBABILITY DISTRIBUTION
GLOSSARY
EXERCISES
12: Continuous Random Variables and Their Probability Distributions
12.1 OVERVIEWS
12.2 CONTINUOUS PROBABILITY DISTRIUTIONS
12.3 THE NORMAL DISTRIBUTION
12.4 THE EXPONENTIAL DISTRIBUTION
GLOSSARY
EXERCISES
13: Sampling Distributions
13.1 OVERVIEW
13.2 SAMPLING DISTRIBUTION OF A SAMPLE MEAN
13.3 SAMPLING DISTRIBUTION OF A SAMPLE PROPORTION
13.4 THE CENTRAL-LIMIT THEOREM (CLT)
14: Single-Population Estimation
14.1 OVERVIEW
14.2 MEANING OF A CONFIDENCE LEVEL
14.3 ESTIMATING A POPULATION MEAN
14.4 ESTIMATING A POPULATION PROPORTION
14.5 ESTIMATING A POPULATION VARIANCE
GLOSSARY
EXERCISES
15: Control Methods
15.1 OVERVIEW
15.2 INTRODUCTION
15.3 CONTROL LOGIC
15.4 STATISTICAL CONTROL SYSTEMS
15.5 STATISTICAL CONTROL
15.6 PREVENTION VERSUS DETECTION
15.7 A PROCESS CONTROL SYSTEM DEFINITION
15.8 VARIATION
15.9 PROCESS OUT OF CONTROL
15.10 FUNDAMENTALS OF PROCESS CONTROL
15.11 CONTINUOUS STATISTICAL PROCESS CONTROL (SPC) TOOLS
15.12 INTERPRETING PROCESS CONTROL
15.13 STATISTICAL PROCESS CONTROL AND STATISTICAL PROCESS MONITORING
15.14 THE FOUNDATION OF SPC
15.15 TOOLS FOR PROCESS CONTROLS - CONTROL CHARTS
15.16 CONTROL LIMITS
15.17 PROCESS OUT-OF-CONTROL CONDITION
15.18 WESTERN ELECTRIC RULES
15.19 CONTROL CHARTS AND HOW THEY ARE USED
15.20 PRECONTROL METHOD
15.21 CONTROL CHARTS FOR VARIABLES
15.22 CONTROL CHART FOR ATTRIBUTES
GLOSSARY
REFERENCES AND NOTES
16: Single-Population Hypothesis Tests
16.1 OVERVIEW
16.2 INTRODUCTION TO HYPOTHESIS TESTING
16.3 TESTING A CLAIM ABOUT A POPULATION MEAN
16.4 HYPOTHESIS TEST ABOUT A POPULATION PROPORTION
GLOSSARY
EXERCISES
17: Estimation and Hypothesis Tests: Two Populations
OVERVIEW
17.2 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION MEANS FOR INDEPENDENT SAMPLES
17.3 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION MEANS FOR PAIRED SAMPLES
17.4 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION PROPORTIONS
GLOSSARY
EXERCISES
18: Chi-Square Tests
18.1 OVERVIEW
18.2 A GOODNESS-OF-FIT TEST
18.3 CONTINGENCY TABLES
18.4 TESTS OF INDEPENDENCE AND HOMOGENEITY
GLOSSARY
EXERCISES
19: Analysis of Variance
19.1 OVERVIEW
19.2 THE F DISTRIBUTION
19.3 ONE-WAY ANALYSIS OF VARIANCE
19.4 PAIRWISE COMPARISONS
19.5 MULTIFACTOR ANALYSIS OF VARIANCE
19.6 WHAT TO DO WHEN THE ASSUMPTIONS ARE UNREASONABLE
GLOSSARY
EXERCISES
20: Linear and Multiple Regression
20.1 OVERVIEW
20.2 SIMPLE REGRESSION MODEL
20.3 LINEAR REGRESSION
20.4 COEFFICIENT OF DETERMINATION AND CORRELATION
20.5 MULTIPLE REGRESSION
20.6 REGRESSION ANALYSIS
20.7 USING THE REGRESSION MODEL
20.8 RESIDUAL ANALYSIS
20.9 CAUTIONS IN USING REGRESSION
GLOSSARY
EXERCISES
21: Measurement Analysis
21.1 OVERVIEW
21.2 INTRODUCTION
21.3 MEASUREMENT
21.4 MEASUREMENT ERROR
21.5 ACCURACY AND PRECISION
21.6 MEASUREMENT SYSTEM AS A PROCESS
21.7 CATEGORIES OF MEASUREMENT ERROR THAT AFFECT LOCATION
