Statistical Quality Control Provides a basic understanding of Statistical Quality Control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept. Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of Statistical Quality Control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide also * Focuses on the learning and understanding of Statistical Quality Control for second and third year undergraduates and practitioners in the field * Discusses aspects of Six Sigma Methodology * Teaches readers to use MINITAB, R, JMP and Python to create and analyze charts * Requires no previous knowledge of statistical theory * Is supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems Statistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of Statistical Quality Control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It's also useful for those who use Six Sigma techniques to improve the quality of products in such areas.
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Bhisham C. Gupta
Professor Emeritus of StatisticsUniversity of Southern MainePortland, ME
This edition first published 2021#169; 2021 John Wiley & Sons, Inc.
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Library of Congress Cataloging‐in‐Publication DataISBN 978‐1‐119‐67163‐3 (hardback)ISBN 978‐1‐119‐67170‐1 (ePDF)ISBN 978‐1‐119‐67172‐5 (ePub)ISBN 978‐1‐119‐67171‐8 (oBook)
Cover image:Cover design by
In loving memory of my parents, Roshan Lal and Sodhan Devi
This is an introductory textbook on statistical quality control (SQC), an important part of applied statistics that is used regularly to improve the quality of products throughout manufacturing, service, transportation, and other industries. The objective of this book is to provide a basic but sound understanding of how to apply the various techniques of SQC to improve the quality of products in various sectors. Knowledge of statistics is not a prerequisite for using this text. Also, this book does not assume any knowledge of calculus. However, a basic knowledge of high school algebra is both necessary and sufficient to grasp the material presented in this book.
Various concepts of the Six Sigma methodology are also discussed. Six Sigma methodology calls for many more statistical tools than are reasonable to address in one book; accordingly, the intent here is to provide Six Sigma team members with some basic statistical concepts, along with an extensive discussion of SQC tools in a way that addresses both the underlying statistical concepts and their applications.
This book is written for second‐year or third‐year college/university students who are enrolled in engineering or majoring in statistics, management studies, or any other related field, and are interested in learning various SPC techniques. The book can also serve as a desk reference for practitioners who work to improve quality in sectors such as manufacturing, service, transportation, medical, oil, and financial institutions, or for those who are using Six Sigma techniques to improve the quality of products in such areas. This book will also serve those who are preparing for certification in Six Sigma training, such as the Six Sigma Green Belt. In the Western world, these kinds of certifications are very common, particularly in the manufacturing, service, transportation, medical, and oil industries.
In Chapter 1, we introduce the basic concept of quality, as discussed by various pioneer statisticians, engineers, and practitioners. A few important pioneers in this area are W. Edwards Deming, Joseph M. Juran, Philip B. Crosby, and Armand V. Feigenbaum. Concepts of quality control and quality improvements are discussed. Finally, we discuss how management can help improve product quality.
In Chapter 2, we introduce the Six Sigma methodology. In the current era, Six Sigma methodology is considered the statistical standard of quality. During the past three decades, Six Sigma methodology has become an integral part of many manufacturing and service companies throughout the world. The benefits of Six Sigma methodology are also discussed.
In Chapter 3, we introduce the different types of data that we encounter in various fields of statistical applications, and we define some terminology, such as population, sample, and different measures. We then introduce various graphical and numerical tools needed to conduct preliminary analyses of data. We introduce the statistical software packages Minitab version 19, Rx64 3.6.0. Finally, some discrete and continuous probability distributions commonly encountered in the application of SQC are discussed.
In Chapter 4, we study different types of sampling methods – simple random sampling, stratified random sampling, cluster random sampling, and systematic random sampling – that are encountered whenever we use sampling schemes to study specific populations. Then we discuss how estimates of various population parameters are derived under different sampling methods.
In Chapter 5, we discuss Phase I control charts for variables. Phase I control charts are used to detect large shifts in a given process in any manufacturing or service industry. Large shifts usually occur when a process is either brand‐new or has undergone drastic changes: for example, a new industry is started and/or new machines are installed in an established industry. This also occurs when a significant new technology is introduced. These kinds of changes can also usually occur whenever a high level of administration is changed: for example, a new CEO or CTO (Chief Technical Officer) is hired.
In Chapter 6, we discuss Phase I control charts for attributes. Control charts for attributes are used whenever we collect a set of counted data. Again, Phase I control charts are used to detect significant shifts in a given process established in any manufacturing or service industry.
