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Major Projects and the (FPRA) Methodology by Using @RISK for Cost Contingency

The full impact of COVID-19 on the Australian construction sector, including major disruption to supply chains and possible difficulty sourcing materials and skilled workers and services, will depend on a number of factors, especially the time and scale availability of its vaccine. On the other side, as recovery post-COVID-19 investment on infrastructure projects over the next decade is increasing by Australian governments for economic recoveries, both public and private decision-makers depend on accurate and reliable risk assessment methodologies to give them confidence in their decisions ensuring the right projects will be delivered for the right money at the right time. There has also been much speculation regarding the outcomes for the industry and potential risk mitigation strategies as well as opportunities to make the sector more resilient against its future challenges.

The use of Quantitative Risk Analysis (QRA), including probabilistic cost and schedule risk analysis, is a good practice during both the development and delivery of major projects. Under current policy settings by the Department of Infrastructure, Transport, Regional Development and Communications (DITRDC), a probabilistic cost estimation process must be used for all projects, for which Commonwealth funding is sought, with a total anticipated outturn cost (including contingency) exceeding $25 million. Probabilistic simulation methods generate distributions of possible outcomes. The First Principles Risk Analysis (FPRA), recommended by the DITRDC Guidance Note 3A (Nov 2018) and the 2nd Edition of Risk Engineering Society (RES) Contingency Guideline for contingency determination at key decision points, e.g. Final Business Case or during execution, is a bottom-up risk-based cost contingency determination approach.
Recorded Oct 27 2020 68 mins
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Presented by
Pedram Danesh-Mand, Director Risk Consulting at KPMG
Presentation preview: Major Projects and the (FPRA) Methodology by Using @RISK for Cost Contingency
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  • 몬테카를로 시뮬레이션 개념 및 @RISK 8.0주요 기능 알아보기 Recorded: Oct 27 2020 73 mins
    이레테크 데이터랩스 민경현 부장
    Palisade사는 지난 50여년간 몬테카를로 시뮬레이션을 지원하는 분석 툴인 @RISK를 전 세계에 보급하고 교육 및 컨설팅 서비스를 제공하고 있습니다. 몬테카를로 시뮬레이션은 많은 산업 및 응용 분야에서 미래의 불확실성에 대한 리스크를 평가하기 위해 활용되는 확률론적 방법입니다. @RISK는 엑셀에 에드인(Add-in) 되어 제공되는 분석 툴로써 기존에 엑셀 기반으로 정량적 분석을 했던 기존의 수리적 모델에 @RISK를 활용하면 쉽고 편리하게 몬테카를로 시뮬레이션을 수행할 수가 있습니다. @RISK로 실행된 분석 결과들은 리스크에 대한 확률론적 정보를 제공함으로써 기존의 결정론적(점추정) 방법에서 제공되는 하나의 값으로 판단해야 하는 정보의 한계를 넘어 리스크 발생 가능성인 확률을 고려하는 의사결정에 대한 통찰력을 제시할 것입니다.
    Palisade는 몬테카를로 시뮬레이션을 활용하고자 하거나 이미 활용하고 있는 사용자들이 보다 쉽고 편리하게 분석을 수행할 수 있도록 지속적인 노력을 해 오고 있으며 이에 2020년 3월에 새로운 버전인 @RISK 8.0버전이 출시하게 되었습니다.
    이에 본 웨비나에서는 몬테카를 시뮬레이션에 대한 이해도를 높이기 위해 기본적인 개념들과 새롭게 출시한 @RISK 8.0버전에서 개선된 주요 기능에 대해서 이야기를 하고자 합니다.
    많은 분들의 관심과 참여 부탁 드립니다.

    아젠다
    순서 내용 발표자
    1 몬테카를로 시뮬레이션 이해 ㈜이레테크 데이터랩스
    민경현 부장
    2 @RISK 8.0 주요 기능 소개
    3 예제를 활용한 데모 시연
    4 Q&A
  • Major Projects and the (FPRA) Methodology by Using @RISK for Cost Contingency Recorded: Oct 27 2020 68 mins
    Pedram Danesh-Mand, Director Risk Consulting at KPMG
    The full impact of COVID-19 on the Australian construction sector, including major disruption to supply chains and possible difficulty sourcing materials and skilled workers and services, will depend on a number of factors, especially the time and scale availability of its vaccine. On the other side, as recovery post-COVID-19 investment on infrastructure projects over the next decade is increasing by Australian governments for economic recoveries, both public and private decision-makers depend on accurate and reliable risk assessment methodologies to give them confidence in their decisions ensuring the right projects will be delivered for the right money at the right time. There has also been much speculation regarding the outcomes for the industry and potential risk mitigation strategies as well as opportunities to make the sector more resilient against its future challenges.

    The use of Quantitative Risk Analysis (QRA), including probabilistic cost and schedule risk analysis, is a good practice during both the development and delivery of major projects. Under current policy settings by the Department of Infrastructure, Transport, Regional Development and Communications (DITRDC), a probabilistic cost estimation process must be used for all projects, for which Commonwealth funding is sought, with a total anticipated outturn cost (including contingency) exceeding $25 million. Probabilistic simulation methods generate distributions of possible outcomes. The First Principles Risk Analysis (FPRA), recommended by the DITRDC Guidance Note 3A (Nov 2018) and the 2nd Edition of Risk Engineering Society (RES) Contingency Guideline for contingency determination at key decision points, e.g. Final Business Case or during execution, is a bottom-up risk-based cost contingency determination approach.
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    Dr. Steve Van Drew
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    This webinar is intended for current users of the Industrial version of @RISK 8.0 who are unfamiliar with the Time Series feature, or users of the Professional version who are considering upgrading to the Industrial version.
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    Roy Nersesian, Author & Professor
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  • Energy Portfolio Analysis and Risk Management Recorded: Oct 1 2020 67 mins
    Glen Justis, Senior Partner, Experience on Demand
    Palisade is excited to offer this five-part webcast series providing an overview of advanced economic analysis using Microsoft Excel and Palisade DecisionTools Suite. Webcast content and examples will be specifically tailored to electric utilities and other power sector participants.

