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Pjt Risk Management Process

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시간 내용

09:00~09:30

(30’) 행사 등록 및 접수

09:30~09:50

(20’) 개회사 및 환영인사

09:50~10:40

(50’)

주제 1: 프로젝트 성과향상을 위한 Risk 관리

PM 인사이드 한동환 대표

10:40~10:55

(15’) Coffee Break

10:55~11:45

(50’)

주제 2: Crystal Ball 을 활용한 비재무리스크 측정과 관리

연세대학교 경제학과 연구교수 김우환 박사

11:50~12:40

(50’)

주제 3: Monte Carlo 시뮬레이션을 활용한 식품 중 유해물질의 위해성 평가에서의

불확실성 분석 / ㈜엔바이오니아 최시내 차장

12:40~14:00

(80’) Lunch

14:00~14:50

(50’)

주제 4: “BIS 비율, NIM, 유동성 비율 제약조건에서” 은행 자산/부채에 대한 최적

포트폴리오 선택 / 부산은행 김지민 과장

14:55~15:45

(50’)

주제 5: 생명과학 기술가치평가와 R&D 포트폴리오 관리

LG 생명과학 기술원 이승주 차장

15:45~16:05

(20’) Coffee Break

16:05~17:55

(50’)

주제 6: Monte Carlo Simulation 을 이용한 자동차 Door Regulator 의 누적공차

최적화 구현 / 상신브레이크 윤용익 전무이사

17:00~17:40

(40’)

주제 7: Crystal Ball 11.1 의 향상된 도구들을 활용한 모델링 기법 소개

㈜이레테크 소프트웨어사업부 박잉근 대리

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주제 1) 프로젝트 성과향상을 위한 리스크 관리

우리들이 현업에서 진행하고 있는 대부분의 프로젝트들은 미래에 발생할 프로젝트의 성과에 대한

불확실성을 갖고 있습니다. 리스크는 불확실성으로부터 기인하므로 모든 프로젝트의 성공적인 수행을

위해서는 반드시 리스크 관리가 필요합니다. 리스크는 발생 후 대처보다는 발생 전 준비 또는 예방이

더욱 중요하며, 리스크 준비시 미래의 불확실성에 대한 정량적인 측정을 통한 접근이 필요합니다.

이러한 정량적 접근의 방법으로 현 상황을 보다 정확하게 반영할 수 있는 시뮬레이션을 사용하게

됩니다. 이러한 시뮬레이션 기법은 프로젝트관리의 다양한 부분에 적용할 수 있습니다.

프로젝트는 프로세스의 흐름으로 진행하게 되는데, 여러 프로세스에서 미래에 대한 예측을 하게 됩니다.

어떤 프로세스에서 어떻게 리스크를 정량적으로 접근하여 프로젝트의 성과를 향상시킬 수 있는지에 대

해 크리스탈볼을 활용하여 효율적 접근 방안을 제시하고자 합니다.

- 발표자: 한동환 (현)PM인사이드 대표

- (현) 광운대학교 정보통신대학원 겸임교수

- 숭실대학교 Global project management 석사.

- 삼성전자, KT, 대우정보시스템, 포스데이타, 농심데이타시스템, KOICA 외 다수 출강

주제 2) Crystal Ball 을 활용한 비재무리스크 측정과 관리

오늘날 리스크 관리는 단순히 금융기관을 넘어 ERM이라는 이름으로 산업 전분야로 확산되고 있다. 일

반적으로 리스크 관리는 환율, 이자율, 유가 등의 시장요인의 변화 또는 신용과 연관된 사건(부도)의 발

생으로 인해 발생할 수 있는 불리한 영향 등을 재무리스크로 정의하고 이를 측정하고 관리하는 것에 초

점을 맞추고 있다. 재무적 리스크와 별도로 비재무리스크 역시 전사적 관점의 리스크 관리의 핵심 요소

로 인식되고 있다. 비재무리스크는 전략리스크, 평판리스크 등을 아우르는 포괄적인 개념으로 리스크 측

정이 재무리스크에 비해 상대적으로 어렵지만 체계적으로 관리하여야 함은 주지의 사실이다. 바젤II로

불리는 금융기관의 자기자본 협약은 비재무리스크 중 운영리스크를 체계적으로 측정 관리하는 역량을

배양하기를 금융기관에 권고하고 있다. 비재무리스크는 모든 비즈니스 조직이 공통적으로 노출되어 있

기 때문에 이를 잘 관리하는 것이 조직 발전과 이익 창출에 필수불가결한 요인이다. 본 발표는 크리스

탈볼을 활용한 비재무리스크 측정에 관한 실제 사례를 중심으로 비재무리스크 측정과 관리에 필요한 다

양한 내용을 체계적으로 살펴보고자 한다.

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- 발표자 : 김우환 박사 (현) 연세대학교 경제학과 연구교수

- 연세대학교 통계학과 박사, 경제대학원 강사

- FRM, CFA 강의

- 바젤II 신자기자본협약 관련 프로젝트 다수 수행

주제3) Monte Carlo Simulation을 활용한 식품 중 유해물질의 위해성평가에서의 불확실성 분석

식품 중 다양한 유해요인에 대한 위해성평가의 불확실성분석을 위한 기법으로써, Monte-Carlo

Analysis를 포함한 확률론적 분석기법의 적용이 이루어지고 있다. 최근 위해성평가의 노출평가수행이

보수적인 가정을 토대로 엄격한 결과값을 도출하는 것으로부터, 보다 실질적인 관리가 가능한,

실제노출현황으로부터 이루어지는 결과값 도출로 전환되면서, 노출평가에서 사용된 변수 대부분이

단일치보다 구간치로 존재하고 있으며, 이러한 구간치를 활용하는 것이 점추정치가 나타내는

불확실성을 최소화할 수 있는 방안으로 제안되고 있다.

즉, 노출평가에서 활용되는 노출요인들의 구간값(분포)을 분포로 결정하고 결합시킴으로써

노출시나리오에서 발생가능한 불확실성을 최소화하고, 사용가능한 모든 자료를 활용함으로써 최대한의

정보를 활용할 수 있는 방안이 적용되고 있는 것이다. 위해성평가의 용량-반응평가에서도, 독성학적

기준형성에 근거가 되는 독성자료들에서 Monte-Carlo 분석이 적용될 수 있으며, 용량-반응평가시

활용되는 독성시험결과가 단일 실험이나 종말점에 근거하는 것을 피할 수 있는 방법으로서 이러한

확률론적 분석이 활용되고 있다. 또한, 확률론적 분석은 위해성관리자와 대중에게 더 많은 정보를

전달하기 위한 기반을 제공하는데, 이는 위해성평가의 결과인 인체노출량과 위해도를 단일 점추정치로

표현하는 대신, Monte-Carlo Analysis를 도입함으로써 결과값을 노출량 및 위해도수준의 범주로써

나타내고, 이에 따른 발생의 경향을 규명할 수 있게 함으로써, 결과에 영향을 주는 주요 요인과 다른

요인과의 관계, 노출의 전체범위를 쉽게 규명할 수 있게 하고, 위해도관리에 있어서도 결과된

위해도결과 분포를 통해 쉽게 설명함으로써, 위해도 전달을 용이하게 하는 것이다.

- 발표자: 최시내 차장 (현) (주)엔바이오니아 정보화사업팀

- 한국과학기술연구원 도핑콘트롤센터 자문위원

- 성균관대학교 대학원 약학과 박사과정 수료

- 연세대학교 보건대학원 환경관리학과 석사

주제4) “BIS 비율, NIM, 유동성 비율 제약조건에서” 은행 자산/부채에 대한 최적 포트폴리오 선택

최근의 은행권의 외형확대 및 경쟁 심화 등으로 리스크 관리의 중요성이 더욱더 커지고 있으며

BIS비율(자기자본비율) 및 유동성비율 관리에 대한 관심이 증가되고 있다. 뿐만 아니라 위험과 trade off

관계에 있는 NIM(순이자마진)도 적정수준을 유지하여야 하므로 최적 자산부채선택은 쉽지 않은

의사결정이다. 또한 각 은행들은 BIS비율이나 유동성비율, NIM을 산출하기 위한 시스템을 각각

독립적으로 운용하고 있어 유기적으로 최적화된 시뮬레이션이 어려우며, 비율 관리를 위한 주관부서도

상이한 경우가 많아 부서간 긴밀한 협조가 없으면 은행전체의 최적화된 결론을 얻기 어렵다.

