Abstracts with presentations

Keynote: Managing Risk through Risk Intelligence:
Viewing Risks as Learnable Rather than Random

Presenter: David Apgar, Managing Director, Corporate Executive Board
Industry Focus: General Business

Contrary to conventional wisdom, David Apgar shows that operating, business, and strategy risks are not random but in fact learnable.  As a consequence, businesspeople must develop their risk intelligence—how fast they can learn about the risks of the projects they take up. The talk builds upon Mr. Apgar’s new book Risk Intelligence: Learning to Manage What We Don’t Know, just published in August by Harvard Business School Press.

Simply put, learnable risks must be approached entirely differently from random ones. The traditional approach of treating projects like stocks—that is, subject to random risks—and diversifying project portfolios accordingly is dangerous because it leads managers to take risks they have no natural advantage in assessing.  Unlike stock portfolios, lists of projects where managers have low risk intelligence across the board are likely to fail.

David outlines methodologies for scoring the main kinds of risk that arise in business to identify which projects are likely to yield above- or below-average returns.  He then shows how to conduct a risk strategy audit of all the projects and uses of resources they’re considering.  These audits produce a visual profile of the risk strategy—where they are taking on too many new learning challenges and where they are not experimenting enough to stay profitable and competitive.

David wraps up with the concept of risk networks, or the suppliers, customers, and other business partners who can help manage risks.  He discusses how to determine which partners in the risk network are best positioned to absorb main business risks, after which he will answer questions from the audience.

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Forecasting Capital Project Cost using Monte Carlo Simulation with @RISK for Project


Presenter: Hreinn Thormar, President, Planning & Management Services, Inc. (PMSI)
Industry Focus: Capital Projects
Product Focus: @RISK for Project

Our case study is based on the $80M Renewal and Replacement project for Shilshole Bay Marina “SBM”, and focuses on forecasting capital cost on capital projects and the additional benefits with Monte Carlo simulations and Probabilistic Outcomes for projected cost compared to traditional deterministic project controls and cost forecasting.

Construction projects are a complex entity to manage and each construction project has its own significant changes and challenges like: unforeseen conditions; errors in documents and plans; scope creep; schedule delays etc. The SBM project in particular was challenged with tight budget constraints, execution of several simultaneous construction contracts, and compounded with on-going operations and use of the Marina.

Most owners who manage capital projects have processes in place to manage changes and contingencies and forecast cost. However, when project budgets are tight on money it is not unusual that project stakeholders begin asking questions like –

  • Will we run out of money?
  • Have you considered ….?
  • What assumptions have you made in your numbers?
  • Are your numbers conservative; optimistic or even most likely?

The discussions and explanations become Complex and the Credibility can become questionable – which is every Project Managers worst nightmare!

At Port of Seattle we introduced the Monte Carlo simulation as the Solution to the Problem and we began reporting cost “forecast” based upon probabilistic outcomes. The modified approach has shown SIGNIFICANT results in the Visibility and Credibility in the Project Controls.

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Risk-Based Modeling Approaches for Determining Current Liability for Future Asset Retirement Obligations

Santee Cooper

Presenter: Larry Philbin, Principal Engineer
Industry Focus: Energy; Environmental
Product Focus: @RISK for Excel

Simulation tools are frequently employed by financial professionals to evaluate risk scenarios and confidence levels for uncertain stochastic variables in economic analyses.

In 2003 the Federal Accounting Standards Board (FASB) enacted a requirement for formally determining and booking current liabilities for future retirement of assets that involve a legal requirement (such as EPA regulated hazardous waste repository decommissioning).  These retirements involve numerous uncertainties such as timelines, labor and material costs, cost of capital, inflation rate assumptions, and market risk, and thus are good candidates for simulation-based risk assessment tools.  This presentation will demonstrate the application of @RISK for determination of financial obligation risk profiles and confidence levels for long-term decommissioning of storage facilities for electric power plant waste byproducts.  Potential adverse effects of under funding future obligations due to ignoring uncertainty will be illustrated.  The methodology would be applicable to other areas of financial obligation planning such as pension plan funding, etc.  Simulation models and results from an actual application will be shown.

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Using Bayesian Networks within an
@RISK Monte Carlo Simulation
Environment: Incorporating a Risk Factor Approach
and Qualitative Evidence for Project Risk Analysis

University of Maryland

Presenter: Javier F. Ordonez, MS, PMP
Industry Focus: Construction
Product Focus: @RISK for Excel, @RISK for Project

The goals of successful project management are to finish a project on time, within budget and according to the specifications and quality standards. In reality however, projects are commonly over budget and behind schedule because uncertainties are not accounted for in cost and schedule estimates.  Most often this happens because of events that could have been anticipated but were not considered at the planning stage.

