El VAN y el punto muerto financiero de un proyecto de inversión con una ecuación de demanda PQ=const en función de la tasa de descuento
por Domingo A. Tariza, Universidad Austral - CONICET, XXXI Jornadas Nacionales de Administración Financiera, Septiembre 2011
Se considera un proyecto de inversión simple, con incertidumbre. El autor usa @RISK para analizar las variables aleatorias.

Novel offshore vertical axis wind turbines
by David Parsons and Julia Chatterton, Carbon Brainpoint Case Study, July 2011
This case study used @RISK to considered the potential reduction in greenhouse gases (GHGs) that could be achieved through the installation of NOVA wind turbines, in comparison to conventional horizontal axis wind turbines (HAWTs) for offshore power generation.

Sustainability at UPS 2010
Sustainability at UPS 2010, July 2011
With over 400,000 employees and 1.1 million shipments per day, UPS is one of the world's largest shipping companies. UPS regularly monitors its environmental impact by measuring its greenhouse gas (GHG) emissions. Because there is significant uncertainty around GHG monitoring, UPS turned to @RISK to most accurately estimate these amounts. See Appendix B-84 in the report.

Unknown Unknowns in Project Probabilistic Cost and Schedule Risk Models
by Yuri Raydugin, Risk Services & Solutions Inc., February 2011
Practitioners recognise a requirement to consider unknown unknowns in project risk management. Same time, clear and consistent recommendations on incorporating of unknown unknowns into risk models have yet to be proposed. This article outlines thinking process and comes up with practical recipes on handling unknown unknowns.

Incorporating Uncertainty into Dam Safety Risk Assessment
by Sanjay S. Chauhan and David S. Bowles of Utah State University, 2011
Risk assessment is becoming more widely used to supplement traditional approaches to dam safety decision-making. Dam owners throughout Australia, the U.S. Army Corps of Engineers, and the U.S. Bureau of Reclamation are using risk assessment as a decision support tool.

Monte Carlo Simulation-Based Supply Chain Disruption Management for War Games
by Shilan Jin, Jun Zhuang, and Zigeng Liu, Proceedings of the 2010 Winter Simulation Conference, December, 2010
Monte Carlo simulation experiments are used to evaluate different strategies for coping with disruptions in supply chains in war zones such as Iraq and Afghanistan.

Cancer Risks After Radiation Exposure in Middle Age
by Igor Shuryak, Rainer K. Sachs and David J. Brenner, Journal of the National Cancer Institute, Vol 102, Issue 21, November 3, 2010
Monte Carlo simulation is used to evaluate the risk of cancer after radiation exposure in middle age. The study indicates that radiation-induced cancer risks after exposure in middle age may be up to twice as high as previously estimated.

Using Simulation to Support The Reinsurance Decision of a Medical Stop-Loss Provider
by Lina S. Chan, FSA, MAAA, FCA Managing Partner, CP Risk Solutions, LLC, and Domingo Castelo Joaquin, Illinois State University, from the journal "Insurance Markets and Companies: Analyses and Actuarial Computations" Volume 1, Issue 2, 2010
This article illustrates how simulation modeling can be employed to support the reinsurance decision of a medical insurer. We do this in the context of a simplified but realistic example, where a medical insurer is evaluating a request for proposal to provide stop-loss coverage for a trust, which provides comprehensive medical coverage to employees of a major conglomerate. Simulation is employed to evaluate alternative reinsurance options for the stop-loss provider. We incorporate uncertainty about the true loss distribution through the use of alternative distributions to model total claims.

Schedule Risk and Contingency using @RISK and Probabilistic Analysis
by Ian Wallace, Palisade Corporation, August 2010
Project overruns and failures are all too commonplace. As many of the underlying problems are difficult to address in the short term, it is clear that project managers need some help in obtaining sufficient schedule and cost contingency to avoid overruns. This paper suggests ways of using probabilistic analysis and Monte Carlo simulation so that managers can visualize and quantify the uncertainty in their projects and make thought-provoking predictions of the likelihood of being on-time and on-budget. With this new information, it will be possible to make more informed decisions about target dates, pricing, budgeting and risk management, as well as manage customer and stakeholder expectations more effectively.