본 발표는 여러 가지 시나리오 별로 BIS 비율, NIM, 유동성 비율 제약조건 하에서 여수신 주요

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자산/부채에 대한 최적 포적폴리오 선택에 관한 내용을 담고 있다. 특히, BIS비율 중 신용위험가중자산의

계산을 단순화하기 위해 EAD 전환비율 분포를 만들어 시뮬레이션에 이용하였으며, 주요 계정에 대한

분포는 과거 최대, 최소값을 경계로한 정규분포를 가정하고 분포간 상계관계를 반영하여 시뮬레이션 및

최적화 실행하여 원화유동성비율, 순이자마진, BIS비율 등에 대한 예측하여 리스크를 관리하고자 한다.

- 발표자: 김지민 과장 (현)부산은행 리스크 관리팀

- 부산대학교 경영학과 석사 졸업 / 박사 수료

- 동아대학교 경영학과 졸업

주제 5) 생명과학 기술가치평가와 R&D 포트폴리오 관리

생명과학 분야인 바이오 및 제약 사업에서 개발되는 신약, 각종 건강 식품 등은 제품 개발기간과 비용

회수 기간이 길고, 또 시장에 출시 되었을 때의 risk가 커서 전통적으로 가치평가를 한다는 것이 매우

어려운 분야이다.

이러한 이유로 이미 미국을 비롯한 선진 국가들과 세계 글로벌 기업에서 신제품에 대한 리스크를 보다

정확하게 측정하고 관리하기 위하여 시뮬레이션을 활용한 확률론적 방법론을 이용하고 있다. 이에 본

발표는 시뮬레이션 기법들 중에서 가장 일반적이고 우수한 Monte carlo simulation을 이용하였으며,

sensitivity 분석 및 최적화를 통해서 주요 영향 요인 및 최적화 알고리즘을 통한 포트폴리오 관리법에

대해서 소개하고자 한다.

- 발표자: 이승주 차장 (현) LG생명과학 기술원

- 연세대학교 생화학 학사

- UC Berkeley Biophysics박사

- Stanford Univ Chemistry 포스닥

주제 6) Monte Carlo Simulation 을 이용한 자동차 Door Regulator 의 누적공차 최적화 구현

조립 공차 분석은 전체 생산 원가 절감과 제품 품질 향상을 위해서 산업에서 핵심적인 요소가 되고

있다. 자동차와 같이 최종 제품이 여러 개의 개별 부품으로 구성되는 경우에 개별 부품들이 허용공차

범위 내[內]에서 만들어 진다 해도 조립된 제품에는 누적공차가 발생한다. 생산성을 높이기 위해서

Manufacturing variation 에 영향을 미치는 각 구성 품들의 생산 편차에의 기여도(Sensitivity)를 파악할

수 있다면 누적 공차관리에 보다 효율적일 것이다. 이러한 분석을 위한 상용[商用] 프로그램이

시판[市販]되고 있지만 고가[高價]이고, Application 교육 이외[以外]의 기본적인 원리와 Algorithm 에

대해서는 Open 하지 않고 있는 실정이다. 이에 본 연구에서는 한 시스템을 구성하는 여러

단품[Component Parts] 중에서 누적 공차에 주도적인 영향을 미치는 부품들을 집중 관리하기 위해서 각

단품들의 누적공차에 대한 민감도를 One Way Clutch 모델을 적용하였다. 또한 본 모델에 우리들이 매우

유용하고 쉽게 접할 수 있는 Crystal Ball 이라는 Soft ware 를 이용하여 각 부품의 누적공차에 대한

기여도를 몬테카를로 시뮬레이션을 통해 알아 보았다. 그리고 민감도 분석을 통해 누적공차에 대한 각

구성 품들의 기여도를 분석하여 공차 관리 최적화를 시도하였다. 시뮬레이션을 이용한 상용

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프로그램으로 자동차 Door Regulator 의 누적공차 최적화를 구현하였고 자동차 Door Regulator 와 같이

많은 부품들로 구성되는 System 의 누적공차 분석에 매우 유효한 사례가 되었다.

- 발표자: 윤용익 전무이사 (현) 상신브레이크 연구개발 본부

- 현대자동차 기술연구소 근무

- 울산대학교 자동차 선박대학원 석사

- 한양대학교 정임기계공학과 졸업

주제7) Crystal Ball 11.1의 향상된 도구들을 활용한 모델링 기법 소개

수익과 리스크를 정량화하는 여러 가지 방법론 중에서도 가장 각광받고 있는 기법이 바로 몬테카를로

시뮬레이션이다. Crystal Ball은 엑셀을 이용하여 모델을 구축하고 몬테카를로 시뮬레이션을 쉽게 적용할

수 있도록 도움을 주는 소프트웨어이며, 최적의 의사결정을 내릴 수 있도록 많은 분석 도구들을

제공한다. 2008년 9월을 맞이하여 Crystal Ball 11.1 버전이 출시되었으며, OptQuest 최적화 개선, 분포

가정의 추가 기능, 이산형 분포 적합과 p-value 계산, Crystal Ball 도구의 마법사 기능, 데이터 분석 도구

등의 많은 부분에서 사용자들의 편의성을 증대시키고, 더욱 정확하고 올바른 분석을 수행할 수 있도록

향상되었다. 본 발표에서는 이러한 Crystal Ball의 향상된 기능에 대해서 알아보고, 올바른 입력 값의

선택을 통한 합리적인 모델링 기법에 대해서 알아보고자 한다.

- 발표자: 박잉근 대리 (현)이레테크 소프트웨어사업부 교육팀

- 단국대학교 통계학과 석사

- Crystal Ball을 활용한 리스크 및 통계 관련 내부 및 기업체 강의

8

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

기조연설

Oracle’s EPM Product Strategy & Roadmap

James Franklin VPEnterprise Performance Management and General Manager

Crystal Ball GBU

Page 10: Pjt Risk Management Process
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Agenda

• What is Uncertainty Management?

• How does it relate to Enterprise Performance

M t?Management?

• Best Practices• Best Practices

• Finer Points

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Beware the Naked Number

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

11

Page 12: Pjt Risk Management Process

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

<I t Pi t H ><Insert PPicture Here>

What is UncertaintyyManagement?

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

12

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Uncertainty Management

Is the process for management to add

value by optimally making decisions

b d il bl d d ibased on available data and given

inherent uncertainties in the businessinherent uncertainties in the business

environment.

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Applied Throughout the Enterprise

Industries

Academia/Education Finance/Banking

Aerospace Government

Applications

Business Intelligence Portfolio Management

Capacity Planning Project ManagementAerospace Government

Biotech Healthcare/medical

Chemicals Insurance

Construction Manufacturing

Capacity Planning Project Management

Consulting Real Options

Cost Estimation Risk Analysis

Demand Forecasting Sales ForecastingConstruction Manufacturing

Electronics Mining

Energy/Utilities Oil and Gas

E i i Ph ti l

Demand Forecasting Sales Forecasting

Design for Six Sigma Six Sigma/Quality

Financial Analysis Strategic Finance

Liti ti S l Ch i /I tEngineering Pharmaceutical

Environmental Telecommunications

Litigation Supply Chain/Inventory

Marketing Valuation/M&A

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

13

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Top Enterprises are Managing Uncertainty

Aerospace

Manufacturing

Oil and Gas

Pharmaceutical

Utilities

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Global Usage in Business Schools and Universities

Over 700 business schools and universities globally and dozens of textbooksdozens of textbooks

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

14

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Success Stories

Leading Aerospace & Defense Company

Used Crystal Ball in the project selection process to maximize returns on project portfolio within budget and

resource constraints.

Achieved multi-million dollar savings (10% of budget).

Foremost Disk Drive Manufacturer

Developed an integrated model strategy within its DFSSDeveloped aan integrated mmodel strategy wwithin its DDFSSprocess to produce significant advantages in time- and

resources-to-market and reliability.

Decreases Time to Market (TTM) from 48 to 12 man-months.

International technology, media and financial services companygy, p y

Solved a tolerance stack problem for floating fasteners deemed unsolvable for 40 years.

Reduces costs by opening tolerances by 3X.

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

How an Executive can Think in Ranges

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

15

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Know What Matters

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

How much capital do I need?

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

16

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How much Capital do I need?

