History of Thought in Quality Assurance

Dr. Tommy Gardner

Chief Technology Officer

March 19, 2012

Outline

• Goals and Definitions

• Egypt

• Greece

• Rome

• Middle Ages

• Industrial Age

• Wartime Production and Post War

• Modern Quality Thought

1. Shewhart

2. Taguchi

3. Deming

4. Womack

5. Solzhenitsyn

6. Rickover

2

Goal

1. To show some insight into the hypothesis that the Quality function has been with Mankind throughout the ages.

2. To draw what we can from quality experts of the past.

3. To learn from the past and challenge all on what are the next breakthroughs in Quality

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First a Failure of Quality

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Dilbert

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ASQ: Quality Assurance and Quality Control

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Assurance: The act of giving confidence, the state of being certain or the act of making certain.

Quality Assurance: The planned and systematic activities implemented in a quality system so that quality requirements for a product or service will be fulfilled.

Control: An evaluation to indicate needed corrective responses; the act of guiding a process in which variability is attributable to a constant system of chance causes.

Quality Control: The observation techniques and activities used to fulfill requirements for quality.

The terms “quality assurance” and “quality control” are often used interchangeably to refer to ways of ensuring the quality of a service or product. The terms, however, have different meanings.

Ancient Egypt

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The Pyramids were built around 5,000 BC.

The organization, planning, scheduling, design and calculations involved would be a major challenge today.

Modern Egypt

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Greece

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Alexander the Great conquered the world.

He used extensive logistics training and planning.

He needed repeatable processes and a dependable supply chain.

Aristotle said, "We are what we repeatedly do; excellence, then, is not an act, but a habit"

Rome

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Rome was built on the ideals of the Greeks with financing from Egypt.

Middle Ages up to the Industrial Revolution

During the Middle Ages, guilds adopted responsibility for quality control of their members, setting and maintaining certain standards for guild membership.

Royal governments purchasing material were interested in quality control as customers. For this reason, King John of England appointed William Wrotham to report about the construction and repair of ships. Centuries later, Samuel Pepys, Secretary to the British Admiralty, appointed multiple such overseers.

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The Industrial Revolution

Prior to the extensive division of labor and mechanization resulting from the Industrial Revolution, it was possible for workers to control the quality of their own products. The Industrial Revolution led to a system in which large groups of people performing a specialized type of work were grouped together under the supervision of a foreman who was appointed to control the quality of work manufactured.

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Mass Production

At the time of the First World War, manufacturing processes typically became more complex with larger numbers of workers being supervised. This period saw the widespread introduction of mass production and piece work, which created problems as workmen could now earn more money by the production of extra products, which in turn occasionally led to poor quality workmanship being passed on to the assembly lines.

To counter bad workmanship, full time inspectors were introduced to identify, quarantine and ideally correct product quality failures. Quality control by inspection in the 1920s and 1930s led to the growth of quality inspection functions, separately organized from production and large enough to be headed by superintendents.

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Wartime Production

The systematic approach to quality started in industrial manufacturing during the 1930s, mostly in the USA, when some attention was given to the cost of scrap and rework. With the impact of mass production required during the Second World War made it necessary to introduce an improved form of quality control known as Statistical Quality Control, or SQC.

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SQC includes the concept that every production piece cannot be fully inspected into acceptable and non acceptable batches. By extending the inspection phase and making inspection organizations more efficient, it provides inspectors with control tools such as sampling and control charts, even where 100 per cent inspection is not practicable. Standard statistical techniques allow the producer to sample and test a certain proportion of the products for quality to achieve the desired level of confidence in the quality of the entire batch or production run.

Postwar

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In the period following World War II, many countries' manufacturing capabilities that had been destroyed during the war were rebuilt. General Douglas MacArthur oversaw the re-building of Japan. During this time, General MacArthur involved two key individuals in the development of modern quality concepts: W. Edwards Deming and Joseph Juran. Both individuals promoted the collaborative concepts of quality to Japanese business and technical groups, and these groups utilized these concepts in the redevelopment of the Japanese economy.

