John Oakland is one of the world’s top ten gurus in quality and operational excellence; Executive Chairman, Oakland Group; Emeritus Professor of Quality & Business Excellence at Leeds University Business School; Fellow of the Chartered Quality Institute (CQI); Fellow of the Royal Statistical Society (RSS); Fellow of the Cybernetics Society (CybSoc); Fellow of Research Quality Association (RQA).Robert Oakland is Director in the Oakland Group and works across the globe helping complex organizations to unlock the power in their data using advanced analytical and statistical techniques to improve the quality, cost and delivery of their products and services.

• Preface Part 1 Process understanding1 Quality, processes and controlObjectives 1.1 The basic concepts 1.2 Design, conformance and costs 1.3 Quality, processes, systems, teams, tools and SPC1.4 Some basic tools1.5 SPC, ‘big data’ and data analyticsChapter highlights References and further reading 2 Understanding the process Objectives2.1 Improving customer satisfaction through process management 2.2 Information about the process 2.3 Process mapping and flowcharting 2.4 Process analysis 2.5 Statistical process control and process understanding Chapter highlights References and further reading3 Process data collection and presentation Objectives 3.1 The systematic approach 3.2 Data collection 3.3 Bar charts and histograms 3.4 Graphs, run charts and other pictures3.5 Data quality and sharing 3.6 Conclusions Chapter highlights References and further reading Part 2 Process variability4 Variation – understanding and decision making Objectives 4.1 How some managers look at data 4.2 Interpretation of data 4.3 Causes of variation 4.4 Accuracy and precision 4.5 Variation and management Chapter highlights References and further reading 5 Variables and process variation Objectives 5.1 Measures of accuracy or centering 5.2 Measures of precision or spread 5.3 The normal distribution 5.4 Sampling and averages Chapter highlightsWorked examples using the normal distribution References and further reading Part 3 Process control6 Process control using variables Objectives 6.1 Means, ranges and charts 6.2 Are we in control? 6.3 Do we continue to be in control? 6.4 Choice of sample size and frequency and control limits 6.5 Short-, medium- and long-term variation6.6 Process control of variables in the world of big data Chapter highlightsWorked examples References and further reading7 Other types of control charts for variables Objectives 7.1 Beyond the mean and range chart 7.2 Process control for individual data 7.3 Median, mid-range and multi-vari charts 7.4 Moving mean, moving range and exponentially weighted moving average (EWMA) charts 7.5 Control charts for standard deviation (σ) 7.6 Techniques for short-run SPC 7.7 Summarizing control charts for variables and big data Chapter highlightsWorked example References and further reading 8 Process control by attributes Objectives 8.1 Underlying concepts 8.2 Process control for number of defectives or non-conforming units 8.3 Process control for proportion defective or non-conforming units 8.4 Process control for number of defects/non-conformities 8.5 Attribute data in non-manufacturingChapter highlightsWorked examples References and further reading 9 Cumulative sum (cusum) charts Objectives 9.1 Introduction to cusum charts 9.2 Interpretation of simple cusum charts 9.3 Product screening and pre-selection 9.4 Cusum decision procedures Chapter highlightsWorked examples References and further reading Part 4 Process capability10 Process capability for variables and its measurement Objectives 10.1 Will it meet the requirements? 