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First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality, Second Edition 2nd New edition [Kõva köide]

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(University of Chicago Medicine, Illinois, USA), (Bradley University, Peoria, Illinois, USA)
  • Formaat: Hardback, 634 pages, kõrgus x laius: 234x156 mm, kaal: 1076 g, 7/2012 - Book converted to Micro - 10/15 -EBooks corrected by Aptara; 450 Equations, 94 in text boxes - 8/15-CX book to PG; 69 Tables, black and white; 199 Illustrations, black and white
  • Ilmumisaeg: 29-Aug-2011
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1439840342
  • ISBN-13: 9781439840344
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First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality, Second Edition 2nd New editionSuurem pilt
  • Formaat: Hardback, 634 pages, kõrgus x laius: 234x156 mm, kaal: 1076 g, 7/2012 - Book converted to Micro - 10/15 -EBooks corrected by Aptara; 450 Equations, 94 in text boxes - 8/15-CX book to PG; 69 Tables, black and white; 199 Illustrations, black and white
  • Ilmumisaeg: 29-Aug-2011
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1439840342
  • ISBN-13: 9781439840344
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781439840344.html
Märksõnad:
"Completely revised and updated, A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality, Second Edition contains virtually all the information an engineer needs to function as a quality engineer. The authors not only break things down very simply but also give a full understanding of why each topic covered is essential to learning proper quality management. They present the information in a manner that builds a strong foundation in quality management without overwhelming readers.See what's new in the new edition:Reflects changes in the latest revision of the ISO 9000 Standards and the Baldrige Award criteriaIncludes new mini-projects and examples throughoutIncorporates Lean methods for reducing cycle time, increasing throughput, and reducing wasteContains increased coverage of strategic planningThis text covers management and statistical methods of quality engineering in an integrative manner, unlike other books on the subject that focus primarily on one of the two areas of quality. The authors illustrate the use of quality methods with examples drawn from their consulting work, using a reader-friendly style that makes the material approachable and encourages self-study. They cover the must-know fundamentals of probability and statistics and make extensive use of computer software to illustrate the use of the computer in solving quality problems. Reorganized to make the book suitable for self study, the second edition discusses how to design Total Quality System that works. With detailed coverage of the management and statistical tools needed to make the system perform well, the book provides a useful reference for professionals who need to implement quality systems in any environment and candidates preparing forthe exams to qualify as a certified quality engineer (CQE)"--

"Preface to Second Edition "The average Japanese worker has a more in-depth knowledge of statistical methods than an average American engineer," explained a U.S. business executive returning from a visit to Japan, as a reason why the Asian rivals were able to produce better quality products than U.S. manufacturers. That statement, made almost 30 years ago, may be true even today as Japanese cars are continuously sought by customers who care for quality and reliability. Dr. Deming, recognized as the guru who taught the Japanese how to make quality products, said: "Industry in America needs thousands of statistically minded engineers, chemists, doctors of medicine, purchasing agents, managers" as a remedy to improve the quality of products and services produced in the U.S. He insisted that engineers, and other professionals, should have the capacity for statistical thinking, which comes from learning the statistical tools and the theory behind them. The engineering accreditation agency in the U.S., ABET, abody made up of academics and industry leaders, stipulates that every engineering graduate should have "an ability to design and conduct experiments, as well as to analyze data and interpret results" as part of the accreditation criteria. Yet, we see that most of the engineering majors from a typical "--

