Statistical and Managerial Techniques for Six Sigma Methodology: Theory and Application
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品牌: Stefano BaroneEva Lo Franco
基本信息出版社:Wiley; 1 (2012年3月20日)精装:396页正文语种:英语ISBN:0470711833条形码:9780470711835商品尺寸:17.4 x 2.4 x 24.6 cm商品重量:748 gASIN:0470711833商品描述内容简介Statistical and Managerial Techniques for Six Sigma Methodologyexamines the methodology through illustrating the most widespread tool and techniques involved in Six Sigma application. Both managerial and statistical aspects of Six Sigma will be analyzed, allowing the reader to apply these tools in the field. This book offers an insight on variation and risk management, and focuses on the structure and organizational aspects of the Six Sigma projects. It covers six sigma methodology, basic managerial techniques, basic statistical techniques, methods for variation and risk management and advanced statistical techniques. Clear and practical examples and demonstrations are included throughout the book.Packed with clear examples and case studies to illustrate the concepts and methodologies used in Six Sigma.Looks at both managerial and statistical aspects of Six Sigma, covering both basic and more advanced statistical techniques.Provides a chapter featuring case studies, equipping readers with the tools and techniques to apply methodology to real situations.Suitable for adoption in all courses for Six Sigma Green Belt and Black BeltFeatures a supporting website containing datasetsWell suited for Master level students in engineering and quality management, as well as MBA. Quality Managers, Consultants, and public and private companies implementing Six Sigma will also benefit greatly from the book.目录Preface xiAbout the Authors xiii1 Six Sigma methodology 11.1 Management by process 11.1.1 The concept of ‘process’ 11.1.2 Managing by process 11.1.3 The process performance triangle 21.1.4 Customer satisfaction 31.1.5 The success of enterprise 41.1.6 Innovation and Six Sigma 51.2 Meanings and origins of Six Sigma 51.2.1 Variation in products and processes 51.2.2 Meaning of ‘Six Sigma’ 61.2.3 Six Sigma process 71.2.4 Origins of Six Sigma 71.2.5 Six Sigma: Some definitions 91.3 Six Sigma projects 111.3.1 Why implement Six Sigma projects? 111.3.2 Six Sigma paths 121.4 The DMARIC path 181.4.1 Human resources and training 20References 212 Basic managerial techniques 232.1 For brainstorming 232.1.1 Cause–effect diagram 232.1.2 Affinity diagram (KJ analysis) 262.2 To manage the project 292.2.1 Work breakdown structure 292.2.2 Gantt chart 302.3 To describe and understand the processes 302.3.1 The SIPOC scheme 312.3.2 The flow chart 322.3.3 The ServQual model 332.4 To direct the improvement 372.4.1 The Kano model 37References 393 Basic statistical techniques 413.1 To explore data 413.1.1 Fundamental concepts and phases of the exploratory data analysis 413.1.2 Empirical frequency distribution of a numerical variable 463.1.3 Analysis by stratification 593.1.4 Other graphical representations 603.2 To define and calculate the uncertainty 623.2.1 Definitions of probability 633.2.2 Events and probabilities in the Venn diagram 643.2.3 Probability calculation rules 663.2.4 Dispositions, permutations and combinations 693.3 To model the random variability 703.3.1 Definition of random variable 703.3.2 Probability distribution function 713.3.3 Probability mass function for discrete random variables 713.3.4 Probability density function for continuous variables 713.3.5 Mean and variance of a random variable 723.3.6 Principal models of random variables 743.4 To draw conclusions from observed data 823.4.1 The inferential process 823.4.2 Sampling and samples 823.4.3 Adopting a probability distribution model by graphical analysis of the sample (probability plot) 843.4.4 Point estimation of the parameters