In Chapter 7, we discuss Phase II control charts to detect small shifts. Small shifts usually occur when a well‐established process has just experienced small changes. Some examples include workers having gone through some kind of training or specific machines having had their annual checkups, calibrations, or renovations, etc.
In Chapter 8, we discuss various types of process capability indices (PCIs). PCIs are used when a process is well established and the manufacturer or a service provider wants to ensure that they can deliver a product that meets the consumer’s requirements. In this chapter, we discuss how to use PCIs to help any given industry avoid the severe consequences of heavy losses. Then we discuss various aspects of pre‐control, which is an important tool in SQC. As we discuss later, in Chapter 9, different sampling schemes are used to sort out the defective parts from shipments. Pre‐control is a mechanism to reduce the amount of sampling and inspection required to validate that a process is producing products consistent with customer’s expectations. In the last part of this chapter, we discuss aspects of measurement system analysis (MSA). Sometimes a measurement system is responsible for the bad quality of a product; proper MSA can avoid a measurement system that may become the root cause of a product’s failure.
In Chapter 9, we discuss various kind of acceptance sampling schemes that are still in use in some places in the world. These schemes were used frequently in the United States during World War II, when armaments and ammunitions were being produced in huge quantities. However, these sampling schemes are currently not very popular since they are sometimes very expensive to use and are often not very effective. The methods discussed in the previous chapters are more effective in achieving processes that produce better‐quality products. Finally, we discuss some sampling standards and plans: ANSI/ASQ Z1.4‐2003, ANSI/ASQ Z1.9‐2003, Dodge’s Continuous Sampling Plans and MIL‐STD‐1235B.
Chapter 10 is available for download on the book’s website: www.wiley.com/college/gupta/SQC. This chapter presents more detail about the statistical software packages used in this text. In Chapter 3, we introduce the statistical software packages Minitab and R; but due to lack of space, certain basic concepts of these packages are not discussed. In Chapter 10, we discuss these additional aspects of Minitab and R. We also discuss two other statistical packages: JMP and Python. Then, using JMP and Python, we study SQC techniques using examples handled earlier in the book with Minitab and R. After going through the material presented in this chapter, you will be able to analyze different data situations using Minitab, R, JMP, and/or Python; cover all of the SQC techniques discussed in this book; and implement them in various sectors whenever and wherever high‐quality products are desired.
In this text, we emphasize both theory and application of SQC techniques. Each theoretical concept is supported by a large number of examples using data encountered in real‐life situations. Further, we illustrate how the statistical packages Minitab® version 19, R® version 3.6.0, JMP® Pro‐15, and Python® version 3.7 are used to solve problems encountered when using SQC techniques.
As indicated, we incorporate the statistical packages Minitab and R throughout the text and discuss JMP and Python examples in Chapter 10. Our step‐by‐step approach with these statistical packages means that no prior knowledge of their use is required. After completing a course that includes this book, you will be able to use these statistical packages to analyze statistical data in quality control. Familiarity with the quality control features of these packages will further aid you as you learn additional features pertaining to other related fields of interest.
Data sets with 20 or more data points in examples and exercises are saved as Minitab, CSV (ANSI), and JMP files and available on the book’s website:
. The CSV (ANSI) files can easily be imported into the R and Python software discussed in this book.
Solutions to all of the odd‐numbered review practice problems presented in this text are available to students on the book’s website:
Data sets with 20 or more data points in examples and exercises are saved as Minitab, CSV (ANSI), and JMP files and made available on the book’s website:
. The CSV (ANSI) files can easily be imported into the R and Python software discussed in this book.
Solutions to all review practice problems presented in this text are available to instructors on the book’s website:
PowerPoint slides to aid instructors in the preparations of lectures are available on the book’s website:
I have thoroughly reviewed the text to make sure it is as error‐free as possible. However, any errors discovered will be listed on the book’s website: www.wiley.com/college/gupta/SQC.
If you encounter any errors as you are using the book, please send them to me at [email protected] so that they can be corrected in a timely manner on the website and in future editions. I also welcome any suggestions for improvement you may have, and I thank you in advance for helping me improve the book for future readers.