Arvustused

Praise for the Previous Edition



Professor Krishnamoorthi has designed, assembled, and delivered a milestone textbook in quality engineering education. His use of real-world examples is exemplary. His integration of the managerial aspects and the technical methods within the world of quality is both refreshing and appealing. Indeed, it saves one from having to buy two books as it is, to wit, the only textbook that does it. All topics are presented in a clear, well defined, but not overstated manner... Trevor Hale, Ohio University

Preface to the Second Edition xv
Preface to the First Edition xvii
Authors xxi
1 Introduction to Quality
1(48)
1.1 An Historical Overview
1(8)
1.1.1 A Note about "Quality Engineering"
7(2)
1.2 Defining Quality
9(2)
1.2.1 Product Quality vs. Service Quality
11(1)
1.3 The Total Quality System
11(2)
1.4 Total Quality Management
13(2)
1.5 Economics of Quality
15(1)
1.6 Quality, Productivity, and Competitive Position
16(1)
1.7 Quality Costs
17(22)
1.7.1 Categories of Quality Costs
18(1)
1.7.1.1 Prevention Cost
18(2)
1.7.1.2 Appraisal Cost
20(1)
1.7.1.3 Internal Failure Cost
20(1)
1.7.1.4 External Failure Cost
21(1)
1.7.2 Steps in Making a Quality Cost Study
21(5)
1.7.3 Projects Arising from a Quality Cost Study
26(1)
1.7.4 Quality Cost Scoreboard
27(2)
1.7.5 Quality Costs Not Included in the TQC
29(2)
1.7.6 Relationship among Quality Cost Categories
31(1)
1.7.7 Summary of Quality Costs
32(1)
1.7.8 A Case Study in Quality Costs
32(7)
1.8 Success Stories
39(1)
1.9 Exercise
39(10)
1.9.1 Practice Problems
39(4)
1.9.2 Mini-Projects
43(3)
References
46(3)
2 Statistics for Quality
49(94)
2.1 Variability in Populations
49(1)
2.2 Some Definitions
50(2)
2.2.1 The Population and a Sample
50(1)
2.2.2 Two Types of Data
51(1)
2.3 Quality vs. Variability
52(1)
2.4 Empirical Methods for Describing Populations
53(14)
2.4.1 The Frequency Distribution
53(1)
2.4.1.1 The Histogram
53(1)
2.4.1.2 The Cumulative Frequency Distribution
54(5)
2.4.2 Numerical Methods for Describing Populations
59(2)
2.4.2.1 Calculating the Average and Standard Deviation
61(1)
2.4.3 Other Graphical Methods
61(1)
2.4.3.1 Stem-and-Leaf Diagram
61(2)
2.4.3.2 Box-and-Whisker Plot
63(1)
2.4.4 Other Numerical Measures
64(1)
2.4.4.1 Measures of Location
64(1)
2.4.4.2 Measures of Dispersion
65(1)
2.4.5 Exercise in Empirical Methods
65(2)
2.5 Mathematical Models for Describing Populations
67(46)
2.5.1 Probability
67
2.5.1.1 Definition of Probability
68(1)
2.5.1.2 Computing the Probability of an Event
69(3)
2.5.1.3 Theorems on Probability
72(8)
2.5.1.4 Counting the Sample Points in a Sample Space
80(5)
2.5.2 Exercise in Probability
85(2)
2.5.3 Probability Distributions
87(1)
2.5.3.1 Random Variable
87(2)
2.5.3.2 Probability Mass Function
89(2)
2.5.3.3 Probability Density Function
91(1)
2.5.3.4 The Cumulative Distribution Function
92(1)
2.5.3.5 The Mean and Variance of a Distribution
93(3)
2.5.4 Some Important Probability Distributions
96(1)
2.5.4.1 The Binomial Distribution
96(3)
2.5.4.2 The Poisson Distribution
99(2)
2.5.4.3 The Normal Distribution
101(8)
2.5.4.4 Distribution of the Sample Average X
109(1)
2.5.4.5 The Central Limit Theorem
110(1)
2.5.5 Exercise in Probability Distributions
111(2)
2.6 Inference of Population Quality from a Sample
113(24)
2.6.1 Definitions
114(1)
2.6.2 Confidence Intervals
115(1)
2.6.2.1 CI for the μ of a Normal Population When σ Is Known
115(1)
2.6.2.2 Interpretation of CI
116(1)
2.6.2.3 CI for μ When σ Is Not Known
117(1)
2.6.2.4 CI for σ2 of a Normal Population
118(2)
2.6.3 Hypothesis Testing
120(1)
2.6.3.1 Test Concerning the Mean σ of a Normal Population When σ Is Known
121(2)