Monte Carlo simulation and remote sensing applied to agricultural survey sampling strategy in Taita Hills, Kenya
Eduardo Eiji Maeda, Petri Pellikka, and Barnaby J. F. Clark African Journal of Agricultural Research Vol 5(13), July 4, 2010
Monte Carlo simulation was integrated with Geographical Information Systems (GIS) to create an optimal population sampling strategy. This reduces the cost of field work and improves efficiency. The approach reduced uncertainty in the estimation of crop areas and helped manage errors.

Introduction to Engineering Reliability
by Robert C. Patev, US Army Corps of Engineers, July 2010
In this analysis of reliability in engineering applications, Monte Carlo simulation is used to help determine the probability that a system will perform its intended function.

Estimation of Uncertainty Distributions for Internal Flood Initiators Using Parametric Sensitivity Study
by Robert J. Wolfgang, Consultant, ERIN Engineering and Research, Inc., June 2010
To help understand the effect on the uncertainty associated with the initiating frequency for an internal flood scenario based on contributions from various water systems, a parametric study was performed in which each of the system piping failure rates was fitted to a cumulative distribution. A parametric analysis was performed using @RISK.

Stress Testing State Budgets under
Alternative Business Cycle Scenarios
Appeared in State Tax Notes Vol. 56 (May 3, 2010) written by Ray Nelson, Associate Professor of Finance, Marriott School of Management, Brigham Young University
In this study, @RISK is used to facilitate stress testing, through Monte Carlo simulations of revenues and expenditures.

Quantitative Chemical Exposure Assessment
for Water Recycling Schemes
by Stuart J. Khan, Waterlines Report Series No. 27, March 2010, Australian Government National Water Commission
This study looks at the risks (such as chemical exposure and system reliability) associated with water recycling plans in Australia. @RISK is used for quantitative assessment.

Cost of Universal Influenza Vaccination of Children in Pediatric Practices
By Byung-Kwang Yoo, MD, PhD, Peter G. Szilagyi, MD, MPH, Stanley J. Schaffer, MD, MS, et al., published in Pediatrics 2009
The goals were to estimate nationally representative pediatric practices’ costs of providing influenza vaccination during the 2006 –2007 season and to simulate the costs pediatric practices might incur when implementing universal influenza vaccination for US children aged 6 months to 18 years.

Prediction of Aftershocks Distribution Using Artificial Neural Networks and Its Application on the May 12, 2008 Sichuan Earthquake
by R. Madahizadeh and M. Allamehzadeh, Journal of Sustainable Energy and Environment, Fall 2009, Vol. II, No. 3
Using data from initial earthquake aftershocks, neural networks are used to predict the concentration and trend of more aftershocks from the May 12, 2008 earthquake in Sichuan, China.

The Business Case for a Risk Executive
by KPMG, October 2009
In this paper, KPMG emphasizes the need for comprehensive, strategic risk management across an organization. The report notes that most current risk management efforts are specific to particular departments, projects, or regulations, and do not approach risk from an enterprise level. This had led to critical oversights and missed opportunities. To address this gap, KMPG recommends the appointment of a risk executive dedicated to helping "prepare the organization to respond to change and the risks that emerge in changing times.

Emerging Risks: Strategic Decision Making
in the Face of Uncertainty
by the Financial Times and Oliver Wyman management consultants, August 2009
The research in this paper was based on a comprehensive Fortune 1000 executive survey and focused on new risks facing global organizations, the expected impacts on business and the methods used to identify and assess risks. The survey made clear that not nearly enough firms are implementing critical quantitative practices such as probabilistic analysis, simulation, scenario analysis, and decision trees.

Potential of Using New Technology for Estimating Body Condition Scores
By Jeffrey M. Bewley, Department of Animal Science and Food Sciences, University of Kentucky and Michael M. Schutz, Department of Animal Sciences, Purdue University, April 2009
The economic feasibility of investment in an automated body conditioning system (BCS) was explored using a dynamic, stochastic simulation dairy model designed to examine investments in dairy intervention technologies. The model was created in Microsoft Excel using the @RISK add-in to consider the stochastic nature of key variables with Monte Carlo simulation.

