Pascal B. Xavier

Part II

Pascal B. Xavier

Pascal B. Xavier

Part II

Risk Management Tools

Figure 1: Risk Management Tools (NOT Exhaustive)

Pascal B. Xavier

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Fishbone (Ishikawa) Diagram

Figure 2: A Fishbone Diagram

Figure Courtesy of Dutch Renaissance Press

Fishbone diagram is basically a brainstorming tool to list possible root causes.

FMEA is a living document to list things that could go wrong in a product or process, the severity rated, and what actions are put in place to reduce the likelihood of failure. (MIL-STD-1629A)

Pascal B. Xavier

Part II

Decision Tree Analysis

John is a manager of a profitable gadget factory. He is wondering whether or not he should expand his factory this year. The cost to expand the factory is $1.5M. If he expands the factory, he expects to receive $6M if economy is good and $2M if economy is bad.

If he does nothing and the economy stays good and people continue to buy lots of gadgets he expects $3M in revenue; while only $1M if the economy is bad.

He also assumes that there is a 40% chance of a good economy and a 60% chance of a bad economy.

Draw a Decision Tree showing these choices.

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Part II

Decision Tree Analysis (Cont’d)

Expand Factory

Cost = $1.5 M

Don’t Expand Factory

Cost = $0

40 % Chance of a Good Economy

Profit = $6M

60% Chance Bad Economy

Profit = $2M

Good Economy (40%)

Profit = $3M

Bad Economy (60%)

Profit = $1M NPVExpand = (0.4(6) + 0.6(2)) – 1.5 = $2.1M

NPVNo Expand = 0.4(3) + 0.6(1) = $1.8M

$2.1 M > $1.8M, therefore John should expand the factory

Figure 3: A Decision Tree

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SWOT Analysis

Figure 4: Strengths, Weaknesses, Opportunities and Threats

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Control Charts

Figure 5: Shewhart Control Charts

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Factorial Design of Experiments (DOE)

Figure 6: Design of Experiments

Factor B

Levels b1 b2

a1 20 40

Factor A

a2 50 12

Factor B

Levels b1 b2

a1 20 30

Factor A

a2 40 52

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The Risk Matrix Used by the US Department of Defense (DoD) since the 1980s. This is documented in MIL-STD-882D (Standard Practice for System Safety). AS/NZS 4360:2004 has been superseded by ISO 31000:2009. AS 4360 was first published in 1995. AS4360 includes discussion on risk matrices. So the application of risk matrices has been driven also by the standard. The Harvard Business Review uses it too! While it is useful for quick assessments, one must be careful about its application to risk management which includes risk treatment.

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The Risk Matrix

Consequences

Likelihood Insignificant (1)

Minor (2)

Moderate (3)

Major (4)

Catastrophic (5)

Certain (5) M H I I I

Likely (4) M M H I I

Possible (3) L M H I I

Unlikely (2) L L M H I

Rare (1) L L M H H

Table 7: Qualitative Risk Analysis Matrix

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Table 8: A Simple Example to Illustrate

Risk Identification

Consequence

to Public

Likelihood of O

ccurrence

Risk R

ating

Management or Mitigating Action

Consequence

after Action

Likelihood after A

ction

Residual R

isk R

ating

Hazard: Unbarricaded cliff at the Blue Mountains Risk: Falling Off and Suffering Injury or Death

5 3 I

Remedial Action 1 Install only a safety net at the foot of cliff

3 3 H

Hazard: Unbarricaded cliff at the Blue Mountains Risk: Falling Off and Suffering Injury or Death

5 3 I

Remedial Action 2 Install only a fence at edge of cliff 5 1 H

Hazard: Unbarricaded cliff at the Blue Mountains Risk: Falling Off and Suffering Injury or Death

5 3 I

Remedial Action 3 Install both a fence at edge of cliff and also a safety net at the foot of the cliff

3 1 M

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LIKELIHOOD SCORE

Likelihood Descriptor Probability

1 Rare Exceptional Circumstances <1%

2 Unlikely Seldom Occurs 1% to 5%

3 Possible Occasionally Occurs 5% to 20%

4 Likely Often Occurs 20% to 60%

5 Certain Frequently Occurs >85%

MIL-STD-882D attempts to semi-quantify

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Table 6: More Detail to the Consequence Definition

CONSEQUENCE SCORE

Score Consequence Cost Program Reputation

Community /

Stakeholder

Response

H&S Environment

1 Insignificant <$10,000 <1 week No Local Media

Coverage Minor Complaint No Injuries

Short Term

Damage

2 Minor $10,000 -

$50,000

1-2

Weeks

Minor Effect on Local

Company

Image/Business

Relationship Mildly

Affected

Formal

Correspondence

written by

Stakeholder

First Aid

Required -

near miss

Limited but

Medium Term

Negative Effects

3 Moderate $50,000-

$250,000

2-4

Weeks

Local Media

Exposure/Business

Relationship Affected

Local Member

Intervention.

Official

Information

Request

Medical

Treatment

Major but

Recoverable

Ecological

Damage

4 Major $250,000-

$1,000,000 >1 Month

Nationwide Media

Exposure/Business

Relationship Greatly

Affected

Ministerial

Involvement

Extensive

Injuries

Heavy Ecological

Damage, Costly

Restoration

5 Catastrophic >$1,000,00

0

>3

Months

Permanent Nationwide

Effect on Company

Image/Significant Impact

on Business

Relationship

Political

Intervention or

Commission of

Enquiry

Fatality

Permanent

Widespread

Ecological

Damage

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Presentation and Implementation Issues The context of risk for different categories (financial, political, HR, environmental, stakeholder, etc) may be such that the definition of frequencies (likelihoods) may not be too broad:

Political risk worth millions (defined moderate impact) vs. high environmental risk worth millions (defined high impact) Power outage 5% of the time (defined likely or certain likelihood) vs. trains running late 5% of the time (defined unlikely or possible likelihood)

So do we have scoring matrices for each category?

