Quality management distance learning

quality management distance learning
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I. Contents of quality management distance learning
==================
A number of universities offer distance learning classes in quality assurance management. Learn
how these classes work, and read through the descriptions of some common class offerings.
Essential Information
Quality assurance (QA) professionals have the important task of ensuring that products or
services meet high standards. Students can take classes in quality assurance management
through distance learning programs in the QA field, which are most common at the bachelor's
degree level.
How Distance Learning Quality Assurance Classes Work
Most distance learning classes in quality assurance management are part of quality assurance
degree programs offered by 4-year universities. These courses often use Blackboard, an
academic website program where professors post assignment instructions and even hold class
discussions with forum posts. Some colleges may require each student to find a proctor who
can supervise the final exam. Officials at a local branch of the American Society for
Quality, www.asq.org, may be able to serve as proctors. The American Society for Quality
even offers its own online distance learning classes.
General Class Overviews
While there are multiple types of quality assurance management, some basic principles of
quality are always the same. Classes discussing those principles are common to multiple

programs in quality assurance management. The descriptions below highlight the typical
curriculum of some commonly offered distance learning courses in quality assurance
management.
Total Quality Management and ISO
Basic quality assurance concepts are laid out by the ISO: the International Organization for
Standardization. One basic QA standard is Total Quality Management, which helps all
employees to improve their productivity and work quality. These principles, as laid out by the
American Society for Quality, include:
 Striving for quality even without inspection
 Encouraging on-the-job training, self-improvement and continuing education
 Fostering unity among departments
 Avoiding quotas and annual ratings
 Minimizing employees' fear
Human Factors of Quality Assurance
No commercial or industrial process is perfect, because humans are fallible. Quality assurance
professionals study human operators within systems. They learn how workplaces and
procedures can be handled to maximize productivity.
Quality Cost Principles
This course offered by the American Society for Quality shows students how quality assurance
can improve a company's finances. Students learn to manage their QA projects for the best
monetary result.
Manufacturing Class Overviews
Quality assurance is important to mass industrial manufacturing of commercial products. These
courses support workers in this type of quality assurance management. The following
descriptions represent some classes that may be offered in quality assurance from a
manufacturing standpoint.
Testing and Measurement Techniques
Students learn how to use common measurement tools, equipment and processes to test newly
manufactured products. Teachers present principles of measurement so students can maximize
accuracy.
Sampling and Statistical Quality Control
When testing each unit manufactured is impractical, quality assurance workers can test a
certain number out of each lot. A course in sampling and statistics helps students learn how to
use math and statistical methods to maximize their certainty in this process.
Service Class Overviews
Some quality assurance managers need to work with employees who provide a service directly
to customers. Health care organizations need this type of quality assurance. An online degree

program in quality assurance management may include courses about this assessment specialty.
Read through the overviews of such courses below.
Service Professionals and Statistical Quality Control
Quality assurance students learn statistical methods designed for evaluating service workers.
This class teaches set-up and maintenance of quality control, including sampling processes
Quality Assurance and Customer Satisfaction
To conform service delivery with the customers' desires, professionals must know exactly what
the customers want and where current service falls short. One quality assurance class teaches
students how to determine customers' level of satisfaction and implement quality control
procedures to maximize it
=================
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
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
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
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
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

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]
III. Other topics related to quality management distance learning (pdf
download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards
quality management policy
quality management strategy
quality management books

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