Quality risk management supports a scientific and practical approach to decision-making. It provides documented, transparent and reproducible methods to accomplish steps of the quality risk management process based on current knowledge about assessing the probability, severity and sometimes detectability of the risk.

1. Basic risk
management
facilitation methods
Rutuja Arlekar
Roll no – 01
MPharm PQA Sem 2

2. Introduction
• Quality risk management supports a scientific
and practical approach to decision-making.
• It provides documented, transparent and
reproducible methods to accomplish steps of the
quality risk management process based on
current knowledge about assessing the
probability, severity and sometimes detectability
of the risk.

3. Basic risk management facilitation
methods
These are some simple
techniques that are
commonly used to
structure risk
management by
• Organizing data
• Facilitating decision
making
Area of application
• Failure investigations
• Root cause analysis
1. Flow charts
2. Processes
Mapping
3. Acceptance
Control Charts
4. Cause & effect
diagrams(Fishbon
e/Ishikawa)

4. Flowcharts
• Flowcharts are
pictorial
representations
of a process.
• It is represented by
breaking the
process down into
its constituent steps.

6. Process mapping
• The indicators may be selected based on unit
operations involved in the process.
• It shows how these indicators are interrelated.
• Potential Areas of Use(s) / outcomes
Provides a clear and simple visual representation
of involved steps.
Facilitates understanding, explaining and
systematically analysing complex processes and
associated risks.
A pre-requisite for the use of some other tools.

8. Acceptance control charts
• An acceptance control chart combines
consideration of control implications with
elements of acceptance sampling.
• It is an appropriate tool for helping to make
decisions with respect to process acceptance.
• The bases for the decisions may be defined in
terms of
whether or not a designated percentage of units
of a product or Service derived from that process
will satisfy specification requirements;
whether or not the process has shifted beyond
some allowable zone of process level locations.

9. Example
Sample Data:
• The file bottles.sgd
contains the
measured bursting
strength of n = 100
glass bottles.
• Each row consists of
a sample tested at
10 minute intervals.
• The table shows a
partial list of the
data from that file:
Data Input:
• There are two menu
selections that
create acceptance
charts, one for
individuals data and
one for grouped
data.
• In the case of
grouped data, the
original observations
may be entered, or
subgroup statistics
may be entered
instead.

10. Case #1: Individuals
• The data to be analysed consist of a single numeric column
containing n observations.
• The data are assumed to have been taken one at a time.
• Observations: numeric column containing the data to be analysed.
• Date/Time/Labels: optional labels for each observation.
• LSL, Nominal, USL: optional lower specification limit, nominal
(target) value, and upper specification limit. Enter at least one
specification limit.
• Select: subset selection.

11. Case #2: Grouped Data – Original Observations
• The data to be analysed consist of one or
more numeric columns. The data are
assumed to have been taken in groups, in
sequential order by rows.
1. Observations: one or more numeric
columns. If more than one column is
entered, each row of the file is assumed to
represent a subgroup with subgroup size
m equal to the number of columns
entered. If only one column is entered,
then the Date/Time/Labels or Size field is
used to form the groups.
2. Date/Time/Labels or Size: If each set of m
rows represents a group, enter the single
value m. If the subgroup sizes are not
equal, enter the name of an additional
numeric or non-numeric column
containing group identifiers. The program
will scan this column and place sequential
rows with identical codes into the same
group.
3. LSL, Nominal, USL:
optional lower
specification limit,
nominal (target) value,
and upper specification
limit. Enter at least one
specification limit.
4. Select: subset selection.

12. Case #3: Grouped Data – Subgroup
Statistics
• In this case, the statistics for
each subgroup have been
computed elsewhere and
entered into the datasheet, as in
the given table:
1. Subgroup Statistics: the names
of the column containing the
subgroup means, subgroup
ranges, and subgroup sizes.
2. Date/Time/Labels: optional
labels for each subgroup.
3. LSL, Nominal, USL: optional
lower specification limit,
nominal (target) value, and
upper specification limit. Enter
at least one specification limit.
4. Select: subset selection.

13. Acceptance Chart
• The Acceptance chart plots the observations or subgroup
means together with both control limits and specification
limits.

14. References
• ICH guideline Q9 on quality risk management
https://www.ema.europa.eu/en/documents/scientific-guideline/international-
conference-harmonisation-technical-requirements-registration-pharmaceuticals-
human-use_en-3.pdf
• Guidance for Industry Q9 Quality Risk Management
https://www.fda.gov/media/71543/download
• Willem Albers, "Risk-Adjusted Control Charts for Health Care Monitoring",
International Journal of Mathematics and Mathematical Sciences, vol. 2011,
Article ID 895273, 16 pages, 2011. https://doi.org/10.1155/2011/895273
• Quality Risk Management Principles and Industry Case Studies
https://pqri.org/wp-
content/uploads/2016/03/Quality_Risk_Management_Principles_and_Industry_
Case_Studies_December_28_2008.pdf
• FIRE RISK ASSESSMENT – FLOWCHART
https://portal.oxfordshire.gov.uk/content/public/LandC/Resources/healthsafe/fir
esafe/2.6_Flowchart.pdf
• https://www.slideshare.net/shettyuc/quality-risk-managment-basic-facilitation-
methods
• Acceptance Charts
https://cdn2.hubspot.net/hubfs/402067/PDFs/Acceptance_Charts.pdf