21.8 CATEGORIES OF MEASUREMENT THAT AFFECT SPREAD
21.9 GAGE ACCURACY AND PRECISION
21.10 EXPLORING LINEARITY ERROR
21.11 GAGE REPEATABILITY AND REPRODUCIBILITY (R&R)
21.12 ANOVA METHOD VERSUS X-R METHOD
21.13 ANOVA/VARIANCE COMPONENT ANALYSIS
21.14 RULES OF THUMB
21.15 ACCEPTABILITY CRITERIA
21.16 CHAPTER REVIEW
GLOSSARY
REFERENCES
22: Design of Experiments
22.1 OVERVIEW
22.2 INTRODUCTION
22.3 DESIGN OF EXPERIMENTS (DOE) DEFINITION
22.4 ROLE OF EXPERIMENTAL DESIGN IN PROCESS IMPROVEMENT
22.5 EXPERIMENT DESIGN TOOLS
22.6 PRINCIPLES OF AN EXPERIMENTAL DESIGN
22.7 DIFFERENT TYPES OF EXPERIMENTS
22.8 INTRODUCTION TO FACTORIAL DESIGNS
22.9 FEATURES OF FACTORIAL DESIGNS-ORTHOGONALITY
22.10 FULL FACTORIAL DESIGNS
22.11 RESIDUAL ANALYSIS (22)
22.12 MODELING (22)
22.13 MULTIFACTOR EXPERIMENT
22.14 FRACTIONAL FACTORIAL DESIGNS
22.15 THE ANOVA TABLE
22.16 NORMAL PROBABILITY PLOT OF THE EFFECTS
22.17 MAIN-EFFECTS PLOT
22.18 BLOCKING VARIABLE
22.19 STATISTICAL SIGNIFICANCE
22.20 PRACTICAL SIGNIFICANCE
22.21 FUNDAMENTALS OF RESIDUAL ANALYSIS
22.22 CENTERPOINTS
22.23 NOISE FACTORS
22.24 STRATEGY OF GOOD EXPERIMENTATION
22.25 SELECTING THE VARIABLE LEVELS
22.26 SELECTING THE EXPERIMENTAL DESIGN
22.27 REPLICATION
22.28 ANALYZING THE DATA (ANOVA)
22.29 RECOMMENDATIONS
22.30 ACHIEVING THE OBJECTIVE
22.31 CHAPTER SUMMARY
22.32 CHAPTER EXAMPLES
GLOSSARY
REFERENCES
23: Design for Six Sigma (DFSS), Simulation, and Optimization
23.1 OVERVIEW
23.2 INTRODUCTION
23.3 SIX SIGMA AS STRETCH TARGET
23.4 PRODUCIBILITY
23.5 STATISTICAL TOLERANCES
23.6 DESIGN APPLICATION
23.7 DESIGN MARGIN
23.8 DESIGN QUALIFICATION
23.9 DESIGN FOR SIX SIGMA (DFSS) PRINCIPLES
23.10 DECISION POWER
23.11 EXPERIMENTATION
23.12 EXPERIMENT DESIGN
23.13 RESPONSE SURFACE DESIGNS
23.14 FACTORIAL PRODUCIBILITY
23.15 TOOLBOX OVERVIEW
23.16 MONTE CARLO SIMULATIONS
23.17 DESIGN FOR SIX SIGMA PROJECT SELECTION EXAMPLE
23.18 DEFINING SIMULATION INPUTS
23.19 DEFINING OUTPUTS AND RUNNING A SIMULATION
23.20 STOCHASTIC OPTIMIZATION: DISCOVERING THE BEST PORTFOLIO WITH THE LEAST RISK
23.21 CONCLUSIONS
GLOSSARY
REFERENCES
24: Survey Methods and Sampling Techniques
24.1 OVERVIEW
24.2 INTRODUCTION
24.3 THE SAMPLE SURVEY
24.4 THE SURVEY SYSTEM
24.5 CLEAR GOALS
24.6 TARGET POPULATION AND SAMPLE SIZE
24.7 INTERVIEWING METHOD
24.8 RESPONSE RATE, RESPONDENTS, AND NONRESPONDENTS
24.9 SURVEY METHODS
24.10 SOURCES OF INFORMATION AND DATA
24.11 ORDER OF THE QUESTIONS
24.12 PILOT TESTING THE QUESTIONNAIRE
24.13 BIASED SAMPLE OR RESPONSE ERROR
24.14 SAMPLING-RANDOM AND NONRANDOM SAMPLES
24.15 POPULATION DISTRIBUTION
24.16 SAMPLING DISTRIBUTION
24.17 SAMPLING AND NONSAMPLING ERRORS
GLOSSARY
REFERENCES
Appendix A Statistical Tables
Appendix B Answers to Selected Odd-Numbered Exercises
Index

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