I am grateful to the following reviewers and colleagues whose comments and suggestions were invaluable in improving the text:
Dr. Bill Bailey, Kennesaw State University
Dr. Raj Chikkara, Professor Emeritus, University of Houston
Dr. Muhammad A. El‐Taha, Professor of Mathematics and Statistics, University of Southern Maine
Dr. Sandy Furterer, University of Dayton, Ohio
Dr. Irwin Guttman, Professor Emeritus of Statistics, SUNY at Buffalo and Univ. of Toronto
Dr. Ramesh C. Gupta, Professor of Statistics, University of Maine
Dr. Kalanka P. Jayalath, University of Houston
Dr. Jamison Kovach, University of Houston
Dr. Eric Laflamme, Associate Professor of Statistics, Plymouth State University
Dr. Mary McShane‐Vaughn, Principal, Partner‐University Training Partners
Dr. Daniel Zalewski, University of Dayton, Ohio
Dr. Weston Viles, Assistant professor of Mathematics and Statistics, University of Southern Maine
I would like to thank George Bernier (M.S. Mathematics, M.S. Statistics), who is a lecturer in mathematics and statistics at the University of Southern Maine. He provided assistance in the development of material pertaining to R and also helped by proofreading two of the chapters.
I would also like to express my thanks and appreciation to Dr. Eric Laflamme, Associate Professor of Mathematics and Statistics at Plymouth State University of New Hampshire, for helping by proofreading five of the chapters. Last but not least, I would also like to thank Mark W. Thoren (M.S. Electrical Engineering, staff scientist at Analog Devices) for providing assistance in the development of material pertaining to Python. Finally, I would like to thank Dr. Mary McShane‐Vaughn, Principal at University Training Partners, for providing Chapter 2 on Lean Six Sigma. Her concise explanation of the subject has helped give context to why we must monitor the variability of processes to achieve and sustain improvements.
I acknowledge Minitab® for giving me free access to Minitab version 19 and allowing me to incorporate the Minitab commands and screenshots in this book. Minitab® and the Minitab logo are registered trademark of Minitab. I also thank the SAS Institute for giving me free access to JMP Pro‐15 and allowing me to incorporate the JMP commands and screenshots in this book. JMP® and SAS® are registered trademarks of the SAS institute in the United States and other countries. I would also like to thank all the contributors to the libraries of R version 3.6.0 and Python version 3.7.
I would like to gratefully thank my family and acknowledge the patience and support of my wife, Swarn; daughters, Anita and Anjali; son, Shiva; sons‐in‐law, Prajay and Mark; daughter‐in‐law, Aditi; and wonderful grandchildren, Priya, Kaviya, Ayush, Amari, Sanvi, Avni, and Dylan.
Bhisham C. Gupta
This book is accompanied by a companion website:
The companion websites include:
(instructor and student sites)
SQC Data Folder (instructor and student sites)
PowerPoint Files (instructor site only)
Instructors’ Solution Manual (instructor site only)
Students’ Solution Manual (student site only)
Readers of this book have most likely used or heard the word quality. The concept of quality is centuries old. Many authors have defined quality, and some of these definitions are as follows:
Joseph M. Juran defined
as “fitness for intended use.” This definition implies that
means meeting or exceeding customer expectations.
W. Edwards Deming stated that the customer's definition of
is the one that really matters. He said, “A product is of good quality if it meets the needs of a customer and the customer is glad that he or she bought that product.” Deming also gave an alternative definition of
: “A predictable degree of uniformity and dependability with a quality standard suited to the customer.”
Philip B. Crosby defined
as “conformance to requirements, not as ‘goodness' or ‘elegance.'” By
, he meant that the performance standard must be zero defects and not “close enough.” He is known for his concept of “Do it right the first time.”
The underlying concept in all these definitions is much the same: consistency of performance and conformance with the specifications while keeping the customer's interests in mind.
Statistical quality control (SQC) refers to a set of statistical tools used to monitor, measure, and improve process performance in real time.
A process may be defined as a series of actions or operations that change the form, fit, or function of one or more input(s) as required by a customer. A process may also be defined as a combination of workforce, equipment, raw material, methods, and environment that work together to produce a product. Figure 1.1 shows various steps that usually take place in any process, whether in a manufacturing or non‐manufacturing environment.
The quality of the final product depends on how the process to be used is designed and executed.
The concept of SQC is less than a century old. Dr. Walter Shewhart (1931), working at the Westinghouse Hawthorne plant in Cicero, Illinois, drew the first statistical process control (SPC) chart in 1924. While working at Hawthorne, Shewhart met and influenced W. Edward Deming and Joseph Juran; later, they went on to champion Shewhart's methods. Shewhart, Deming, and Juran are often considered the three founders of the quality improvement movement.
