1. An assignment
On
Contributions of
Walter A. Shewhart
Course code: MGT-408
Course Title: Total Quality Management
Prepared for
Nadia Newaz Rimi
Assistant Professor
Department of Management
Faculty of Business Studies
University of Dhaka
Prepared by- Group -04
20th
batch, Section-B
Department of Management
Date of submission: 5th
October, 2017
2. Group Profile
Group-04
Group Name: Future Entrepreneurs
ID Name Remarks
112 Md. Biplob Hossain
128 Tajul Islam
161 Md. Nasim Uddin
165 Md. Anamul Haque
207 Mahamudur Rahman
3. Walter A. Shewhart
Walter Shewhart was a giant among giants in the quality movement during the first half of the
20th century. His mentoring of other engineers at Western Electric and his groundbreaking work
with control charts arguably led a quality revolution and launched the quality profession.
Background
Walter Andrew Shewhart was born to Anton and Esta Barney Shewhart on March 18, 1891, in
New Canton, IL. Shewhart died on March 11, 1967, in Troy Hills, NJ. He attended the
University of Illinois receiving bachelor’s and master’s degrees. In 1914, he married Edna Hart
and moved to California where he earned his doctoral degree in physics while studying as a
Whiting Fellow at the University of California, Berkeley, in 1917.
He had brief stints of teaching at University of Illinois, University of California at Berkeley, and
La Crosse State Teachers College (renamed Wisconsin State University), but his academic career
was short-lived.
Walter A. Shewhart was founding editor of the Wiley Series in Mathematical Statistics, a role
that he maintained for twenty years.
Originally an Engineer and Statistician, Walter A. Shewhart is above all known as the true
"Father of Modern Quality" whereas W. Edwards Deming was his student and spiritual son.
Shewhart introduced the concept of Statistical Process Control (SPC) in Manufacturing.
Shewhart's Contribution
As influential as SPC is, this is not the essential contribution made by Shewhart. His essential
idea he planted in the head of Deming was the concept of Profound Knowledge and PDCA
(Plan-Do-Check-Action) or PDSA (Plan-Do-Study-Act) spiral: Plan what you want to do, do it,
study the results, make corrections, and start the cycle again (so the spiral not a circle only).
From the late 1930s onwards, Shewhart's interests expanded out from industrial quality to wider
concerns in science and statistical inference. The title of his second book Statistical Method from
the Viewpoint of Quality Control (1939) asks the audacious question: What can statistical
practice and science in general, learn from the experience of industrial quality control?
Shewhart's approach to statistics was radically different from that of many of his contemporaries.
He possessed a strong operationalist outlook, largely absorbed from the writings of pragmatist
philosopher C. I. Lewis, and this influenced his statistical practice. In particular, he had read
Lewis's Mind and the World Order many times.
His more conventional work led him to formulate the statistical idea of tolerance intervals and to
propose his data presentation rules, which are listed below:
4. 1. Data has no meaning apart from its context.
2. Data contains both signal and noise. To be able to extract information, one must separate the
signal from the noise within the data.
Walter Shewhart died at Troy Hills, New Jersey in 1967.In his obituary for the American
Statistical Association, Deming wrote of Shewhart:
As a man, he was gentle, genteel, never ruffled, never off his dignity. He knew disappointment
and frustration, through failure of many writers in mathematical statistics to understand his point
of view.
It was the Inspection Engineering Department of the Western Electric Company at Hawthorne
that Shewhart joined in 1918. He worked there on statistical tools to examine when a corrective
action must be applied to a process. His writings were on statistical control of industrial
processes and applications to measurement processes in science. The control chart techniques
which he developed have been widely adopted.
By the turn of the century, Western Electric had trained individuals as inspectors to assure
specification and quality standards, in order to avoid sending bad products to the customer. In the
1920's, Western Electric's Dr Walter Shewhart took manufacturing quality to the next level -
employing statistical techniques to control processes to minimize defective output. When Dr
Shewhart joined the Inspection Engineering Department at Hawthorne in 1918, industrial quality
was limited to inspecting finished products and removing defective items. That all changed in
May 1924. Dr Shewhart's boss, George 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."
Mr. Edwards had observed the birth of the modem scientific study of process control. That same
year, Dr Shewhart created the first statistical control charts of manufacturing processes, which
involved statistical sampling procedures. Shewhart published his findings in a 1931 book,
Economic Control of Quality of Manufactured Product.
