Muutke küpsiste eelistusi

E-raamat: Six Sigma for Organizational Excellence: A Statistical Approach

4.00/5 (2 hinnangut Goodreads-ist)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Apr-2015
  • Kirjastus: Springer, India, Private Ltd
  • Keel: eng
  • ISBN-13: 9788132223252
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Six Sigma for Organizational Excellence: A Statistical ApproachSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Apr-2015
  • Kirjastus: Springer, India, Private Ltd
  • Keel: eng
  • ISBN-13: 9788132223252
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

The book discusses the integrated concepts of quality engineering and management tools of statistics. The book helps in understanding and applying the concepts of quality through project management and technical analysis by using statistical methods. Prepared in a ready-to-use form, the text will help the practitioners to implement the Six Sigma principles in projects without any hurdles. Necessary tables, graphs, descriptions and checklists are provided to ease the referencing of tools and techniques. The concepts discussed in the book are all critically assessed and explained to enable their use for managerial decision making. The objectives of each chapter and its continuity with subsequent chapters are clearly established for smooth reading. Charts and plots, a number of worked-out examples and exercises are provided for better understanding of the theory. The science of Six Sigma project management, integrated through engineering concepts, is explained through statistical tools, which is the unique features of the book. Undergraduate, postgraduate and research students can make the optimum use of the integrated concepts of quality engineering and management tools of statistics. The book would also serve as a concise book for Six Sigma professionals, Green Belt, Black Belt and Master Black Belt trainers.

Arvustused

The book in fact is a mix of project management, statistics, and Six Sigma topics. The strength of the book is the fact that it builds a very strong bridge between statistics discipline and Six Sigma. The target audience is project managers and Information Technology professional and also students in management and related disciplines. The book would have enough material for a 1 year long course if it is selected as a textbook. (Morteza Marzjarani, Technometrics, Vol. 58 (3), August, 2016)

1 Six Sigma Concepts
1(18)
1.1 Introduction
1(5)
1.2 Six Sigma Methodology
6(2)
1.3 Six Sigma Tools
8(2)
1.4 Six Sigma Tasks
10(1)
1.5 Six Sigma Deliverables
11(1)
1.6 Lean Six Sigma
12(2)
1.7 Six Sigma: The Belt Systems
14(2)
1.8 Relevance for Managers
16(3)
References
17(2)
2 Six Sigma Project Management
19(20)
2.1 Project Management
19(1)
2.2 SWOT Analysis
20(1)
2.3 Project Phases
21(1)
2.4 Alignment with the Business Strategy
22(1)
2.5 Project Stakeholders
23(1)
2.6 Managing the Stakeholders
24(1)
2.7 A Six Sigma Project
25(3)
2.7.1 Probability Model-Based Project
27(1)
2.7.2 Regression Model-Based Project
27(1)
2.8 Quantitative Project Management
28(1)
2.9 Project Risk Assessment
29(2)
2.9.1 Quantifying the Risk
30(1)
2.10 Critical Evaluation of a Project
31(2)
2.11 Role of Computing Technology in Project Management