Figure 1.1 Flow chart of a process.
As mentioned above, SQC is a set of statistical tools used to monitor, control, and improve process performance. These essential tools are (i) SPC, (ii) acceptance sampling plans, and (iii) design of experiments (DOE). DOE is used to improve the process and find important control factors, whereas SPC monitors these factors so that the process remains in a steady state. SPC is one of the important tools that makes up SQC. However, the term statistical process control is often used interchangeably with statistical quality control.
The customer or consumer plays a very important role in achieving quality, for it is the customer who defines the quality of the product. If the customer likes the product and is satisfied with it the way it functions, then the probability is high that they will be willing to buy the product again in the future, indicating that you have a quality product. However, quality can also be achieved through innovation. Quality is not static; rather, it is an ongoing process. For example, a given product may be of great quality today – but if no further innovative improvements are made, it may become obsolete in the future and consequently lose its market share. It should be obvious that the required innovation can only be defined by the producer.
The customer is not in a position to tell how a product should look like 5 or 10 years from now. For example, five decades ago, a customer could not imagine electric cars or self‐driven cars, or small computers replacing the huge computers that used to occupy entire rooms. The customer is only the judge of the product in its current form. In other words, a concern about quality begins with the customer, but the producer must carry it into the future. The producer or their team has to incorporate their innovative ideas at the design stage. This is called continuous improvement or quality forever. We will have a brief look at this concept later in this chapter.
It is important to note that a customer can be internal or external. For example, a paper mill could be an internal or external customer of a pulp mill. If both the paper and the pulp mill are owned by the same organization, then the paper mill is an internal customer; otherwise, it is an external customer. Similarly, various departments are internal customers of the Human Resources department. Another example is that students from various departments of a university taking a course from another department are internal customers, whereas a part‐time student from outside the university is an external customer. In such cases, the company or organization should not assume that if its internal customers are satisfied, external customers are also automatically satisfied. The needs of external customers may be entirely different from those of internal customers, and the company must strive to meet both sets of needs. Furthermore, the goal of a company or an organization should be that all customers are satisfied not only for the moment but forever.
In summary, to achieve quality and competitiveness, you must first achieve quality today and then continue to improve the product for the future by introducing innovative ideas. To do this, an organization must take the following steps:
Make the customer its top priority. In other words, it should be a customer‐focused organization.
Make sure the customer is fully satisfied and, as a result, becomes a loyal customer. A
customer is the one who will always give reliable feedback.
Create an environment that provides the most innovative products and has as its focus quality improvement as an ongoing process.
Take data‐driven action to achieve quality and innovation. That is, the organization must collect information systematically, following appropriate sampling techniques to obtain data from internal as well as external customers about their needs and analyzing it to make necessary improvements. This process should be repeated continuously.
Different authors have taken different steps to achieve quality improvement. In this chapter, we quote the steps suggested by four prominent advocates of quality who revolutionized the field of SQC: Philip B. Crosby, W. Edwards Deming, Joseph M. Juran, and Armand V. Feigenbaum. We first discuss ideas suggested by Crosby, Feigenbaum, and Juran; later, we will look those from W. Edwards Deming.
Following are Juran's 10 steps to achieve quality improvement (Uselac 1993, p. 37; Goetsch and Davis 2006):
Build awareness of both the need for improvement and opportunities. Identify gaps.
Set goals for improvement.
Organize to meet the goals that have been set. They should align with the company's goal.
Implement projects aimed at solving problems.
Keep scores. Sustain these and continue to perfection.
Maintain momentum by building improvement into the company's regular system.
Next, we summarize Armand V. Feigenbaum's philosophy for total management (Tripathi 2016; Watson 2005):
Quality of products and services is directly influenced by nine Ms: Markets, Money, Management, Men, Motivation, Material, Machines and Mechanization, Modern information methods, and Mounting product requirements.
Three steps to quality: (i) management should take the lead in enforcing quality efforts and should be based on sound planning; (ii) traditional quality programs should be replaced by the latest quality technology to satisfy future customers; (iii) motivation and continuous training of the entire workforce gives insights about organizational commitment to the continuous quality improvement of products and services.
Elements of total quality to enable a
total customer focus
are as follows:
Quality is the customer's perception.
Quality and the cost are the same, not different.
Quality is an individual and team commitment.
