1. quality management system in pharmaceutical industry
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I. Contents of quality management system in pharmaceutical industry
==================
Risk taking is an important part of any business endeavor. Entrepreneurs and investors take risks
every time they fund a start-up. Business executives take risks every day as they make decisions
about which products, services, ideas and people to advance within an organization. Risk taking
can be enormously profitable. But in the pharmaceutical industry, excessive risk taking can have
devastating results; product delays, recalls, and enforcement actions by the Food and Drug
Administration (FDA) have led to the demise of many small- to mid-size pharmaceutical
manufacturers. Most importantly, consumer safety can be compromised by excessive risk taking.
In the pharmaceutical industry, risk must be tempered by caution. And the mechanism for
tempering risk is a robust pharmaceutical quality system based on the latest FDA guidance.
Quality cannot be an afterthought. Implementing an effective quality system involves up-front
costs. An effective quality system should be in place at the earliest stages of product research and
development. While the pursuit of quality can be a costly line item on a financial statement or
business plan, failure to implement an effective quality system can have even more costly effects
on the bottom line.
Based on the latest guidance from the FDA, an effective pharmaceutical quality system should
help ensure compliance with cGMPs by focusing on:
 Quality management
 Quality assurance
 Evaluation analysis and quality risk management tools

2.  Preventive action
 Risk management
 Continuous improvement
This latest guidance does not replace previous FDA regulations, which require every
pharmaceutical quality system to include Standard Operating Practices (SOPs), adequate
personnel and training systems, and an adequate system for recordkeeping. The new guidance is
simply aimed at addressing advances in manufacturing technologies, quality systems and risk
management approaches that have been developed since 1978. The latest guidance is also aimed
at harmonizing the cGMPs with other widely used quality management systems, including the
FDA’s own medical device quality system regulations. Developing a modern, quality system
approach can provide the necessary framework for implementing continuous improvement and
risk management efforts in the drug manufacturing process.
While a culture of quality should permeate the entire organization, management plays a very
important role in the successful functioning, design, implementation and management of a
modern quality system. Not only should management align the quality system plan with the
company’s strategic plan, it must demonstrate strong support for quality systems. It’s essential
for senior leaders of pharmaceutical manufacturers to encourage internal communication about
quality issues and support the production, quality and manufacturing activities needed to produce
quality products.
But what does a modern pharmaceutical quality system look like? Imagine the hub and spokes of
a wheel. The quality system itself is at the center (the hub), but it is connected to five other
manufacturing systems (the spokes). When you include the quality system as a subsystem at the
center of it all, the six subsystems of a modern pharmaceutical quality system are the:
 Quality System
 Production System
 Facilities and Equipment System
 Laboratory Controls System
 Materials System
 Packaging and Labeling System
The quality subsystem at the center provides the foundation for the five manufacturing
subsystems and helps them achieve compliance. Each subsystem has an impact on the others and
they all have to work together to consistently produce a quality product. But it’s important to
understand that none of the individual subsystems equate to a functional group in an organization
or manufacturing facility. For example, the Materials System does not simply apply to
warehouse personnel. This subsystem includes the warehouse personnel who receive, store and

3. handle components and raw materials and distribute final products, but it also includes the
purchasers who buy components from qualified vendors, the manufacturing workers who request
and receive components and transfer final products to the warehouse, the quality assurance
specialists responsible for component and lot release, and the quality control employees who
sample and test components and products.
In the coming months we will take a closer look at each of these subsystems in the hopes it will
help you develop and maintain a strong overall quality system in your organization. In the end,
firms with strong quality systems will be more likely to meet and exceed cGMPs while also
enjoying many other advantages, including improved product and process understanding for
better decision making; continuous improvement; the ability to manage change to prevent
unintended consequences; minimized product variability; enhanced test method accuracy;
reduced costs due to fewer investigations, deviations and rejections; minimized product loss and
costs associated with scrap, disposal, rework and recalls; reduced downtime with more reliable
equipment and fewer repair interruptions; and decreased labor costs associated with automation
of manufacturing processes.
==================
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)

4.  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.

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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