Although there were many individuals trying to lead United States industries towards a more comprehensive approach to quality, the U.S. continued to apply the Quality Control (QC) concepts of inspection and sampling to remove defective product from production lines, essentially ignoring advances in QA for decades.

Deming Juran

Breakout - Modern Quality Thought

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- Shewhart

- Taguchi

- Deming

- Womack

- Solzhenitsyn

- Rickover

Shewhart

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Father of statistical quality control

“Shewhart simulated theoretical models by marking numbers on three different sets of metal-rimmed tags. Then he used an ordinary kitchen bowl – the Shewhart bowl – to hold each set of chips as different sized samples were drawn from his three different populations. There was a bowl, and it played a vital role in the development of ideas and formulation of methods culminating in the Shewhart control charts.”

– Ellis R. Ott, Tribute to Walter A. Shewhart, 1967

The industrial age was easing into its second century when a young engineer named Walter A. Shewhart came along and altered the course of industrial history. Shewhart, ASQ’s first Honorary member, successfully brought together the disciplines of statistics, engineering, and economics and became known as the father of modern quality control.

The lasting and tangible evidence of that union for which he is most widely known is the control chart, a simple but highly effective tool that represented an initial step toward what Shewhart called “the formulation of a scientific basis for securing economic control.”

Shewhart was concerned that statistical theory serve the needs of industry. He exhibited the restlessness of one looking for a better way. A man of science who patiently developed and tested his ideas and the ideas of others, he was an astute observer of developments in the world of science and technology. While the literature of the day discussed the stochastic nature of both biological and technical systems, and spoke of the possibility of applying statistical methodology to these systems, Shewhart actually showed how it was to be done; in that respect, the field of quality control can claim a genuine pioneer in Shewhart.

ASQ Shewhart’s Legacy

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His monumental work, Economic Control of Quality of Manufactured Product,published in 1931, is regarded as a complete and thorough exposition of the basic principles of quality control.

Shewhart wrote Statistical Method from the Viewpoint of Quality Control in 1939 and gained recognition in the statistical community. In addition, he published numerous articles in professional journals, and many of his writings were held internally at Bell Laboratories. One of these was the historic memorandum of May 16, 1924, in which he proposed the control chart to his superiors.

An element in Shewhart’s success was his searching out other bright and knowledgeable individuals for their ideas, methodically cultivating these sources and drawing from them information and advice in a way that endeared him to all. In a series of tributes to Shewhart published in Industrial Quality Control in August 1967, the most striking comment from the contributors—many of whom were themselves important figures in the development of the quality control field—was their respect for Shewhart’s gentlemanly approach and sincere interest in the work and concerns of others.

Shewhart’s influence on ASQ runs deep. Shortly before his death, he remarked to members that they “extended the field beyond my early visions and saw areas of service that pleased and amazed me. I hope that you continue.”

Shewhart’s Control Chart

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When Dr. Shewhart joined the Western Electric Company Inspection Engineering Department at the Hawthorne Works in 1918, industrial quality was limited to inspecting finished products and removing defective items. That all changed on May 16, 1924. Dr. Shewhart's boss, George D. Edwards, recalled: "Dr. Shewhart prepared a little memorandum only about a page in length. About a third of that page was given over to a simple diagram which we would all recognize today as a schematic control chart. That diagram, and the short text which preceded and followed it, set forth all of the essential principles and considerations which are involved in what we know today as process quality control."[1]

Shewhart's work pointed out the importance of reducing variation in a manufacturing process and the understanding that continual process-adjustment in reaction to non-conformance actually increased variation and degraded quality.

Shewhart framed the problem in terms of assignable-cause and chance-cause variation and introduced the control chart as a tool for distinguishing between the two. Shewhart stressed that bringing a production process into a state of statistical control, where there is only chance-cause variation, and keeping it in control, is necessary to predict future output and to manage a process economically. Dr. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments.

Taguchi

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Genichi Taguchi provided a whole new way to evaluate the quality of a product. Traditionally, product quality has been a correlation between loss and market size for the product. Actual quality of the product was thought of as an adherance to product specifications. Loss due to quality has usually only been thought of as additional costs in manufacturing (i.e. materials, re-tooling, etc.) to the producer up to the time of shipment or sale of the product. It was believed that after sale of the product, the consumer was the one to bear costs due to quality loss either in repairs or the purchase of a new product. It has actually been proven in most cases that in the end the manufacturer is the one to bear the costs of quality loss due to things like negative feedback from customers.

