Statistical and Managerial Techniques for Six Sigma Methodology

Name: Statistical and Managerial Techniques for Six Sigma Methodology | Theory and Application
Brand: Wiley
Price: 65.99 EUR
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Theory and Application

Stefano Barone Eva Lo Franco(Autor*in)

Wiley (Verlag)

Erschienen am 3. Januar 2012

400 Seiten

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978-1-119-96839-9 (ISBN)

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Intro
Statistical and Managerial Techniques for Six Sigma Methodology
Contents
Preface
About the Authors
1 Six Sigma methodology
1.1 Management by process
1.1.1 The concept of 'process'
1.1.2 Managing by process
1.1.3 The process performance triangle
1.1.4 Customer satisfaction
1.1.5 The success of enterprise
1.1.6 Innovation and Six Sigma
1.2 Meanings and origins of Six Sigma
1.2.1 Variation in products and processes
1.2.2 Meaning of 'Six Sigma'
1.2.3 Six Sigma process
1.2.4 Origins of Six Sigma
1.2.5 Six Sigma: Some definitions
1.3 Six Sigma projects
1.3.1 Why implement Six Sigma projects?
1.3.2 Six Sigma paths
1.4 The DMARIC path
1.4.1 Human resources and training
References
2 Basic managerial techniques
2.1 For brainstorming
2.1.1 Cause-effect diagram
2.1.2 Affinity diagram (KJ analysis)
2.2 To manage the project
2.2.1 Work breakdown structure
2.2.2 Gantt chart
2.3 To describe and understand the processes
2.3.1 The SIPOC scheme
2.3.2 The flow chart
2.3.3 The ServQual model
2.4 To direct the improvement
2.4.1 The Kano model
References
3 Basic statistical techniques
3.1 To explore data
3.1.1 Fundamental concepts and phases of the exploratory data analysis
3.1.2 Empirical frequency distribution of a numerical variable
3.1.3 Analysis by stratification
3.1.4 Other graphical representations
3.2 To define and calculate the uncertainty
3.2.1 Definitions of probability
3.2.2 Events and probabilities in the Venn diagram
3.2.3 Probability calculation rules
3.2.4 Dispositions, permutations and combinations
3.3 To model the random variability
3.3.1 Definition of random variable
3.3.2 Probability distribution function
3.3.3 Probability mass function for discrete random variables
3.3.4 Probability density function for continuous variables
3.3.5 Mean and variance of a random variable
3.3.6 Principal models of random variables
3.4 To draw conclusions from observed data
3.4.1 The inferential process
3.4.2 Sampling and samples
3.4.3 Adopting a probability distribution model by graphical analysis of the sample (probability plot)
3.4.4 Point estimation of the parameters of a Gaussian population
3.4.5 Interval estimation
3.4.6 Hypothesis testing
References
4 Advanced managerial techniques
4.1 To describe processes
4.1.1 IDEF0
4.2 To manage a project
4.2.1 Project evaluation and review technique
4.2.2 Critical path method
4.3 To analyse faults
4.3.1 Failure mode and effect analysis
4.3.2 Fault tree analysis
4.4 To make decisions
4.4.1 Analytic hierarchy process
4.4.2 Response latency model
4.4.3 Quality function deployment
References
5 Advanced statistical techniques
5.1 To study the relationships between variables
5.1.1 Linear regression analysis
5.1.2 Logistic regression models
5.1.3 Introduction to multivariate statistics
5.2 To monitor and keep processes under control
5.2.1 Process capability
5.2.2 Online process control and main control charts
5.2.3 Offline process control
5.3 To improve products, services and production processes
5.3.1 Robustness thinking
5.3.2 Variation mode and effect analysis
5.3.3 Systemic robust design
5.3.4 Design of experiments
5.3.5 Four case studies of robustness thinking
5.4 To assess the measurement system
5.4.1 Some definitions about measurement systems
5.4.2 Measurement system analysis
5.4.3 Lack of stability and drift of measurement system
5.4.4 Preparation of a gauge R&R study
5.4.5 Gauge R&R illustrative example
References
6 Six Sigma methodology in action: Selected Black Belt projects in Swedish organisations
6.1 Resource planning improvement at SAAB Microwave Systems
6.1.1 Presentation of SAAB Microwave Systems
6.1.2 Project background
6.1.3 Define phase
6.1.4 Measure phase
6.1.5 Analyse phase
6.1.6 Improve phase (ideas and intentions)
6.1.7 Control phase (ideas and intentions)
6.2 Improving capacity planning of available beds: A case study for the medical wards at Sahlgrenska and O¨ stra Hospitals
6.2.1 Presentation of Sahlgrenska and O¨ stra Hospitals
6.2.2 Project background
6.2.3 Define phase
6.2.4 Measure phase
6.2.5 Analyse phase
6.2.6 Improve phase (ideas and intentions)
6.2.7 Control phase (ideas and intentions)
6.3 Controlling variation in play in mast production process at ATLET
6.3.1 Presentation of Atlet AB
6.3.2 Project background
6.3.3 Define phase
6.3.4 Measure phase
6.3.5 Analyse phase
6.3.6 Improve phase (ideas and intentions)
6.3.7 Control phase (ideas and intentions)
6.4 Optimising the recognition and treatment of unexpectedly worsening in-patients at K¨arnsjiukhuset, Skaraborg Hospital
6.4.1 Presentation of Skaraborg Hospital
6.4.2 Project background
6.4.3 Define phase
6.4.4 Measure phase
6.4.5 Analyse phase (ideas and intentions)
6.4.6 Improve phase (ideas and intentions)
6.4.7 Control phase (ideas and intentions)
6.5 Optimal scheduling for higher efficiency and minimal losses in warehouse at Structo Hydraulics AB
6.5.1 Presentation of Structo Hydraulics AB
6.5.2 Project background
6.5.3 Define phase
6.5.4 Measure phase
6.5.5 Analyse phase
6.5.6 Improve phase (planning)
6.5.7 Control phase (planning)
6.6 Reducing welding defect rate for a critical component of an aircraft engine
6.6.1 Presentation of Volvo Aero Corporation
6.6.2 Project background
6.6.3 Define phase
6.6.4 Measure phase
6.6.5 Analyse phase
6.6.6 Improve phase (ideas and intentions)
6.6.7 Control phase (ideas and intentions)
6.7 Attacking a problem of low capability in final machining for an aircraft engine component at VAC - Volvo Aero Corporation
6.7.1 Presentation of Volvo Aero Corporation
6.7.2 Project background
6.7.3 Define phase
6.7.4 Measure phase
6.7.5 Analyse phase
6.7.6 Improve phase (ideas and intentions)
Index

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