2.6.3.2 Why Place the Claim Made about a Parameter in H1?
123(1)
2.6.3.3 The Three Possible Alternate Hypotheses
124(1)
2.6.3.4 Test Concerning the Mean σ of a Normal Population When σ Is Not Known
125(2)
2.6.3.5 Test for Difference of Two Means When σs Are Known
127(2)
2.6.4 Tests for Normality
129(1)
2.6.4.1 Use of the Normal Probability Plot
129(1)
2.6.4.2 Normal Probability Plot on the Computer
130(3)
2.6.4.3 A Goodness-of-Fit Test
133(1)
2.6.5 The P-Value
134(1)
2.6.6 Exercise in Inference Methods
135(1)
2.6.6.1 Confidence Intervals
135(1)
2.6.6.2 Hypothesis Testing
136(1)
2.6.6.3 Goodness-of-Fit Test
137(1)
2.7 Mini-Projects
137(6)
References
142(1)
3 Quality in Design
143(74)
3.1 Planning for Quality
143(1)
3.1.1 The Product Creation Cycle
143(1)
3.2 Product Planning
144(22)
3.2.1 Finding Customer Needs
145(1)
3.2.1.1 Customer Survey
146(4)
3.2.2 Quality Function Deployment
150(2)
3.2.2.1 Customer Requirements and Design Features
152(1)
3.2.2.2 Prioritizing Design Features
153(1)
3.2.2.3 Choosing a Competitor as Benchmark
154(1)
3.2.2.4 Targets
154(1)
3.2.3 Reliability Fundamentals
155(1)
3.2.3.1 Definition of Reliability
156(1)
3.2.3.2 Hazard Function
156(2)
3.2.3.3 The Bathtub Curve
158(3)
3.2.3.4 Distribution of Product Life
161(1)
3.2.3.5 The Exponential Distribution
161(1)
3.2.3.6 Mean Time to Failure
162(3)
3.2.3.7 Reliability Engineering
165(1)
3.3 Product Design
166(31)
3.3.1 Parameter Design
166(1)
3.3.2 Design of Experiments
167(1)
3.3.2.1 22 Factorial Design
168(2)
3.3.2.2 Randomization
170(1)
3.3.2.3 Experimental Results
171(1)
3.3.2.4 Calculating the Factor Effects
172(1)
3.3.2.5 Main Effects
173(1)
3.3.2.6 Interaction Effects
174(1)
3.3.2.7 A Shortcut for Calculating Effects
175(1)
3.3.2.8 Determining the Significance of Effects
175(3)
3.3.2.9 The 23 Design
178(4)
3.3.2.10 Interpretation of the Results
182(1)
3.3.2.11 Model Building
183(1)
3.3.2.12 Taguchi Designs
184(1)
3.3.3 Tolerance Design
185(1)
3.3.3.1 Traditional Approaches
185(1)
3.3.3.2 Tolerancing According to Dr. Taguchi
186(1)
3.3.3.3 Assembly Tolerances
187(1)
3.3.3.4 The RSS Formula
188(3)
3.3.3.5 Natural Tolerance Limits
191(1)
3.3.4 Failure Mode and Effects Analysis
191(4)
3.3.5 Concurrent Engineering
195(1)
3.3.5.1 Design for Manufacturability/Assembly
196(1)
3.3.5.2 Design Reviews
197(1)
3.4 Process Design
197(12)
3.4.1 The Process Flow Chart
198(2)
3.4.2 Process Parameter Selection: Experiments
200(4)
3.4.3 Floor Plan Layout
204(1)
3.4.4 Process FMEA
205(1)
3.4.5 Process Control Plan
205(1)
3.4.6 Other Process Plans
205(1)
3.4.6.1 Process Instructions
205(2)
3.4.6.2 Packaging Standards
207(1)
3.4.6.3 Preliminary Process Capabilities
207(1)
3.4.6.4 Product and Process Validation
207(1)
3.4.6.5 Process Capability Results
208(1)
3.4.6.6 Measurement System Analysis
208(1)
3.4.6.7 Product/Process Approval
208(1)
3.4.6.8 Feedback, Assessment, and Corrective Action
208(1)
3.5 Exercise
209(8)
3.5.1 Practice Problems
209(3)
3.5.2 Mini-Projects
212(2)
References
214(3)
4 Quality in Production---Process Control I
217(80)
4.1 Process Control
217(1)
4.2 The Control Charts
218(3)
4.2.1 A Typical Control Chart
219(2)
4.2.2 Two Types of Data
221(1)
4.3 Measurement Control Charts
221(24)
4.3.1 X- and R-Charts
222(6)
4.3.2 A Few Notes about the X- and R-Charts
228(1)
4.3.2.1 The Many Uses of the Charts
228(1)
4.3.2.2 Selecting the Variable for Charting
229(1)
4.3.2.3 Preparing Instruments
230(1)
4.3.2.4 Preparing Check Sheets
230(1)
4.3.2.5 False Alarm in the X-Chart
231(1)
4.3.2.6 Determining Sample Size
231(1)