Taguchi changed the perspective of quality by correlating quality with cost and loss in dollars not only at the manufacturing level, but also to the customer and society in general.

You will most likely encounter Taguchi methods in a manufacturing context. They are statistical methods developed by Genichi Taguchi to improve the quality of products. Where as statisticians before him focused on improving the mean outcome of a process, Taguchi recognized that in an industrial process it is vital to produce a product on target , and that the variation around the mean caused poor manufactured quality. For example, car windshields that have the target average mean are useless if they each vary significantly from the target specifications.

Taguchi Quality Loss Function

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Taguchi's key argument was that the cost of poor quality goes beyond direct costs to the manufacturer such as reworking or waste costs. Traditionally manufacturers have considered only the costs of quality up to the point of shipping out the product. Taguchi aims to quantify costs over the lifetime of the product. Long term costs to the manufacturer would include brand reputation and loss of customer satisfaction leading to declining market share. Other costs to the consumer would include costs from low durability, difficulty interfacing with other parts, or the need to build in safety margins.

Think for a moment about how the costs of quality would vary with the products deviation on either side of the mean. Now if you were to plot the costs versus the diameter of a nut, for example, you would have a quadratic function, with a minimum of zero at the target diameter. We expect therefore that the loss (L) will be a quadratic function of the variance (σ, or standard deviation) from the target (m). The squared-error loss function has been in use since the 1930's, but Taguchi modified the function to represent total losses. Next we will walk though the derivation of the Taguchi Loss Function.

QLF Description

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Where:

L = Loss in Dollars

y = Quality Characteristic (diameter, concentration, etc)

m = Target Value for y

k = Constant (defined below)

The Taguchi quality loss function is a way to assess economic loss from a deviation in quality without having to develop the unique function for each quality characteristic. As a function of the traditionally used process capability index, it also puts this unitless value into monetary units.

A graphical representation of the Nominal Characteristic is shown at left. As the output value (y) deviates from the target value (m) increasing the mean squared deviation, the loss (L) increases. There is no loss when the output value is equal to the target value (y = m).

Deming

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Deming said when asked by the government why won’t you help us and he replied because you do not want to do this.

Womack

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Based on the Toyota (lean) model, which combines operational excellence with value-based strategies to produce steady growth through a wide range of economic conditions.

Solzhenitsyn

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Aleksandr Isayevich Solzhenitsyn from The Gulag Archipelago, "Dwell on the past you'll lose an eye, Forget the past you'll lose both eyes." Old Russian Proverb

Rickover

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One of the great icons of the 20th Century was Admiral Hyman Rickover. He is known as the “father” of our nuclear navy, and his efforts have made America safer. Born in a ghetto in Warsaw in 1900, Rickover rose to rank of Admiral and directed the development of our nuclear navy, which has a tremendous safety record. He recognized he was dealing with a highly risky, highly complex issue, and he developed rules for success.

How can these rules help you in your highly complex, highly risky world of government operations? How did his focus on quality control penetrate the organization so deeply so as to reach to the line employee level in the nuclear navy? Let’s take a look at each of these rules.

Rickover Rules

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Rule 1. You must have a rising standard of quality over time, and well beyond what is required by any minimum standard.

Rule 2. People running complex systems should be highly capable.

Rule 3. Supervisors have to face bad news when it comes, and take problems to a level high enough to fix those problems.

Rule 4. You must have a healthy respect for the dangers and risks of your particular job.

Rule 5. Training must be constant and rigorous.

Rule 6. All the functions of repair, quality control, and technical support must fit together.

Rule 7. The organization and members thereof must have the ability and willingness to learn from mistakes of the past.

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BACKUP MATERIAL

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Rickover Rule 1

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Rule 1. You must have a rising standard of quality over time, and well beyond what is required by any minimum standard.

