Statistical and managerial techniques for six sigma methodology: theory and application

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010307154	HD62.15 B367 2012	Open Access Book	Book	Searching... Unknown
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Six Sigma methodology is a business management strategy which seeks to improve the quality of process output by identifying and removing the causes of errors and minimizing variability in manufacturing and business processes. This book examines the Six Sigma methodology through illustrating the most widespread tools and techniques involved in Six Sigma application. Both managerial and statistical aspects are analysed allowing the reader to apply these tools in the field. Furthermore, the book offers insight on variation and risk management and focuses on the structure and organizational aspects of Six Sigma projects.

Key features:

* Presents both statistical and managerial aspects of Six Sigma, covering both basic and more advanced statistical techniques.

* Provides clear examples and case studies to illustrate the concepts and methodologies used in Six Sigma.

* Written by experienced authors in the field.

This textbook is ideal for graduates studying Six Sigma for Black Belt and Green Belt qualifications as well as for engineering and quality management courses. Business consultants and consultancy firms implementing Six Sigma will also benefit from this book.

Author Notes

Stefano Barone, University of Palermo, Italy; and Chalmers University of Technology, Sweden
Eva Lo Franco, University of Palermo, Italy

Preface	p. xi
About the Authors	p. xiii
1 Six Sigma methodology	p. 1
1.1 Management by process	p. 1
1.1.1 The concept of 'process'	p. 1
1.1.2 Managing by process	p. 1
1.1.3 The process performance triangle	p. 2
1.1.4 Customer satisfaction	p. 3
1.1.5 The success of enterprise	p. 4
1.1.6 Innovation and Six Sigma	p. 5
1.2 Meanings and origins of Six Sigma	p. 5
1.2.1 Variation in products and processes	p. 5
1.2.2 Meaning of 'Six Sigma'	p. 6
1.2.3 Six Sigma process	p. 7
1.2.4 Origins of Six Sigma	p. 7
1.2.5 Six Sigma: Some definitions	p. 9
1.3 Six Sigma projects	p. 11
1.3.1 Why implement Six Sigma projects?	p. 11
1.3.2 Six Sigma paths	p. 12
1.4 The DMARIC path	p. 18
1.4.1 Human resources and training	p. 20
References	p. 21
2 Basic managerial techniques	p. 23
2.1 For brainstorming	p. 23
2.1.1 Cause-effect diagram	p. 23
2.1.2 Affinity diagram (KJ analysis)	p. 26
2.2 To manage the project	p. 29
2.2.1 Work breakdown structure	p. 29
2.2.2 Gantt chart	p. 30
2.3 To describe and understand the processes	p. 30
2.3.1 The SIPOC scheme	p. 31
2.3.2 The flow chart	p. 32
2.3.3 The ServQual model	p. 33
2.4 To direct the improvement	p. 37
2.4.1 The Kano model	p. 37
References	p. 39
3 Basic statistical techniques	p. 41
3.1 To explore data	p. 41
3.1.1 Fundamental concepts and phases of the exploratory data analysis	p. 41
3.1.2 Empirical frequency distribution of a numerical variable	p. 46
3.1.3 Analysis by stratification	p. 59
3.1.4 Other graphical representations	p. 60
3.2 To define and calculate the uncertainty	p. 62
3.2.1 Definitions of probability	p. 63
3.2.2 Events and probabilities in the Venn diagram	p. 64
3.2.3 Probability calculation rules	p. 66
3.2.4 Dispositions, permutations and combinations	p. 69
3.3 To model the random variability	p. 70
3.3.1 Definition of random variable	p. 70
3.3.2 Probability distribution function	p. 71
3.3.3 Probability mass function for discrete random variables	p. 71
3.3.4 Probability density function for continuous variables	p. 71
3.3.5 Mean and variance of a random variable	p. 72
3.3.6 Principal models of random variables	p. 74
3.4 To draw conclusions from observed data	p. 82
3.4.1 The inferential process	p. 82
3.4.2 Sampling and samples	p. 82
3.4.3 Adopting a probability distribution model by graphical analysis of the sample (probability plot)	p. 84
3.4.4 Point estimation of the parameters of a Gaussian population	p. 88
3.4.5 Interval estimation	p. 90
3.4.6 Hypothesis testing	p. 91
References	p. 93
4 Advanced managerial techniques	p. 95
4.1 To describe processes	p. 95