33(1)
2.12 Launch and Execution Process
34(1)
2.13 Closure of the Project
34(1)
2.14 The Climate for Success
34(1)
2.15 Relevance for Managers
35(4)
References
36(3)
3 Six Sigma Process
39(10)
3.1 Introduction
39(3)
3.2 Process Characterization
42(2)
3.3 Process Perception
44(1)
3.4 Process Capability
44(1)
3.5 Process Performance
45(1)
3.6 Process Improvement
46(1)
3.7 Process Control
46(1)
3.8 Relevance or Managers
47(2)
References
48(1)
4 Understanding Variation
49(18)
4.1 Types of Variation
50(2)
4.1.1 Special Cause Variation
50(1)
4.1.2 Common Cause Variations
50(2)
4.2 Causes of Variations
52(1)
4.3 Need for Measuring Variation
53(1)
4.4 Measurement Variations
54(1)
4.5 Measurement System Characteristics
55(2)
4.6 Measures of Variations
57(5)
4.7 Relevance for Managers
62(5)
References
65(2)
5 Sigma Estimation
67(14)
5.1 Introduction
67(1)
5.2 Some General Estimators of Standard Deviation
68(6)
5.3 Estimation of Standard Deviation Through Control Charts
74(3)
5.3.1 Default Method Based on Individual Measurements
74(1)
5.3.2 Sigma Estimation for Subgroups
75(1)
5.3.3 MVLUE Method Based on Subgroup Ranges
76(1)
5.3.4 MVLUE Method Based on Subgroup Standard Deviations
76(1)
5.4 Relevance for Managers
77(4)
References
79(2)
6 Sample Size Determination
81(18)
6.1 Accuracy and Precision
81(2)
6.2 Sample Size When Characteristic of Interest Is Mean
83(3)
6.3 Sample Size When Characteristic of Interest Is Proportion
86(5)
6.4 Sample Size When Characteristic of Interest Is Counts
91(1)
6.5 Sample Size When Characteristic of Interest Is Difference of Means
92(1)
6.6 Sample Size When Characteristic of Interest Is Difference of Proportions
93(2)
6.7 Relevance for Managers
95(4)
References
97(2)
7 Define Phase
99(24)
7.1 Project Charter
99(7)
7.1.1 The Problem Statement
99(1)
7.1.2 The Goal (or Result) Statement
100(1)
7.1.3 Customer Identification
101(3)
7.1.4 Process Models
104(2)
7.2 Defining Team Roles
106(3)
7.3 Managing the Project Team
109(2)
7.4 Planning Tools
111(3)
7.4.1 Gantt Chart
112(1)
7.4.2 Affinity Diagram
113(1)
7.5 Process Map and Flowchart
114(2)
7.6 Quality Function Deployment (QFD)
116(2)
7.6.1 House of Quality
116(1)
7.6.2 Kano's Model
117(1)
7.7 Understanding Defects, DPU, and DPMO
118(2)
7.8 Incorporating Suggestions, Improvements, and Complaints
120(1)
7.9 Readying for the Next Phase
120(1)
7.10 Define Check Sheets
121(1)
7.11 Relevance for Managers
121(2)
References
122(1)
8 Measure Phase
123(114)
8.1 Initiating Measure Phase
123(1)
8.2 Process Mapping
124(4)
8.2.1 Voice of Customer
125(2)
8.2.2 Voice of Process
127(1)
8.3 Adding Value Through Customer Service
128(1)
8.4 Value Stream Mapping
129(1)
8.5 Data Collection Plan
130(4)
8.5.1 Unit of Analysis
131(1)
8.5.2 Characteristics of Interest
131(1)
8.5.3 Data Types
132(2)
8.6 Cycle Time, Takt Time, Execution Time, and Delay Time
134(1)
8.7 Measurement System Analysis
135(11)
8.7.1 Assessing Bias in Continuous Measurements
136(5)
8.7.2 Assessing Bias of Attribute Data
141(5)
8.8 Descriptive Statistics
146(7)
8.8.1 Measures of Accuracy
147(2)
8.8.2 Measures of Symmetry and Shape
149(4)
8.9 Describing Sources of Variation
153(7)
8.9.1 Pareto Chart
153(1)
8.9.2 Control Charts
154(1)
