1. onlinequality management system
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I. Contents of online quality management system
==================
To understand the importance of online quality management software in regulated environments,
one has to grasp the concept of quality control. W. Edwards Deming, the world-famous
proponent of quality control, believed that quality should be incorporated at the start of the
manufacturing process, instead of inspecting products for defects at the end.
Deming helped Japan turn around its post-World War II economy by helping Japanese
manufacturers produce high-quality products that dominated the global market. The application
of Deming's concept of quality eventually spread from general manufacturing to life science and
other regulated industries. He has greatly influenced the prevalent concept of quality
management system (QMS) today, as well as the use of online quality management software and
online QMS in regulated industries.
Life Science Companies and Online Quality Management Software
Deming famously said that "quality is everyone's responsibility." In the life science industry,
quality is incorporated in the entire development and manufacturing process, making it
everyone's concern. Using online quality management software and establishing a QMS make it
easier to connect all stakeholders so quality becomes everybody's business.
The U.S. FDA's regulations and guidelines for the life science industry reflect the importance of
quality management in keeping medicines, medical devices, and other regulated products safe
and effective. In particular, the FDA's Quality System Regulation (21 CFR 820) for medical
devices outlines what constitutes an effective quality system. Medical device and other FDA-

2. regulated companies were among the first to utilize online quality management software as the
platform for their online QMS.
The FDA's 21 CFR 211 requires pharmaceutical companies to establish a quality control unit,
whose major responsibility is to ensure quality in all facets of the manufacturing process. A
quality management system, whether it's manual or online, is the quality control unit's
responsibility. If a pharmaceutical company decides to use online quality management software,
then the unit typically spearheads the effort to purchase the system.
ISO Standards and Online Quality Management Software
ISO 13485 for medical device manufacturers and the ISO 9000 series for general manufacturers
emphasize the importance of an effective quality management system. Online quality
management software that exists in the market today is largely based on those ISO standards, in
addition to FDA and other regulations.
ISO 13485 requires the establishment of a QMS that covers quality and remedial procedures.
Similarly the ISO 9000 series requires the establishment of a QMS, including documentation of
quality processes. Manufacturers are increasingly switching from paper-based or hybrid systems
to online QMS.
MasterControl Online Quality Management Software and Regulated Companies
MasterControl's online quality management software is widely used in life science and other
regulated industries. Its enduring appeal lies in its fundamental characteristics - it is easy to use,
configurable, and all applications are built on a common web-based platform. Users attest that
the software is easy to implement and easy to validate.
MasterControl All Access offers a single solution for managing different quality processes
critical to compliance. It covers all essential components of any effective online QMS, including
document control, training management, CAPA, change control, BOM, and audit management.
For companies that prefer to use individual modules, consider the following MasterControl
modules:
 MasterControl Documents: This module offers an essential capability for companies
switching from a manual process to a fully electronic system. It automates routing,
follow-up, escalation, and approval of SOPs and other critical documents. MasterControl
is web-based so it is accessible to all authorized users from virtually anywhere 24/7. It
provides robust document management features for any online QMS.
 MasterControl CAPA: This module automates and streamlines the CAPA process from
start to finish. It offers industry best-practice forms that help reduce error in data entry. It
gives users the capability to launch a CAPA form from another form, such as
nonconformance or deviation.
 MasterControl Training: This module automates distribution and monitoring of
training tasks, as well as online grading of tests. Unlike other online quality management

3. software solutions, MasterControl Training has the capability to sequence training, so a
trainee who completes a prerequisite course will automatically get the next training.
 MasterControl Change Control: Change control is a fundamental part of a compliant
QMS. MasterControl provides a best-practice change control form that incorporates
priority level, risk assessment, and classification of the change. The form can be launched
directly from a CAPA form, connecting the change control process with CAPA.
 MasterControl Audit: A key advantage of MasterControl over other online software
solutions is its capability to connect the audit management process with all critical quality
processes. This module automates all audit-related tasks and schedules and provides
robust tools for managing and conducting regular audits.
 MasterControl Mobile: MasterControl Mobile allows users to access documents, tasks,
and processes via a tablet or a smartphone. Those users will be able to participate in
documents - and forms-based processes quickly and conveniently. Mobile access will
increase the "mileage" and utility of any existing QMS.
==================
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

4. 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

5. 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

6. 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.

7. 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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