of a Gaussian population 883.4.5 Interval estimation 903.4.6 Hypothesis testing 91References 934 Advanced managerial techniques 954.1 To describe processes 954.1.1 IDEF0 954.2 To manage a project 984.2.1 Project evaluation and review technique 984.2.2 Critical path method 1044.3 To analyse faults 1094.3.1 Failure mode and effect analysis 1104.3.2 Fault tree analysis 1144.4 To make decisions 1224.4.1 Analytic hierarchy process 1224.4.2 Response latency model 1294.4.3 Quality function deployment 135References 1435 Advanced statistical techniques 1455.1 To study the relationships between variables 1455.1.1 Linear regression analysis 1455.1.2 Logistic regression models 1565.1.3 Introduction to multivariate statistics 1575.2 To monitor and keep processes under control 1715.2.1 Process capability 1725.2.2 Online process control and main control charts 1745.2.3 Offline process control 1835.3 To improve products, services and production processes 1895.3.1 Robustness thinking 1895.3.2 Variation mode and effect analysis 2005.3.3 Systemic robust design 2095.3.4 Design of experiments 2125.3.5 Four case studies of robustness thinking 2435.4 To assess the measurement system 2595.4.1 Some definitions about measurement systems 2595.4.2 Measurement system analysis 2605.4.3 Lack of stability and drift of measurement system 2625.4.4 Preparation of a gauge R&R study 2635.4.5 Gauge R&R illustrative example 263References 2656 Six Sigma methodology in action: Selected Black Belt projects in Swedish organisations 2676.1 Resource planning improvement at SAAB Microwave Systems 2696.1.1 Presentation of SAAB Microwave Systems 2696.1.2 Project background 2696.1.3 Define phase 2706.1.4 Measure phase 2756.1.5 Analyse phase 2756.1.6 Improve phase (ideas and intentions) 2806.1.7 Control phase (ideas and intentions) 2826.2 Improving capacity planning of available beds: A case study for the medical wards at Sahlgrenska and O¨ stra Hospitals 2836.2.1 Presentation of Sahlgrenska and O¨ stra Hospitals 2846.2.2 Project background 2846.2.3 Define phase 2846.2.4 Measure phase 2866.2.5 Analyse phase 2886.2.6 Improve phase (ideas and intentions) 2936.2.7 Control phase (ideas and intentions) 2966.3 Controlling variation in play in mast production process at ATLET 2966.3.1 Presentation of Atlet AB 2976.3.2 Project background 2976.3.3 Define phase 2986.3.4 Measure phase 3026.3.5 Analyse phase 3076.3.6 Improve phase (ideas and intentions) 3126.3.7 Control phase (ideas and intentions) 3136.4 Optimising the recognition and treatment of unexpectedly worsening in-patients at K¨arnsjiukhuset, Skaraborg Hospital 3146.4.1 Presentation of Skaraborg Hospital 3146.4.2 Project background 3146.4.3 Define phase 3156.4.4 Measure phase 3216.4.5 Analyse phase (ideas and intentions) 3286.4.6 Improve phase (ideas and intentions) 3296.4.7 Control phase (ideas and intentions) 3296.5 Optimal scheduling for higher efficiency and minimal losses in warehouse at Structo Hydraulics AB 3306.5.1 Presentation of Structo Hydraulics AB 3306.5.2 Project background 3316.5.3 Define phase 3326.5.4 Measure phase 3356.5.5 Analyse phase 3386.5.6 Improve phase (planning) 3436.5.7 Control phase (planning) 3486.6 Reducing welding defect rate for a critical component of an aircraft engine 3506.6.1 Presentation of Volvo Aero Corporation 3506.6.2 Project background 3506.6.3 Define phase 3516.6.4 Measure phase 3546.6.5 Analyse phase 3596.6.6 Improve phase (ideas and intentions) 3646.6.7 Control phase (ideas and intentions) 3656.7 Attacking a problem of low capability in final machining for an aircraft engine component at VAC – Volvo Aero Corporation 3656.7.1 Presentation of Volvo Aero Corporation 3656.7.2 Project background 3666.7.3 Define phase 3666.7.4 Measure phase 3676.7.5 Analyse phase 3716.7.6 Improve phase (ideas and intentions) 373Index 375