10.2 Process capability indices 10.3 Interpreting capability indices 10.4 The use of control chart and process capability data 10.5 Service industry example of process capability analysis Chapter highlightsWorked examples References and further readingPart 5 Process improvement11 Process problem solving and improvement Objectives 11.1 Introduction 11.2 Pareto analysis 11.3 Cause and effect analysis 11.4 Scatter diagrams 11.5 Stratification 11.6 Summarizing problem solving and improvement Chapter highlightsWorked examples References and further reading 12 Managing out-of-control processes Objectives 12.1 Introduction 12.2 Process improvement strategy 12.3 Use of control charts and data analytics for trouble-shooting12.4 Assignable or special causes of variation and big data Chapter highlights References and further reading 13 Designing the statistical process control system with big data Objectives 13.1 SPC and the quality management system 13.2 Teamwork and process control/improvement 13.3 Improvements in the process 13.4 Taguchi methods13.5 System performance – the confusion matrix 13.6 Moving forward with big data analytics and SPC Chapter highlights References and further reading 14 Six-sigma process quality Objectives 14.1 Introduction 14.2 The six-sigma improvement model 14.3 Six-sigma and the role of design of experiments 14.4 Building a six-sigma organization and culture 14.5 Ensuring the financial success of six-sigma projects 14.6 Concluding observations and links with excellence models and data analytics Chapter highlightsReferences and further reading 15 Data governance and data analyticsObjectives 15.1 Introduction – data attributes 15.2 Data governance strategies 15.3 Data analytics and insight15.4 Future of process control and assurance Chapter highlightsReferences and further readingAppendicesA The normal distribution and non-normality B Constants used in the design of control charts for mean C Constants used in the design of control charts for range D Constants used in the design of control charts for median and range E Constants used in the design of control charts for standard deviation F Cumulative Poisson probability curves G Confidence limits and tests of significance H OC curves and ARL curves for X and R charts I Autocorrelation J Approximations to assist in process control of attributes K Glossary of terms and symbols Index

John Oakland is one of the world’s top ten gurus in quality and operational excellence; Executive Chairman, Oakland Group; Emeritus Professor of Quality & Business Excellence at Leeds University Business School; Fellow of the Chartered Quality Institute (CQI); Fellow of the Royal Statistical Society (RSS); Fellow of the Cybernetics Society (CybSoc); Fellow of Research Quality Association (RQA).Robert Oakland is Director in the Oakland Group and works across the globe helping complex organizations to unlock the power in their data using advanced analytical and statistical techniques to improve the quality, cost and delivery of their products and services.

• Preface Part 1 Process understanding1 Quality, processes and controlObjectives 1.1 The basic concepts 1.2 Design, conformance and costs 1.3 Quality, processes, systems, teams, tools and SPC1.4 Some basic tools1.5 SPC, ‘big data’ and data analyticsChapter highlights References and further reading 2 Understanding the process Objectives2.1 Improving customer satisfaction through process management 2.2 Information about the process 2.3 Process mapping and flowcharting 2.4 Process analysis 2.5 Statistical process control and process understanding Chapter highlights References and further reading3 Process data collection and presentation Objectives 3.1 The systematic approach 3.2 Data collection 3.3 Bar charts and histograms 3.4 Graphs, run charts and other pictures3.5 Data quality and sharing 3.6 Conclusions Chapter highlights References and further reading Part 2 Process variability4 Variation – understanding and decision making Objectives 4.1 How some managers look at data 4.2 Interpretation of data 4.3 Causes of variation 4.4 Accuracy and precision 4.5 Variation and management Chapter highlights References and further reading 5 Variables and process variation Objectives 5.1 Measures of accuracy or centering 5.2 Measures of precision or spread 5.3 The normal distribution 5.4 Sampling and averages Chapter highlightsWorked examples using the normal distribution References and further reading Part 3 Process control6 Process control using variables Objectives 6.1 Means, ranges and charts 6.2 Are we in control? 6.3 Do we continue to be in control? 