This presentation talks about how risks in a project are analyzed and quantified. The presentation also discusses benefits of quantification of risks and the various tools at our disposal to manage risks effectively through quantification.

1. RISK ANALYSIS AND
QUANTIFICATION
Presented By
K. Malini
Goli Pimida
Feba Jose
Kavya .S
Kavana K.S
Semester – III
Presidency Business School
30th September 2019
Under the guidance of
Prof Venkatesh G
Associate Professor (Marketing)

2. Introduction
 Risk quantification is a process to evaluate
identified risks to produce data that can be
used in deciding a response to corresponding
risks.
 The objective of project risk quantification is to
prepare contingencies in terms of costs, time,
or human resources and prioritize them.

3.  Focus is typically on schedule and budget.
 This affects most aspects of a project including ,
evaluation, assessment, selection, planning,
collaboration, execution, and control.
 Goal is to develop reliable risk management-solution
throughout the enterprise.
 This involves analysis of risk and also involves alerting
senior management of potential problems at an early
stage.
 Risk quantification reduces uncertainty, controls costs,
and improves decision-making within projects and
organizations.

4.  If risk is not proactively managed-seriously threatened by
unplanned events-unexpected expenditures, project
delays, quality issues or failing to meet corporate
objectives.
 Better risk management-software that delivers a cross-
project and company-wide picture of risk exposure in
real-time.
 Helps identify and mitigate risks, reduces uncertainty.

5. Risk quantification: PMBOK
 Evaluating risks and risk interactions to assess the
range of possible outcomes.
 Risk quantification is a process of evaluating the risks
that have been identified and developing the data that
will be needed for making decisions as to what should
be done about them.
 The objective of risk quantification is to prepare
contingencies in terms of costs, time, or human
resources and prioritize them in terms of their severity
and likelihood, so that appropriate action can be
taken accordingly.

6. Concept & Purpose
 The objective of risk quantification is to prepare contingencies
in terms of costs, time, or human resources and prioritize
them in terms of their severity and likelihood, so that
appropriate action can be taken accordingly.
 In order to quantify risk, it needs to be identified first. Once
risk is identified then it is analyzed in terms of probability of
occurrence and impact that it could print on the outcome
 The probability is assigned either based on intuition or the
previous data of failure rates available for similar events in
datasheets. Once probabilities of all events are calculated, a
criterion for the likelihood of all the events is defined
 The risk(R) is calculated by multiplying probability(P) with the
impact(I) or severity

7. Qualitative risk management
 Qualitative risk management- to manage
project uncertainties in current projects, but
also to provide input for future projects.
 With the available database the current
project risks can be judged
 Assessment of risks of future project will be
done based on objective analysis of risks in
current project.

8. Quantitative risk management
 Quantitative risk management helps
determine the chance that a project will be
completed on time and within a budget,
identify critical project parameters that affect
the project schedule, determine project
success rate, make a decision about viable
project alternative.

9. Risk Management Process:
 Risk Identification
It's an analysis of complete job scope
and is conducted as a group session which
involves all key members of project- "Risk
Register“-probable risks on the job.
 Risk Quantification
Risk Analysis and ranking is
performed. Risk Analysis is done by identifying
the nature of risk, location of risk , magnitude of
risk , distributing the risk and performing
distribution

10.  Risk Response Development
How to respond to risks such as, Avoidance ,
Transference , Mitigation and acceptance
 Risk Response Control
Developing risk plan and monitoring it on regular basis.

11. Objective of quantification
 In the simplest form, risks can be ranked as of
high, medium and low severity.
 Various statistical techniques such as PERT
(Program Evaluation and Review Technique),
statistical sampling, sensitivity analysis,
decision tree analysis, financial ratios, Monte
Carlo and critical chain are used in evaluating
and quantifying risks.

12. RISK QUALITATIVE ANALYSIS TABLE….

13.  Care should be exercised in the use of
quantitative techniques as the cost of applying
the technique and the use of data can
sometimes be more than cost of risks .

14. Tools for Risk Quantification
 Monte Carlo simulation determines how risks will
affect project schedule.
 Event chain methodology. According to this
methodology, an activity in most real projects is
not a continuous uniform process. It is affected by
the external events, which transform risks from
one state to another. These events should be
properly captured in qualitative risk management
software.

15. Computer simulations
 Today computer simulations are quite simple and
inexpensive to use. Not many years ago simulations were
done on analog computers, which made them expensive
and not very accurate.
 The digital computers that most people have on their
desks now are able to run simulations quite easily.
Simulations use a model to simulate the real phenomena
that we are trying to find out something about.
 There are two reasons to use simulations. One is that
solving the problem mathematically is very difficult and
expensive or even impossible. The second is that studying
the actual phenomena is impossible or impractical in full
scale. In either case simulation or modeling can be
practical.

16. EXPERT OPINION
 Expert opinion is an extremely useful tool in risk
assessment and is often overlooked as a separate
technique in the quantitative actuarial world. It is
particularly useful where relevant data are scarce,
for example where conditions have changed
materially (reducing the usefulness of past
experience), or where the risks are very company-
specific as would often be the case for lapse rates.
In essence, the prudent assumption setter was
providing one expert opinion on the risk. .
However, it will often be appropriate to seek input
from a range of experts across different
disciplines.

17. DELPHI METHOD
 The Delphi method was developed by the RAND
Corporation to address these possible
shortcomings, and came in response to a US
military request to prepare a forecast of future
technological capabilities. However, the
forecasting techniques developed have since been
applied in a much wider range of areas.

18. HISTORICAL SIMULATION
 A fairly straightforward approach to risk
quantification is simply to gather as much past
data as possible and use this history as a
simulation of the future. For example, we can
gather the daily price changes for the last 1,000
days for the shares we are currently holding in our
portfolio. This generates 1,000 different scenarios
for the performance of our portfolio over the
coming day. If we take the 5th-worst performance
of the portfolio then we will have generated the
99.5th percentile portfolio return over a one-day
time horizon.

19. NORMAL DISTRIBUTION ASSUMPTION
 Another way of exploiting past data is simply to
observe the mean and standard deviation of a
particular factor, for instance equity market returns,
and assume that the factor is normally distributed. The
basic properties of the normal distribution then allow
us to generate the chosen confidence interval around
the mean by taking particular multiples of the standard
deviation. This approach generally gives far less
weight to the outliers in the data than would a historical
simulation.

20. MONTE CARLO SIMULATION
 Monte Carlo simulation is a statistical sampling
technique for solving complex differential equations.
Basically, we assume that the evolution of a
particular item of interest can be described by a
probability density function; the Monte Carlo
simulation is then carried out by sampling from this
probability density function and tallying the results.
This is a powerful technique and may not strictly be
required if ‘closed form’ (that is, formula based)
solutions exist.

21. BENEFITS OF RISK
QUANTIFICATION
 First, risk quantification helps in preparing
contingencies for time and cost estimates.
 Second, It helps organizations in taking a rational
decision in the presence of uncertainty.
 Third, it provides confidence of dealing
unforeseeable events in future rather than acting
irrationally.

22. TO SUM UP
 The most robust approach is to employ as many
methods as possible to help understand the risk
better and to provide a reasonableness check on
the results from the other methods.
 The methods deployed will also give an
indication of the model risk involved. From the
options available, the most appropriate method
should be chosen for use in quantifying risk. This
selection itself requires no little judgment.