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Now entitled Statistical Process Control and Data Analytics, this revised and updated eighth edition retains its focus on processes that require understanding, have variation, must be properly controlled, have a capability and need improvement – as reflected in the five sections of the book. In this book the authors provide not only an 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 consulting work with thousands of organizations worldwide. A new chapter on data governance and data analytics reflects the increasing importance of Big Data in today’s business environment.
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 software, the book also retains its valued textbook quality through clear learning objectives and online end-of-chapter discussion questions. It can still serve as a textbook for both student and practicing engineers, scientists, technologists, managers and anyone wishing to understand or implement modern statistical process control techniques and data analytics.
‘Big Data’ (very large complex data sets, sometimes explained by the use of five Vs: volume, variety, veracity, value and velocity), machine learning (ML), robotics, Artificial Intelligence (AI), the Internet of Things (IoT) and cloud computing will continue to greatly impact industry and business/organization models. The amount of data is constantly growing, but learning from more data depends massively on the data being collected and used correctly if we are not going to see increases in variation in processes through lack of understanding of aggregated or filtered data with selected algorithms. That is why the application of modified SPC approaches, methods and tools is still an essential part of making this transformation in the world of big data. When designing and using any large complex system involving data streams, data collection/aggregation, data analytics and algorithms, it is important to measure its performance in terms of the system’s ‘correctness’ – how often it gives the right answer, the right decision/instruction.
Preface
Process understanding
Quality, processes and control
Understanding the process
Process data collection and presentation
Process variability
Variation – understanding and decision making
Variables and process variation
Process control
Process control using variables
Other types of control charts for variables
Process control by attributes
Cumulative sum (cusum) charts
Process capability
Process capability for variables and its measurement
Process improvement
Process problem solving and improvement
Managing out-of-control processes
Designing the statistical process control system
Six-sigma process quality
Data governance and data analytics
Appendices