8.9.3 Cause and Effect Diagram
155(3)
8.9.4 Prioritization Matrix
158(2)
8.10 Dealing with Uncertainty: Probability Concepts
160(7)
8.10.1 Principles of Counting
163(4)
8.11 Random Variables and Expectation
167(15)
8.11.1 Discrete Random Variables
167(3)
8.11.2 Continuous Random Variables
170(4)
8.11.3 Jointly Distributed Random Variables
174(8)
8.12 Probability Models
182(32)
8.12.1 Binomial Distribution
182(3)
8.12.2 Poisson Distribution
185(5)
8.12.3 Hypergeometric Distribution
190(4)
8.12.4 Normal Distribution
194(4)
8.12.5 Distributions Arising from the Normal
198(5)
8.12.6 Exponential Distribution
203(4)
8.12.7 Gamma Distribution
207(1)
8.12.8 Weibull Distribution
208(3)
8.12.9 Sampling Distributions
211(3)
8.13 Capability Analysis
214(5)
8.13.1 Process Potential Index (Cp Index)
215(1)
8.13.2 Process Performance Index (Cpk Index)
215(4)
8.14 Baseline Performance Evaluation
219(1)
8.15 Measure Checklists
220(1)
8.16 Relevance for Managers
221(16)
References
235(2)
9 Analyze Phase
237(126)
9.1 Process Mapping
237(1)
9.2 Brainstorming
238(2)
9.3 Failure Modes and Effects Analysis
240(4)
9.3.1 Design FMEA
242(1)
9.3.2 Process FMEA
243(1)
9.4 Histogram and Normality
244(3)
9.4.1 Probability Plotting
244(1)
9.4.2 Normal Probability Plot
245(2)
9.5 Parameter Estimation
247(10)
9.5.1 Point Estimation
248(3)
9.5.2 Confidence Interval Estimation
251(6)
9.6 Testing of Hypothesis
257(32)
9.6.1 Parametric Tests
259(10)
9.6.2 Nonparametric Tests
269(14)
9.6.3 Goodness-of-Fit Tests
283(6)
9.7 Modeling Relationship Between Variables
289(36)
9.7.1 Scatter Diagram and Correlations Study
290(5)
9.7.2 Regression Analysis
295(19)
9.7.3 Nonlinear Regression
314(11)
9.8 Analysis of Variance
325(20)
9.8.1 One-Way Classification or One-Factor Experiments
326(7)
9.8.2 Two-Way Classification or Two-Factor Experiments
333(8)
9.8.3 Three-Way Classification
341(4)
9.9 Root Cause Analysis
345(3)
9.9.1 Fault Tree Analysis
346(2)
9.9.2 5-Why's Techniques
348(1)
9.10 Readying for the Improve Phase
348(1)
9.11 Analyze Checklists
349(1)
9.12 Relevance for Managers
349(14)
References
361(2)
10 Improve Phase
363(62)
10.1 Balanced Scorecard (BSC)
363(2)
10.2 Kaizen Events
365(1)
10.3 5S Implementation
366(1)
10.4 The 3M's Technique
367(1)
10.5 Kanban
368(1)
10.6 Design of Experiments
369(27)
10.6.1 Principles of Experimentation
372(2)
10.6.2 Classification of Design of Experiments
374(1)
10.6.3 General Two-Factor Factorial Design
375(2)
10.6.4 22 Factorial Design
377(7)
10.6.5 23 Factorial Design
384(12)
10.7 Robust Designs
396(7)
10.7.1 Robust Parameter Design
398(5)
10.8 Process Mapping for Improvement
403(7)
10.8.1 Improving a Process Data
404(5)
10.8.2 Improving a Stable Process
409(1)
10.9 Simulation Techniques
410(7)
10.9.1 Model Selection and Validation
411(6)
10.10 Implementation and Validation
417(2)
10.11 Improve Check Sheets
419(1)
10.12 Relevance for Managers
419(6)
References
423(2)
11 Control Phase
425(76)
11.1 Control Plans
425(2)
11.2 Statistical Process Control
427(48)
11.2.1 Describing Variations
428(1)
11.2.2 Control Charts
429(2)
11.2.3 Control Charts for Variables
431(11)
11.2.4 Control Charts for Attributes
442(15)
11.2.5 Cumulative Sum Chart
457(4)
11.2.6 EWMA Chart
461(3)
11.2.7 Economic Design of Control Charts
