Statistical Process Control

The business, commercial and public-sector world has changed dramatically since John Oakland wrote the first edition of Statistical Process Control – a practical guide in the mid-eighties. Then people were rediscovering statistical methods of ‘quality control’ and the book responded to an often desperate need to find out about the techniques and use them on data. Pressure over time from organizations supplying directly to the consumer, typically in the automotive and high technology sectors, forced those in charge of the supplying production and service operations to think more about preventing problems than how to find and fix them. Subsequent editions retained the ‘took kit’ approach of the first but included some of the ‘philosophy’ behind the techniques and their use. The theme which runs throughout the 7th edition is still processes - that require understanding, have variation, must be properly controlled, have a capability, and need improvement - the five sections of this new edition. SPC never has been and never will be simply a ‘took kit’ and in this book the authors provide, not only the instructional guide for the tools, but communicate the management practices which have become so vital to success in organizations throughout the world. The book is supported by the authors' extensive and latest consulting work within thousands of organisations worldwide. Fully updated to include real-life case studies, new research based on client work from an array of industries, and integration with the latest computer methods and Minitab software, the book also retains its valued textbook quality through clear learning objectives and end of chapter discussion questions. It can still serve as a textbook for both student and practicing engineers, scientists, technologists, managers and for anyone wishing to understand or implement modern statistical process control techniques.

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 Chapter highlights References and further reading Discussion questions 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 Discussion questions 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 Conclusions Chapter highlights References and further reading Discussion questions Part 2 Process Variability 1. Variation: understanding and decision making Objectives 1.1 How some managers look at data 1.2 Interpretation of data 1.3 Causes of variation 1.4 Accuracy and precision 1.5 Variation and management Chapter highlights References and further reading Discussion questions 2. Variables and process variation Objectives 2.1 Measures of accuracy or centring 2.2 Measures of precision or spread 2.3 The normal distribution 2.4 Sampling and averages 2.5 Chapter highlights References and further reading Discussion questions Worked examples using the normal distribution 99 Part 3 Process Control 1. Process control using variables Objectives 1.1 Means, ranges and charts 1.2 Are we in control? 1.3 Do we continue to be in control? 1.4 Choice of sample size and frequency, and control limits 1.5 Short-, medium- and long-term variation: a change in the standard practice 1.6 Summary of SPC for variables using X and R charts Chapter highlights References and further reading Discussion questions Worked examples 2. Other types of control charts for variables Objectives 2.1 Life beyond the mean and range chart 2.2 Charts for individuals or run charts 2.3 Median, mid-range and multi-vari charts 159 2.4 Moving mean, moving range and exponentially weighted moving average (EWMA) charts 2.5 Control charts for standard deviation (s) 2.6 Techniques for short run SPC 2.7 Summarizing control charts for variables Chapter highlights References and further reading Discussion questions Worked example 3. Process control by attributes Objectives 3.1 Underlying concepts 3.2 np-charts for number of defectives or non-conforming units 3.3 p-charts for proportion defective or non-conforming units 3.4 c-charts for number of defects/non-conformities 3.5 u-charts for number of defects/non-conformities per unit 3.6 Attribute data in non-manufacturing Chapter highlights References and further reading Discussion questions Worked examples 4. Cumulative sum (cusum) charts Objectives 4.1 Introduction to cusum charts 4.2 Interpretation of simple cusum charts 4.3 Product screening and pre-selection 4.4 Cusum decision procedures Chapter highlights References and further reading Discussion questions Worked examples Part 4 Process Capability 4. Process capability for variables and its measurement Objectives 4.1 Will it meet the requirements? 4.2 Process capability indices 4.3 Interpreting capability indices 4.4 The use of control chart and process capability data 4.5 A service industry example: process capability analysis in a bank 269 Chapter highlights References and further reading Discussion questions Worked examples Part 5 Process Improvement 1. Process problem solving and improvement Objectives 1.1 Introduction 1.2 Pareto analysis 1.3 Cause and effect analysis 1.4 Scatter diagrams 1.5 Stratification 1.6 Summarizing problem solving and improvement Chapter highlights References and further reading Discussion questions Worked examples 2. Managing out-of-control processes Objectives 2.1 Introduction 2.2 Process improvement strategy 2.3 Use of control charts for trouble-shooting 2.4 Assignable or special causes of variation Chapter highlights References and further reading Discussion questions 3. Designing the statistical process control system Objectives 3.1 SPC and the quality management system 3.2 Teamwork and process control/improvement 3.3 Improvements in the process 3.4 Taguchi methods 3.5 Summarizing improvement Chapter highlights References and further reading Discussion questions 4. Six-sigma process quality Objectives 4.1 Introduction 4.2 The six-sigma improvement model 4.3 Six-sigma and the role of Design of Experiments 4.4 Building a six-sigma organization and culture 4.5 Ensuring the financial success of six-sigma projects 4.6 Concluding observations and links with Excellence Chapter highlights References and further reading Discussion questions 5. The implementation of statistical process control Objectives 5.1 Introduction 5.2 Successful users of SPC and the benefits derived 5.3 The implementation of SPC 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 404 F. Cumulative Poisson probability tables G. Confidence limits and tests of significance H. OC curves and ARL curves for –– 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 10 gurus in quality & operational excellence; Executive Chairman, Oakland Consulting; Emeritus Professor of Quality & Business Excellence at Leeds University Business School , a Fellow of the Chartered Quality Institute (CQI) and a Member of American Society for Quality. Robert Oakland works across the globe helping complex organisations to design and implement large-scale quality and operational excellence programmes to improve quality, cost and delivery of products and services. He is a consultant at Oakland Consulting, UK.