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

The General Management Problem

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

17

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Data to Decisions: Delivering on Insight to Action

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

<I t Pi t H ><Insert PPicture Here>

UM and EPM

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

18

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Oracle’s EPM Vision: Extend Operational Excellence to Management Excellenceg

Competitive

Advantage

MANAGEMENT EXCELLENCE

Time

OPERATIONAL EXCELLENCE

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Time

ERP Has Leveled the Playing Field Creating Operational Efficienciesp

Firm Infrastructure

Human Resource Management

T h l D l t

Invest to Retire

Technology Development

ProcurementDevelop to Release

Inbound Operations

Marketing &

SService

Outbound

L i ti

Procure to Pay

O d t C hLogisticsOperations

SalesService

Logistics

Source to Procure

Order to Cash

Based on Michael E. Porter’s Value Chain

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

19

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To Achieve Management Excellence

Rationalize YourSMART

Rationalize YourManagement Systems

Leverage Best PracticesAGILE

Leverage Best Practicesand Advanced Integration

ALIGNEDShare Insights Across the Extended EnterpriseALIGNED Extended Enterprise

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Connecting Management ProcessesStrategy to Success Framework

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

20

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Integrating Key Management ProcessesOracle EPM System

EXTERNAL REPORTING

FINANCIAL MODELING

PREDICTIVE MODELING

INTEGRATED BUSINESS PLANNING

PROFITABILITY & COST MANAGEMENT

SCORECARDING

MONITORING, ANALYSIS & REPORTING

FINANCIAL REPORTING

BI REPORTING TOOLS & APPLICATIONS

OLAP

DATA QUALITY, DATA INTEGRATION & MASTER DATA

APPLICATION & METADATA MANAGEMENT

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Oracle’s Enterprise Performance Management System, Fusion Edition

EPM Workspace

BI A li ti

EPM WWorkspace

BI A li tiPerformance Management

BI AApplicationsBI AApplicationsg

Applications

Business Intelligence Foundation

Fusion Middleware

OLTP & ODSS t

Data WarehouseD t M t

SAP, Oracle, Siebel,P l S ft C t

ExcelXML

BusinessP

OLAP

Copyright © 2008, Oracle and / or its affiliates. All rights reserved. 22

Systems Data Mart PeopleSoft, Custom XML Process

21

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Market Leading Performance Management Applications

EPM Workspace

BI A li ti

EPM WWorkspace

PERFORMANCE MANAGEMENT APPLICATIONS

BI AApplicationsBI ApplicationsStrategy

Management

Business

Planning

Profitability

Management

Financial

Reporting &

Compliance

Business Intelligence Foundation

Fusion Middleware

OLTP & ODS Data Warehouse SAP, Oracle, Siebel, Excel BusinessOLAP

Copyright © 2008, Oracle and / or its affiliates. All rights reserved. 23

Systems Data Mart PeopleSoft, Custom XML Process

New Predictive Planning CapabilityIntegration of Crystal Ball with Planning

• Monte Carlo Simulations

• Assign Probability Distributions to Uncertain Assumptions

• Run Thousands of Simulations

A l R f O• Analyze Range of OOutcomesand Probabilities

• Assess Model Sensitivities

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

22

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<I t Pi t H ><Insert PPicture Here>

Best PracticesBest Practices

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

<Insert Picture Here>

H h WilliHugh Williamson

Risk and Cost Advisor Drilling and Completions, British Petroleum

“Crystal Ball is involved in

Completions, British Petroleum

Crystal Ball is involved inevery major investment

d i i th t k fdecision that we make forwells.”

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

23

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How to Best Organize for UM

1.Centralize Assumptions

2.Decentralize the tool set

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Drive Better Conversations

• Instead of creating analysis to justify a gut feel…

• Have the business process owners describeHave the business process owners describe

• What is the ‘train wreck’ scenario?

• What is the ‘everything goes our way’ scenario?y g g y

• What is a ‘center of gravity’ scenario?

• What is the distribution within those ranges?

• Which ranges are correlated together?

• Like ‘Wisdom of the Crowds’ have each owner INDEPENDENTLY

submit their estimates

• Then get consensus on parameters…or use second-order

assumptions.

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

24

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Let the Data Guide You, Not Rule You

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

80% is Good Enough

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

25

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Personally, Where Should I Invest?

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

Optimized Solution: 150 bp higher

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

26

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Further Reading

<I t Pi t H >

“Why Can’t You Just Give Me the Number?”<Insert PPicture Here>

Patrick Leach

“Financial Modeling with Crystal Ball” Johng y

Charnes

“Against the Gods: the Remarkable Story of Risk”Against the Gods: the Remarkable Story of Risk

Peter Bernstein

“ h l k ” i h l l b“The Black Swan” Nicholas Taleb

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

For More Information

search.oracle.com

Crystal Ball

or

oracle.com/crystalball

Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

27

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Copyright © 2008, Oracle and / or its affiliates. All rights reserved.

28

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

프로젝트 성과향상을 위한 Risk관리

발표자: 한동환 대표발표자: 한동환 대표

소속: PM인사이드

E-Mail: [email protected]

Page 30: Pjt Risk Management Process
Page 31: Pjt Risk Management Process

2008 Korea Crystal Ball User Conference

1. Project Management Overview

1 1 Hi t f P j t t1.1 History of Project management

1.2 Management Areas

1 3 St d d of P oje t M e e t1.3 Standards of Project Management

2. Risk Management of Project

2 1 What is a Risk?2.1 What is a Risk?

2.2 Project Risk Management Processes

2 3 Case study using CB2.3 Case study using CB

3. Wrap up

2008 Korea Crystal Ball User Conference

31

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1. Project Management Overview

1.1 History of yProject Management

2008 Korea Crystal Ball User Conference

History of Project Management

Modern project management began with the Manhattan Projectbegan with the Manhattan Project,which the U.S. military led to develop the atomic bombdevelop the atomic bomb

The Manhattan Project was the project to develop the first nuclear weapon (atomicdevelop the first nuclear weapon (atomicbomb) during World War II by the United States, the United Kingdom, and Canada.

2008 Korea Crystal Ball User Conference

32

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History of Project Management

I 1917 H G ttIn 1917 Henry Ganttdeveloped the Gantth l fchart as a tool for

scheduling work in job shops

A Gantt chart is a popular type of bar chart that illustrates a projectA Gantt chart is a popular type of bar chart that illustrates a projectschedule. Gantt charts illustrate the start and finish dates of the terminal elements and summary elements of a project.

2008 Korea Crystal Ball User Conference

History of Project Management

In 1958, the Navy developed PERT charts

The Program Evaluation and Review Technique commonlyThe Program Evaluation and Review Technique, commonlyabbreviated PERT, is a model for project management designed to analyze and represent the tasks involved in completing a given projectcompleting a given project.

This model was invented by Booz Allen Hamilton, Inc. under contract to the United States Department of Defense's US Navy Special Projects Office in 1958 as part of the Polaris mobile submarine-launched ballistic missile projectmissile project.

2008 Korea Crystal Ball User Conference

33

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History of Project Management

In the 1970s, the military began using project management software, as did the construction industryy

2008 Korea Crystal Ball User Conference

History of Project Management

fBy the 1990s, virtually every industry was using some form

of project management

2008 Korea Crystal Ball User Conference

34

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History of Project Management

The PMBOK Guide – 2004 Edition is an ANSI standard

PMI’s certification department earned ISO 9000 certification

Hundreds of new books, articles, and presentations related to , , p

project management have been written in recent years

2008 Korea Crystal Ball User Conference

1. Project Management Overview

1.2 Management Areas1.2 Management Areas

2008 Korea Crystal Ball User Conference

35

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Management Areas?

2008 Korea Crystal Ball User Conference

Management Areas?

Project

2008 Korea Crystal Ball User Conference

36

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1. Project Management Overview

1 3 S d d f1.3 Standards ofProject ManagementProject Management

2008 Korea Crystal Ball User Conference

Standards of Project Management

• America - PMBOK® Guide

• A Guide to the Project

Management Body of Knowledge

• Participated in 29 Countries, 266

Volunteers

• Over 2million copies distribution.

2008 Korea Crystal Ball User Conference

37

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Standards of Project Management

• UK – PRINCE2

• PRojects IN Controlled Environments

• De-facto standard for project management

in the UK

• http://prince2.com

2008 Korea Crystal Ball User Conference

Standards of Project Management

• Japan – P2M

• A Guidebook of Project & Program

Management for Enterprise InnovationManagement for Enterprise Innovation

• http://www.pmaj.or.jp

It reflects an intention to enlarge the scope of project and program f h l f k f h llmanagement from the conventional focus on work front to the overall

organization including the general management level.