4.3.2.7 Why 3-Sigma Limits?
232(1)
4.3.2.8 Frequency of Sampling
233(1)
4.3.2.9 Rational Subgrouping
233(2)
4.3.2.10 When the Sample Size Changes for X- and R-Charts
235(1)
4.3.2.11 Improving the Sensitivity of the X-Chart
236(1)
4.3.2.12 Increasing the Sample Size
236(2)
4.3.2.13 Use of Warning Limits
238(1)
4.3.2.14 Use of Runs
238(2)
4.3.2.15 Patterns in Control Charts
240(1)
4.3.2.16 Control vs. Capability
240(2)
4.3.3 X and S-Charts
242(3)
4.4 Attribute Control Charts
245(17)
4.4.1 The P-Chart
245(3)
4.4.2 The C-Chart
248(3)
4.4.3 Some Special Attribute Control Charts
251(1)
4.4.3.1 The P-Chart with Varying Sample Sizes
251(3)
4.4.3.2 The nP-Chart
254(1)
4.4.3.3 The Percent Defectives Chart (100P-Chart)
255(1)
4.4.3.4 The U-Chart
255(3)
4.4.4 A Few Notes about the Attribute Control Charts
258(1)
4.4.4.1 Meaning of the LCL on the P- or C-Chart
258(1)
4.4.4.2 P-Chart for Many Characteristics
259(1)
4.4.4.3 Use of Runs
259(1)
4.4.4.4 Rational Subgrouping
259(3)
4.5 Summary on Control Charts
262(6)
4.5.1 Implementing SPC on Processes
263(5)
4.6 Process Capability
268(7)
4.6.1 Capability of a Process with Measurable Output
268(1)
4.6.2 Capability Indices Cp and Cpk
269(5)
4.6.3 Capability of a Process with Attribute Output
274(1)
4.7 Measurement System Analysis
275(14)
4.7.1 Properties of Instruments
275(2)
4.7.2 Measurement Standards
277(2)
4.7.3 Evaluating an Instrument
279(1)
4.7.3.1 Properties of a Good Instrument
279(1)
4.7.3.2 Evaluation Methods
279(1)
4.7.3.3 Resolution
280(3)
4.7.3.4 Bias
283(1)
4.7.3.5 Variability (Precision)
284(3)
4.7.3.6 A Quick Check of Instrument Adequacy
287(2)
4.8 Exercise
289(8)
4.8.1 Practice Problems
289(4)
4.8.2 Mini-Projects
293(3)
References
296(1)
5 Quality in Production---Process Control II
297(78)
5.1 Derivation of Limits
298(6)
5.1.1 Limits for the X-Chart
298(3)
5.1.2 Limits for the R-Chart
301(1)
5.1.3 Limits for the P-Chart
302(1)
5.1.4 Limits for the C-Chart
303(1)
5.2 Operating Characteristics of Control Charts
304(11)
5.2.1 Operating Characteristics of an X-Chart
304(1)
5.2.1.1 Computing the OC Curve of an X-Chart
305(2)
5.2.2 OC Curve of an R-Chart
307(2)
5.2.3 Average Run Length
309(3)
5.2.4 OC Curve of a P-Chart
312(1)
5.2.5 OC Curve of a C-Chart
313(2)
5.3 Measurement Control Charts for Special Situations
315(24)
5.3.1 X- and R-Charts When Standards for μ and/or σ are Given
315(1)
5.3.1.1 Case I: μ Given, σ Not Given
316(1)
5.3.1.2 Case II: μ and σ Given
316(3)
5.3.2 Control Charts for Slow Processes
319(1)
5.3.2.1 Control Chart for Individuals (X-Chart)
320(2)
5.3.2.2 Moving Average and MR Charts
322(2)
5.3.2.3 Notes on Moving Average and Moving Range Charts
324(2)
5.3.3 The Exponentially Weighted Moving Average Chart
326(5)
5.3.3.1 Limits for the EWMA Chart
331(2)
5.3.4 Control Charts for Short Runs
333(1)
5.3.4.1 The DNOM Chart
333(2)
5.3.4.2 The Standardized DNOM Chart
335(4)
5.4 Topics in Process Capability
339(10)
5.4.1 The Cpm Index
340(1)
5.4.2 Comparison of Cp, Cpk, and Cpm
341(1)
5.4.3 Confidence Interval for Capability Indices
342(2)
5.4.4 Motorola's 6σ Capability
344(5)
5.5 Topics in the Design of Experiments
349(20)
5.5.1 Analysis of Variance
349(6)