    The series progresses from basic to advanced technical concepts in the 1st four sessions, concluding with a final session covering management approaches for building internal analytic capabilities. Example models will be demonstrated to show how the theory is put into practice.

    Participants will gain a great understanding of the following:
    • What is Monte Carlo analysis and how is it particularly beneficial for electric utilities?
    • What types of economic analyses are worth the added complexity of Monte Carlo analysis?
    • What are best practices for building models that can make full usage of @RISK capabilities?
    • How do you explain the results and promote adoption of advanced techniques?
    • What are best practices for introducing and promoting the adoption of advanced methods such as Monte Carlo analysis?
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    • Energy risk management fundamentals and metrics
    • Importance of simulation techniques for electric utility energy risk management
    • Demonstration of mid-term energy portfolio analysis model using @RISK
    • Program implementation considerations
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    Manuel Carmona, Director Edy Training LTD
    Projecting oil prices under conditions of uncertainty has always been and will always remain a challenge. What makes this more of a challenge is the acceptance that the oil, and possibly other commodity and stock markets, do not behave in a random fashion generating normal distribution patterns.

    As we will show, a partially manipulated/partially random model for oil prices incorporating uncertainty generates U shaped distributions. These distributions are difficult to model with a single best fitting distribution, but a segmented approach to obtaining several best-fitting distributions of the prescribed ranges will be shown to be a useful substitute.

    This practical webinar will explore the subject of distribution fitting with @RISK and is based in a chapter of the book energy risk modeling by Roy Nersesian published by Palisade.
  • Modelos de Simulación para el Agro y la Ganadería Recorded: Sep 23 2020 76 mins
    José García, Consultant & Project Manager
    El cambio de escala de conocimiento mediante la transformación de datos en información útil es el aporte clave del análisis avanzado.

    La rentabilidad de la agricultura de precisión depende fuertemente del correcto modelado de la incertidumbre de sus variables. Reconocer esta realidad incierta, aprender a usar las herramientas correctas para modelar su comportamiento y medir el impacto en los resultados, son los principales objetivos de esta presentación.

    Estudios recientes en nuestra región indican que las variaciones en el rendimiento del suelo se explican en el 15% de las condiciones climáticas, lo que lleva a las variables del manejo del cultivo a un aporte del 85% de los rendimientos.

    Es por lo anterior que propongo un ciclo de seminarios donde se exponen modelos de rendimiento agrícolas y ganaderos, aplicando las herramientas de Simulación de Montecarlo, ajuste de series de datos y optimización, para obtener información justificable que contribuye a la toma de decisiones de los productores.

    En este primer seminario mostraré un modelo simple de rendimiento de cultivos (soja, maíz, trigo y avena). Demostraré cómo es posible transformar modelos basados en escenarios deterministas en modelos estocásticos, que capturan la incertidumbre de las múltiples variables de una producción agronómica.
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    This webinar will review the most common ways of applying @RISK into Life and Non-Life insurance models that are commonly used in the Insurance Industry. Functions such as RISKGENERAL and RISKCOMPOUND will be covered in order to simulate a life table and events where a combination of Frequency & Severity is required. The Distribution Fitting tool will also be discussed in depth to find suitable probability functions according to the Goodness of fit tests that are available in the software.
  • Introduction to Monte Carlo Simulation using @RISK 8 Recorded: Sep 18 2020 81 mins
    Rishi Prabhakar, Senior Consultant & Jitesh T Dave, Director SYSTECH Technocraft Services Pvt.Ltd
    Despite a highly uncertain future, both short- and long-term, governments and businesses must strive to make informed decisions. Should we invest in a new opportunity, and how much? What is an appropriate contingency for this project? What revenues could we anticipate from our core products? Which key value drivers should we focus on? Which product mix is optimal?

    Monte Carlo Simulation provides a technique that assists decision-makers with these, and many other questions, by taking the uncertainty into account in a spreadsheet model. @RISK 8 performs the simulation directly in your spreadsheet model to provide reasonable and justifiable statistical answers to your most important questions. What is the probability of failure? What is the expected value of next year’s profit? What is the P90 of the total project cost?

    In this introductory webinar, we will explore how @RISK 8 integrates with Excel, and models uncertainty with access to a wide range of distributions, time series, and correlation structures. Historical data can be used to build these, or estimation methods can be used. @RISK 8 has many reporting features to communicate your results, with both standard and customizable reports as well as user-defined templates.

    We will use @RISK’s built-in example files to highlight these features, so you can easily follow along and revisit or reproduce the results at a later date.
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  • Title: Major Projects and the (FPRA) Methodology by Using @RISK for Cost Contingency
  • Live at: Oct 27 2020 1:00 am
  • Presented by: Pedram Danesh-Mand, Director Risk Consulting at KPMG
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