2008 Korea Crystal Ball User Conference

38

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2. Risk Management of Project

2.1 What is a Risk?

2008 Korea Crystal Ball User Conference

What is a Risk?

If d i lIf you do not actively

attack the risksattack the risks,

they will actively attack you !

-Tom Gilb -

http://www.gilb.com

2008 Korea Crystal Ball User Conference

39

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What is a Risk?

Project Risk is an uncertain event or condition

that, if it occurs, has a positive or negative

ff t j t bj tieffect on a project objective.By PMBOK®

2008 Korea Crystal Ball User Conference

What is a Risk?

Initiating

PlanningPlanning

Executing

ain

ty

Closing

Un

cert

aU

2008 Korea Crystal Ball User Conference

40

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What is a Risk?

Initiating Planning Executing Closing

tain

ty Imp

Un

cert

pact

2008 Korea Crystal Ball User Conference

Two types of Risk

Represent identified potential problems, such as the

possibility of a strike when a labor contract expires.

The problems that arrive unexpectedly

2008 Korea Crystal Ball User Conference

p p y

41

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2. Risk Management of Project

2.2 Project Risk ManagementProcess

2008 Korea Crystal Ball User Conference

Pjt Risk Management Process

1.Risk1.RiskManagementManagement

Pl iPl i

2.Risk2.RiskIdentificationIdentification

Reg

PlanningPlanning IdentificationIdentification gular ch

e

3.Qualitative3.QualitativeRi k A l iRi k A l i

4.Quantitative4.QuantitativeRi k A l iRi k A l i

eck for n

e

Risk AnalysisRisk AnalysisRisk AnalysisRisk Analysis

ew p

roje

5.Risk5.RiskResponseResponse

ect risks

ResponseResponsePlanningPlanning Risk Monitoring

and control

2008 Korea Crystal Ball User Conference

42

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Pjt Risk Management Process

1.Risk Management Planning1.Risk Management Planning

Project Scope Project Scope StatementStatement

Risk Management Risk Management PlanPlan

Planning Meetings

StatementStatement PlanPlan

Deciding how to approach and conduct the risk

i i i fmanagement activities for a project.

2008 Korea Crystal Ball User Conference

Pjt Risk Management Process

2. Risk Identification

RiskRiskManagementManagement Risk RegisteRisk Registe

Document review

Assumption analysisManagementManagementPlanPlan

Risk RegisterRisk RegisterAssumption analysis

Brainstorming

Interview

D t i hi h i k i ht ff t th j t dDetermines which risks might affect the project and

documents their characteristics.

2008 Korea Crystal Ball User Conference

43

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Pjt Risk Management Process

3. Qualitative Risk Analysis3. Qualitative Risk Analysis

Risk Register Risk RegisterP-I MatrixRisk Register s eg ste(Update)

Prioritizing the identified risksPrioritizing the identified risks

2008 Korea Crystal Ball User Conference

Pjt Risk Management Process

P-I Matrix

5

ab

ilit

y 4

Pro

ba

2

3

1

2

Impact

1

1 2 3 4 5

2008 Korea Crystal Ball User Conference

Impact

44

Page 45: Pjt Risk Management Process

Pjt Risk Management Process

3. Qualitative Risk Analysis

Probability of

risk event (Pe)

XRiskprobability

Probability of

impact (Pi)

Expected loss

(Le)=

X

TotalTotal loss (Lt)

Totalamount of

if risk occur

2008 Korea Crystal Ball User Conference

Pjt Risk Management Process

Hi h

ty s

High

HighRi k

ob

ab

ilit

of

Lo

ss

Moderate

Risk

Proo

Low

ModerateRisk

Low

LowRisk

Magnitude of ImpactNo

ImpactLargeImpact

2008 Korea Crystal Ball User Conference

45

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Pjt Risk Management Process

4. Quantitative Risk Analysis4. Quantitative Risk Analysis

Risk RegisterMonte Carlo simulationRisk Register Risk RRegister

(Update)

Monte CCarlo simulation

Decision tree analysis

Analysis of potentially and substantially impactingAnalysis of potentially and substantially impacting

the project’s competing demands.

2008 Korea Crystal Ball User Conference

Pjt Risk Management Process

5. Risk Response Planning5. Risk Response Planning

Strategies for Negative Risks or ThreatsRisk RegisterRisk Register Risk RegisterRisk Register

(Update)(Update)Strategies for

Positive Risks or Opportunities

gg(Update)(Update)

Developing options, and determining actions toh t iti d d th tenhance opportunities and reduce threats

to the project’s objectives.

2008 Korea Crystal Ball User Conference

46

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2. Risk Management of Project

2 3 Case study using CB2.3 Case study using CB

2008 Korea Crystal Ball User Conference

Case study using CB

Case 1. Project Cost Estimating

By PMBOK®

2008 Korea Crystal Ball User Conference

47

Page 48: Pjt Risk Management Process

Pjt Risk Management Process

Case 2. Project Critical Path Analysis

2008 Korea Crystal Ball User Conference

3 W Up3.Wrap Up

2008 Korea Crystal Ball User Conference

48

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Wrap Up

2008 Korea Crystal Ball User Conference

Reference

• A Guide to the PMBOK 3rd Edition, PMI, 2004

P j t M ' S tli ht Ri k M t Ki H ld• Project Manager's Spotlight on Risk Management, Kim Heldman,

Sybex, 2006

P ti Ri k M t P t G S ith d G M M itt• Proactive Risk Management, Preston G. Smith and Guy M. Merritt,

Productivity Press, 2002

C t P j t t i ti S l J M t l J• Core concepts Project management in practice, Samuel J. Mantel Jr.

Wiley, 2005

htt // th d123• http://www.method123.com

• http://www.cvr-it.com/

2008 Korea Crystal Ball User Conference

49

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

Crystal Ball을 활용한 비재무리스크 측정

발표자: 김우환 박사발표자 김우환 박사

소속: 연세대학교 경제학과

E-Mail: [email protected]

Page 52: Pjt Risk Management Process
Page 53: Pjt Risk Management Process

Ri k ( t t i t )

Risk• The difference between expectation and realization Variance

• Adverse impact Value at Risk

Risk (exposure to uncertainty)

Uncontrollable Risk

• Adverse impact Value at Risk

Environmental Risk

Controllable Risk

• , , ,

Financial Risk Non-Financial Risk

M k t Ri k

Controllable Risk

Market Risk

Credit Risk

Strategic Risk• , ,

Liquidity RiskOperational Risk

. , ,

2008 Korea Crystal Ball User Conference

,

,

.

• ,

.

• , , IT ,, , IT ,

BIS

, ( )

,

· ,

2008 Korea Crystal Ball User Conference

53

Page 54: Pjt Risk Management Process

1995 230 (Barings)

ING 1ING 1

(Nick Leeson)

(SIMEX)

• 1993 10% 1

• 1994

95 1

8 2,700

ING 1ING 1 ,

6

2008 Korea Crystal Ball User Conference

•– (Capital at •(Capital at

Risk)

•–

(RAROC,RAPM)(1)

•-

Note: (1) Risk Adjusted Return On Capital Risk Adjusted Performance Measurement

• Contingency Plan- ,

Action Plan

2008 Korea Crystal Ball User Conference

Note: (1) Risk Adjusted Return On Capital, Risk Adjusted Performance Measurement

54

Page 55: Pjt Risk Management Process

2008 Korea Crystal Ball User Conference

2008 Korea Crystal Ball User Conference

55

Page 56: Pjt Risk Management Process

Monte Carlo Simulation

- Loss Distribution Approach (LDA)

Probability

Loss Distribution Approach (LDA)

Loss Distribution Approach

Frequency

Convolution

Loss

Severity

Convolution

EL UL VaR

2008 Korea Crystal Ball User Conference

1

(ex : 100,000)

• Monte Carlo Simulation

2008 Korea Crystal Ball User Conference

56

Page 57: Pjt Risk Management Process

Poisson

Poisson

• Poisson :

=

exp( )( )!