6.4 Choice of sample size and frequency and control limits 6.5 Short-, medium- and long-term variation6.6 Process control of variables in the world of big data Chapter highlightsWorked examples References and further reading7 Other types of control charts for variables Objectives 7.1 Beyond the mean and range chart 7.2 Process control for individual data 7.3 Median, mid-range and multi-vari charts 7.4 Moving mean, moving range and exponentially weighted moving average (EWMA) charts 7.5 Control charts for standard deviation (σ) 7.6 Techniques for short-run SPC 7.7 Summarizing control charts for variables and big data Chapter highlightsWorked example References and further reading 8 Process control by attributes Objectives 8.1 Underlying concepts 8.2 Process control for number of defectives or non-conforming units 8.3 Process control for proportion defective or non-conforming units 8.4 Process control for number of defects/non-conformities 8.5 Attribute data in non-manufacturingChapter highlightsWorked examples References and further reading 9 Cumulative sum (cusum) charts Objectives 9.1 Introduction to cusum charts 9.2 Interpretation of simple cusum charts 9.3 Product screening and pre-selection 9.4 Cusum decision procedures Chapter highlightsWorked examples References and further reading Part 4 Process capability10 Process capability for variables and its measurement Objectives 10.1 Will it meet the requirements? 10.2 Process capability indices 10.3 Interpreting capability indices 10.4 The use of control chart and process capability data 10.5 Service industry example of process capability analysis Chapter highlightsWorked examples References and further readingPart 5 Process improvement11 Process problem solving and improvement Objectives 11.1 Introduction 11.2 Pareto analysis 11.3 Cause and effect analysis 11.4 Scatter diagrams 11.5 Stratification 11.6 Summarizing problem solving and improvement Chapter highlightsWorked examples References and further reading 12 Managing out-of-control processes Objectives 12.1 Introduction 12.2 Process improvement strategy 12.3 Use of control charts and data analytics for trouble-shooting12.4 Assignable or special causes of variation and big data Chapter highlights References and further reading 13 Designing the statistical process control system with big data Objectives 13.1 SPC and the quality management system 13.2 Teamwork and process control/improvement 13.3 Improvements in the process 13.4 Taguchi methods13.5 System performance – the confusion matrix 13.6 Moving forward with big data analytics and SPC Chapter highlights References and further reading 14 Six-sigma process quality Objectives 14.1 Introduction 14.2 The six-sigma improvement model 14.3 Six-sigma and the role of design of experiments 14.4 Building a six-sigma organization and culture 14.5 Ensuring the financial success of six-sigma projects 14.6 Concluding observations and links with excellence models and data analytics Chapter highlightsReferences and further reading 15 Data governance and data analyticsObjectives 15.1 Introduction – data attributes 15.2 Data governance strategies 15.3 Data analytics and insight15.4 Future of process control and assurance Chapter highlightsReferences and further readingAppendicesA The normal distribution and non-normality B Constants used in the design of control charts for mean C Constants used in the design of control charts for range D Constants used in the design of control charts for median and range E Constants used in the design of control charts for standard deviation F Cumulative Poisson probability curves G Confidence limits and tests of significance H OC curves and ARL curves for X and R charts I Autocorrelation J Approximations to assist in process control of attributes K Glossary of terms and symbols Index

John Oakland is one of the world’s top ten gurus in quality and operational excellence; Executive Chairman, Oakland Group; Emeritus Professor of Quality & Business Excellence at Leeds University Business School; Fellow of the Chartered Quality Institute (CQI); Fellow of the Royal Statistical Society (RSS); Fellow of the Cybernetics Society (CybSoc); Fellow of Research Quality Association (RQA).Robert Oakland is Director in the Oakland Group and works across the globe helping complex organizations to unlock the power in their data using advanced analytical and statistical techniques to improve the quality, cost and delivery of their products and services.

