Statistical and Managerial Techniques for Six Sigma Methodology: Theory and Application

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.   

Table of Contents:
Preface xi About the Authors xiii 1 Six Sigma methodology 1 1.1 Management by process 1 1.1.1 The concept of ‘process’ 1 1.1.2 Managing by process 1 1.1.3 The process performance triangle 2 1.1.4 Customer satisfaction 3 1.1.5 The success of enterprise 4 1.1.6 Innovation and Six Sigma 5 1.2 Meanings and origins of Six Sigma 5 1.2.1 Variation in products and processes 5 1.2.2 Meaning of ‘Six Sigma’ 6 1.2.3 Six Sigma process 7 1.2.4 Origins of Six Sigma 7 1.2.5 Six Sigma: Some definitions 9 1.3 Six Sigma projects 11 1.3.1 Why implement Six Sigma projects? 11 1.3.2 Six Sigma paths 12 1.4 The DMARIC path 18 1.4.1 Human resources and training 20 References 21 2 Basic managerial techniques 23 2.1 For brainstorming 23 2.1.1 Cause–effect diagram 23 2.1.2 Affinity diagram (KJ analysis) 26 2.2 To manage the project 29 2.2.1 Work breakdown structure 29 2.2.2 Gantt chart 30 2.3 To describe and understand the processes 30 2.3.1 The SIPOC scheme 31 2.3.2 The flow chart 32 2.3.3 The ServQual model 33 2.4 To direct the improvement 37 2.4.1 The Kano model 37 References 39 3 Basic statistical techniques 41 3.1 To explore data 41 3.1.1 Fundamental concepts and phases of the exploratory data analysis 41 3.1.2 Empirical frequency distribution of a numerical variable 46 3.1.3 Analysis by stratification 59 3.1.4 Other graphical representations 60 3.2 To define and calculate the uncertainty 62 3.2.1 Definitions of probability 63 3.2.2 Events and probabilities in the Venn diagram 64 3.2.3 Probability calculation rules 66 3.2.4 Dispositions, permutations and combinations 69 3.3 To model the random variability 70 3.3.1 Definition of random variable 70 3.3.2 Probability distribution function 71 3.3.3 Probability mass function for discrete random variables 71 3.3.4 Probability density function for continuous variables 71 3.3.5 Mean and variance of a random variable 72 3.3.6 Principal models of random variables 74 3.4 To draw conclusions from observed data 82 3.4.1 The inferential process 82 3.4.2 Sampling and samples 82 3.4.3 Adopting a probability distribution model by graphical analysis of the sample (probability plot) 84 3.4.4 Point estimation of the parameters of a Gaussian population 88 3.4.5 Interval estimation 90 3.4.6 Hypothesis testing 91 References 93 4 Advanced managerial techniques 95 4.1 To describe processes 95 4.1.1 IDEF0 95 4.2 To manage a project 98 4.2.1 Project evaluation and review technique 98 4.2.2 Critical path method 104 4.3 To analyse faults 109 4.3.1 Failure mode and effect analysis 110 4.3.2 Fault tree analysis 114 4.4 To make decisions 122 4.4.1 Analytic hierarchy process 122 4.4.2 Response latency model 129 4.4.3 Quality function deployment 135 References 143 5 Advanced statistical techniques 145 5.1 To study the relationships between variables 145 5.1.1 Linear regression analysis 145 5.1.2 Logistic regression models 156 5.1.3 Introduction to multivariate statistics 157 5.2 To monitor and keep processes under control 171 5.2.1 Process capability 172 5.2.2 Online process control and main control charts 174 5.2.3 Offline process control 183 5.3 To improve products, services and production processes 189 5.3.1 Robustness thinking 189 5.3.2 Variation mode and effect analysis 200 5.3.3 Systemic robust design 209 5.3.4 Design of experiments 212 5.3.5 Four case studies of robustness thinking 243 5.4 To assess the measurement system 259 5.4.1 Some definitions about measurement systems 259 5.4.2 Measurement system analysis 260 5.4.3 Lack of stability and drift of measurement system 262 5.4.4 Preparation of a gauge R&R study 263 5.4.5 Gauge R&R illustrative example 263 References 265 6 Six Sigma methodology in action: Selected Black Belt projects in Swedish organisations 267 6.1 Resource planning improvement at SAAB Microwave Systems 269 6.1.1 Presentation of SAAB Microwave Systems 269 6.1.2 Project background 269 6.1.3 Define phase 270 6.1.4 Measure phase 275 6.1.5 Analyse phase 275 6.1.6 Improve phase (ideas and intentions) 280 6.1.7 Control phase (ideas and intentions) 282 6.2 Improving capacity planning of available beds: A case study for the medical wards at Sahlgrenska and O¨ stra Hospitals 283 6.2.1 Presentation of Sahlgrenska and O¨ stra Hospitals 284 6.2.2 Project background 284 6.2.3 Define phase 284 6.2.4 Measure phase 286 6.2.5 Analyse phase 288 6.2.6 Improve phase (ideas and intentions) 293 6.2.7 Control phase (ideas and intentions) 296 6.3 Controlling variation in play in mast production process at ATLET 296 6.3.1 Presentation of Atlet AB 297 6.3.2 Project background 297 6.3.3 Define phase 298 6.3.4 Measure phase 302 6.3.5 Analyse phase 307 6.3.6 Improve phase (ideas and intentions) 312 6.3.7 Control phase (ideas and intentions) 313 6.4 Optimising the recognition and treatment of unexpectedly worsening in-patients at K¨arnsjiukhuset, Skaraborg Hospital 314 6.4.1 Presentation of Skaraborg Hospital 314 6.4.2 Project background 314 6.4.3 Define phase 315 6.4.4 Measure phase 321 6.4.5 Analyse phase (ideas and intentions) 328 6.4.6 Improve phase (ideas and intentions) 329 6.4.7 Control phase (ideas and intentions) 329 6.5 Optimal scheduling for higher efficiency and minimal losses in warehouse at Structo Hydraulics AB 330 6.5.1 Presentation of Structo Hydraulics AB 330 6.5.2 Project background 331 6.5.3 Define phase 332 6.5.4 Measure phase 335 6.5.5 Analyse phase 338 6.5.6 Improve phase (planning) 343 6.5.7 Control phase (planning) 348 6.6 Reducing welding defect rate for a critical component of an aircraft engine 350 6.6.1 Presentation of Volvo Aero Corporation 350 6.6.2 Project background 350 6.6.3 Define phase 351 6.6.4 Measure phase 354 6.6.5 Analyse phase 359 6.6.6 Improve phase (ideas and intentions) 364 6.6.7 Control phase (ideas and intentions) 365 6.7 Attacking a problem of low capability in final machining for an aircraft engine component at VAC – Volvo Aero Corporation 365 6.7.1 Presentation of Volvo Aero Corporation 365 6.7.2 Project background 366 6.7.3 Define phase 366 6.7.4 Measure phase 367 6.7.5 Analyse phase 371 6.7.6 Improve phase (ideas and intentions) 373 Index 375

About the Author :
Stefano Barone, Assistant Professor at University of Palermo, Italy and Associate Professor at Chalmers University of Technology, Sweden. Eva Lo Franco, Assistant Researcher at the University of Palermo, Italy.