x

f xx

• : , 2

<

1( ) (1 )x yx yf ( ) (1 )x yf x p p

y

2k O E2

1

ki i

i i

O EX

E

2008 Korea Crystal Ball User Conference

, ,

• Inverse Gaussian

max ( ) ( )NKS F x F x

11

1 2 ln ( ) ln 1 ( )N

i N ii

iAD N F X F XN

2008 Korea Crystal Ball User Conference

57

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2008 Korea Crystal Ball User Conference

236

12052

80

2536

120

5280

Lossessizes(in $)

Lossessizes(in $)

10

2422

715

2021 18

25

10

24

36

22

715

52

20218

25 Threshold

7

Time Time

Peaks over Threshold (P.O.T.)Fits Generalised Pareto Distribution

Distribution of Maxima over a certain period - Fits theFits Generalised Pareto Distribution

(G.P.D.)a certain period Fits the

Generalised Extreme Dist (GEV)

• POT

2008 Korea Crystal Ball User Conference

58

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Shape parameter1 /

,

1 1 , 0x

G xx

p p

GPD1 exp , 0x

Scale parameterScale parameter

N o rm al D is t r ib u t io n

M ean

S k ew ed D is t r ib u t io n

M e a n

Std Devia tion

Value a t R isk

(95th

Percentile)

ea

S td D e v ia tio n

2 4 6 8 10 12 14 16 18 20

L o s s

Tail VaR 95

(Average VaR in

Shaded Area)

0 .0 0 0 5 0 .0 0 0 1 0 0 .0 0 0 1 5 0 .0 0 0 2 0 0 .0 0 0 2 5 0 .0 0 0 3 0 0 .0 0 0 3 5 0 .0 0 0 4 0 0 .0 0 0

L o s s

V a lu e a t R isk

(95th

P e rcen tile )

T a il V a R 9 5

(A ve rage V aR in

S haded A rea)

2008 Korea Crystal Ball User Conference

(GPD)

- :

.

- :

.

MEF(Mean Excess function) :MEF

1

( )( )

n

iin

X ue u

I( u : ( ) )

Hill Estimates :Hill Estimates .

( )1

iX uiI

Hill Estimates .

11

( ) ( )1

[( 1) log ] logm

H i mi

m x x ( m : ,position)

2008 Korea Crystal Ball User Conference

1i

59

Page 60: Pjt Risk Management Process

B d, Body

Tail GPD .

(P i )(Poisson) .

Poisson (k) , Uniform (0,1) k

Uniform Body. Uniform Body

, Tail .

.

~ 10,000 , 99.9 Percentile .

2008 Korea Crystal Ball User Conference

60

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

Monte Carlo Simulation을 활용한 식품 중

유해물질의 위해성 평가에서의 불화실성 분석

발표자: 최시내 차장

소속: ㈜엔바이오니아

E-Mail: [email protected]

Page 62: Pjt Risk Management Process
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Background

• Human and Environmental Risk Assessment

• 1950s : “black and white” insight obtained from toxicity testing

• 1970s : scientific and regulatory communities

• 1980s : public began to develop an expectation that risk analysis- Unmanagerable quantity of scientific and medical data regarding theUnmanagerable quantity of scientific and medical data regarding the

potential health hazards posed by physical and chemical agents

- National Academy of Sciences, 1983

• 1990s : the field of risk assessment matured significantly(Graham• 1990s : the field of risk assessment matured significantly(Graham,1995; NRC, 1994)- Risk managers, policymakers, and the public a range of potions, each

h i ifi t d b fit(G h d H t ll 1997)having a specific cost and benefit(Graham and Hartwell, 1997)

- Superfund and Resource Consevation and Recovery Act(RCRA) sites

• Measure better exposure data more balanced risk characterization• Measure - better exposure data, more balanced risk characterization,the use of Monte Carlo techniques

• Dose extrapolation – improved PBPK model

2008 Korea Crystal Ball User Conference

Background

• Delaney Clause of the 1950s

• Regulation in US- Resource Conservation and Recovery Act(RCRA)Resource Conservation and Recovery Act(RCRA)

- Safe Drinking Water Act (SDWA)

- Clean Air Act(CAA)

- Clean Water Act(CWA)

- Toxic Substance Control Act(TSCA)

- Comprehensive Environmental Response, Compensation and LiabilityComprehensive Environmental Response, Compensation and LiabilityAct (CERCLA, Superfund)

- Food, Drug and Cosmetics Act(FDCA)

- Federal Insecticide, fungicide and Rodenticide(FIFRA)

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Elements of the Risk Assessment and Risk Management Processes (NAS, 1983)

RESEARCH RISK ASSESSMENT RISK MANAGEMENT

Laboratory and fieldobservations ofadverse health effectsand exposures to

ti l t

HazardIdentification(Does the agentcause the adverseeffect?)

Development ofregulatoryoptions

particular agent

Information onextrapolation

th d

Dose-responseAssessment(What is the

Evaluation of public health economic, social,political

methodsfor high to low doseand animal to human

(What is therelationshipbetween dose andincidence in human?)

RiskCharacterization(What is the estimated incidence of the adverse effect i i

Field measurements,estimated exposures,characterization ofpopulations

ExposureAssessment(What exposures arecurrentlyexperienced

in a given population?

Agencydecisions and actionsexperienced

or anticipated underdifferent conditions?

2008 Korea Crystal Ball User Conference

An Environmental Health Paradigm and Its Relationship

to the Risk Assessment Framework

( f S )

Mechanistic Basis for the Environmental Risk AssessmentSequence of Events in the Health Framework

(Adapted from Sexton et al., 1995)

Sequence of Events in the Health FrameworkEnvironmental Health Paradigm Paradigm

ExposureAssessment

Emission What

Biological,Chemical,Physical,andSociologicalDeterminants What is

PollutantTransport,

Transformation,and Fate Process

source

Environmentalconcentration

Whatenvironmental

exposure occur orare expected to

occur for human populations

and what is the resulting dose to

RiskCharacterization

Determinantsof the CriticalEvents Leadingof Release ofToxic Agents into the environment to

the estimated human health

risk from anticipatedexposures ?

Demographic,Geographic, and

lifestyle Attributes

Pharmacokinetica

HumanExposure

resulting dose tothe target tissue

What is the relationship

Dose-ResponseAssessment

-ronment toResultingDisease or injury in People

Pharmacokinetic

Pharmacodynamicb

Internal Dose

Adverse

relationshipbetween dose tothe target tissue

and adverse effects in humans

HazardId ifi i

a.Pharmacokinetic - what the body does to the agentb.Pharmacodynamic - what the agent does to the body

EffectIs the environmental

agent capable of causing an adverseeffect in humans?

Identification

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Risk and Hazard

Risk : The probability of injury, disease, or death from

exposure to a chemical agent or a mixture of chemicals.

In quantitative terms, risk is expressed in values

ranging from zero (representing the certainty that harm

will not occur) to one (representing the certainty that ) ( p g y

harm will occur).

Hazard: A potential source of harm.

U.S.EPA, Glossary of IRIS Terms (1999)

2008 Korea Crystal Ball User Conference

, y ( )

Risk

• Total risk = (How much or How often) x (Some risk per unit of action, or per event)

• Risk = Probability(Exposure) x Severity(Toxicity)= S{probability x severity x weight}

: Likelihood(probability) that an adverse event will occur when a product containing a hazardous chemical is used in permitted waysproduct containing a hazardous chemical is used in permitted ways

• Measures of Risk : Deaths, Injuries, et al.

• Risk Assessment: The determination of the probability that an adverse effect will

result from a defined exposure

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Risk

Biostatistics

Risk

OxicologyEpidemiology

RiskAssessment

AssumptionsExposureassessment

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Risk Analysis

Science Risk Assessment

Risk Management Policy

Risk Communication

Risk Management

Risk Communication

Risk Assessment : Bridge between science and policy (Hertz-Piccioto, 1995)

Topics : To identify causal relationships and health-protective exposure limits

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Purpose of Risk Assessment

• To provide pertinent information to risk managers,

specifically, policymakers and regulators, so that the best

possible decisions can be made.

• Social benefits vs. technological risk (Starr, 1969)

• Risk management, assessment, and acceptability (Starr, 1985)

• Voluntary and involuntary risks (Crouch & Wilson, 1982)

2008 Korea Crystal Ball User Conference

Definition of Risk Assessment (NAS, 1983)

• Risk Assessment to mean the characterization of the potential adverse health effects of human exposures to p penvironmental hazards. Risk assessments include several elements: description of potential adverse health effects based on an evaluation of results of epidemiologic clinicalbased on an evaluation of results of epidemiologic, clinical,toxicologic, and environmental research; extrapolationfrom those results to predict the type and estimate the extent of health effects in humans under given conditions of exposure; judgments as the number and characteristics of persons exposed at various intensities and durations;of persons exposed at various intensities and durations;and summary judgments on the existence and overall magnitude of the public health problem. Risk assessment also includes characterization of the uncertainties inherent in the process of inferring risk.