The Joint Confidence Level Paradox at NASA:
A History of Denial
by Glenn Butts and Ken Linton at the 2009 NASA Cost Symposium, April 2009
This paper is intended to provide a reliable methodology for estimating construction and R&D costs at NASA. It consists of a collection of cost-related engineering detail and project fulfillment information from early agency days to the present. The authors use @RISK, Monte Carlo simulation, and distribution fitting in their cost analyses.

Examination of Pig Farm Technology by Computer Simulation
by Szilvia Szoke– Lajos Nagy– Sándor Kovács– Péter Balogh of University of Debrecen, 4th Aspects and Visions of Applied Economics and Informatics, March 2009
Agricultural production is among the riskiest production activities. This computer simulation was performed using @RISK. The study concentrates on the factors affecting the number of offspring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model.

Drug Development: Valuing the Pipeline – a UK study
Mayer Brown Pharma & Biotech, March 2009
Drug discovery, research and development follows a sequence of distinct stages, each of which aims to generate “economically valuable specific knowledge” about the drug candidate in question. In this way, the implementation of a drug development project generates intellectual assets capable of transfer or licensing. Determining the NPV of these intellectual assets is accomplished through Monte Carlo simulation and decision tree modeling.

Coastal Inundation at Narrabeen Lagoon:
Optimising Adaptation Investment
Prepared by AECOM for the Australia Government Department of Climate Change
This study uses @RISK and RISKOptimizer to examine climate change impacts on coastal flooding in the Pittwater area of Sydney, Australia.

Put More Science into Quantitative Risk Analyses
by John Zhao, Quality and Risk Manager, Statoil ASA
Zhao argues that risk practitioners are responsible for putting more science into risk analyses to foster and bolster the credibility of risk quantification.

Optimizing Global Clinical Trial Investments
by Todd D. Clark – President, Value of Insight Consulting, Inc.
Advanced Risk Modeling for Patient Enrollment Forecasting in 23 Country Phase III Trial Saves Millions for Pharmaceutical Company.

A Case for Simulation
Steven Bonacorsi
For better risk analysis and decision-making, companies that practice Lean Six Sigma should consider process modeling and simulation techniques. In this paper, Steven Bonacorsi of Bonacorsi Consulting LLC outlines the benefits of process modeling and simulation, gives examples, and provides a list of tools including Palisade's @RISK.

Decision Analysis for Medical Applications
by A. Indrayan, adapted from Medical Biostatistics, 2nd Ed, 2008
Decision trees play and important role in the effective diagnosis and treatment of medical conditions. With decision trees, various possibilities can be visualized, and appropriate action taken. Decision trees take into account the probabilities of various outcomes, the value of different actions taken, and the utility assigned to different possible outcomes.

Climbing Out of the Credit Crunch
by the Association of Chartered Certified Accountants, September 2008
Failure to appreciate risks, lack of influence of risk management departments, and weaknesses in risk reporting are key factors which led to the credit crunch.

Simulating the Financial Consequences of the Subprime Mortgage Crisis
by Roy Nersesian, September 22, 2008
Roy Nersesian describes how the selling of mortgages as investments (collateralized mortgage obligations, or CMOs), coupled with lax governmental regulation and the greed of house flippers fueled the flames of the home buying and building frenzy. He uses an @RISK simulation to show exactly how and where the investors lost in the ensuing housing market meltdown.

Risk Analysis and Contingency
Determination Using Range Estimating
by Dr. Kenneth K. Humphreys, PE CCE; AACE International Recommended Practice No. 41R-08, June 25, 2008
This Recommended Practice (RP) of AACE International describes the process known as range estimating, a methodology to determine the probability of a cost overrun (or profit underrun) for any level of estimate and determine the required contingency needed in the estimate to achieve any desired level of confidence. The process uses range estimating and Monte Carlo analysis techniques. The RP provides the necessary guidelines for properly applying range estimating and Monte Carlo analysis to determine probabilities and contingency in a reliable manner using risk analysis software packages.

The Use of Video Tutorials in a Mathematical Modeling Course
by Aimee J. Ellington and Jill R. Hardin, redOrbit, 28 May 2008
In Mathematical Modeling, a semester-long course at Virginia Commonwealth University, students are exposed to the role that technology plays in modern applications of mathematical models. PrecisionTree and @RISK, which "represent the state of the art in solution methodology," are used in the course. This paper describes the modeling course, the video tutorials, and an assessment of the usefulness of the tutorials.