The Bell Telephone Laboratories were founded in 1925 and Shewhart moved to them when the
Laboratories opened and worked there until his retirement in 1956. He expanded his interests to a
broader use of statistics over this period. During this period he published many articles papers in
the Bell System Technical Journal. In addition, he published Random sampling in the American
Mathematical Monthly in 1931. In 1939 he published the important book Statistical Method from
the Viewpoint of Quality Control. It is interesting to read the publisher's description of the book:-
The application of statistical methods in mass production makes possible the most efficient use
of raw materials and manufacturing processes, economical production, and the highest standards
5. of quality for manufactured goods. In this classic volume, based on a series of ground-breaking
lectures given to the Graduate School of the Department of Agriculture in 1938, Dr Shewhart
illuminates the fundamental principles and techniques basic to the efficient use of statistical
method in attaining statistical control, establishing tolerance limits, presenting data, and
specifying accuracy and precision.
In the first chapter, devoted to statistical control, the author broadly defines the three steps in
quality control: specification, production and inspection; he then outlines the historical
background of quality control. This is followed by a rigorous discussion of the physical and
mathematical states of statistical control, statistical control as an operation, the significance of
statistical control and the future of statistics in mass production.
Chapter II offers a thought-provoking treatment of the problem of establishing limits of
variability, including the meaning of tolerance limits, establishing tolerance limits in the simplest
cases and in practical cases, and standard methods of measuring. Chapter III explores the
presentation of measurements of physical properties and constants. Among the topics considered
are measurements presented as original data, characteristics of original data, summarizing
original data (both by symmetric functions and by Chebyshev's theorem), measurement
presented as meaningful predictions, and measurement presented as knowledge.
Finally, Dr Shewhart deals with the problem of specifying accuracy and precision - the meaning
of accuracy and precision, operational meaning, verifiable procedures, minimum quantity of
evidence needed for forming a judgment and more.
In this book Shewhart asks:-
What can statistical practice, and science in general, learn from the experience of industrial
quality control?
He wrote in this book:-
The definition of random in terms of a physical operation is notoriously without effect on the
mathematical operations of statistical theory because so far as these mathematical operations are
concerned random is purely and simply an undefined term. The formal and abstract mathematical
theory has an independent and sometimes lonely existence of its own. But when an undefined
mathematical term such as random is given a definite operational meaning in physical terms, it
takes on empirical and practical significance. Every mathematical theorem involving this
mathematically undefined concept can then be given the following predictive form: If you do so
and so, then such and such will happen.
Shewhart's Contribution
Engineers at Bell Telephone had been working to improve the reliability of their transmissions
systems. Business dictated a need to reduce the frequency of failures and repairs to their
amplifiers, connectors and other equipment that were buried underground. Bell Telephone had
already realized that reducing variation in manufacturing processes would have a positive impact
6. on repair costs. At the same time the company determined that continual adjustments in process
parameters reacting to non-conformances resulted in increased variation and a degradation of
quality.
Bell Telephone’s discoveries in product variation resulted in the institution of an inspection
program, ensuring specification and quality standards to avoid sending defective products to
customers. Even though this program was somewhat effective, it was very costly to deal with
inspecting and sorting of finished goods.
By 1924, Shewhart determined the problem of variability in terms of assignable cause and
chance cause (Deming referred to this as common cause). On May 16, 1924, Shewhart prepared
a memorandum of less than one page in length and forwarded it to his manager, George
Edwards. About 1/3 of the page was devoted to a simple diagram that we would today recognize
as a control chart. This memorandum set forth the essential principles and considerations that
became known as process quality control.
Shewhart’s principle was that bringing a process into a state of statistical control would allow the
distinction between assignable and chance cause variations. By keeping the process in control, it
would be possible to predict future output and to economically manage processes. This was the
birth of the modern scientific study of process control.
SPC Moves Mainstream
At its creation in 1925, Shewhart moved to the Bell Telephone Laboratories working to advance
his theories and to bring together the disciplines of statistics, engineering and economics.
In 1931 he published a book, “Economic Control of Quality of Manufactured Product.” It
challenged the inspection-based approach to quality and introduced the modern era of quality
management. Up until this time, statistical process control was largely a Bell Telephone quality
tool. Shewhart’s book popularized statistical control and its use then spread throughout industry.