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History of Health Risk Assessment• Environmental Risks(ca. A.D 500-1300)o e ta s s(ca 500 300)

• Potential health problem : Contamination of the Air, water, and land• Occupational Hazard Assessment (ca. 1300-1900)• Occupational Disease Recognition(1900-1930)• Toxicological Studies and Risk Assement(1930-1940)

• Friess(1987) has suggested that we currently call risk assessment began roughly in the 1930s

• Concern over relatively Modest Health Risk(1940-1980)• Concern over relatively Modest Health Risk(1940 1980)• Black Death(1348-1349)• Silent spring(Carson,1962) – organic chemicals

• Setting Acceptable Daily Intakes(1950-1970)• NOEL, safety factor(uncertainty factor)

• The Cancer Hazard(1970-1985)• The dose makes the poison (1974), VC experience

Th h ld d• Threshold dose• The acceptance of models that predicted the potential upper-bound excess cancer

risk pf very low doses• Concerns Regarding the Accuracy of Risk Assessment(1980-1995)g g y ( )• Biologically Based Disposition Models (1985-2000), 68 chemicals• Biologically Based Cancer Models(1985-2000)• The Mathematical Modeling and Benchmark Dose Method(1995-2000)

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• Guidance Documents(1985-2000) : USEPA

Hazard Identification

• Does the agent cause the adverse effect?

• Determination of whether a particular chemical is a causalDetermination of whether a particular chemical is a causal

factor for particular health outcomes

H d id tifi ti i l l lit ti t hi h• Hazard identification is a largely qualitative step which

involves a description of the toxicity of the agent as

influenced by its physical and chemical properties, and its

environmental and biological fate and interactions.

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• Hazard Identification

• Exposure Assessment

D R A t• Dose-Response Assessment

• Risk CharacterizationRisk Characterization

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Hazard Identification

• Toxicological studies- Animal Studies can identify new hazards before human

exposure occurs

• Epidemiological studies• Epidemiological studies- Epidemiologic studies can directly demonstrate human

health risks related to existing environmental hazards

• US Surgeon General’s report(1964) grouped the available scientific evidence into two main categories

Experimental studies of animals- Experimental studies of animals

- Clinical and epidemiologic studies of human

- Criteria(cf. lung cancer vs. smoking) : Consistency, Strength, Specificity, Temporal relationship, Coherence

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• Environmental fate and transport

• Example of Useful databasep

- IRIS (Integrated Risk Information System) : EPA, USA

- HSDB (Hazardous Substances Data Bank) : NIH USA- HSDB (Hazardous Substances Data Bank) : NIH, USA

2008 Korea Crystal Ball User Conference

Exposure Assessment

• What exposures are currently experienced or

ti i t d d diff t diti ?anticipated under different conditions?

• The step that quantifies the intake of an agent as the

result of contact with various environmental media (e gresult of contact with various environmental media (e.g.

air, water, soil, food)

• Exposure assessments can address past, current or future

anticipated exposures, although uncertainties will

compound when addressing questions on what might have

happened or what will happen.

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Exposure Assessment

• Duration of exposure : the period of time over which the exposure

occurs

- Acute : single exposure(less than a day)

- Subchronic : between acute and chronic

- Chronic : substantial portion of the subject’s lifetime

• Exposure scenario (Qualitative step)

Types of exposure / exposure pathway : ingestion inhalation dermal- Types of exposure / exposure pathway : ingestion, inhalation, dermal

intake et al.

- Exposure factorp

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Estimate of Exposure Dose

• Measurement of Exposure- Monitoring : personal monitoring, biological monitoring,

biomonitoring, biomarker

- Estimation : modelingEstimation : modeling

• Modeling- Fate, transport, and dispersion modeling : atmospheric

models, surface-water models, ground water and unsaturated-zone models, multimedia models, food-chain model

• Population analysis

• Aggregate(multi-route exposure), Cumulative

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E i t l E t H E• Environmental Exposure assessment- Transport/Transformation model

(Environmental fate model)

Human Exposure assessment– Human exposure model

A i rA i r

W a t e rW a t e r H u m a nH u m a n

A n im a lA n im a l

P la n tP la n t

F i s hF i s h

S o i lS o i l

Fugacity model

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Environmental Fate Model

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Example) Exposure Pathways for Power Plant or Incinerator -

Source : Paustenbach 2000

2008 Korea Crystal Ball User Conference

Source : Paustenbach, 2000

Basic equation

EC x IR x AF x EF x EDDDose =

BW x AT x 365(days/year)

• Dose = exposure intake ( mg/kg bw-day )• EC = the environmental concentration• IR = daily intake or contact rate• AF = absorption factor• EF = exposure frequency • ED = exposure duration • BW = body weight• AT = averaging time : for evaluation of carcinogens – 70 years

for evaluation of non-carcinogens – duration of exposure

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Exposure factor

Ingestion

Drinking water (l/day)

2.1 (RME adult)

1.4 (adult average)

1.0 (child)

0 1 (incident ingestion

Inhalation m3/day 102030

Body weight (kg) 13 2 (2~5yr)0.1 (incident ingestion

during swimming)

Soil (mg/day) 200 (child average)800 (child 90th average) Lifespan (yr) 70 (average)

Body weight (kg) 13.2 (2~5yr)20.8 (6yr)70 (adult average)

Foodbeef(home-grown)

100g/day(all sources)Dairy(home-grown)

2000 (adult, total)44 (average)75 (RME)160 (average)

Exposed skin area (m2) 0.2 (adult average)0.53 (adult, RME)1.94 (male bathing)1.69 (female bathing)D y( m g w )

400 g/day(all sources)Fruit(home-grown)

140 g/day(all sources)Vegetables(home-grown)

( g )300 (RME)28 (average)42 (RME)50 (average)

. 9 (f m g)

Showering (5 min. shower uses 40 gallons

7 min (average)12 min (90th percentile)

200 g/day(all sources)Sport fish

80 (RME)30 (average)140 (RME)

Residence time 9 yr (average)30 yr (RME)

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U.S.EPA / Exposure factor Handbook

Dietary Exposure

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EnvironmentalH lth P di

Intake and

Health Paradigm

ImportantDepartment(mechanisms)

Emission Sources

UptakeProcesses

Pathway(s)

Exposure ConcentrationIntake

Exposure Factors- biological- chemical

sociological

Potential Dose

Applied Dose

- Inhalation- Ingestion

- sociological

Applied Dose

U t k (Ab ti )

ASORPTION BARRIERS

Pharmacokinetics- bioavailability- absorption- disposition

Skin, Lung,G.I. tract

Internal Dose

Delivered Dose

Uptake(Absorption)- Inhalation- Ingestion

p- metabolism- elimination

Biologically Effective Dose

Biological Effect(s)

Pharmacodynamics- compensation- damage

2008 Korea Crystal Ball User Conference

Biological Effect(s)

Adverse Effect(s)

g- repair

Dose-response Assessment

Wh t i th l ti hi b t d d i id i• What is the relationship between dose and incidence in

human?

• The process of characterizing the quantitative relationships between the dose (or intake) of an agent and the resultant biological response.

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Developing the dose-response relationship in risk assessment - two major steps -

• Extrapolation : high dose low dose• Extrapolation : high dose low dose

- Experimental Dose(High Dose)

t L D (E i t l D )to Low-Dose (Environmental Dose)

- Using Mathematical model

• Dose-Scaling : Animal Dose to Human Dose

- Considering body weight, surface area, et al.