» View the tutorials designed for the course

Consideration of Uncertainty Factors in Search for High Risk Events of Power Systems Caused by Natural Disasters
by Tetsushi Miki, WSEAS Transactions on Power Systems, Issue 3, Vol 3, March 2008
Power systems are large and complex, and vulnerable to high impact risks. Monte Carlo simulation can be used to analyze risk events from natural disasters.

Monte Carlo method for determining earthquake recurrence parameters from short paleoseismic catalogs: Example calculations for California
by Tom Parsons, USGS Open File Report 2007-1437C, 2008
Monte Carlo simulation has been used to determine earthquake recurrence in California, even in the absence of large amounts of data.

Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses
By David T. Hulett, Keith Hornbacher, and Waylon T. Whitehead; Hulett Associates, LLC, 2008
This presentation outlines limitations of the traditional three-point estimate approach to quantitative risk analysis.
The authors introduce the Risk Driver method to cost risk analysis in projects. The approach uses @RISK, and a
sample spreadsheet model with risk registers is included so you can try it yourself. A simple refinery construction
example is included.

Download sample spreadsheet model

Information Analysis Using PrecisionTree
By Kan Shao, Mitchell J. Small, Department of Civil and Environmental Engineering, Carnegie Mellon University, March 2007
This paper uses PrecisionTree as an example to illustrate the application of commercial software in information analysis for cancer risk assessment.

Factor of Safety and Probability of Failure with @RISK
Dr. Evert Hoek, from Chapter 8 of his book Practical Rock Engineering, 2007
This paper discusses how to use Monte Carlo simulation with @RISK to determine the probability of failure in engineering designs.

Actuaries Excel: But What About Their Software?
Louise Pryor et al, General Insurance Convention, 26 September 2006
Independent survey of actuaries across North America and Europe revealed that @RISK is the third most widely used software by actuaries, after Microsoft Office and in-house reserves software.

@RISK and BestFit Models Provide Insight on
Biological Control Impact on Monarch Butterflies.
R. L. Koch et al pub: Biological Invasion, 2006
A quantitative risk assessment Study from the University of Minnesota.

Practical Calculation of Expected and Unexpected
Losses in Operational Risk by Simulation Methods
Enrique Navarrete, Scalar Consulting, 2006
This paper surveys the main difficulties involved with the quantitative measurement of operational risk and proposes simulation methods as a practical solution for obtaining the aggregate loss distribution. An example that calculates both expected and unexpected losses as well as operational risk VAR is provided.

Modeling Financial Scenarios: A Framework for the Actuarial Profession
By Kevin C. Ahlgrim, Illinois State University, Stephen P. D’Arcy, University of Illinois at Urbana-Champaign and Richard W. Gorvett, University of Illinois at Urbana-Champaign in Proceedings Of The Casualty Actuarial Society Volume 92, 2005
This paper summarizes the research project on Modeling of Economic Series Coordinated with Interest Rate Scenarios initiated by the joint request for proposals by the Casualty Actuarial Society and the Society of Actuaries. It is intended to serve as a practical guide to understanding the financial scenario model in order to facilitate the use of this model for such actuarial applications as Dynamic Financial Analysis, development of solvency margins, cash flow testing, operational planning, and other financial analyses of insurer operations.

Fast and Robust Monte Carlo CDO Sensitivities and
their Efficient Object Oriented Implementation
Marius Rott and Christian Fries, DefaultRisk.com, May 31, 2005
In this paper we present a simple yet generic method for fast and robust Monte-Carlo calculation of sensitivities of Collateralized Debt Obligations (CDOs).

Application of a Monte Carlo Simulation Method for
Predicting Voltage Regulation on Low-voltage Networks
Power Systems IEEE Transactions, February 2005, Vol 20, Issue 1
Estimating voltage regulation on Low-Voltage (LV) networks is "bread and butter" work for many electricity network engineers. This paper summarizes research work on Monte Carlo modeling of residential electricity demand and its application to LV regulation problems.

Schedule Risk Analysis Simplified
David Hulett, Hulett and Associates, 2004
This slide show was presented by David Hulett of Hulett and Associates at the Project Management Institute annual meeting. It is a brief overview showing the benefits of accounting for uncertainty in your project models.

