1. Quality management essay
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I. Contents of quality management essay
==================
Total Quality Management is an essential tool that makes an improvement reaction to firms and
companies. It is a technique of managing the future outcomes, and it does consist more features
than just ensuring product and service quality, as it is a technique of running people and business
processes in order to guarantee customer satisfaction in every phase. With TQM, it helps
organizations to do the right thing at the right time from its first attempt. Therefore, we will be
introducing the principle of TQM and how it reacts with organizations along with different pros
and cons that could affect using of this vital technique.
Definition of Quality
Quality in manufacturing is defined as a measure of excellence or defects free that is taken by the
adherence to measurable and provable standards to reach consistency of a specific output that
will satisfy a certain customer. Total Quality Management is one of the techniques used to
achieve a specific standard to serve customer requirements. A frequent quality description is
delighting the customer by fully achieving their desire and expectations; this could include
performance, delivery of item, reliability, cost effectiveness, and appearance. Therefore, it is
essential for the company to know what are these desire and expectations, as well as identify
them, understand, and measure their ability to meet them.
Quality commences with a detailed market investigation to determine the actual requirements of
a certain product and the customer's need. The main role of quality in organizations is
cooperation of everyone, as it is compulsory to achieve a total quality organization.

2. What is Total Quality Management (TQM)?
TQM is a customer focused approach program that focuses on customer satisfaction by
delivering the best quality product at lowest possible price. It is an organization strategy that
involves everybody for contribution. The main aspect is prevention of defects, by working on a
target of zero defects. Moreover, TQM is methodical as it depends on the information gained and
it is a continuous improvement process.
Throughout the years before, TQM has reached to be an important and outstanding for firm's
process capabilities improvement to lead to a fit and maintained competitive advantages.
http://www.leanmanufacturingconcepts.com/DefinitionTotalQualityManagement.htm
History of TQM
In the present world we hear a lot about quality control and management, which even did not
exist in eighteenth and nineteenth century. However there were some quality control actions
taken by individuals at a small level. The quality control and management developed and
evolved during the entire twentieth century. In early 1900s Fredrick W. Taylor presented the
quality concepts, he is known as the ‘father of scientific management'. In 1913, JC Penney
became one of the first person to introduce the fundamentals of total quality management by
bringing up ideas like “customer satisfaction”, “quality”, “value”, “training” and “rewards for
performance” to the managerial bases for the business.
The history of TQM starts through Elton Mayo's Hawthorne experiments from 1927 to 1932.
The Hawthorne experiments showed that the workers involvement in the decision making
process actually enhanced the production. In 1930s the Western Electric Company considered
lighting levels, work day lengths and rest period length in the Hawthorne plant of to maximize
the productivity. And the researchers found that as the lights were brighter, worker's productivity
increased and vice versa. This change of the behavior of the employees is called the Hawthorne
effect.
In 1940s US was in World War II. World War II pushed the standardization, quality control and
manufacturing practices to a higher level. The idea of TQM grew very slowly in the USA even
though many TQM aspects were developed in the USA in the 1950s. Quality was implemented
in the American and European industries only in the 1980s because there was no preparedness of
the business and governmental organizations to take adequate steps concerning the findings of
technical and statistical work. The decision making structures at that period were not ideal to
solve the quality control problems. In 1960s, the idea of “Zero-defects” gained favor.Philip
Crosby, who was the founder of “Zero defects” idea concentrated on employee motivation and
awareness.
In Japan the quality before 1940s was limited to inspection quality. The post war era saw
dramatic progress in the Japanese quality and that happened over a small period of time. Actually
the quality control was introduced to Japan by few American experts. In 1950s Edward Deming
imparted statistical methods and Dr. Juran imparted the quality management methods to the

3. Japanese. Edward Deming is known as the ‘father of modern quality'. It was in 1950s when
Armand Feigenbaun wrote Total Quality Control. This was the first work that initiated Total
Quality Management theories. The Japanese realized the need and benefits of quality
management. A proper effort was initiated in 1956. In 1962 the Japanese had innovated the
concept of quality control. In 1968 they had developed their own version of TQM, and presented
it as Company Wide Quality Control (CWQC) and the most key features of TQM in Japan was
achieved between 1950-1965.
"We have learned to live in a world of mistakes and defective products as if they were necessary
to life. It is time to adopt a new philosophy in America." W. Edwards Deming(1900-1993)
In the 1980s, quality management systems, as given in the ISO standards 9000 series, paved the
way for a quality-related business management. Advanced companies today are in a post-ISO-
era, in search of business brilliance to meet the tests of the globalization in all market sectors.
How to implement TQM
There are stages in order to be able to implement total quality to a firm which requires
commitment and work to successfully implement total quality where the first step is to start
setting goals and achieving them, also the management team should be motivate because it is
important for continues improvement. By continues improvement it is important to identify the
stages of improvement and create the required responsibility before implementing where it is
also important pre-plan every step before the actual implementation. And therefore it would be
recommended to assign every employer to be his or her own inspector while working thus
increasing productivity and lowering the costs.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are

4. read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of

5. quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

6. A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are

7. numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

8. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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