5.5.2 The General 2k Design
355(1)
5.5.3 The 24 Design
356(1)
5.5.4 2k Designs with Single Trial
357(2)
5.5.5 Fractional Factorials: One-Half Fractions
359(2)
5.5.5.1 Generating the One-Half Fraction
361(1)
5.5.5.2 Calculating the Effects
361(1)
5.5.6 Resolution of a Design
362(7)
5.6 Exercise
369(6)
5.6.1 Practice Problems
369(3)
5.6.2 Mini-Projects
372(1)
References
373(2)
6 Managing for Quality
375(34)
6.1 Managing Human Resources
375(27)
6.1.1 Importance of Human Resources
375(1)
6.1.2 Organizations
376(1)
6.1.2.1 Organization Structures
376(2)
6.1.2.2 Organizational Culture
378(2)
6.1.3 Quality Leadership
380(1)
6.1.3.1 Characteristics of a Good Leader
380(1)
6.1.4 Customer Focus
381(2)
6.1.5 Open Communications
383(2)
6.1.6 Empowerment
385(2)
6.1.7 Education and Training
387(1)
6.1.7.1 Need for Training
387(1)
6.1.7.2 Benefits from Training
388(1)
6.1.7.3 Planning for Training
388(2)
6.1.7.4 Training Methodology
390(1)
6.1.7.5 Finding Resources
391(1)
6.1.7.6 Evaluating Training Effectiveness
392(1)
6.1.8 Teamwork
392(1)
6.1.8.1 Team Building
393(1)
6.1.8.2 Selecting Team Members
393(1)
6.1.8.3 Defining the Team Mission
393(1)
6.1.8.4 Taking Stock of the Team's Strength
394(1)
6.1.8.5 Building the Team
394(1)
6.1.8.6 Basic Training for Quality Teams
395(1)
6.1.8.7 Desirable Characteristics among Team Members
396(1)
6.1.8.8 Why a Team?
397(1)
6.1.8.9 Ground Rules for Running a Team Meeting
397(1)
6.1.8.10 Making the Teams Work
398(1)
6.1.8.11 Different Types of Teams
399(1)
6.1.8.12 Quality Circles
400(1)
6.1.9 Motivation Methods
401(1)
6.1.10 Principles of Management
402(1)
6.2 Strategic Planning for Quality
402(5)
6.2.1 History of Planning
402(2)
6.2.2 Making the Strategic Plan
404(1)
6.2.3 Strategic Plan Deployment
405(2)
6.3 Exercise
407(2)
6.3.1 Practice Problems
407(1)
6.3.2 Mini-Project
408(1)
References
408(1)
7 Quality in Procurement
409(48)
7.1 Importance of Quality in Supplies
409(1)
7.2 Establishing a Good Supplier Relationship
410(1)
7.2.1 Essentials of a Good Supplier Relationship
410(1)
7.3 Choosing and Certifying Suppliers
411(3)
7.3.1 Single vs. Multiple Suppliers
411(1)
7.3.2 Choosing a Supplier
412(1)
7.3.3 Certifying a Supplier
413(1)
7.4 Specifying the Supplies Completely
414(1)
7.5 Auditing the Supplier
415(1)
7.6 Supply Chain Optimization
416(4)
7.6.1 The Trilogy of Supplier Relationship
417(1)
7.6.2 Planning
417(1)
7.6.3 Control
418(1)
7.6.4 Improvement
419(1)
7.7 Using Statistical Sampling for Acceptance
420(33)
7.7.1 The Need for Sampling Inspection
420(2)
7.7.2 Single Sampling Plans for Attributes
422(1)
7.7.2.1 The Operating Characteristic Curve
422(1)
7.7.2.2 Calculating the OC Curve of a Single Sampling Plan
423(3)
7.7.2.3 Designing an SSP
426(1)
7.7.2.4 Choosing a Suitable OC Curve
426(2)
7.7.2.5 Choosing a Single Sampling Plan
428(3)
7.7.3 Double Sampling Plans for Attributes
431(1)
7.7.3.1 Why Use a DSP?
432(1)
7.7.3.2 The OC Curve of a DSP
432(2)
7.7.4 The Average Sample Number of a Sampling Plan
434(2)
7.7.5 MIL-STD-105E (ANSI Z1.5)
436(2)
7.7.5.1 Selecting a Sampling Plan from MIL-STD-105E
438(9)
7.7.6 Average Outgoing Quality Limit
447(4)
7.7.7 Some Notes about Sampling Plans
451(1)