Traditional methods project management are deterministic in nature and fail to address uncertainties and the inherent variability of the real world.  Research and practice is now addressing this problem, often by using Monte Carlo methods to simulate the effect of variances in work package costs and durations on total cost and date of completion.

However, many such project risk approaches ignore the large impact of probabilistic correlation in work package cost and duration on predictions.  This session presents a risk analysis methodology that integrates schedule and cost uncertainties while considering the effect of correlations. Current approaches deal with correlation typically by using a correlation matrix in input parameters. This is conceptually correct, but the number of correlation coefficients to be estimated grows combinatorially with the number of variables, and analysts are often forced to use unreliable “expert opinion.”

An alternative is the integration of Bayesian belief networks (BBN) within an integrated cost-schedule Monte Carlo simulation (MCS) model using risk factors. Most of the current applications incorrectly assume that costs are independent from the project schedule. The fact that schedule delays can cause project cost overruns requires a simultaneous analysis of cost and schedule risk.

BBN can be used to implicitly generate dependency among risk factors and to examine non-additive impacts.  @RISK is used to model independent events, which are propagated through the BBN to assess dependent posterior probabilities of cost and time to completion.

BBN can also include qualitative considerations and project characteristics when soft evidence is acquired.

The proposed methodology allows us to develop a risk analysis model that can respond to questions such as what is the probability of finishing a project by certain date and within a certain cost. We will also be able to anticipate work packages that are prone to be affected by project risks and allocate contingencies that will safeguard the cost and time objectives.

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Using Simulation to Promote Undergraduate Learning: Going Beyond Simple Answers and Getting Students to Analyze, Synthesize, and Evaluate

U.S Military Academy, West Point

Presenter: Major Ernest Wong, Analyst
Industry Focus: Education
Product Focus: @RISK for Excel, BestFit

To instruct undergraduates on how to build spreadsheet simulation models, Major Wong asked students to make actual investment recommendations. Their ideas, ranging from savings accounts to poker, allowed him to generate a financial portfolio through which the class could analyze its projected returns. By analyzing those results, students improved their understanding of probabilities and learned the benefits of diversification. In this session, Major Wong describes the interactive learning environment fostered by employing student-originated ideas, shows how students used @RISK and BestFit to simulate financial portfolio returns, and presents the results of a few student "what if?" analyses.

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Monte Carlo Analysis for Earthquake Mitigation:
Oil Refinery Case Study Complex

Pertamina, Indonesia State Oil & Gas Company

Presenter: Ari Pramano
Industry Focus: Oil, Gas, and Energy
Product Focus: @RISK for Excel

Being in a geologically unstable zone, Indonesia is always exposed to the risk of damaging earthquakes.  The devastating damage caused by the two latest major earthquake occurrences in the area (Aceh and Yogyakarta) have led Pertamina management to initiate mitigation efforts to reduce the risk to the company’s infrastructure, especially oil refineries.  The impact of an earthquake is quite significant on a construction-heavy refinery complex.  The session will discuss the use of Monte Carlo simulation in supporting the effort to develop an earthquake mitigation model in one of Indonesia's biggest oil refineries.

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Seasonal Plant
Optimization Project

Mettler Consulting, Inc.

Presenter: Don Mettler, Principal
Industry Focus: Manufacturing
Product Focus: Evolver, @RISK, and Developer's Kits

This case study focuses on genetic modeling to find optimal machine routings and quantities given a product forecast.  The Fortune 500 manufacturer for which the solution was developed provides corrugated packing boxes for specific California agricultural products.  The unique challenge of this project was high demand fluctuations due to the seasonality of the produce that the boxes support, and a limited production capacity in the manufacturing facilities that could not meet demands of a given produce type at peek demand, while avoiding building large amounts of inventory.

The genetic inventory optimization model solution recorded the optimal settings for each product type to a database on a nightly basis.  The model’s findings were then implemented by reviewing incoming orders and recommending order quantities - either higher to build inventory when going into season, or lower to pull from inventory when leaving the season - based on the optimized settings stored in the database. Customer services personnel reviewed the recommendations to ensure reliability.  Orders were then produced while the inventory was updated with the new quantities, which were then fed back into the optimization model for that night’s run.  Some recommendations resulted in zero production (if a particular inventory item was fully stocked), allowing for the order will be pulled from inventory, thus reducing the amount of inventory – the goal of the project.

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Neural Network Forecasting:
The Next Generation

Presenter: Barbara Tawney, Ph. D.
Industry Focus: Healthcare; Disaster Recovery
Product Focus: NeuralTools

This presentation focuses on the benefits of using Excel based forecasting during critical events such as patient overloads on an entire metropolitan area, call volume load during the critical recovery from Katrina and other 2005 Gulf hurricanes, and the forecasting of the outcome for those reported missing from the hurricanes.