We have to get better and better at what we do. Our public deserves it. Our personnel deserve it. We must be constantly looking for a better way to do things. Status Quo — we have always done it this way — is no longer acceptable. On an organizational level, there are better ways to get and keep good people. There are better ways to build your policy manual. There are better ways to train your personnel. There are better ways to supervise. There are better ways to discipline errant employees. On an operational level, we must improve our performance in response times, quality and timeliness of written reports, training, candor in performance evaluations, equipment and vehicle maintenance, physical conditioning, and anything else that we can measure. Continuous improvement has got to be part of the way we do business.

Rickover Rule 2

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Rule 2. People running complex systems should be highly capable. Successful government operations require people who know how to think. Fifty years ago, you did not need to be all that sharp to be a government employee. Things have changed. Technology, equipment, strategies and tactics involved in providing services to our constituents have all changed. This is an extremely complex job, and if you hire people who can’t think things through, you are in route to disaster. If you allow the hiring of problem employees, they will not disappoint you — they will always be problem employees. In view of the consequences that can occur when things do not go right in your complex, high-risk job — this may end being the cause of a future tragedy. Every nickel you spend in weeding out problem employees up front has the potential to save you a million dollars. And I can prove that statement if you want me to.

Rickover Rule 3

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Rule 3. Supervisors have to face bad news when it comes, and take problems to a level high enough to fix those problems. When you take an honest look at tragedies in any aspect of government, from the lawsuits to the injuries, deaths, embarrassments, internal investigations and even the rare criminal filing, so many of them get down to supervisors not behaving like supervisors. The primary mission of a supervisor is “systems implementation.” If you promote people who either can’t or won’t enforce policy, you are in route to tragedy. To be sure, the transition from line employee to supervisor is a difficult one, but the people you choose to be supervisors have to like their people so much, that they will enforce the policy to protect each of them from loss. Not to beat this point to death, but you show me a tragedy in government operations — including some in the news today — and I will show you the fingerprints of a supervisor not behaving like a supervisor.

Rickover Rule 4

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Rule 4. You must have a healthy respect for the dangers and risks of your particular job. Many government jobs are high risk in nature, and the consequences for not doing things right can be dramatic. Remember the basic rules of Risk Management. RPM — Recognize, Prioritize, Mobilize. You must do a risk assessment on each job in every government department and identify the tasks that have the highest probability of causing you grief. Then you must prioritize these tasks in terms of potential frequency, severity, and available time to think prior to acting. Finally, you must mobilize (act) to address the recognized risks appropriately and prevent consequences.

Rickover Rule 5

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Rule 5. Training must be constant and rigorous. Every day must be a training day! We must focus the training on the tasks in every job description that have the highest probability of causing us grief. These are the high risk, low frequency, non-discretionary time events. We must assure that all personnel are adequately trained to address the tasks that give them no time to think, and that they understand the value of thinking things through when time allows.

Rickover Rule 6

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Rule 6. All the functions of repair, quality control, and technical support must fit together. Audits and inspections are an important part of your job as a leader in government. We cannot assume that all is going well. We must have control measures in place to assure things are being done right. This is not micro-management — it is called doing your job. If you do not have the audits (formal and informal) in place, you will not know about problems until they become consequences, and then you are in the domain of lawyers. That is too late for action, as all you can do then is address the consequences. And if you take the time to study the life of Admiral Rickover, you will quickly learn that he was widely despised in the Navy because of his insistence on using the audit process as a tool to hold people accountable.

Rickover Rule 7

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Rule 7. The organization and members thereof must have the ability and willingness to learn from mistakes of the past.

Analysis of past data is the foundation for almost all of risk management. We (government operations) keep on making the same mistakes over and over again. As I read the lawsuits, injuries and deaths, organizational embarrassments, internal investigations and even the rare criminal filing against our personnel, I know that we can learn so much by studying the mistakes we have made in the past.

SPC

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Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce conforming product. Under SPC, a process behaves predictably to produce as much conforming product as possible with the least possible waste. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output. Key tools in SPC are control charts, a focus on continuous improvementand designed experiments.

Much of the power of SPC lies in the ability to examine a process and the sources of variation in that process using tools that give weight to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over other quality methods, such as inspection, that apply resources to detecting and correcting problems after they have occurred.