464(2)
11.2.8 Role of Process Monitoring
466(2)
11.2.9 Nonparametric Control Charts
468(7)
11.3 Process Capability Studies
475(6)
11.4 Poke Yoke
481(1)
11.5 Designed for Six Sigma
482(2)
11.6 Quality Function Deployment
484(1)
11.7 Standardization
485(1)
11.8 Standard Operating Procedures and Work Instructions
485(1)
11.9 Process Dashboards
486(2)
11.10 Change Management and Resistance
488(1)
11.11 Documentation
488(1)
11.12 Control Check Sheets
489(1)
11.13 Relevance for Managers
490(11)
References
498(3)
12 Sigma Level Estimation
501(16)
12.1 Sigma Level for Normal Process
501(5)
12.2 Sigma Level for Non-normal Process
506(1)
12.3 Long-Term Versus Short-Term Sigma
506(6)
12.4 Cost of Poor Quality
512(1)
12.5 Relevance for Managers
513(4)
References
514(3)
13 Continuous Improvement
517(16)
13.1 Deming's Quality Philosophy
518(2)
13.2 Crosby's Quality Philosophy
520(2)
13.3 Juran's Quality Philosophy
522(1)
13.4 Feigenbaum's Quality Philosophy
523(1)
13.5 Ishikawa Quality Philosophy
523(1)
13.6 Taguchi Quality Philosophy
524(2)
13.7 Management Systems Standards
526(2)
13.8 Six Sigma Quality Philosophy
528(1)
13.9 Lean Six Sigma
529(2)
13.10 Relevance for Managers
531(2)
References
532(1)
14 Marketing Six Sigma
533(16)
14.1 What is Six Sigma Marketing?
534(3)
14.2 The Leading and Lagging Indicators
537(1)
14.3 Measurement-Based Key Marketing Indicators
538(1)
14.4 Relevance of Supply Chain Metrics in Marketing
539(2)
14.5 Importance of Data in Marketing
541(2)
14.6 Six Sigma Marketing Value Tools
543(2)
14.7 Relevance for Managers
545(4)
References
546(3)
15 Green Six Sigma
549(10)
15.1 Introduction
549(2)
15.2 Green Six Sigma Tools and Techniques
551(2)
15.3 Sustainability Issues of Green Six Sigma
553(1)
15.4 Benefits of Green Six Sigma
554(1)
15.5 Green Six Sigma: Some Quality Guidelines
554(2)
15.6 Green Six Sigma: Moving Toward Excellence
556(1)
15.7 Relevance for Managers
556(3)
References
557(2)
16 Six Sigma: Some Pros and Cons
559(10)
16.1 Introduction
559(1)
16.2 Six Sigma: Advantages and Disadvantages
560(3)
16.3 Six Sigma: Limitations
563(1)
16.4 Six Sigma: Dos and Don'ts
564(2)
16.5 Six Sigma: The Future
566(1)
16.6 Relevance for Managers
567(2)
References
568(1)
17 Six Sigma: Some Case Studies
569(14)
17.1 Introduction
569(1)
17.2 Case Study-1: Reduction in Extruder-Specific Power Consumption in Duplex
570(4)
17.3 Case Study-2: To Improve Product and Service Quality of CFL Lamps
574(5)
17.4 Case Study-3: Customer Complaint Resolution Through Re-engineering Debit Card and PIN Issuance Process
579(3)
17.5 Relevance for Managers
582(1)
Appendix 583(30)
Glossary 613
K. Muralidharan is professor and head of the Department of Statistics, Faculty of Science, Maharajah Sayajirao University of Baroda, Vadodara. In addition to being the director of the Population Research Centre of this university, Professor Muralidharan is also an adjunct faculty at Indian Institute of Technology Gandhinagar, India. He did post-doctoral fellowship from the Institute of Statistical Science, Academia Sinica, Taiwan. He is an internationally certified Six Sigma Master Black Belt from Indian Statistical Institute, Bangalore.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97881322232522e.html
Märksõnad: