This document provides an overview of quality management including definitions, principles, and common tools. It defines total quality management as a customer-focused, systematically strategic approach to continual improvement that involves all employees. Common quality management tools are then described, including check sheets to collect data, control charts to monitor processes, Pareto charts to identify key factors, and scatter plots to illustrate correlations between variables. Various online resources for additional quality management information are also listed.

1. About quality management
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I. Contents of about quality management
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Total quality management can be summarized as a management system for a customer-focused
organization that involves all employees in continual improvement. It uses strategy, data, and
effective communications to integrate the quality discipline into the culture and activities of the
organization.
 Customer-focused. The customer ultimately determines the level of quality. No matter what
an organization does to foster quality improvement—training employees, integrating quality
into the design process, upgrading computers or software, or buying new measuring tools—
the customer determines whether the efforts were worthwhile.
 Total employee involvement. All employees participate in working toward common goals.
Total employee commitment can only be obtained after fear has been driven from the
workplace, when empowerment has occurred, and management has provided the proper
environment. High-performance work systems integrate continuous improvement efforts with
normal business operations. Self-managed work teams are one form of empowerment.
 Process-centered. A fundamental part of TQM is a focus on process thinking. A process is a
series of steps that take inputs from suppliers (internal or external) and transforms them into
outputs that are delivered to customers (again, either internal or external). The steps required
to carry out the process are defined, and performance measures are continuously monitored in
order to detect unexpected variation.

2.  Integrated system. Although an organization may consist of many different functional
specialties often organized into vertically structured departments, it is the horizontal processes
interconnecting these functions that are the focus of TQM.
o Micro-processes add up to larger processes, and all processes aggregate into the business
processes required for defining and implementing strategy. Everyone must understand the
vision, mission, and guiding principles as well as the quality policies, objectives, and
critical processes of the organization. Business performance must be monitored and
communicated continuously.
o An integrated business system may be modeled after the Baldrige National Quality
Program criteria and/or incorporate the ISO 9000 standards. Every organization has a
unique work culture, and it is virtually impossible to achieve excellence in its products and
services unless a good quality culture has been fostered. Thus, an integrated system
connects business improvement elements in an attempt to continually improve and exceed
the expectations of customers, employees, and other stakeholders.
 Strategic and systematic approach. A critical part of the management of quality is the
strategic and systematic approach to achieving an organization’s vision, mission, and goals.
This process, called strategic planning or strategic management, includes the formulation of a
strategic plan that integrates quality as a core component.
 Continual improvement. A major thrust of TQM is continual process improvement.
Continual improvement drives an organization to be both analytical and creative in finding
ways to become more competitive and more effective at meeting stakeholder expectations.
 Fact-based decision making. In order to know how well an organization is performing, data
on performance measures are necessary. TQM requires that an organization continually
collect and analyze data in order to improve decision making accuracy, achieve consensus,
and allow prediction based on past history.
 Communications. During times of organizational change, as well as part of day-to-day
operation, effective communications plays a large part in maintaining morale and in
motivating employees at all levels. Communications involve strategies, method, and
timeliness.
These elements are considered so essential to TQM that many organizations define them, in
some format, as a set of core values and principles on which the organization is to operate.
==================
III. Quality management tools
1. Check sheet

3. 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
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

4. 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
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

5. algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
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

6. 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
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

7. 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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