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end.

Progression of Charting

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When excessive variation is identified by the control chart detection rules, or the process capability is found lacking, additional effort is exerted to determine causes of that variance. The tools used include Ishikawa diagrams, designed experiments and Pareto charts. Designed experiments are critical to this phase of SPC, as they are the only means of objectively quantifying the relative importance of the many potential causes of variation.

Once the causes of variation have been quantified, effort is spent in eliminating those causes that are both statistically and practically significant (i.e. a cause that has only a small but statistically significant effect may not be considered cost-effective to fix; however, a cause that is not statistically significant can never be considered practically significant).

For digital SPC charts, so-called SPC rules usually come with some rule specific logic that determines a 'derived value' that is to be used as the basis for some (setting) correction. One example of such a derived value would be (for the common N numbers in a row ranging up or down 'rule'); derived value = last value + average difference between the last N numbers (which would, in effect, be extending the row with the to be expected next value).

Most SPC charts work best for numeric data with Gaussian assumptions. Recently a new control chart: The real-time contrasts chart[6]was proposed to handle process data with complex characteristics, e.g. high-dimensional, mix numerical and categorical, missing-valued, non-Gaussian, non-linear relationship.

Quality Process Steps

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Steps for a typical quality assurance process

There are many forms of QA processes, of varying scope and depth. The application of a particular process is often customized to the production process.

A typical process may include:

test of previous articles

plan to improve

design to include improvements and requirements

manufacture with improvements

review new item and improvements

test of the new item

Failure testing

Valuable processes to perform on a whole consumer product is failure testing or stress testing. In mechanical terms this is the operation of a product until it fails, often under stresses such as increasing vibration, temperature, and humidity. This exposes many unanticipated weaknesses in a product, and the data is used to drive engineering and manufacturing process improvements. Often quite simple changes can dramatically improve product service, such as changing to mold-resistant paint or adding lock-washer placement to the training for new assembly personnel.

TQM

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Statistical control

Many organizations use statistical process control to bring the organization to Six Sigma levels of quality,[citation needed] in other words, so that the likelihood of an unexpected failure is confined to six standard deviations on the normal distribution. This probability is less than four one-millionths. Items controlled often include clerical tasks such as order-entry as well as conventional manufacturing tasks.[citation needed]

Traditional statistical process controls in manufacturing operations usually proceed by randomly sampling and testing a fraction of the output. Variances in critical tolerances are continuously tracked and where necessary corrected before bad parts are produced.

Total quality management

The quality of products is dependent upon that of the participating constituents, some of which are sustainable and effectively controlled while others are not. The process(es) which are managed with QA pertain to Total Quality Management.

If the specification does not reflect the true quality requirements, the product's quality cannot be guaranteed. For instance, the parameters for a pressure vessel should cover not only the material and dimensions but operating, environmental, safety, reliability andmaintainability requirements.

Models and standards

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ISO 17025 is an international standard that specifies the general requirements for the competence to carry out tests and or calibrations. There are 15 management requirements and 10 technical requirements. These requirements outline what a laboratory must do to become accredited. Management system refers to the organization's structure for managing its processes or activities that transform inputs of resources into a product or service which meets the organization's objectives, such as satisfying the customer's quality requirements, complying with regulations, or meeting environmental objectives.

Other standards:

ISO 9000:2008, ISO 20000, CMMI, ITIL

During the 1980s, the concept of “company quality” with the focus on management and people came to the fore. It was realized that, if all departments approached quality with an open mind, success was possible if the management led the qualityimprovement process.

In the system of Company Quality, the work being carried out was shop floor inspection which did not reveal the major quality problems. This led to quality assurance or total quality control, which has come into being recently.

CMMI

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In 1988, the Software Engineering Institute introduced the notion that SPC can be usefully applied to non-manufacturing processes, such as software engineering processes, in the Capability Maturity Model (CMM). This idea exists today within the Level 4 and Level 5 practices of the Capability Maturity Model Integration (CMMI). This notion that SPC is a useful tool when applied to non-repetitive, knowledge-intensive processes such as engineering processes has encountered much skepticism, and remains controversial today.