Background technical information on neural network forecasting and NeuralTools as an Excel add-in will be covered fist, followed by three case studies.  Case Study 1 focuses on patient load at hospitals in an entire metropolitan area over four years. A methodology is developed for forecasting the patient load in the near term using NeuralTools applied to existing load information. The methodology of parsing the native hospital patient billing data into metropolitan hospital patient load is presented.

During the hurricane recovery in Louisiana, the methodology developed in the metropolitan area hospitals patient load and NeuralTools functionality are applied to the Find Family National Call Center (FFNCC) to assist with planning for call volume load.  Case Study 2 discusses how, on a short turnaround basis, the call center predicted call volumes - load, periodicity, trending, and forecasting - using NeuralTools.

For Case Study 3, NeuralTools’ forecasting capability is presented illustrating prediction accuracy, with an error rate of less than 2%, of the actual status (living or deceased) of over 800 persons following the Gulf Coast hurricanes. These results were verified by an independent organization.  This forecast was to help determine the possible outcome of those persons who were listed as missing, months after the hurricanes.

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Value Optimization
in a World of Choices

Triangle Economic Research

Presenter: Leigh Hostetter, Senior Project Economist, Timothy J. Havranek, MBA, PMP, Senior Business Analyst
Industry Focus: Manufacturing; Oil, Gas, Energy
Product Focus: @RISK for Excel, RISKOptimizer

Industrial companies face a multitude of choices when deciding what type and size of equipment to utilize when designing a new production facility.  Facilities are often over-designed with respect to equipment capacity due to issues such as: allowances for demand uncertainty, use of default design safety factors, manufactured incremental step sizes in equipment and materials, and a lack of design definition.  Total life cycle cost and NPV analysis are typically performed using deterministic models that do not adequately account for demand, cost (capital and O&M) and equipment life uncertainties. Capital budgeting decisions that consider such uncertainties usually do so by creating deterministic scenarios intended to represent best, most likely and worst cases.  Given the number uncertainties and their potential impact, this is simply not adequate.  Any quantitative model to facilitate the design process must be able to identify the optimum decision in light of project uncertainties and limitations.

Triangle Economic Research (TER) has designed a value optimization model to facilitate the design process that takes project uncertainties and limitations into account by combining the use of Palisade’s @RISK and RISKOptimizer tools.  TER has built a template that allows for the input of ten pieces of equipment with ten different options for each (i.e., ten types of pumps, ten types of cooling systems).  For each option, probability distributions based on expert judgment or statistical analysis of data are used to represent capital costs, installation costs, operation and maintenance costs, and equipment life. The capacity of each equipment option and a distribution representing potential product demand are also inputs to the model. A key component of this process is to have a facilitated meeting with the design team to identify all these inputs and to identify which equipment options cannot be paired with others (i.e., pump A can not function with cooling system C).  The constraints are then added using the RISKOptimizer interface.

The model has the capability to optimize facility design by desired system capacity, minimizing total cost, maximizing average net present value (NPV) of annual revenue, and maximizing average NPV of annual profit.  Discount and inflation rates are incorporated into calculations to convert future dollars into NPV. The ability to look at many types of optimization and the ability to incorporate uncertainty make this model useful in facilitating efficient design plans. This model can be applied to all types of industrial facilities including creating new and expanding existing refineries or in any type of manufacturing setting. Combining the expertise of a design team’s knowledge and the strengths of optimization techniques allows for better decision making in an industrial setting.

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Overview of @RISK 5.0 and
DecisionTools Suite 5.0

Sam McLafferty, CEO, Palisade Corporation

This major upgrade to @RISK and the DecisionTools Suite is the direct result of extensive customer input, as well as attention to the improvements with Excel 2007. Far more than a set of mere cosmetic changes, the upgrade acknowledges and meets numerous customer mandates. Sam will highlight the most prevalent of these, including requests for a more intuitive user interface that is completely integrated into the spreadsheet environment. The discussion will also address how @RISK 5.0 is designed to better meet the needs of corporate-wide usage of risk analysis tools.

PrecisionTree and TopRank are also being released in all-new 5.0 versions. Improvements to these important DecisionTools Suite components will be discussed as well. PrecisionTree 5.0 is covered more fully in its own session, “Introduction to PrecisionTree 5.0."

How to Lie with
Statistical Graphics

Successful Statistics, LLC

Presenter: Andrew Sleeper, Six Sigma expert, General Manager
Industry Focus: Manufacturing
Product Focus: @RISK for Excel, StatTools

All graphs are lies, because each graph presents a filtered, aggregated, and artfully designed version of the data, which selectively emphasizes some aspects of the data over others. A completely truthful graph is neither possible nor desirable. The whole truth is too much information for a graph to contain or for a viewer to absorb. It is more important that a graph have integrity, which is when the conclusions of a reasonable viewer are consistent with the physical system that produced the data. This presentation reviews techniques of filtering, aggregation, and design, which may enhance or obstruct integrity. Examples are chosen from scientific applications and current events.