From the 1930s forward, Shewhart’s interests expanded from industrial quality to wider concerns
in science and statistical inference. In 1934, W. Edwards Deming and another physicist,
Raymond T. Birge, published a paper on measurement error in science. However, after
collaboration with Shewhart, they recast their approach and launched a long collaboration
between Shewhart and Deming.
Shewhart’s charts were adopted by the American Society for Testing Materials (ASTM) in 1933.
Shewhart and Deming impacted the improvement of production material during World War II in
American War Standards Z1.1-1941, Z1.2-1941 and Z1.3-1942. Frequently, he was called upon
as a consultant to the U.S. War Department, the United Nations and the government of India.
Deming continued to champion Shewhart’s ideas, methodologies and theories throughout his
career. While working with Japan, Deming further developed some of Shewhart’s
methodological proposals of scientific interference, which had been named the Shewhart Cycle
and was represented by the plan-do-check-act elements.
7. Shewhart lectured extensively on the subjects of quality control and applied statistics in India, at
the University of London, at Stevens Institute of Technology and at the graduate schools of the
U.S. Department of Agriculture. He also was a member of many societies and governmental
agencies.
During the 1990s Shewhart’s work was rediscovered by a third generation of industrial engineers
and managers, and this time it was repackaged and incorporated into the Six Sigma approach.
Shewhart received many honors and awards that include:
Holley Medal of the American Society of Mechanical Engineers
ASQ’s 1st Honorary Member
Founding Member and Fellow of the Institute of Mathematical Statistics
Fellow of the American Statistical Association
Fellow of the International Statistical Institute
Fellow of the Royal Society of Mechanical Engineers
Shewhart believed that statistical theory should serve the needs of industry and society as a
whole. He challenged the norms of his day and showed manufacturers the better way that
revolutionized industry.
Upon his death in 1967, there were a multitude of commentaries from many contributors who
were themselves important figures in the development of the quality field. An excerpt from a
speech by the chairman of the committee that awarded the first ASQ Shewhart Medal captured
Shewhart’s character in the following:
“Shewhart’s legacy lives in mementos of him-a simple bowl and some numbered chips, a bronze
medal, some books and writings. It lives in the succession of other prominent individuals he
influenced, and it lives in the society of professionals who carry on the work he started.”
8. Plan Do Check Act cycle
PDCA (plan–do–check–act or plan–do–check–adjust) is an iterative four-step management
method used in business for the control and continual improvement of processes and products.[1]
It is also known as the Deming circle/cycle/wheel, the Shewhart cycle, the control circle/cycle,
or plan–do–study–act (PDSA). Another version of this PDCA cycle is OPDCA. The added "O"
stands for observation or as some versions say: "Observe the current condition." This emphasis
on observation and current condition has currency with the literature on lean manufacturing and
the Toyota Production System.
Meaning
The PDCA cycle
Continuous quality improvement with PDCA
Plan
9. During the plan phase, establish the objectives and processes necessary to deliver results in
accordance with the expected output (the target or goals). By establishing output expectations,
the completeness and accuracy of the specification is also a part of the targeted improvement.
When possible start on a small scale to test possible effects.
Do
During the do phase, implement the plan, execute the process, make the product. Collect data for
charting and analysis in the following check and act steps.
Check
In the check phase, study the actual results (measured and collected in do phase above) and
compare against the expected results (targets or goals from the plan phase) to ascertain any
differences. Look for deviation in implementation from the plan and also look for the
appropriateness and completeness of the plan to enable the execution, i.e., the doing. Charting
data can make this much easier to see trends over several PDCA cycles and in order to convert
the collected data into information. Information is needed for the next step: act.
Act
If the check phase shows that the plan phase which was implemented in do phase is an
improvement to the prior standard (baseline), then that becomes the new standard (baseline) for
how the organization should act going forward (new standards are thus said to be enACTed).
Instead, if the check phase shows that the plan phase which was implemented in do phase is not
an improvement, then the existing standard (baseline) will remain in place. In either case, if the
check phase showed something different than expected (whether better or worse), then there is
some more learning to be done... and that will suggest potential future PDCA cycles. Note that
some who teach PDCA assert that the act phase involves making adjustments or corrective
actions, but generally it would be counter to PDCA thinking to propose and decide upon
alternative changes without using a proper plan phase, or to make them the new standard
(baseline) without going through do and check steps.
