1. As9100 qualitymanagement system
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I. Contents of as9100 quality management system
==================
AS9100 QMS is a quality management standard designed specifically for aviation, space, and
defense companies. Prior to discussing the benefits of implementing AS9100 quality
management software, let us examine the standard itself, and why it is so important.
When it comes to building aircrafts and other sophisticated aerospace equipment, there is no
margin for error. Mistakes in the manufacturing process can cause the aircraft or aviation device
to malfunction, which can result in product and delivery delays at best, and the loss of human life
at worst. To avoid such calamities, aerospace manufacturers must ensure that all of their
manufacturing processes and procedures are well documented and carried out accurately and
consistently. The AS9100 quality management system standard was developed to promote and
continuous product and process improvement in the aerospace supply chain, with an additional
focus on ensuring on-time and on-quality deliveries.
AS9100 QMS was introduced in 1999 by the Society for Automotive Engineers (SAE). As
previously mentioned, the AS standard is based on ISO 9001; however, it has been supplemented
with standards specific to civil and military aerospace applications. In 2009, the AS9100 quality
management system standard was revised as AS9100:2009 Revision C or AS9100C. The
additions to the AS9100 can be onerous, which is why best-in-class aerospace manufactures have
come to rely on automated quality management software like MasterControl to help them
achieve AS9100 certification.

2. Business Advantages of Using AS9100 Quality Management Software
When implemented and used correctly, an automated quality management system can greatly
assist aviation, space, and defense companies in achieving AS9100 QMS certification which, in
turn, delivers many important business advantages. These advantages include:
 AS9100 certification assures a consistent commitment to quality services and enhanced
reliability.
 Registration provides a company’s marketing team with a competitive advantage.
 Risk management capabilities of AS9100 quality management software can help
minimize production risks, which can lead to refined procedures that reduce waste and
yield higher profits.
 Achievement of AS9100 quality management system certification enhances company
image in the eyes of customers, employees, and stakeholders.
 AS9100 compliance verifies the existence of a strong QMS and qualifies the company as
a reliable manufacturer to its customers, including the U.S. Government and Department
of Defense.
 AS9100 certification is universally recognized, which allows an AS9100 QMS certified
company to gain access to worldwide markets.
 An effective AS9100 quality management software system improves the manufacturer’s
entire operating system.
Achieve AS9100 QMS Certification with MasterControl’s Automated AS9100 Quality
Management Software
Over the years, companies involved in the production or manufacturing of parts and products
used by the aviation industry have adopted a wide range of IT solutions. As a result, they are
now left with a cluster of homegrown systems and a hodgepodge of legacy products. These
disconnect systems and products often result in miscommunication, lost or misplaced documents,
manufacturing errors, and production delays—the very issues the AS9100 quality management
system standard was designed to address.
MasterControl's AS9100 quality management software electronically organizes, archives, and
controls all compliance-related documentation into one lean, paperless QMS. In addition,
MasterControl’s AS9100 QMS add-on products allow users to create a unified solution for
managing a wide-range of AS9100 compliance process requirements, such as audit risk
management, audit management, and supplier management, across the enterprise.
 Risk Management—AS9100 certification aims at eliminating production risks.
MasterControl Risk™ unifies risk-related activities and documents in a single,
centralized repository. The solution provides a complete and accurate picture of the
AS9100 risk landscape across product lines, business processes, and business units,
averting the brand and reputation damage that can result from recalls, delivery delays,
and regulatory nonconformance. Effective AS9100 quality management software

3. products like MasterControl Risk can help aerospace manufactures quickly and
efficiently identify and mitigate their long-term systemic risk.
 Audit Management—AS9100 certification hinges on passing a comprehensive AS9100
QMS audit. MasterControl Audit™, an integrated part of the MasterControl AS9100
quality management system software suite, enables aviation, space, and defense
organizations to electronically streamline and manage all of their audit procedures,
including the development of easy-to-use checklists that can be customized to address a
variety of AS9100 audit types (external, internal, and supplier). These comprehensive
checklists promote audit consistency, which helps to ensure that the manufacturer is in
compliance with applicable guidelines. And MasterControl’s AS9100 quality
management software also offers powerful online analytics and reporting tools, which
allow aerospace manufacturers to spot trends of potential systemic issues and make
faster, more productive critical decisions.
 Supplier Management— The AS9100 quality management system standard was
specifically developed to promote process improvement in the aerospace supply chain,
MasterControl Supplier™ offers a single repository for accessing all supplier quality data
and documentation. The solution makes it extremely easy to add and track approved
suppliers. The robust AS9100 quality management software solution is also able to link
approved parts, materials, and services directly to a specific supplier, and Supplier’s
built-in reporting tool allows its users to filter and trend data relevant to each individual
supplier.
==================
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:

4.  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

5. 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
numerically identical, the scatters fall on the identity line

7. 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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