Quality and innovation are interrelated and mutually beneficial.
Managing quality is managing the business.
Quality is a principle.
Quality is not a temporary or quick fix.
Productivity is gained by cost‐effective, demonstrably beneficial quality investment.
Implement quality by encompassing suppliers and customers in the system.
Feigenbaum was the first to define a system engineering approach to quality. He believed that total quality control combines management methods and economic theory with organizational principles, resulting in commercial leadership. He also taught that widespread quality improvement performance in a nation's leading businesses is directly related to quality's long‐term economic impact.
Philip B. Crosby is well known for his “Quality Vaccine” and 14 steps to quality improvement. The Quality Vaccine consists of the following three ingredients:
Crosby's suggested set of 14 steps to quality improvement are as follows (Goetsch and Davis 2006):
Make it clear that management is committed to quality for the long term.
Form cross‐departmental quality teams.
Identify where current and potential problems exist.
Assess the cost of quality and explain how it is used as a management tool.
Increase the quality awareness and personal commitment of all employees.
Take immediate action to correct problems that have been identified.
Establish a zero‐defects program.
Train supervisor to carry out their responsibilities in the quality program.
Periodically hold “zero defects days” to ensure that all employees are made aware there is a new direction.
Encourage individuals and teams to establish both personal and team improvement goals.
Encourage employees to tell management about obstacles they face in trying to meet quality goals.
Recognize employees who participate.
Implement quality councils to promote continual communication.
Repeat everything to illustrate that quality improvement is a never‐ending process.
Note that many of these steps are covered if the projects in the Six Sigma methodology are well executed.
During and after World War II, America was under a lot of pressure to increase productivity. The managers of manufacturing companies in America believed that productivity and quality were not compatible, and their way to increase productivity was to hire more workers and put “quality” on the back burner. Japan and Germany were also coming out of the ashes of World War II. So, until 1960, America dominated the world with its productivity – but in 1948, Japanese companies started to follow the work that many pioneers such as Shewhart, Juran, and Deming practiced at Westinghouse. The managers of Japanese companies observed that improving quality not only make their products more attractive but also increased productivity. However, this observation did not sink into the minds of the managers of American companies: they continued working with the assumption that improving quality cost more and inhibited productivity and consequently would mean lower profits.
Deming's famous visit to Japan in (1950) brought about a quality revolution in Japan, and the country became a very dominant power of quality throughout the world. During his visit, he gave a seminar that was attended not only by engineers but also by all the top managers. He told the Japanese managers that “they had an obligation to the world to uphold the finest of management techniques.” He warned them against mistakenly allowing into Japanese companies the use of certain Western management practices, such as management by objective and performance standards, saying that “these practices are largely responsible for the failure of Western industry to remain competitive.” As Deming noted in his book Out of the Crisis, which resulted from his visit to Japan, the chain reaction shown in Figure 1.2 became engraved in Japan as the way of industrial life. This chain reaction was on the blackboard during every meeting he held with top management in Japan.
Furthermore, Deming noted that “Once management in Japan adopted the chain reaction, everyone there from 1950 onward had one common aim, namely, quality.” But as remarked earlier, this idea was not adopted by American management until at least the late 1980s. In the 1960s and 1970s, American companies continued to dominate in productivity, mainly by increasing their workforce. However, as a result of ignoring the quality scenario, America started to lose its dominance in terms of competitiveness and thus productivity. During this period, Germany and South Korea also became competitors with America. Ultimately, in the 1990s, American management started to work on quality; and as a result, America began to reemerge as a world‐class competitor.
The gurus and advocates for quality – Deming, Feigenbaum, Juran, and Crosby – were the most influential people in making the move from production and consumption to total quality control and management. According to Joseph A. DeFeo, president and CEO of the Juran Institute, “the costs of poor‐quality account for 15 to 30% of a company's overall costs.” When a company takes appropriate steps to improve its performance by reducing deficiencies in key areas (cycle time, warranty costs, scrap and rework, on‐time delivery, billing, and others), it reduces overall costs without eliminating essential services, functions, product features, and personnel increases as outlined by Goetsch and Davis (2006). Feigenbaum also said that up to 40% of the capacity of a plant is wasted through not getting it right the first time.
Figure 1.2 A chain reaction chart used by the Japanese companies in their top management meetings.