Designing for Robust Assembly and Function -
In the Real World of Manufacturing Variation

Cummins Inc.

Presenter: Hanna Huo, Technical Advisor in Dimensional Variation Analysis (DVA)
Industry Focus: Manufacturing
Product Focus: @RISK for Excel

Cummins has developed a Dimensional Variation Analysis (DVA) program and developed a virtual team dedicated to deploying advanced processes and tools in Statistical Tolerance Analysis.  The case used Analysis Led Design approach.  The products are analyzed at early stage to improved reliability, reduce cycle time, and make product robust.  DVA makes link between design and manufacturing effectively with concurrent engineering communication.  DVA is one of the key and effective tools used in DFSS in Cummins.


Presenting Large Scale Forecast
Results in an Intuitive and Informative
way: A Tobacco Industry Case Study

Moore Stephens

Presenter: David Edison, Senior Consultant, Forecasting
Industry Focus: Manufacturing/Tobacco
Product Focus: @RISK for Excel, StatTools

THE PROJECT: To produce a generic stochastic model making automated daily, monthly and annual sales forecasts for all tobacco products across a range of European regions, allowing users to apply subjectivity at any level to the forecasts being made.

THE SYSTEM: An Excel based model using both @RISK and StatTools, deemed to be the optimal solution following a pilot study. The core model involves a regression analysis of sales volumes against a range of historic variables, followed by stochastic sampling of future values of those variables, resultant sales and error terms, as well as incorporating subjective user assumptions. The model automatically sources its 17,000 separate sets of data (product / geography combinations) from ‘cubes’ and the data warehouse, and forecast statistics are returned to cubes for ease of use. Cubes containing forecast data at every percentile of confidence hold approximately 100 million data points but are extremely quick and simple to use and interrogate.

THE RESULTS AND BENEFIT: The client has a proven core generic forecasting model which can be used for ad hoc modeling and experimentation, but which also sits at the heart of a fully automated and integrated forecasting system. The whole system runs automatically on a monthly basis, and the user has the ability to make any subjective adjustments within the system in a simple way at any product level, at any geographical level, for any time period and at any confidence level. The end results of the automated forecasts are pre-defined summary exhibits, along with Business Intelligence ‘briefing books’ - a powerful way of allowing users to have access to all forecast statistics in pre-defined but flexible views, where the click of the mouse allows the same view to be seen for a different dataset, or broken down or analyzed in a different way, instantaneously. No more need for a hundred spreadsheets, and no more bulging ring binders!

Cash Flow @RISK Modeling for Industrial Companies with Time Series Inputs

McKinsey and Company

Presenter: Martin Pergler, Manager, Corporate Risk Special Initiative
Industry Focus: General Business
Product Focus: @RISK for Excel

To make better strategic decisions, companies need a holistic understanding of the impact of key risks they face. This session describes some of the modeling techniques often useful in this regard, including building top-down cash flow @RISK models using Monte Carlo simulation techniques, and incorporating mean-reverting stochastic time series for key commodity and other time-varying inputs. The emphasis is on 80/20 approaches which give useful input to major strategic decisions rather than risk reporting/compliance or trading.

Making ¢ents From Baseball:
An Econometric Approach

Chaos Group, Inc.

Presenter: Clay Graham, CEO
Industry Focus: Sports; Gaming
Product Focus: @RISK, BestFit, RiskOptimizer and StatTools

Ever since the ancient Greeks first flipped a Drachma, gaming and probabilities have gone hand-in-hand.  From the endless examples of dice throwing, coin tossing, and card dealing in statistics classes, to the innovative approach to card counting by Edward Thorp (Beat the Dealer….) there has always been a desire to “beat the house.” This has been a most elusive goal at best.  Sport gaming has proliferated in Las Vegas, over the internet and even in the hallowed halls of Wall Street.

Baseball is unique in its finite nature of options when a batter faces a pitcher.  There is no sports endeavor on earth that quantifies performance more than baseball.  However, utilizing such information for gaming purposes has generally been relegated to the “tipster” or “insiders”.  

Taking a scientific approach to baseball and gaming has traditionally yielded less than spectacular results . . . until now. Runs are the currency of baseball.  Linking what causes runs production is at the foundation of this economically based research.  The challenge of building an effective baseball production function (in the spirit of Cobb-Douglass and Robert Solow) has been answered.  The presentation will cover:

  • Analyzing the cause and effect of run production,
  • Building a detailed stochastic forecasting model of a baseball game,
  • Integrating “lines and odds” into the economics of Expected Values for Return on Investment,
  • Creating a wagering decision algorithm,
  • Incorporating a control system. 

