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Risk Assessments. From qualitative to quantitative. Agenda. Introduction Case presentation Introduction to distributions Risk quantification Risk simulation Discussion and wrap-up. About DNV. Purpose: to safeguard life, property and the environment

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Risk assessments l.jpg

Risk Assessments

From qualitative to quantitative


Agenda l.jpg
Agenda

  • Introduction

  • Case presentation

  • Introduction to distributions

  • Risk quantification

  • Risk simulation

  • Discussion and wrap-up


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About DNV

  • Purpose: to safeguard life, property and the environment

  • Independent foundation created in 1864

  • Over 9,000 employees working from 300 offices in 100 countries providing:

    • Assessment, Advisory, Certification, Assurance and Training services

  • DNV assists organisations in a wide range of sectors to indentify, assess, manage and communicate on risk and sustainability, building sustainable value, resilience and stakeholder trust


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Introduction

Workshop

Introduction

Case

Quantification

Simulation

End


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Assumptions

  • You are familiar with thinking about risks

  • You are comfortable with risk registers

  • You are familiar with qualitative risk identification and assessment


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Why should we quantify risks?

  • Makes a risk register richer and potentially more nuanced

  • May simplify discussions relating to risk appetite

  • Can summarise a risk register into one figure

  • Simulation results may change priorities


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Why shouldn’t we quantify risks?

  • Some risks don’t lend themselves to quantification

    • Reputation risks are notoriously hard to pin down

  • When too many simplifying assumptions must be made

    • E.g. Gaussian cupolas and credit default swap pricing

  • When risks are too diverse

    • Don’t compare apples to motorcycles



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Changing definition of risk

  • Measurable uncertainty

    Knight, Frank H. (1971), “Risk, Uncertainty and Profit” (University of Chicago Press), Orig. pub. 1921

  • Combination of the probability of occurrence of harm and the severity of that harm

    Source: ISO/IEC Guide 51:1999

  • Combination of the probability of an event and its consequence

    ISO/IEC Guide 73:2002

  • Chance of something happening that will have an impact on objectives

    AS/NZS 4360:2004

  • Effect of uncertainty on objectives

    ISO 31000:2009


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For this workshop session

Unwanted event → Negative consequence


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Short case description

Workshop

Introduction

Case

Quantification

Simulation

End


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Hi-tech corporation

Hi-tech corporation is a multi-national company that produces and sells hardware and services on several continents. They pride themselves in a good reputation for corporate governance, social responsibility and good worker practices.


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Description

Hi-tech corporation use outsourcing and complex supply chains very efficiently

They use raw materials and components sourced from all over the world


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Market situation is extremely competitive

Marketing research has shown that customers in Western countries are willing to pay a premium for Hi-tech corporation’s products

Surveys identified their reputation for fairness to employees, and that their products are perceived as having high quality as the main reason for this


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Financial performance last year

Revenue

3bn

4% increase

year on year

R&D expenses

£80 m

After tax profit

£500

Launched 4

new products




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Risk quantification

Workshop

Introduction

Case

Quantification

Simulation

End


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Risk quantification in this model is in two steps

  • Quantify probability of occurrence

  • Quantify loss distribution on occurrence



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Step two – Quantify the loss if the unwanted event occurs

  • Statisticians have spent many years identifying distributions in data sets

  • Ideally, use historical data to identify impact of risks and fit a distribution


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We will simplify – By using the Trigen function

  • Need three values

    • P10

    • Most likely

    • P90


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How do we quantify using the trigen function?

  • Imagine 10 outcome scenarios ranging from worst case to best case

  • Your P10 value is your second best outcome scenario

  • Your P90 value is your second worst outcome scenario

  • Most likely is the median, which usually is somewhere in the middle ..



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Each group will quantify one risk

  • Assign

    • P10

    • Most likely

    • P90

Group exercise


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Simulation

Workshop

Introduction

Case

Quantification

Simulation

End


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Summary of model outputs

  • Mean value

    • The average expected loss over the next year

    • In a humdrum year, we would expect our humdrum loss to be this value

  • Standard deviation

    • Quantifies the range of the possible outcomes

    • The narrower the “better”, as it increases our confidence in the mean value

  • ±1.96 * Standard deviation

    • Our 95% confidence band

    • We are 95% certain our losses will not exceed/be inferior to these thresholds


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Did our risk priorities change?

  • The tornado graphs show correlation between simulated losses related to each risk and total simulated losses

  • This information is useful with regards to prioritising risk mitigating actions and controls

  • Our qualitative register suggests we start with mitigating risk 8 – Stolen product designs

  • What does our model say?


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And for those of a statistical persuasion

  • Fit the aggregate exposure to statistical distributions

  • If this is consistent over a 10 – 15 year period you may consider using …



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Thank you

  • Please fill in the feedback forms

  • May your risk management make you sleep well at night


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Safeguarding life, property and the environment

www.dnv.com