Furthermore, we note that often, flexibility in manufacturing can increase productivity without affecting quality. For example, the best Japanese automaker plants can send a minivan, pickup truck, or SUV down the same assembly line one after another without stopping the line to retool or reset. One Nissan plant can assemble five different models on one line. This flexibility obviously translates into productivity (Bloomberg Businessweek 2003).
Earlier in this chapter, we noted that the characteristic of quality improvement is not static; rather, it is an ongoing process. It becomes the responsibility of all management that all appropriate steps are taken to implement quality improvement. The first step by management, of course, should be to transform “business as usual” into an improved business by instilling quality into it. Deming's 14‐point philosophy is very helpful to achieve this goal:
Create constancy of purpose for improving products and services.
Adopt the new philosophy. That is, management must learn about the new economic age and challenges such as competitiveness, and take responsibility for informing and leading their business.
Cease dependence on inspections to achieve quality.
End the practice of awarding business based on price alone; instead, minimize total costs by working with a single supplier.
Constantly improve every process for planning, production, and service.
Institute training on the job.
Adopt and institute leadership.
Drive out fear.
Break down barriers between staff areas.
Eliminate slogans, exhortations, and targets for the workforce.
Eliminate numerical quotas for the workforce and numerical goals for management.
Remove barriers that rob people of pride of workmanship, and eliminate the annual rating or merit system.
Institute a vigorous program of education and self‐improvement for everyone.
Put everybody in the company to work to accomplish the transformation.
This philosophy can be used in any organization to implement total quality management (TQM). For more details and examples, we refer you to Out of the Crisis (Deming 1986).
The outcomes of quality control are obvious. Some of these outcomes are the following:
The quality of the product or service will improve, which will make it more attractive and durable. Better quality will result in a higher percentage of the product meeting the specifications of the customer. Consequently, only a small percentage (or none) of the products will be rejected.
Since few or no products are rejected, fewer need rework, and consequently there are fewer delays in delivery. This makes the customer happy, and they are bound to buy the product again. All this adds up to more savings, and that results in a lower price for the product – which makes it more competitive.
Consequently, there will be better use of resources, such as manpower, raw material, machine hours, etc. All of these outcomes result in lower costs, better quality, higher productivity, and hence a larger market share.
Quality control helps an organization to create products that, simply put, are of better quality. Continuous quality improvement makes operators, engineers, and supervisors more focused on customer requirements, and consequently, they are less likely to make any “mistakes.”
Quality control may use a technique called acceptance sampling to improve quality. An acceptance sampling plan is a method for inspecting a product. Acceptance sampling may inspect only a small portion of a lot or 100% of the lot. In some cases, inspecting 100% of the lot means all products in that lot will be destroyed. For example, if we are testing the life of a new kind of bulbs for a particular type of projector, then inspecting 100% of the lot means all the bulbs in that lot will be destroyed.
But acceptance sampling plans increase quality only of the end product or service, not of what is still being manufactured or of services that are still being performed, which means any defects or errors that occurred during the production process will still exist. In certain service industries, nothing can be done until the service has been fully provided or after it has been provided. For example, if a patient is receiving treatment, then nothing can be done during or after the treatment if the treatment was bad. Similarly, if a dentist has pulled out the wrong tooth, then nothing can be done after the dentist has completed the service. Thus quality improvement is extremely important in such situations. In manufacturing, acceptance sampling very often requires rework on defective units; after rework, these units may turn out to be acceptable or not, depending on what kind of defects these units had in the first place. All of this implies that acceptance sampling is not a very effective method for quality improvement. We will study acceptance sampling plans in more detail in Chapter 9.
We turn now to process control. In process control or statistical process control, steps are taken to remove any defects or errors before they occur by applying statistical methods or techniques of five kinds: define, measure, analyze, improve, and control. We discuss these techniques in Chapter 2. Deming describes statistical quality as follows: “A state of statistical control is not a natural state for the manufacturing process. It is instead an achievement, arrived at by eliminating one by one, by determined effort, the special causes of excessive variation.” Another way of describing statistical quality is as an act of taking action on the process based on the result obtained from monitoring the process under consideration. Once the process‐monitoring tools (discussed in detail in Chapters 5–8) have detected any cause for excessive variation (excessive variation implies poor quality), the workers responsible for the process take action to eliminate the cause(s) and bring the process back into control. If a process uses statistical control, there is less variation; consequently, quality is better and is continuously improved. If the process is under control, then it is more likely to meet the specifications of the customer or management, which helps to eliminate or significantly reduce any costs related to inspection.