7.7.7.1 What Is a Good AQL?
451(1)
7.7.7.2 Available Choices for AQL Values in the MIL-STD-105E
451(1)
7.7.7.3 A Common Misconception about Sampling Plans
452(1)
7.7.7.4 Sampling Plans vs. Control Charts
452(1)
7.7.7.5 Variable Sampling Plans
452(1)
7.8 Exercise
453(4)
References
454(3)
8 Continuous Improvement of Quality
457(52)
8.1 The Need for Continuous Improvement
457(1)
8.2 The Problem-Solving Methodology
458(6)
8.2.1 Deming's PDCA Cycle
458(1)
8.2.2 Juran's Breakthrough Sequence
459(2)
8.2.3 The Generic Problem-Solving Methodology
461(3)
8.3 Quality Improvement Tools
464(29)
8.3.1 Cause-and-Effect Diagram
465(1)
8.3.2 Brainstorming
466(1)
8.3.3 Benchmarking
467(4)
8.3.4 Pareto Analysis
471(1)
8.3.5 Histogram
472(2)
8.3.6 Control Charts
474(2)
8.3.7 Scatter Plots
476(3)
8.3.8 Regression Analysis
479(1)
8.3.8.1 Simple Linear Regression
479(2)
8.3.8.2 Model Adequacy
481(1)
8.3.8.3 Test of Significance
482(5)
8.3.8.4 Multiple Linear Regression
487(2)
8.3.8.5 Nonlinear Regression
489(1)
8.3.9 Correlation Analysis
490(1)
8.3.9.1 Significance in Correlation
491(2)
8.4 Lean Manufacturing
493(11)
8.4.1 Quality Control
496(1)
8.4.2 Quantity Control
497(1)
8.4.3 Waste and Cost Control
498(1)
8.4.4 Total Productive Maintenance
499(1)
8.4.5 Stable, Standardized Processes
500(1)
8.4.6 Visual Management
500(2)
8.4.7 Leveling and Balancing
502(2)
8.4.8 The Lean Culture
504(1)
8.5 Exercise
504(5)
8.5.1 Practice Problems
504(2)
8.5.2 Term Project 8.1
506(1)
References
507(2)
9 A System for Quality
509(66)
9.1 The Systems Approach
509(1)
9.2 Dr. Deming's System
510(8)
9.2.1 Long-Term Planning
511(1)
9.2.2 Cultural Change
512(1)
9.2.3 Prevention Orientation
512(1)
9.2.4 Quality in Procurement
513(1)
9.2.5 Continuous Improvement
513(1)
9.2.6 Training, Education, Empowerment, and Teamwork
514(4)
9.3 Dr. Juran's System
518(9)
9.3.1 Quality Planning
519(3)
9.3.2 Quality Control
522(1)
9.3.3 Quality Improvement
523(4)
9.4 Dr. Feigenbaum's System
527(3)
9.5 Baldrige Award Criteria
530(13)
9.5.1 Criterion 1: Leadership
533(1)
9.5.2 Criterion 2: Strategic Planning
534(1)
9.5.3 Criterion 3: Customer Focus
535(2)
9.5.4 Criterion 4: Measurement, Analysis, and Knowledge Management
537(1)
9.5.5 Criterion 5: Workforce Focus
538(2)
9.5.6 Criterion 6: Process Management
540(1)
9.5.7 Criterion 7: Results
541(2)
9.6 ISO 9000 Quality Management Systems
543(3)
9.6.1 The ISO 9000 Standards
543(1)
9.6.2 The Eight Quality Management Principles
544(1)
9.6.3 Documentation in ISO 9000
545(1)
9.7 ISO 9001:2008 Requirements
546(15)
9.7.1 Quality Management System (4)
547(1)
9.7.2 Management Responsibility (5)
548(3)
9.7.3 Resource Management (6)
551(1)
9.7.4 Product Realization (7)
552(6)
9.7.5 Measurement, Analysis, and Improvement (8)
558(3)
9.8 Six Sigma System
561(8)
9.8.1 Six Themes of Six Sigma
562(1)
9.8.2 The 6σ Measure
563(2)
9.8.3 The Three Strategies
565(1)
9.8.4 The Two Improvement Processes
566(1)
9.8.5 The Five-Step Road Map
566(3)
9.8.6 The Organization for the Six Sigma System
569(1)
9.9 Summary of Quality Management Systems
569(2)
9.10 Exercise
571(4)
9.10.1 Practice Problem
571(2)
9.10.2 Mini-Projects
573(1)
References
573(2)
Appendix 1 575(12)
Appendix 2 587(8)
Index 595