1. Effective quality management system
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I. Contents of effective quality management system
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It’s no secret that regulated companies that fail to embed an automated, effective QMS solution
into their manufacturing and value chain operations expose their brands to increased compliance
risks, and thus weaken their competitive standings in the market. But automating your quality
processes, such as document control, training, corrective action, and risk management, into one
easy-to-use, easy-to-access effective quality management system can give your regulated
organization the competitive edge it needs to achieve compliance success, and win the race to
market.
Here are a few of the advantages you can expect to gain by implementing an automated,
effective QMS:
 You’ll save time—and lots of it: Manual or paper-based quality management systems
are time-consuming, inefficient, and error-prone, and these issues are compounded when
your teams or suppliers are geographically dispersed. Automating your quality processes
into one holistic, effective quality management system will not only enhance visibility
and collaboration, but also allow you to stop chasing paper, and devote more time to
running your business.
 You’ll improve profitability—and ROI: Let’s face it—when you save time, you save
money. Whether you save that money by avoiding costly product recalls or cutting your
corrective action cycle time in half, you’ve already seen a return on your QMS
investment.

2.  You’ll accelerate compliance: Saving time and money is great, but, for regulated
companies, it all comes down to compliance—and the risk of non-compliance has never
been higher. Using MasterControl’s effective QMS to manage your quality process
enables you to minimize costly and embarrassing product shortages and/or recalls. This is
because effective quality management systems like MasterControl are specifically
designed to drastically reduce these risks by controlling the quality and integrity of your
quality management processes, which, in turn, helps you ensure compliance with the
FDA and other global regulatory agencies.
 You’ll deliver safer, higher quality products to your customers:> Gaining a
competitive advantage is only part of the appeal of an effective QMS. Managers in best-
of-breed life science companies are realizing that effective quality management systems
also support the next generation of quality management, which views improving product
safety and efficacy as important as satisfying regulatory requirements. MasterControl’s
QMS help you reduce costs, increase efficiency, and reduce risk within your organization
while helping you to achieve and maintain your required regulatory agenda. These
benefits, which all effective quality management systems should provide, result in the
production and delivery of higher quality products.
Effective Quality Management Systems: Build vs. Buy
The advantages of automating your quality processes into an effective QMS are compelling. Any
company that wants to stay competitive and compliant needs an automated effective quality
management system. But should you build your own homegrown system or buy a proven,
validated QMS? Building a homegrown QMS has its advantages, but it also presents unique
challenges and raises important questions: Will you build it yourself or work with a consultant?
Who will write the software? Will it be reliable? Will it fulfill regulatory requirements? Will it
scale? Will routine maintenance consume too much precious IT staff time? Ultimately, will it
work? It’s not unheard of for companies to start building an effective QMS only to end up
buying a system anyway, resulting in an enormous waste of financial and human resources.
Over the years, we’ve helped hundreds of companies who’ve struggled with the “build vs. buy”
dilemma, so we understand the unique challenges regulated companies face. Based on the
lessons we’ve learned, we’ve developed MasterControl to be configurable, off-the-shelf, end-to-
end effective quality management system designed to significantly improve efficiency,
accelerate compliance, and reduce cost by helping users save time and effort. With
MasterControl, you can start reaching your compliance goals almost immediately.
How MasterControl’s Effective Quality Management System Differs from Other Systems
MasterControl is a leading provider of effective quality management systems to regulated
companies worldwide. Unlike other effective QMS solutions, MasterControl been designed to
integrate with other enterprise systems quickly and easily. Furthermore, MasterControl allows
you to create a unified method for streamlining and managing all of your critical quality
processes such as document control, risk management, audit, training control, quality event
management (complaints, MDR, NCMR, CAPA, deviations, nonconformances), as well as BOM

3. management, project management, and change control (ECR,ECO). Few other effective quality
management systems offer such one-stop-shopping.
One of the biggest advantages of using MasterControl is that the system allows you to start with
a point solution that can be easily developed into a full-fledged, fully integrated effective QMS
later on. Integrating quality processes under a single enterprise platform makes the QMS more
transparent to management, users, auditors, and regulatory inspectors. Documents and processes
are easier to review and maintain. Compliance requirements are less likely to fall through the
cracks.
MasterControl is also web-based, so users can access the system from virtually anywhere in the
world, at any time. Users can also access the system using a tablet or a smartphone. This
capability is a distinct advantage over other less effective quality management systems. Mobile
access is particularly useful to field employees or those who often travel or work off-site. Those
users will be able to participate in quality processes even when they are not in front of their
computers in the office, increasing efficiency and productivity across the entire enterprise.
==================
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,

4. 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.

5. 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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