4.1.1 IDEFO	p. 95
4.2 To manage a project	p. 98
4.2.1 Project evaluation and review technique	p. 98
4.2.2 Critical path method	p. 104
4.3 To analyse faults	p. 109
4.3.1 Failure mode and effect analysis	p. 110
4.3.2 Fault tree analysis	p. 114
4.4 To make decisions	p. 122
4.4.1 Analytic hierarchy process	p. 122
4.4.2 Response latency model	p. 129
4.4.3 Quality function deployment	p. 135
References	p. 143
5 Advanced statistical techniques	p. 145
5.1 To study the relationships between variables	p. 145
5.1.1 Linear regression analysis	p. 145
5.1.2 Logistic regression models	p. 156
5.1.3 Introduction to multivariate statistics	p. 157
5.2 To monitor and keep processes under control	p. 171
5.2.1 Process capability	p. 172
5.2.2 Online process control and main control charts	p. 174
5.2.3 Offline process control	p. 183
5.3 To improve products, services and production processes	p. 189
5.3.1 Robustness thinking	p. 189
5.3.2 Variation mode and effect analysis	p. 200
5.3.3 Systemic robust design	p. 209
5.3.4 Design of experiments	p. 212
5.3.5 Four case studies of robustness thinking	p. 243
5.4 To assess the measurement system	p. 259
5.4.1 Some definitions about measurement systems	p. 259
5.4.2 Measurement system analysis	p. 260
5.4.3 Lack of stability and drift of measurement system	p. 262
5.4.4 Preparation of a gauge R&R study	p. 263
5.4.5 Gauge R&R illustrative example	p. 263
References	p. 265
6 Six Sigma methodology in action: Selected Black Belt projects in Swedish organisations	p. 267
6.1 Resource planning improvement at SAAB Microwave Systems	p. 269
6.1.1 Presentation of SAAB Microwave Systems	p. 269
6.1.2 Project background	p. 269
6.1.3 Define phase	p. 270
6.1.4 Measure phase	p. 275
6.1.5 Analyse phase	p. 275
6.1.6 Improve phase (ideas and intentions)	p. 280
6.1.7 Control phase (ideas and intentions)	p. 282
6.2 Improving capacity planning of available beds: A case study for the medical wards at Sahlgrenska and Östra Hospitals	p. 283
6.2.1 Presentation of Sahlgrenska and Östra Hospitals	p. 284
6.2.2 Project background	p. 284
6.2.3 Define phase	p. 284
6.2.4 Measure phase	p. 286
6.2.5 Analyse phase	p. 288
6.2.6 Improve phase (ideas and intentions)	p. 293
6.2.7 Control phase (ideas and intentions)	p. 296
6.3 Controlling variation in play in mast production process at ATLET	p. 296
6.3.1 Presentation of Atlet AB	p. 297
6.3.2 Project background	p. 297
6.3.3 Define phase	p. 298
6.3.4 Measure phase	p. 302
6.3.5 Analyse phase	p. 307
6.3.6 Improve phase (ideas and intentions)	p. 312
6.3.7 Control phase (ideas and intentions)	p. 313
6.4 Optimising the recognition and treatment of unexpectedly worsening in-patients at Kärnsjiukhuset, Skaraborg Hospital	p. 314
6.4.1 Presentation of Skaraborg Hospital	p. 314
6.4.2 Project background	p. 314
6.4.3 Define phase	p. 315
6.4.4 Measure phase	p. 321
6.4.5 Analyse phase (ideas and intentions)	p. 328
6.4.6 Improve phase (ideas and intentions)	p. 329
6.4.7 Control phase (ideas and intentions)	p. 329
6.5 Optimal scheduling for higher efficiency and minimal losses in warehouse at Structo Hydraulics AB	p. 330
6.5.1 Presentation of Structo Hydraulics AB	p. 330
6.5.2 Project background	p. 331
6.5.3 Define phase	p. 332
6.5.4 Measure phase	p. 335
6.5.5 Analyse phase	p. 338
6.5.6 Improve phase (planning)	p. 343
6.5.7 Control phase (planning)	p. 348
6.6 Reducing welding defect rate for a critical component of an aircraft engine	p. 350
6.6.1 Presentation of Volvo Aero Corporation	p. 350
6.6.2 Project background	p. 350
6.6.3 Define phase	p. 351
6.6.4 Measure phase	p. 354
6.6.5 Analyse phase	p. 359
6.6.6 Improve phase (ideas and intentions)	p. 364
6.6.7 Control phase (ideas and intentions)	p. 365
6.7 Attacking a problem of low capability in final machining for an aircraft engine component at VAC - Volvo Aero Corporation	p. 365
6.7.1 Presentation of Volvo Aero Corporation	p. 365
6.7.2 Project background	p. 366
6.7.3 Define phase	p. 366
6.7.4 Measure phase	p. 367
6.7.5 Analyse phase	p. 371
6.7.6 Improve phase (ideas and intentions)	p. 373
Index	p. 375

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Author Notes

Table of Contents