Introduction to Statistical Process Control. Muhammad Amir Aslam
– M.A.
To the memory of my father
– A.S.
To my parents, without whom none of my success would be possible
– L.A.
About the Authors
Muhammad Aslam is a full professor of statistics in the Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia. He did his MSc in Statistics (2004) from GC University Lahore with Chief Minister of the Punjab merit scholarship, MPhil in Statistics (2006) from GC University Lahore with the Governor of the Punjab merit scholarship, and PhD in Statistics (2010) from National College of Business Administration & Economics (NCBAE), Lahore, under the kind supervision of Professor Dr. Munir Ahmad. He also worked as research assistant in the Department of Statistics, GC University Lahore from 2006 to 2008. Then he joined the Forman Christian College University as a lecturer in August 2009. He worked as an assistant professor in the same university from June 2010 to April 2012. He worked in the same department as an associate professor from June 2012 to October 2014. He worked as an associate professor of statistics in the Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia, from October 2014 to March 2017. He taught summer course as a visiting faculty of statistics at Beijing Jiaotong University, China, in 2016. He has published more than 390 research papers in national and international well‐reputed journals. He is the author of two books. He received Meritorious Service Award in research from NCBAE, Lahore, in 2011. He also received Research Productivity Award for the year 2012 from Pakistan Council for Science and Technology. He was the second‐listed statistician in the Directory of Productivity Scientists of Pakistan 2013. He was the first‐listed statistician in the Directory of Productivity Scientists of Pakistan 2014. He got 371th position in the list of top 2210 profiles of Scientist of Saudi Institutions 2016. He received King Abdulaziz University Excellence Awards in scientific research.
Neutrosophic statistical quality control (NSQC) was introduced for the first time by Professor Muhammad Aslam. He is also the founder of neutrosophic inferential statistics. He developed neutrosophic statistics theory for the inspection and process control. He originally developed theory in these areas under the neutrosophic statistics. He extended the classical statistics theory to neutrosophic statistics originally in 2018.
Aamir Saghir is currently working as an associate professor of statistics in Mirpur University of Science and Technology, Azad Jammu and Kashmir, Pakistan. He obtained his PhD degree in probability theory and mathematical statistics from Zhejiang University, China, in 2014. He obtained his MS degree in statistics from Quaid‐i‐Azam University, Islamabad, Pakistan, in 2008. His current research interests include statistical process monitoring and probability modeling. He published more than 34 articles in internationally well‐reputed International Scientific Indexing journals. He also served as a peer reviewer for many international journals in the field of statistics and mathematics.
Liaquat Ahmad is currently working as an associate professor in the Department of Statistics and Computer Science, University of Veterinary and Animal Sciences (UVAS), Lahore, Pakistan. He has published 35 research articles in highly reputed international impact factor journals. He has also reviewed more than 30 articles of international journals in the field of statistics. His areas of interest are quality control charts, acceptance sampling plans, experimental design, biostatistical data analysis, and statistical softwares.
Preface
This book is about the use of modern statistical methods for quality control. It provides comprehensive coverage of the subject from basic principles to state‐of‐the‐art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of situations. Although statistical techniques are emphasized throughout, the book has a strong engineering and management orientation. Extensive knowledge of statistics is not a prerequisite for using this book. Readers whose background includes a basic course in statistical methods will find much of the material in this book easily accessible.
The book originally grew out of our teaching, research, and consulting in the application of statistical methods for various fields, particularly for industries. It is designed as a textbook for students enrolled in colleges and universities, who are studying engineering, statistics, management, and related fields and are taking a first course in statistical quality control. The basic quality control course is often taught at the junior or senior level. All the standard topics for this course are covered in detail. We have also used the text materials extensively in programs for professional practitioners, including quality and reliability engineers, manufacturing and development engineers, product designers, managers, procurement specialists, marketing personnel, technicians and laboratory analysts, inspectors, and operators.
The book contains eight chapters. Chapter 1 is an introduction of history and background of control charts. This chapter also includes descriptive statistics, the basic notions of probability and probability distributions, and types of control charts. These topics are usually covered in a basic course in statistical methods; however, their presentation in this text is from the quality engineering viewpoint. It describes that quality has become a major business strategy and that organizations that successfully improve quality can increase their productivity, enhance their market penetration, and achieve greater profitability and a strong competitive advantage. Chapter 2 presents the Shewhart‐type attribute control charts for counts. It highlights the importance of attribute data and presents the proper use of statistical methods to implement quality control. Concept of dispersed data is introduced, and control charts used for monitoring over‐ or under‐dispersed data are discussed along with application in industries.
In Chapter 3, control charts for monitoring the variable data processing are presented with basic concepts and implantation on real data sets. These Shewhart control charts certainly are not new, but its use in modern‐day business and industries is of tremendous value. Chapter 4 contains the new idea of control charts for multiple dependent state (MDS) sampling. The MDS sampling showed the efficiency of the attribute control chart over the traditional Shewhart attribute control chart in terms of average run length. The use of MDS sampling in the area of control chart has increased the sensitivity of the control charts to detect a small shift in the manufacturing process. For decision‐making, it uses the current subgroup information and previous subgroup information to make the decision about the state of the process. In Chapter 5, exponentially weighted moving average (EWMA) control charts using repetitive group sampling scheme are introduced. The methods for EWMA‐based control charts for a variety of situations, such as the average and the dispersion monitoring charts, single sampling, double sampling, multiple sampling, sequential sampling, repetitive sampling, ranked set sampling, and the MDS sampling charts have been developed in this chapter.
Chapter 6 presents the different sampling schemes used to construct the control charts. Some of these sampling schemes are very simple to develop and understand, while some schemes are much complex to develop and understand. Each of the sampling schemes has advantages and disadvantages; therefore the quality control personnel can select according to the situation and the available resources. The use of modern statistical software has made a very simple task of developing a control chart for the non‐statisticians' quality control personnel as there is no need to develop and understand the complex sampling schemes. In Chapter 7, memory‐type control charts for monitoring attributes, such as the cumulative sum chart, the EWMA chart, and the moving average charts,