When CAPM Is Not Reliable Anymore, Simulation
and Optimization Tools Will Get the Job Done
Guilherme Marques Calôba, UFRJ, Regis da Rocha Motta, UFRJ, Alfredo Julio Souza Prates, Petrobrás, Marcelo Marinho Simas, Petrobrás, 2004
The authors compare portfolio optimization by two distinct methodologies. The first is the usual Capital Asset Pricing Model (CAPM) efficiency frontier, which considers that returns from every asset are normally distributed. Many apply this type of optimization to assets whose return distribution is simply non-normal.

Health and Economic Impact of Posttranfusion Hepatitis B
Arturo Pereira, Transfustion, Vol 43, February 2003
@RISK is used to evaluate the effectiveness of new HBsAG assays in donor testing in order to reduce the risk of posttransfusion hepatitis B tranmission.

» Read summary case study.

Evaluating Risk Management Alternatives for Indiana Crop Producers
By Ana R. Rios & George F. Patrick - Purdue University, July 2003
Given the changes which have occurred with respect to government farm policy and risk management tools, it is not fully understood how risk management strategies may affect the level and variability of net farm revenue. This study evaluates some risk management alternatives under current conditions in three areas of Indiana to develop guidelines for corn and soybean producers.

Monte Carlo Simulation for Schedule Risks
by Brenda McCabe, University of Toronto, 2003
This paper discussed the development of a probabilistic model to assist in estimating lower and upper duration estimates required in the preparation of a schedule risk analysis using Monte Carlo simulation. The model was tested in 14 projects with excellent results. The author believes that Monte Carlo simulation can provide valuable information to the owner and the contractor.

Schedule Risk Analysis:
Why It Is Important and How To Do It
by Stephen A. Book, Chief Technical Director, MCR, presented at GSAW 2002
Schedule-risk analysis is the process of associating a degree of confidence with each schedule-duration estimate. The combination of defining probability distributions for various scheduled task durations and establishing network relationships among the tasks allows one to forecast the probability of meeting the targeted dates of key milestone events. Monte Carlo simulation with @RISK for Project is used to perform probabilistic analysis.

The Economics of Litigation
Samson Vermont, 2001
This five-part series of articles outlines how decision trees and risk analysis techniques can be applied to a patent litigation case.

Combining Preference Theory and CAPM Efficient Frontier: Towards and Optimum Portfolio of Upstream Projects
SEG/San Antonio 2001 Expanded Abstracts, 2001
Regis da Rocha Motta et. al.

Sufficiency Analysis in Surface Combatant
Force Structure Studies
by Michael S. Morris, Johns Hopkins APL Technical Digest, Vol 21, No 3, 2000
Monte Carlo simulation was used in conjunction with wargaming and other modeling in a series of studies commissioned by the Surface Warfare Division of the Chief of Naval Operations to determine the required force structure of surface combatant warships in the US Navy.

Using Decision Analysis to Explore Cable Television Delivery
2000, by Keith A. Willoughby and Christopher J. Zappe
This paper demonstrates the efficacy of decision analysis using PrecisionTree in determining the most efficient strategy for installing cable television in the residence halls of Bucknell University.

Investment and Risk Analysis
Applied to the Petroleum Industry
Regis da Rocha Motta et. al.

Estimating the Cost of Preventive Services in Mental
Health and Substance Abuse Under Managed Care
Substance Abuse and Mental Health Administration, Department
of Health and Human Services, December 2001

A Comparison of Failure Probability Estimates by Monte Carlo Sampling and Latin Hypercube Sampling
by Charles N. Zeeb and Patrick J. Burns, Colorado State University, January 1999
This report covers two methods to determine failure probability and other related quantities. One issue is how the values used in each trial are determined. In this report, two methods are explored, Monte Carlo sampling (MCS) and Latin hypercube sampling (LHS).

Why Sex? Monte Carlo Simulations of Survival After Catastrophes
by J.A. Sa Martins and S. Moss de Oliveira, International Journal of Modern Physics C, Vol 9, No 3, March 1998
Monte Carlo simulation has been used to simulate the likelihood of survival of various populations of species in areas affected by natural disasters.