2008 Korea Crystal Ball User Conference

General approaches to deriving toxicity criteria based on toxicological end-points

Approach Methods Toxicological endpoint

I it ti

Dose-response Assessment

Threshold effects

N Th h ld

NOAEL/LOAEL with uncertainty factorsBenchmark dose with uncertainty factors

Linearinzed multistage model

IrritationNon-cancer effectsEpigenetic carcinogenic effects

R d ti ff tNon-Thresholdeffects

Pre-derived

Linearinzed multistage modelOther low-dose extrapolation modelsBenchmark dose with extrapolation

Reference doses(RfDs)

Reproductive effectsGenotoxic carcinogenic effects

All non-carcinogenic effectsPre derivednon-cancer values

Pre-derived l

Reference doses(RfDs)Reference concentration(RfCs)

Unit cancer risk(UCRs)Cancer potency factors(CPFs)

All non carcinogenic effectsexcept for irritation

Carcinogenic effectscancer values

Pharmacokinetics

Cancer potency factors(CPFs)Virtually safe doses(VSDs)

Physically based pharmacokinetic Models (PBPK)

Carcinogenic effects

Target organ effects (including cancer)

Monte-Carloanalysis

Models (PBPK)

Combination of several approaches, data sets, or expert opinions

cancer)

All effects

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Reference Dose (RfD) or Reference Concentration (RfC)

- RfD : An estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an(including sensitive subgroups) that is likely to be without anappreciable risk of deleterious effects during a lifetime.

NOAEL or LOAEL UF :Uncertainty facter 3-10, from animals to humans UF MF 3 10 h i bilitUF x MF 3-10, human variability

5-10, less than chronic data 5-10, LOAEL instead of NOAEL10 incomplete data base10, incomplete data baseModifying factor 1< MF <10

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Benchmark Dose

• Suggested by Crump(1984)

• Modeling from the actual dose-response curve and determining a lower confidence bound for a dose at some specified response level.

• BMD is usually selected at a response rate between 1 and 10 % and a y p95% lower confidence bound on the dose used to derive a conservative value.

• Benchmark DoseP(dp)-P(0)

p = ------------------1 - P(0)

- P(dp) : a number of between 0~1 representing the risk for exposure level d.- P(0) : the response of the control group and P(the

benchmark response) is a specified level of risk.f d t th b h k (BMR)- p : referred to as the benchmark response(BMR)

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Estimation of Benchmark Dose(BMD)

Responsew

p

w

w

Fitting curve

w

0.1

w

w

Dose95% Lower boundBMD5 BENCHMARK DOSE

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Graphical presentation of data and extrapolation- cancer potency (slop factor) -

Extrapolation using Mathematical model

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Ex) Cancer Potency

Dose-Response Data inputAnimal cancer dataAnimal cancer dataRoute/dose units : Gavage(mg/kg/day)

Dose Response0 6 / 646 13 / 64

18 26 / 64Intraspecies Doses Scaling

STEP 2

18 26 / 6439 14 / 64

p gAnimal Dose -> Human Dose

animal life span(weeks) 3Q * * [ ]

Response

P(0) = q0 + q1*d1 + q2d2....

p ( ) 3Q1a* = q1* x [ ---------------------------------------- ]

experimental duration(weeks)

q1* : 95% upper confidence bound on the q term

Q1*

Extrapolation

on tthe q1 termq1a* : adjusted q1*

body weight(human) 3/4Q1H* = q1a* x [ --------------------------- ]

STEP 1

Extrapolationfrom High to Low DoseDose

Q1H q1a [ ]body weight(animal)

q1H* : Predicted human q1*

2008 Korea Crystal Ball User Conference

Using mathematical models

• Multi-hit model

• Weibull model

• Multi-stage model

• Linearized multistage model• Linearized multistage model

- P(d) = 1-exp(-q0 + q1d1 + q2d2 + … + qkdk)

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New Proposed Guidelines for Carcinogen Risk Assessment

• This proposed guidelines state that "If in a particular case, the evidence indicated a threshold as in the case ofthe evidence indicated a threshold, as in the case ofcarcinogenicity being secondary to another toxicity that has a threshold, the margin of exposure analysis for toxicity is th i d f d i t"the same as is done for a non-cancer endpoint".

• The new guidelines allow methodology more appropriate to• The new guidelines allow methodology more appropriate tochemicals acting through threshold-based, non-genotoxic mechanisms when, indeed, sufficiently strong supportive evidence existsevidence exists.- The nonlinear approach advocated in the proposed guidelines is

the MOE, which is defined in the guidelines as the LED10(or other i f d h NOEL) di id d b i lpoint of departure, such as NOEL) divided by environmental

exposure of interest.

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Approaches of Risk Estimation

C Ri k [ C P t ( /k /d ) 1 ]• Cancer Risk = [ Cancer Potency (mg/kg/day)-1 ]x [ Lifetime Average Daily Intake Dose

(LADD, mg/kg/day) ]( , g/ g/ y) ]

LED10(mg/kg/day) or other point of departure(NOEL) • MOE =

Human dose(LADD, mg/kg/day)

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Graphical presentation of data and extrapolation- Margin of Exposure (MOE) -

Dose-response Assessment

"If in a particular case, the evidence indicated a threshold, as in the case of carcinogenicity being secondary to another toxicity that has a threshold, the margin of exposure analysis for toxicity is the same as is done for a non-cancer endpoint". (U.S.EPA. Proposed guidelines for Carcinogen Risk Assessment)

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( p g g )

Risk Characterization

• Non-carcinogen What is the estimated incidence

of the adverse effect in a given population?of the adverse effect in a given population?

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Risk Characterization

• Threshold approach

• Hazard quotient = [exposure level] / [reference dose]• Hazard quotient [exposure level] / [reference dose]- exposure reference dose .

• Non-Threshold approach- linear low-dose model

- Risk = CDI(chronic daily intake average developing cancer)

x CP(cancer potency)

- one-hit model

- Risk = 1-exp(CDI x SF)

• Aggregate Risks for multiple substances : sum of individual risk

• Combining risks across exposure pathways : sum of exposure by all pathways

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Uncertainty Analysis : Monte Carlo Simulation

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Monte-Carlo Simulation : pointvalue ( )(uncertainty analysis)

Illustration of Monte-Carlo Simulation for the Quantitative Exposure Assessment

(uncertainty analysis)

for the Quantitative Exposure Assessment

* During each iteration,a randomly selected valuefrom each input parameter

Body weightp p

distribution(left) is combinedto estimate the probability

of exposure

Forecast: D46

Average TimeAverage Time

A6

Frequency Chart

.022

.029

66

88

3,000 Trials 56 Outliers

Forecast: D46

Pollution Level by FoodPollution Level by Food

-7.86 1.76 11.37 20.99 30.60

.000

.007

.015

0

22

44

0.000E+0 6.250E-5 1.250E-4 1.875E-4 2.500E-4

Exposure durationExposure duration

B11

Probability distribution for the ingestion exposure

Consumption Level Consumption Level by Foodby Food0.09 0.16 0.24 0.31 0.38

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by FFoodby Food

Using Tools on Uncertainty Analysis

• @Risk Palisade Corporation• @Risk, Palisade Corporation

Crystal Ball Oracle• Crystal Ball, Oracle

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Probabilistic Approach

• Exposure Assessment

- Estimation of Exposure dose

- Exposure factors, Monitoring data

• Dose-response assessmentDose response assessment

- Reference dose, Benchmark dose….

• Risk Characterization

- Estimation of risk….

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)

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)

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)

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A- A (ADI) 0.012 mg/kg BW/day

AA

A

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

은행 자산/부채에 대한 최적포트폴리오 선택

- BIS비율, NIM, 유동성비율 제약조건하에서 -

발표자: 김 지 민 과장

소속: 부산은행 리스크 관리팀

E-Mail: [email protected]

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Max BIS

NIM >= 2.4%

>110%NIM

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NIM( )

NIMNIM

NIM = /

= [ - ] /

= [ / ] [ / ]= [ / ] - [ / ]

= - [ / ]

NIS( - ) NIM

,

,

( - )

NIM

2008 Korea Crystal Ball User Conference

(PD ), (PD,LGD,EAD)( ), ( , , )

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Performance

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Performance

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

Valuation in Life Sciences

& Portfolio Management

발표자: 이승주 차장

소속: LG생명과학 의약연구소

E-Mail: [email protected] / [email protected]

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History of LG Life Sciences

Genetic engineering department in LG Chem.

Intermax™- First biodrug in Korea

Refocus on chemical drug discovery and biopharmaceutical developmentand biopharmaceutical development

Strategic alliances for worldwide dev.

