This document discusses the key components of an effective quality management system. It begins by outlining the benefits of implementing quality management software, such as automating processes like corrective and preventive action plans. It then lists several essential components that a quality management system and software should include, such as facilitating regulatory compliance, customizable workflows, and integrating with other systems. The document also provides examples of commonly used quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. In closing, it emphasizes that a robust quality management system connects all departments and processes to help companies improve quality and regulatory compliance.

1. Componentsof a quality management system
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I. Contents of components of a quality management system
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A high-caliber electronic QMS is comprised of configurable, easy-to-use, and connected
applications for automating, streamlining, and effectively managing all essential components of
quality management systems (i.e., document control, change control, training control, audits,
corrective/preventive action, customer complaints, and other crucial documents- and forms-
based quality and business processes) under a single Web-based platform.
Without specifically designed software for managing quality processes such as corrective and
preventive actions (CAPA), organizations may not realize how inefficient their processes are
until they’ve already lost to the competition. For instance, when a company doesn’t meet the
fundamental components of quality management systems, they may end up running full CAPA
processes that should have never been instigated in the first place simply because that is the way
things have always been done internally and no one has ever bothered to fix faulty processes.
Such reliance on outdated processes and paper-based systems inevitably results in wasted time
and increased costs because valuable resources are being focused on the wrong problems and
spread too thin.
Benefits of Top-of-the-Line Quality Management System Software Solutions
Here are a few of the fundamental components of quality management systems that need to be
considered when organizations are seeking to implement an electronic QMS solution:

2.  The QMS solution should give your organization the ability to accelerate and automate
essential quality processes like CAPA, customer complaints, deviations, out-of-
specifications, change control, audits, non-conformance, and so forth.
 The technology should provide the ability to automate documentation processes such as
document change, distribution, notification, and approval in order to accelerate
compliance and minimize the time it takes for products to get to market.
 A system that is ready right out of the box is valuable, but it should also customizable
enough to fit your individual organizational needs.
 A QMS system that is specially designed to be implemented and validated within a
reasonable time frame can drastically reduce overall costs and maximize your return on
investment.
 A good quality management system must ensure that the integrity of regulatory
documentation and standard operating procedures (SOPs) can be maintained throughout a
document’s lifecycle.
 The QMS software should also offer the capability to route documents electronically
along pre-defined workflows for more efficient distribution, collaboration, and approvals.
Most companies doing business in regulatory environments require the following components of
quality management systems:
 A Web-based quality management architecture
 Accelerates compliance
 Must facilitate compliance with stringent regulatory requirements such as 21 CFR Part 11
 Supports standard databases used in life sciences and similarly regulated environments
 Takes advantage of form-to-form launching
 Offers the capability to track forms by status or history (as “in process,” “complete,” etc.)
 Maintains active links so users can review a completed process to see what event caused
or merged with another event
 Integrates with existing document repositories and enterprise applications such as ERP,
LIMS, etc., without custom coding
 Integrates forms with training control (i.e., any change to a document or process that calls
for new training should automatically trigger training tasks)
Bringing Together All the Vital Components of Quality Management Systems
A state-of-the-art quality management software solution allows a regulated company to enhance
the efficiency of its quality systems while also ensuring that those systems are also compliant,
connected, and cost effective. Proven QMS management software solutions are vital components
of maintaining quality management system fundamentals and achieving regulatory compliance.
Plus, robust QMS software can be invaluable in helping companies keep pace with industry
trends. Companies may vary in scope and size, but if they’re doing business in environments
where regulatory bodies are pushing toward the usage of electronic systems, it is mandatory that
they effective quality management software solutions to enhance and harmonize their overall
quality processes.

3. An automated QMS management system also connects all departments, product lifecycles, and
quality processes. User-friendly quality software helps simplify, streamline, and effectively
manage quality control processes. In addition, an electronic system’s ability to automatically
route tasks and escalate them when necessary ensures that task completion, approval cycles, and
inter-departmental input occur in a timely manner. When quality is built directly in to the QMS
system, every department—from engineering to quality assurance and manufacturing to
regulatory —stays connected.
A QMS software solution helps companies overcome the common challenges discussed above
and also establish solid QMS fundamentals. Consider, for instance, that an electronic system can
help users effectively determine root causes by providing problem-solving algorithms based on
critical thinking logic, thus preventing those issues from reoccurring. Electronic QMS systems
connect processes and propagate the CAPA process throughout the entire organization. So, for
example, the resolution of a CAPA can be set to cause the system to automatically trigger a
change control which prompts a change in an SOP and coordinates retraining of employees on
that SOP.
When a company possesses all the necessary components of quality management systems,
companies can improve both quality and speed to market. The bottom line is that if all the QMS
bases are covered, more products of a higher quality can be manufactured at a greatly reduced
cost.
To learn more about MasterControl’s Components of Quality Management Systems (QMS)
solutions, contact a MasterControl representative.
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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.

4. 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

5. 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
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]
III. Other topics related to Components of a quality management system (pdf
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