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 understanding

1 Quality, processes and control

Objectives

1.1 The basic concepts

1.2 Design, conformance and costs

1.3 Quality, processes, systems, teams, tools and SPC

1.4 Some basic tools

1.5 SPC, 'big data' and data analytics

Chapter highlights

References and further reading

2 Understanding the process

Objectives

2.1 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 reading

3 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 pictures

3.5 Data quality and sharing

3.6 Conclusions

Chapter highlights

References and further reading

Part 2 Process variability

4 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 highlights

Worked examples using the normal distribution

References and further reading

Part 3 Process control

6 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 variation

6.6 Process control of variables in the world of big data

Chapter highlights

Worked examples

References and further reading

7 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 (¿)

7.6 Techniques for short-run SPC

7.7 Summarizing control charts for variables and big data

Chapter highlights

Worked 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-manufacturing

Chapter highlights

Worked 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 highlights

Worked examples

References and further reading

Part 4 Process capability

10 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 highlights

Worked examples

References and further reading

Part 5 Process improvement

11 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 highlights

Worked 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-shooting

12.4 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 methods

13.5 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 highlights

References and further reading

15 Data governance and data analytics

Objectives

15.1 Introduction - data attributes

15.2 Data governance strategies

15.3 Data analytics and insight

15.4 Future of process control and assurance

Chapter highlights

References and further reading

Appendices

A 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 Xand R charts

I Autocorrelation

J Approximations to assist in process control of attributes

K Glossary of terms and symbols

Index