Definition of terms, methods of formulation, examples, and results will all be graphically presented.
Picking a winner is not the same thing as making a smart bet.  Building probabilities of winning and run production are brought together in a pragmatic manner (making smart bets) by utilizing the family of Palisade products.


Presenter: Thompson Terry, Consultant, Palisade Corporation

This course will provide hands-on training to cover the basic elements of @RISK.  Attendees will learn how to create, run, and interpret basic risk analysis models using Monte Carlo simulation.  The content is suitable for @RISK beginners and those with experience who may require some reminders.  Some experience building basic models in Excel is needed.  The session will cover:

  1. Introduction to risk analysis and @RISK
    1. What is risk analysis?
    2. Example model: basic business plan
    3. Interpretation of output
  2. Further aspects of risk analysis using @RISK

Getting More
out of @RISK

Thompson Terry, Consultant, Palisade Corporation

This presentation takes @RISK beyond the level of the introductory course and adds several powerful tools and concepts. The Alternate Parameter function is analyzed closely for its usefulness as well as subtleties to be mindful of. Decision variables and policy choices are analyzed using RiskSimtable, and then with RISKOptimizer for even better results. The Advanced Options such as Goal Seek and Stress Testing will be explored as powerful risk analysis tools.  

These functions and more (such as creating standardized reports on demand) will be enhanced by introducing VBA for Excel into the working of the models. Learn how to smooth and simplify processes by utilising powerful, yet user-friendly code. Reduce tedious manual work to nothing more than the click of a button!

Getting More out of
the DecisionTools Suite

Thompson Terry, Consultant, Palisade Corporation

This presentation will show you how to integrate the elements of the DecisionTools Suite into a complete risk program. PrecisionTree will help identify the model and decision analysis that needs to be done, as well as provide an efficient and effective way of presenting the results. We will use TopRank to set us on the right path to creating a probabilistic model out of a deterministic one. @RISK (including BestFit and RISKview) adds the uncertainty to the model and runs the Monte Carlo simulations required for effective risk analysis. RISKOptimizer produces the values for decision variables in your model optimize your required outcome.

Real Options Modeling using
@RISK and PrecisionTree

Dr. Michael Rees, Senior Consultant, Palisade Corporation

In this session, the core concepts behind real options will be introduced, including the value of flexibility in decisions taken under uncertainty. A range of real options models will be shown using @RISK, and then followed by a demonstration of how PrecisionTree may be used for valuing certain real options situations, such as the value associated with structuring a project into phases.

Prediction and Data Analysis
with NeuralTools and StatTools

Barbara Tawney, Ph. D.

Neural networks predict unknown information by finding patterns in data, and the technology behind NeuralTools is inspired by the functioning of the brain. This session will show you how to solve some specific problems to demonstrate both what neural nets can do, and how easy it is to apply this advanced technology using Palisade’s NeuralTools. Problems in banking, biology, medicine or other fields will be solved, time permitting.  Palisade’s StatTools software will also be covered, so you can learn how to use regression and other statistical analyses to enhance your overall model.

Introduction to
@RISK for Project

Thompson Terry, Consultant, Palisade Corporation

The aim of this session is to give people a basic understanding of how to perform quantitative project risk analysis using @RISK for Project.  You will learn how @RISK for Microsoft Project works and gain hands-on experience for setting up and running simulations, and interpreting the results.

Attendees will learn about the key functionality within @RISK for Project in step-by-step method, enabling them to quickly become familiar with basic concepts and terminology.

In addition to graphing and quantifying the risk in a business plan, you will learn how @RISK for Project, using Monte Carlo simulation, enables you to:

  • Calculate the probability of success
  • Graph the margin of error around the most likely outcome
  • Quantify and prioritize the risk drivers
  • Quantify the amount ‘@RISK’

@RISK and
Oil & Gas Projects

Dr. Michael Rees, Senior Consultant, Palisade Corporation

The course is designed to introduce attendees to the concepts and methods necessary to develop a risk assessment and to make a defensible decision under uncertainty.  Attendees will discover how to translate their deterministic Excel analysis into an @RISK model that can be used to quantify exposure and test mitigation strategies. Examples will be presented which will demonstrate how to effectively use the software and to interpret the results.

Examples used include probabilistic reserves estimation both for individual fields and for corporations, analysis of exploration, of discovery and of drilling situations, production forecasting, pricing models, cash flow analysis, and optimal facility sizing.