• Preface Part 1 Process understanding1 Quality, processes and controlObjectives 1.1 The basic concepts 1.2 Design, conformance and costs 1.3 Quality, processes, systems, teams, tools and SPC1.4 Some basic tools1.5 SPC, ‘big data’ and data analyticsChapter highlights References and further reading 2 Understanding the process Objectives2.1 Improving customer satisfaction through process management 2.2 Information about the process 2.3 Process mapping and flowcharting 2.4 Process analysis 2.5 Statistical process control and process understanding Chapter highlights References and further reading3 Process data collection and presentation Objectives 3.1 The systematic approach 3.2 Data collection 3.3 Bar charts and histograms 3.4 Graphs, run charts and other pictures3.5 Data quality and sharing 3.6 Conclusions Chapter highlights References and further reading Part 2 Process variability4 Variation – understanding and decision making Objectives 4.1 How some managers look at data 4.2 Interpretation of data 4.3 Causes of variation 4.4 Accuracy and precision 4.5 Variation and management Chapter highlights References and further reading 5 Variables and process variation Objectives 5.1 Measures of accuracy or centering 5.2 Measures of precision or spread 5.3 The normal distribution 5.4 Sampling and averages Chapter highlightsWorked examples using the normal distribution References and further reading Part 3 Process control6 Process control using variables Objectives 6.1 Means, ranges and charts 6.2 Are we in control? 6.3 Do we continue to be in control? 6.4 Choice of sample size and frequency and control limits 6.5 Short-, medium- and long-term variation6.6 Process control of variables in the world of big data Chapter highlightsWorked examples References and further reading7 Other types of control charts for variables Objectives 7.1 Beyond the mean and range chart 7.2 Process control for individual data 7.3 Median, mid-range and multi-vari charts 7.4 Moving mean, moving range and exponentially weighted moving average (EWMA) charts 7.5 Control charts for standard deviation (σ) 7.6 Techniques for short-run SPC 7.7 Summarizing control charts for variables and big data Chapter highlightsWorked example References and further reading 8 Process control by attributes Objectives 8.1 Underlying concepts 8.2 Process control for number of defectives or non-conforming units 8.3 Process control for proportion defective or non-conforming units 8.4 Process control for number of defects/non-conformities 8.5 Attribute data in non-manufacturingChapter highlightsWorked examples References and further reading 9 Cumulative sum (cusum) charts Objectives 9.1 Introduction to cusum charts 9.2 Interpretation of simple cusum charts 9.3 Product screening and pre-selection 9.4 Cusum decision procedures Chapter highlightsWorked examples References and further reading Part 4 Process capability10 Process capability for variables and its measurement Objectives 10.1 Will it meet the requirements? 10.2 Process capability indices 10.3 Interpreting capability indices 10.4 The use of control chart and process capability data 10.5 Service industry example of process capability analysis Chapter highlightsWorked examples References and further readingPart 5 Process improvement11 Process problem solving and improvement Objectives 11.1 Introduction 11.2 Pareto analysis 11.3 Cause and effect analysis 11.4 Scatter diagrams 11.5 Stratification 11.6 Summarizing problem solving and improvement Chapter highlightsWorked examples References and further reading 12 Managing out-of-control processes Objectives 12.1 Introduction 12.2 Process improvement strategy 12.3 Use of control charts and data analytics for trouble-shooting12.4 Assignable or special causes of variation and big data Chapter highlights References and further reading 13 Designing the statistical process control system with big data Objectives 13.1 SPC and the quality management system 13.2 Teamwork and process control/improvement 13.3 Improvements in the process 13.4 Taguchi methods13.5 System performance – the confusion matrix 13.6 Moving forward with big data analytics and SPC Chapter highlights References and further reading 14 Six-sigma process quality Objectives 14.1 Introduction 14.2 The six-sigma improvement model 14.3 Six-sigma and the role of design of experiments 14.4 Building a six-sigma organization and culture 14.5 Ensuring the financial success of six-sigma projects 14.6 Concluding observations and links with excellence models and data analytics Chapter highlightsReferences and further reading 15 Data governance and data analyticsObjectives 15.1 Introduction – data attributes 15.2 Data governance strategies 15.3 Data analytics and insight15.4 Future of process control and assurance Chapter highlightsReferences and further readingAppendicesA The normal distribution and non-normality B Constants used in the design of control charts for mean C Constants used in the design of control charts for range D Constants used in the design of control charts for median and range E Constants used in the design of control charts for standard deviation F Cumulative Poisson probability curves G Confidence limits and tests of significance H OC curves and ARL curves for X and R charts I Autocorrelation J Approximations to assist in process control of attributes K Glossary of terms and symbols Index