Spin off from LG Chem Investment as LG Life Sciences

FACTIVE® (gemifloxacin)(g )Korea’s first drug to be approved in FDA

VALTROPIN® (rhGH)World’s 2nd biosimilar approved in EU, FDA

•New Chemical Drug Discovery

•Biosimilar Development & Manufacturing

•New Chemical Drug Discovery

•Biosimilar Development & Manufacturing

2008 Korea Crystal Ball User Conference

•Biosimilar Development & Manufacturing

•FDA, EMEA Approval

•Biosimilar Development & Manufacturing

•FDA, EMEA Approval

Pipeline: NCE

Area Project Indication DS PCClinical

App MKTP I P II P III

Infectious

Disease

Factive Antibiotic

HBV ntRTI* Hepatitis B

MetabolicDPP4 Inhibitor Diabetes

GK A ti t Di b tDisease

GK AActivator Diabetes

N.D.** Obesity

CV DiseasePAR-1 Inhibitor Atherothrombosis

P2Y12 Inhibitor Atherothrombosis

Cell SurvivalCaspase Inhibitor HCV, NASH, IPF

Necrosis Inhibitor*** Fibrosis

*HBV ntRTI: HBV nucleotide analog reverse transcriptase inhibitor** Not Disclosed** Necrosis inhibitor of unknown target

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Pipeline: Biopharmaceutical

Area Project Indication DS PCClinical

App MKTP I P II P III

V lt i GHD TS/CRI*

Biogeneric

Valtropin GHD, TS/CRI*

BEPO Anemia

rFSH Infertility

SR-hGH Adult & Pediatric GHDSR product

SR hGH Adult && Pediatric GGHD

SR-IFN Hepatitis C

Antibody

Amevive Psoriasis

TNFmab Rheumatoid

ArthritisTNFcept

Vaccine

DTaP-HepB Combination

VaccineDTwP-HepB

Hib M i itiHib Meningitis

HA Product

HA MesoglowFacial plastic

surgery supplementHA Filler

Esthelis

*GHD: Growth Hormone Deficiency, TS: Turner’s Syndrome, CRI: Chronic Renal Insufficiency

2008 Korea Crystal Ball User Conference

Long Term Planning R&D

R&DValueMaximization g

ValuationValuation

2008 Korea Crystal Ball User Conference

Sales Forecast Licensing Forecast Cost Forecast SuccessProbability

Time Estimate

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Financial Model & Decision Analysis

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Decision Tree Analysis Risk

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Valuation Long Term Planning Expected Cash Flow

2008 Korea Crystal Ball User Conference

Risk Analysis: Innovative vs Non-innovative

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New Run Valuation Model

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New Run Valuation Model: Cash Flow Analysis

• cash flow update• : Revenue, Cost

b

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• 2 a,b• eNPV, NPV update

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Risk Analysis: Monte Carlo Analysis/What if Scenario

What if…

License out milestone ?

Peak sales ?

?

Risk

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Risk Analysis : High Risk High Return or Low Risk Low Return?

2008 Korea Crystal Ball User Conference

Datamonitor “Pharmaceutical Portfolio Management”

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Peak Sales: Quartile -> Triangular Distribution

• Quartile Peak Sales• Risk Analysis

Lower Quartile=5% Median=11% Upper Quartile 19%

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Lower Quartile 5% Median 11% Upper Quartile 19%

Risk Analysis: Sensitivity Analysis

eNPV ?

- eNPV , ?

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- , sales peak ?

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Future Plan:Portfolio Management Resource Optimization

• PJT eNPV- R&D constraint- Risk constraint

2008 Korea Crystal Ball User Conference

Portfolio ManagementResource Optimization -> Value Maximization

• PJT

• Value Maximization

,

Reference: “How SmithKline Beecham Makes Better Resource-Allocation Decisions”, HBR, 1998“Optimal Marketing”, HBR 2003

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User ConferenceUser Conference

Monte Carlo Simulation을 이용한 자동차

Door Regulator의 누적공차 최적화 구현

발표자: 윤용익 전무이사

소속: 상신브레이크

E-Mail: [email protected]

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1) System .

2) .

3) Program .) g

- Source program open .

- Program

4) Computer Simulation Program .

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2008 Korea Crystal Ball User Conference

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2. Window Regulator - 1

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2. Window Regulator - 2

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4. Monte Carlo Simulation

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• :

• : Monte Carlo Simulation

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5. Source

SourceGeometric SourceGeometric

1. ( )

2.

( [Flatness], [Roundness], [Angularity])

3. Kinematic variations ( )

2008 Korea Crystal Ball User Conference

6. Fortini's Clutch

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Vector Loop for the Clutch Assembly

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8. Sensitivities

2008 Korea Crystal Ball User Conference

1) Hub Radius Simulation

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2) Ring Radius Simulation

50.80

0.00

2008 Korea Crystal Ball User Conference

3) Roller Radius Simulation

11.43

0.00

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10. Contact Angle Simulation - 1

Trial Summary

Number of trials run (random ) : 20,000

Mean : 7.01

M di 7 02Median : 7.02

STD Deviation : 0.22

Variance : 0.05

Skewness : 0 08311Skewness : -0.08311

Kurtosis : 3.02

Minimum : 6.14

Maximum : 7 82Maximum : 7.82

Range Width : 1.69

Mean Std. Error : 0.00

Monte Carlo Random seed

Precision control on Confidence level : 95.00%

Entire range is from 6.14 to 7.82

Base case is 7.02

After 20,000 trials, the std. error of the mean is 0.00

2008 Korea Crystal Ball User Conference

After 20,000 trials, the std. error of the mean is 0.00

10. Contact Angle Simulation - 2

Contact Angleg

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Variable Name Basic Size Initial Tolerance

1/2 Hub Width a 27.645mm 0.05mm

R ll R di 11 430 0 01Roller Radius c 11.430mm 0.01mm

Ring Radius e 50.800mm 0.0125mm

Variable Correlation Coefficient Contribution (%)

a -0.90 82.9

c -0.35 12.5

e 0.21 4.5

2008 Korea Crystal Ball User Conference

12. Sensitivity

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13. Simulation

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14. Remodeling

Carrier Plate

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1. Monte Carlo Simulation

[Simulation]

.

[Door] [ ]

.

2. [Sensitivity] 1),

. [Risk] ,

Monte Carlo Simulation

6

.

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2008 Korea Crystal Ball2008 Korea Crystal Ball2008 Korea Crystal Ball 2008 Korea Crystal Ball

User ConferenceUser Conference

Crystal Ball 11.1의 향상된 도구들을 활용한

모델링 기법 소개

발표자: 박잉근 대리

소속: ㈜이레테크

E-Mail: [email protected]

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• Lognormal Location

•••• P-Value

• Crystal Ball

• OptQuest

2008 Korean Crystal Ball User Conference

Lognormal

•• Location(Thresholds: ), (Mean), (Standard Deviation)

•• , 0 ••••

••

• ,0

• ,

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Lognormal location

• Lognormal• \11,000• \15,400\15,400• , \0• , \4.000• . ,. ,

,

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• AutoSelect• Distribution fitting Ranking method• , Chi-square ranking method, Chi square ranking method

(Yes-No )• , Anderson-Darling ranking method

2008 Korean Crystal Ball User Conference

– P-value

• P-Value• , Crystal Ball 7.xx

, Crystal Ball 11.1 P-value

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Crystal Ball

• Crystal Ball

2008 Korean Crystal Ball User Conference

Crystal Ball

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Crystal Ball

• Decision Table

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Crystal Ball

• 2D Simulation

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• Data Analysis Tool• Crystal Ball

•• 4•• ,

• 4•

• Data Analysis ToolData Analysis Tool

2008 Korean Crystal Ball User Conference

• Data Analysis Tool –

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• Data Analysis Tool –

2008 Korean Crystal Ball User Conference

OptQuest

• Crystal Ball 11.1

• Crystal Ball

• Crystal Ball Control Panel

••••• : , ,

• Developer’s Kit(API)

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OptQuest

• – Six Sigma Project Selection• 8

•••

•• ,

2008 Korean Crystal Ball User Conference

OptQuest

• OptQuest

Crystal Ball Simulation

. . .

?

Yes

.

No

Y

No

Yes

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OptQuest

• OptQuest

2008 Korean Crystal Ball User Conference

OptQuest

• OptQuest

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2008 Korea Crystal Ball User ConferenceThe Better, The Faster & The Easier for Risk Analysis

일시: 2008년 10월 28일장소: 대한상공회의소주최: ㈜이레테크

How to Contact us

연락처 : 031-436-1101~9,이메일 : [email protected]홈페이지 : www crystalball co kr홈페이지 : www.crystalball.co.kr

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