Selecting the
Right Distribution

Dr. Michael Rees, Senior Consultant, Palisade Corporation

How often have you looked at the palette of distributions in @RISK and related tools and wondered which one you should use? A crucial aspect of risk modeling is the selection of the appropriate distribution to use to represent key uncertain variables. @RISK offers a wealth of probability distributions – some are very intuitive like the Uniform and Triangular, others are somewhat familiar to anyone with a scientific, engineering or finance background, like the Normal and Lognormal. However, many of the other distributions offered in @RISK gives us access to sophisticated probability thinking that can greatly extend and simplify your risk models.

This session will explain, in simple terms and illustrated with example models, the thinking behind the most powerful distributions, what they model, and how they can be put to use in your risk analyses.

Introduction to
PrecisionTree 5.0

Erik Westwig, Software Engineer, Palisade Corporation

This presentation combines an introduction of the enhanced user interface, tighter Excel integration, and new features of PrecisionTree 5.0, with demonstrations of how PrecisionTree can be used to analyze various problems in decision analysis. A complementary beta version of PrecisionTree 5.0 will be available for all attendees.

Meet Palisade

Sam McLafferty, Erik Westwig, Dr. Michael Rees

Sam McLafferty is not only Palisade’s founder, president, and CEO, but the head of software development. Sam wrote the original @RISK program, and is actively involved with engineering behind all Palisade software. Erik Westwig is a senior software engineer whose work includes virtually all of PrecisionTree and much of @RISK.  Mike Rees gives consulting and training classes on most Palisade software and has an extensive business consulting and modeling background. All three have seen @RISK and other tools applied in dozens of different industries. Join them for this exciting roundtable discussion, where you’ll be able to provide feedback about Palisade tools and seek advice for your particular modeling issues. So bring your spreadsheet models and your software wish list while you get to know the people behind @RISK, the DecisionTools Suite, and more.

Expert Session and
Licensing Consultation

Thompson Terry, Erik Westwig, Javier Ordonez

Get individual advice for your particular modeling issues from industry experts and Palisade consultants. Palisade sales staff will also be available to help meet your software licensing needs. So bring your spreadsheet files, deployment issues, and pricing questions as you get to know your account representatives and consulting staff.

Como Seleccionar la
Distribución Adecuada?


Presentadore: Javier F. Ordonez, Consultor, Palisade Corporation
Producto: @RISK para Excel

Cuántas veces se ha preguntado que distribución debería utilizar para sus modelos entre las 38 disponibles en @RISK? Un aspecto crucial en la creación de modelos para análisis de riesgo es la selección de las distribuciones de probabilidad apropiadas para representar las variables más representativas que son afectadas por incertidumbre.

@RISK ofrece una generosa selección de distribuciones de probabilidad; algunas muy intuitivas come la Uniforme y Triangular; otras que son conocidas para alguien con experiencia en ingeniería, finanzas o aplicaciones científicas, como la Normal y la Log-Normal. A pesar de aquello, otras distribuciones ofrecidas en @RISK nos dan la oportunidad de optar de un razonamiento probabilístico más sofisticado que puede mejorar y simplificar sus modelos de riesgo.

Esta sesión pretende explicar en términos simples y con presentación de ejemplos, el razonamiento y la utilización de distribuciones de probabilidad mucho más flexibles y de gran beneficio para el usuario; se explorarán sus aplicaciones y usos, y como pueden ser utilizadas en sus análisis de riesgo.

Modelo de Análisis de Riesgos
Crediticios para Banca Comercial


Presentadore: Fernando J. Hernández, MBA
Industria: Banca comercial
Producto:@RISK para Excel, Risk Developers Kit (RDK)

En un banco comercial centroamericano enfocado en el sector corporativo, se desarrolló una aplicación informática multi-usuario multi-moneda para el análisis de riesgos de cada facilidad crediticia. Basado en una plantilla Excel sofisticada, cada uno de los usuarios – analistas de crédito y administradores – introducen información histórica de estados financieros y parámetros de proyección futura para cada expediente de solicitud de crédito. Bajo un interfaz en donde queda oculta la plantilla Excel subyacente, el analista proyecta estados financieros, flujos de efectivo, razones financieras y comparaciones industriales, creando en el proceso, una base de datos SQL server que almacena y administra integralmente una base de datos unificada de expedientes de clientes.

La aplicación está desarrollada en Visual Studio .Net y posee un módulo de análisis de riesgos de sensibilización de flujos de caja y estados financieros proforma utilizando el Risk Developers Kit (RDK) de Palisade. El analista puede modificar los parámetros de pronóstico, definir e insertar distribuciones de frecuencia estocásticas en su modelo, ejecutar simulaciones Monte Carlo, realizar todo tipo de análisis y reportes de sensibilidad a riesgos (particularmente cambiarios y de crecimiento operativo) y visualizar gráficos y reportes bajo el interfaz de uso amigable.