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Presentation and Implementation Issues (Cont’d)

Consequences

Likelihood Insignificant (1)

Minor (2)

Moderate (3)

Major (4)

Catastrophic (5)

Certain (5) 5 10 15 20 25

Likely (4) 4 8 12 16 20

Possible (3) 3 6 9 12 15

Unlikely (2) 2 4 6 8 10

Rare (1) 1 2 3 4 5

Table 9: Multiplication of Row and Column to Give Rating

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Presentation and Implementation Issues (Cont’d)

Consequences

Likelihood Insignificant Minor Moderate Major Catastrophic

Certain 11 16 20 23 25

Likely 7 12 17 21 24

Possible 4 8 13 18 22

Unlikely 2 5 9 14 19

Rare 1 3 6 10 15

Table 10: Cells Numbered Progressively Left, Bottom to Top ,Right

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Some Inputs into Risk Estimation - Heuristics • "Rule of thumb" applies a broad approach to problem

solving and allows an individual to make an approximation without having to do exhaustive research.

• "Absurdity” is applied when a claim or a belief seems silly, or seems to defy common sense.

• "Common sense" is applied as a practical and prudent approach to a decision where the right and wrong answers seems relatively clear cut.

• "Familiarity” allows someone to approach an issue or problem based on familiarity of the situation.

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The Subjectivity of Risk Estimation Contextual factors like health beliefs, religion, ethics,

history, politics, class status, family, etc., form perceptions about risk characteristics and situation e.g. polygamy and its affect on HIV risk.

Semantics (i.e. words with multiple meanings e.g. crash; no meaning e.g. Pennzoil with F-7), determines risk sources, people and circumstances of situation.

Trust and credibility of actors involved in assessing risk is an important aspect of risk analysis. e.g. lawyer risk assessing the need for a medication

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The Subjectivity of Risk Estimation (Cont’d) Availability bias e.g. if news readily available about

deaths by lightning, then risk of being struck is regarded as significant (i.e. probability overestimated).

Anchoring effect e.g. if waste is incinerated, the association formed between waste and its toxic nature (i.e. impact overestimated)

Distribution effect of risk over time e.g. road accidents occurring throughout the year i.e. perception of lower probability of occurrence

Assessment bias e.g. low risks overestimated and high risks underestimated.

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The Various Scales of Measurement Nominal - A nominal scale simply places data into categories, without any order or structure e.g. Yes/No. Ordinal - An ordinal scale allows interpretation of gross order and not the relative positional distances i.e. No objective distance between any two points on your subjective scale e.g. rate 5 different beers. Interval - An interval scale because it is assumed to have equidistant points between each of the scale elements. This means that we can interpret differences in the distance along the scale e.g. 7-point satisfaction survey.

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Limitations of using Risk Matrices Range Compression – Only a minority of the scale is used for majority of the ratings given e.g. lumping of a range of 1-18% likely as “likely”, and lumping a loss impact of $100m-$250m as “major”. Thus 1% chance of losing $100m given same risk rating (‘Red’ or ‘Intolerable’) as 18% chance of losing $250m! Presumption of Regular Intervals between Scales – see definition of ordinal scale (above). Is there a same magnitude difference between “possible” and “likely” vs. “likely” and “certain”? How about “Moderate” and “High” vs. “High” and “Catastrophic”?

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Limitations of using Risk Matrices (Cont’d) Presumption of independence – The scoring methods do not account for possible correlation [Conrow (2003)] among the various factors and risks i.e. two or more moderate likelihood, moderate impact risks allocated in a risk matrix may show as a much higher risk rating outcome if they happened together. (Monte Carlo analysis include correlation and dependencies) Human cognitive bias - Systematic translations of objective probability and value as judged by human subjects. [Smith, Siefert and Drain (2008) refers to Prospect Theory by Kahneman and Tversky (1979)]

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Limitations of using Risk Matrices (Cont’d) Likelihood-Consequence Correlation – For negatively correlated likelihoods and consequences, risk matrices can mistakenly assign qualitatively higher/lower risk ratings to quantitatively smaller/larger risks.

High Likelihood, Low Impact – Risk exaggerated Low Likelihood, High Impact – Risk ignored

Sub-optimal Risk Allocation – Effective allocation of resources for risk mitigating measures cannot be based on risk ratings prescribed by risk matrices. [Cox (2008), pp506-510]

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References Anna Korombel and Piotr Tworek (2007), “Qualitative

Risk Analysis as a Stage of Risk Management in Investment Projects: Advantages and Disadvantages of Selectred Methods – Theoretical Approach, Journal of Interdisciplinary Research, pp51-54.

Eric D. Smith, William T. Siefert and David Drain (2008),”Risk Matrix Input Data Biases”, Systems Engineering, 12, 2, pp344-360. Louis Anthony (Tony) Cox, Jr. (2008), “What’s

Wrong with Risk Matrices?”, Risk Analysis, 28, 2, pp497-512

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References (Cont’d) Stroie Elena Ramona (2011),”Advantages and

Disadvantages of Quantitative and Qualitative Information Risk Approaches”, Chinese Business Review, 10 (12), pp1106-1110.

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Bibliography Edmund H. Conrow (2003), “Effective Risk

Management – Some Keys to Success (2nd Edition)”, Appendix H: Some Characteristics and Limitations of Ordinal Scales in Risk Analyses, American Institute of Aeronautics and Astronautics, pp461-479

Douglas W. Hubbard (2009), “The Failure of Risk Management – Why It Is Broken and How To Fix It”, John Wiley and Sons, pp1-273