1. Quality risk management sop
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I. Contents of quality risk management sop
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In most countries compliance with good manufacturing practices (GMP) (1, 2) (including
validation), drug regulatory activities and inspections, together with supply chain controls
throughout the product life-cycle, provide good assurance that risks are largely controlled. However,
where control is less effective, patients may be put at risk through the production of medicines of
inadequate quality. The assessment of individual risks related to specific products and starting
materials and the recognition of hazards at specific stages of production or distribution should
permit regulatory authorities to improve control of medicines by increasing the effectiveness of their
activities within the limits of the available resources. Quality risk management (QRM) is a process
that is relevant to all countries and should provide a rationale to understand risk and mitigate it via
appropriate and robust controls.
The aim of this guideline is to assist the development and implementation of effective QRM
covering activities such as research and development, sourcing of materials, manufacturing,
packaging, testing, storage and distribution. In the past, hazard analysis and critical control point
(HACCP) methodology, traditionally a food safety management system but subsequently applied to
other industries, has been the basis of WHO risk management guidance to the pharmaceutical
industry (3).

2. Since then international guidance has emerged (2, 4-8) that is of specific relevance to the
pharmaceutical industry and which addresses the full scope of pharmaceutical industry QRM more
effectively than HACCP principles, including how to structure regulatory filings using a risk-based
approach. Consequently, this WHO guideline has been developed as an update of WHO advice to
the pharmaceutical industry, taking account of this new guidance.
In order to protect patients, in terms of quality, safety and efficacy, international medicines
regulatory authorities (MRAs) are recommending pharmaceutical manufacturers to adopt a risk-
based approach to the life-cycle of a pharmaceutical product. Some MRAs are requiring the
adoption of a risk-based approach for certain specific areas in the life-cycle of a pharmaceutical
product, e.g. for environmental monitoring for sterile products manufacturing.
While the choice of the tools to support the QRM approach is optional and may vary, they need to
be appropriate for the intended use.
In return for using this approach, there are potential opportunities for both MRAs and
pharmaceutical manufacturers (9) as summarized in the following sections.
a) Quality risk management (QRM) principles can be applied to both MRAs and pharmaceutical
manufacturers:
 MRAs: systematic and structured planning of reviews and inspections that are risk-based.
The submission review and inspection programmes can also operate in a coordinated and
synergistic manner.
 Manufacturers: design, development, manufacture and distribution, i.e. the life-cycle of a
pharmaceutical product. QRM should be an integral element of the pharmaceutical
quality system (PQS).
b) Science-based decision-making can be embedded into QRM processes:
 MRAs: decisions regarding review, inspection or inspection frequency should consider
product risk and GMP compliance of the manufacturer. The MRA accepts residual risks
through understanding the QRM decisions involved.
 Manufacturers: quality decisions and filing commitments can be based on science-based
process understanding and QRM (when utilizing the quality by design approach). Its

3. effective application should offer manufacturers greater freedom on how to meet
principles of GMP, and this, therefore, should encourage innovation.
The control strategy for the process focuses on critical quality attributes and critical
process parameters. Uncertainty can be addressed explicitly.
c) Resources can be focused on risks to patients:
 MRAs: QRM can be used to determine best allocation of inspection resource, both in
terms of product types and for specific areas of focus for a given inspection. This enables
the most efficient and effective scrutiny of the most significant health risks. Those
manufacturers with poor histories of GMP compliance can also be more closely and
frequently evaluated by on-site inspection than those manufacturers with better records.
 Manufacturers: evaluation of quality risk through science-based decisions can be linked
ultimately to protection of the patient by ensuring the quality, safety and efficacy of the
product. A corporate culture is supported to produce cost-effective medicines, without
compromising quality, while maintaining focus on the patient as a primary stakeholder in
all activities.
d) Restrictive and unnecessary practices can be avoided:
 MRAs: regulatory scrutiny adjusted to level of risk to patients. Improvement and
innovation by manufacturers should be encouraged.
 Manufacturers: instead of having systems designed to inhibit change and minimize
business risk, changes can be managed within a company’s quality management
system. Innovation and the adoption of the latest scientific advances in manufacturing
and technology are supported. Unnecessary testing can be eliminated, for example,
with real-time release testing.
e) Communication and transparency are facilitated:
 MRAs: facilitate dialogue with pharmaceutical manufacturers and clarify to the industry
and the public on how the inspection programme may be adjusted based on the risk to
patients. Information-sharing between MRAs will contribute to a better risk management
approach globally.
 Manufacturers: matrix team approach, stakeholders kept informed via science-based
decisions. Culture of trust and “one-team” mindset with focus on product and patient.

4. QRM is the overall and continuing process of appropriately managing risks to product quality
throughout its life-cycle in order to optimize its benefit/risk balance. It is a systematic process for
the assessment, control, communication and review of risks to the quality of the medicinal product.
It can be applied both proactively and retrospectively.
This guideline will align with the general framework described within other current international
guidance on this subject.
==================
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
of the week)
 Why the data were collected

5. 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

6. 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
the control of the experimenter. If a parameter exists that

7. 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.

8. 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

9. 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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