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Evaluación de Portfolios
Agrícolas con @RISK

Universidad de Buenos Aires

Presentadore: Ariadna Berger, Profesora, Facultad de Agronomíca, Universidad de Buenos Aires
Industria: Agricultura
Producto: @RISK for Excel

Las principales fuentes de riesgo en la producción agrícola son los precios y los rindes de los cultivos, que están sujetos a la variabilidad climática. Ante la necesidad de reducir el riesgo, la diversificación de actividades es una de las herramientas que se adoptan con mayor frecuencia. Una estrategia muy eficiente para la reducción del riesgo en el sector agrícola es la diversificación espacial o geográfica. Los fondos de inversión y los pooles de siembra son ejemplos de diversificación espacial. Son empresas que siembran en varias zonas, en lo posible alejadas para que la correlación de rindes sea lo más baja posible, con lo cual reducen el riesgo productivo.

Con el objetivo de cuantificar y administrar el riesgo de estos portfolios, se han desarrollado programas usando Excel y @RISK. Estos programas son planillas de cálculo lo suficientemente complejas como para reproducir en detalle las particularidades del negocio agrícola local.

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Utilización de @RISK para simular
EaR – Earning at Risk y CFaR –
Cash Flow at Risk para Interconexión Eléctrica

Grupo Empresarial ISA

Presentadore: Diego Fernando Arboleda Jaramillo y Marcos Alexis Hincapie Cifuentes
Industria: Fabricación
Producto: @RISK for Excel

Para la modelación, análisis y sensibilidad de las variables más relevantes del mapa de riesgos corporativos de la empresa sobre los estados financieros – Flujo de Caja y Estado de Resultados y su posterior utilización para la toma de decisiones de tipo financiero, se deben realizar tres etapas:

1er Etapa:
Para la generación de las variables macroeconómicas y de riesgos corporativos, se utiliza técnicas de simulación y generación de variables Aleatorias Empíricas; las cuales a su vez requieren de números aleatorios correlacionados obtenidos mediante la técnica matemática conocida como Descomposición de Cholesky, Esta modelación genera la cantidad requerida de datos para ser llevados al aplicativo de BestFit y de este modo ajustar los datos a una distribución de probabilidad específica que arroje los parámetros necesarios para ingresar al modelo financiero como funciones de @RISK.

2da Etapa:
Los anteriores parámetros representan los inputs necesarios para realizar las posteriores simulaciones haciendo uso de @RISK dentro del plan financiero - Estado de Resultado y Flujo de Caja. Para subsanar el faltante de caja en uno de los anteriores estados, se ejecuta una macro desarrollada en VisualBasic for Applications de Excel que activa la aleatoriedad de las funciones de @RISK.

Después de seleccionar las variables de entrada y salida y aplicar los Simulation Settings (número de iteraciones, simulaciones, tipo de muestreo y el macro descrito anteriomente), se corre la simulación para el flujo de caja y el estado de resultados las n-veces seleccionadas.

3ra Estapa:
La tercera etapa consiste en la modelación, obtención e interpretación de los resultados arrojados producto de la simulación, durante esta etapa en la opción estadística de los resultados se revisan las probabilidades acumuladas a la derecha que se presentan para cada uno de los intervalos conformados por las desviaciones estándar de la nueva distribución formada por las variables de salida en estudio y se calcula la Utilidad en Riesgo y Flujo de caja en riesgo para cada una de las empresas del grupo ISA.

Para el análisis de sensibilidad, la empresa utiliza además de la ventana de resultados entragada por @RISK, el aplicativo de TopRank para realizar interpretaciones basadas en el análisis de tornado, determina las variables críticas y cambia cada una de ellas para realizar un If-analysis.

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Aplicación de Redes Bayesian y Simulación
Monte Carlo para el Análisis de Riesgo en
Proyectos de Infraestructura

Universidad de Maryland, USA, Programa de Gerencia de Proyectos

Presentador: Javier F. Ordonez, MS, PMP, Candidato a Ph.D.
Industria: Construcción
Producto: @RISK for Excel, @RISK for Project

Los objetivos principales de una gestión exitosa de proyectos son ejecutarlos dentro de los plazos establecidos y de los presupuestos aprobados de acuerdo las especificaciones de diseño y requerimientos de calidad; desafortunadamente en la mayoría de casos, cuando dichos proyectos son concluidos cuestan más de lo presupuestado y duran más de lo que se anticipó. Esto se debe principalmente a que incertidumbres y/o riesgos que afectan un proyecto y que pudieron ser analizados en la etapa de planeación no fueron considerados en la estimación de costos y cronogramas de programación.