Quality improvement is judged by the customer. Very often, when a customer is not satisfied with quality improvement, they do not bother to file a complaint or demand compensation if the product is not functioning as it is expected to. On the other hand, if there is significant quality improvement, the customer is bound to buy the product repeatedly. These customers we may define as loyal customers. So, quality improvement is best judged by loyal customers, and loyal customers are the biggest source of profit. If there is no significant improvement in quality, then not only do we lose dissatisfied customers but we also lose some of the loyal customers. The loss due to dissatisfied customers or losing loyal customers usually is not measurable – but such a loss is usually enormous, and sometimes it is not recoverable and can cause the collapse of the organization. Thus, quality control and quality improvement are the best sources of good health for any company or organization.
Deming's 14‐point philosophy helped Western management transform old‐fashioned “business as usual” to modern business, where concern for quality is part of the various problems that face any business. Note, however, that there is a danger that these concerns may spread like wildfire, to the detriment of the business as a whole. Further, some problems are what Deming calls “deadly diseases” and become hurdles on the way to fully implement the transformation (Deming 1986, Chapter 3). Deming describes the deadly diseases as follows:
Lack of constancy of purpose to plan products and services that have a market sufficient to keep the company in business and provide jobs.
Emphasis on data analysis, a data‐based decision approach, and short‐term profits. Short‐term thinking that is driven by a fear of an unfriendly takeover, and pressure from bankers and shareholders to produce dividends.
Performance evaluations, merit ratings, or annual reviews without giving sufficient resources to accomplish desired goals.
Job hopping by managers for higher ranks and compensation.
Using only visible data or data at hand in making decisions, with little or no consideration of what is unknown or unknowable.
Excessive medical costs.
Excessive liability cost that is jacked up by lawyers who work on contingency fees and unfair rewards given by juries.
Deadly diseases 1, 3, 4, and 5 can usually be taken care by using a total quality approach to quality management, but this topic is beyond the scope of this book. However, deadly diseases 2, 6, and 7, add major costs to the organization without contributing to the health of the business. They are more cultural problems, but they pressure companies to implement quality improvement and consequently compete globally.
Numerical quotas for hourly workers to measure work standards have been a common practice in America. This is done by the Human Resources (HR) department to estimate the workforce that the company needs to manufacture X number of parts. While doing these estimates, it could be that nobody bothers to check how many of the manufactured parts that have been produced are defective, or how many of them meet the specifications set by customers or will be rejected/returned. HR normally does not take into account the cost of such events – which, of course, the company has to bear because of rework on defective or nonconforming parts or rejected and trashed parts. All of this adds to the final cost.
In setting up numerical quotas, the average number of parts produced by each worker is often set as a work standard. When we take an average, some workers produce a smaller number of parts than the set standard, and some produce more than the set standard. No consideration is given, while setting the standard, to who produced a small or large number of parts that meet customer specifications. Thus, in this process, workers who produce more parts than the set standard – regardless of whether the parts meet the specifications – are rewarded, while other workers are punished (no raises, no overtime, etc.). This creates differences between workers and, consequently, chaos and dissatisfaction among workers. The result is bad workmanship and more turnover, and workers are unable to take the pride in their workmanship to which they are entitled.
It is the responsibility of top management to lead the quality improvement program and, by removing any barriers, to implement the quality improvement program. Then the process owners have the responsibility to implement quality improvement in their company or organization. To do this, they first must understand that quality improvement takes place by reducing variation. The next step for them to understand is what factors are responsible for causing variation. To control such factors, the best approach is if management collaborates with the workers who work day in and day out to solve problems, who know their jobs, and who know what challenges they are facing. By collaborating with workers, management can come to understand everything about quality improvement and what is essential to achieve it. This, of course, can be achieved if management has better communication with those workers who do work for quality improvement.
The next step after the implementation of quality improvement is to focus on customers and their needs. By focusing on customers, loyal customers are created, and they can be relied on for the future of the company. Note that when there is focus on the needs of customers, the use of SPC tools becomes essential to achieve the company's goals, which in turn helps improve quality on a continuous basis. According to Crosby, quality improvement is based on four “absolutes of quality management”:
Quality is defined as
conformance to requirements
(a product that meets the specifications), not as “goodness” or “elegance.”
The system for causing quality is
special and common causes
by using SPC tools), not appraisal.
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