La gran mayoría de métodos para analizar riesgos en proyectos ignoran el alto impacto de la correlación probabilística en la predicción del costo y duración total de un proyecto. Esta sesión presenta una metodología para el análisis de riesgo de proyectos que integra incertidumbres en el cronograma de ejecución y costos incluyendo el efecto de correlación  a través de la integración de Redes Bayesian en un modelo de simulación Monte Carlo usando factores de riesgo. Las Redes Bayesian pueden también incluir en el análisis aspectos cualitativos y de características del proyecto cuando evidencia relacionada con el mismo es adquirida. La metodología propuesta permite responder a preguntas como, cual es la probabilidad de terminar un proyecto para cierta fecha y dentro de determinado costo.

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Introducción a @RISK /
Refrescamiento del @RISK

Presentator: Fernando Hernández, InfoMasters

Este curso proveerá entrenamiento práctico para cubrir los elementos básicos del @RISK. Los participantes aprenderán cómo crear, ejecutar e interpretar modelos básicos de análisis de riesgos utilizando simulación Monte Carlo. El contenido es idóneo para usuarios principiantes de @RISK y para aquellos con experiencia que requieran recordar algunas cosas.  Se requiere cierta experiencia básica en la construcción de modelos en Excel. La sesión cubrirá:

  • Introducción al análisis de riesgos y el @RISK
    • ¿Qué es el análisis de riesgos?
    • Ejemplo modelo: plan básico de negocios
    • Interpretación de variables de salida
  • Aspectos adicionales de riesgo utilizando el @RISK

Obteniendo lo Máximo
de DecisionTools Suite

Presentator: Fernando Hernández, InfoMasters

Esta presentación le mostrará cómo integrar los elementos de la Suite de DecisionTools dentro de un programa completo de riesgos. El PrecisionTree le ayudará a identificar el modelo y el tipo de análisis de decisiones que debe ser llevado a cabo, así como también proveerá de una forma eficiente y efectiva de presentación de resultados. Utilizaremos el TopRank para definir la ruta adecuada en la creación de un modelo probabilístico partiendo desde un modelo determinístico. El @RISK (que incluye al BestFit y al RISKView) añade incertidumbre al modelo y ejecuta la simulación requerida para realizar un análisis de riesgos efectivo. El RISKOptimizer produce los valores de las variables de decisión en su modelo que optimizar en resultado requerido.

Predicción y Análisis de Datos
con NeuralTools y StatTools

Presentator: Fernando Hernández, InfoMasters

Las redes neuronales predicen información desconocida por medio de la búsqueda de patrones en los datos, y la tecnología subyacente a NeuralTools está inspirada en el funcionamiento del cerebro. Esta sesión le enseñará cómo resolver algunos problemas específicos para demostrar tanto lo que una red neuronal puede realizar, como qué tan fácil es aplicar esta sofisticada tecnología utilizando el NeuralTools de Palisade. Si el tiempo lo permite, se resolverán algunos problemas de banca, biología, medicina y de otros campos. El software de Palisade StatTools también será cubierto, de forma tal que usted pueda aprender cómo utilizar un análisis de regresión u otros análisis estadísticos para mejorar su modelo integral.

Administración integral de riesgos
para el sector energético


Presentador: Salvador Huete, Gerente de Consultoría estratégica y económica
Presentador: Arturo Gayoso, Gerente de Consultoría estratégica y económica

Enfoque de industria: Oil & Gas
Soluziona ha desarrollado una metodología basada en software para dinamizar e implementar la función y gestión de análisis de riesgos en grandes corporaciones integrando los siguientes elementos:

  • Gestión estratégica y control de las incertidumbres en todos los niveles de la organización: junta directiva, directores ejecutivos, gerentes, asistentes, etc.
  • El proceso para identificar y definir el modelo de riesgo para cada negocio, enfocándose en aspectos que podrían ser oportunidades o amenazas potenciales.
  • El sistema de información que permita la evaluación del riesgo en el contexto de cada industria, utilizando técnicas estadísticas para la medición del riesgo.

La aplicación de análisis y gestión de riesgos desarrollada por Soluziona integra el kit de desarrollador de @RISK como el núcleo de la simulación Monte Carlo, y provee de los siguientes beneficios a grandes corporaciones:

  • La habilidad para analizar la exposición al riesgo para una determinada cartera de inversiones compuesta de activos nacionales e internacionales, permitiendo así la facilidad en las decisiones de riesgo versus rendimiento.
  • El apoyo al proceso de toma de decisiones relacionado a las nuevas oportunidades de inversión y la facilitación del análisis del perfil de riesgo.
  • La facilitación de la gestión de la cartera por medio de: a) el seguimiento de indicadores operacionales y financieros y la probabilidad de alcanzar las metas propuestas y b) el análisis teórico de un portafolio ideal basado en el model de la frontera eficiente.

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