# White Papers

Take 2 and Make 360
by Roy Nersesian
Accurately modeling the power output of wind and solar alternative energy is necessary to determine any seasonal fluctuations. In this paper, Roy Nersesian uses @RISK to build out a probability distribution for every day of the year. Roy can be contacted directly for the example models

Postpone It!
by Roy Nersesian
Postponing the completion of a finished product until after the sale eliminates the need for inventory control but adds additional material needs. Roy Nersesian uses @RISK and a paint store example to show how to model the demand to reduce stockouts and minimize total costs associated with the sale of paint. Roy can be contacted directly for the example models.

MODEL THIS
by Roy Nersesian
Building off a model from the book Utility Risk Modeling, Roy Nersesian utilizes Evolver to model an unusual distribution example of energy demand in 15-minute intervals. Follow along as he shows how to translate a beta distribution into a Pert distribution and converts a previous spreadsheet to work with Evolver. Roy can be contacted directly for the example models.

When Reliability Meets Uncertainty
by Roy Nersesian
Solar and wind are unreliable sources of energy. A wind farm offshore Europe can supply the power needs of one million households, yet there have been days when total power output couldn't heat water for a cup of tea. Several years ago, there was an eclipse over Europe during calm weather reducing renewable (wind and solar) power to nil - without 100% backup, the lights would have gone out.

Electricity demand is uncertain, but its uncertainty can be bracketed within known parameters based on an analysis of past demand including a projection for growth. Meeting uncertain demand with reliable supply (fossil fuel, nuclear, hydro except in dry seasons) is the normal course of business for an operating utility. Matching up unreliable supply with undcertain demand is a newly emerging trend with the advent of renewables. The challenge is becoming more prominent with the growth in the contribution of solar and wind to electricity supply.

This chapter uses @RISK to describe the risks of matching unreliability with uncertainty via a simulation of a utility with a notable commitment to renewables.

Transmission of foot and mouth disease at the wildlife/livestock interface of the Kruger National Park, South Africa: Can the risk be mitigated?
by Ferran Jori and Eric Etter
Preventative Veterinary Medicine – 126 (2016) 19–29

Foot and mouth disease (FMD) is considered one of the most important infectious animal diseases in the world, mainly because it inflicts severe economic losses due to restrictions in trade of livestock and its products within infected countries. In this paper, the authors developed a stochastic quantitative model to assess the annual risk of FMD virus transmission from buffalo to cattle herds present at Kruger National Park using @RISK. The model suggests that proper measures of immunization and reducing buffalo/cattle contact is efficient to reduce infective events to every 5.5 years while an increased number of buffalo in the KNP and the reduction of immunization increases the risk of transmission by three times resulting in one infective event each year.

Financial Sustainability in the Oil Industry
by Roy L. Nersesian
Usually @RISK is used to measure profitability, but in this example it is applied to demonstrate financial stability for a company under stable conditions. The purpose of this paper is to set a framework for others to begin examining financial stabiliity for other applications and and in a more general way.

Estimating Bovine Spongiform Encephalopathy Infection and Detectable Prevalence in Cattle
by Katsuaki Sugiura, Takeshi Haga, Takashi Onodera, Preventive Veterinary Medicine, April 2014
The authors use @RISK to model the infection prevalence of Bovine Spongiform Encephalopathy (BSE) in Japanese cattle born in the period 2000–2012, using maximum likelihood methods and BSE surveillance data of these birth cohorts. From this, they predicted the number of infected cattle and test positives in years 2004–2020.The number of BSE cases actually detected was within the 95% confidence interval of the predicted numbers. The detectable prevalence (predicted number of test positives/number of cattle tested) was predicted to be highest in 2005. In 2014 the simulation predicts that one animal out of 160,000 tested would test positive. The model further predicts that detectable prevalence would decline exponentially to zero in subsequent years.

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.

Hitting Three Birds with One Stone: Using a Case Study of United Way in Classroom Teaching
by Hikaru Hanawa Peterson, NACTA Journal, March 2006
Modern universities have multi-faceted missions, typically encompassing teaching, research, and outreach. Under financial pressure and changes in societal expectations, increased efficiency in meeting multiple missions is advocated. This article documents the use of a teaching case that involved research and resulted in community outreach. Risk management students at Kansas State University used @RISK to help develop fundraising campaign plans for a local United Way Chapter.

» View the tutorials designed for the course

## Agriculture

Estimating Bovine Spongiform Encephalopathy Infection and Detectable Prevalence in Cattle
by Katsuaki Sugiura, Takeshi Haga, Takashi Onodera, Preventive Veterinary Medicine, April 2014
The authors use @RISK to model the infection prevalence of Bovine Spongiform Encephalopathy (BSE) in Japanese cattle born in the period 2000–2012, using maximum likelihood methods and BSE surveillance data of these birth cohorts. From this, they predicted the number of infected cattle and test positives in years 2004–2020.The number of BSE cases actually detected was within the 95% confidence interval of the predicted numbers. The detectable prevalence (predicted number of test positives/number of cattle tested) was predicted to be highest in 2005. In 2014 the simulation predicts that one animal out of 160,000 tested would test positive. The model further predicts that detectable prevalence would decline exponentially to zero in subsequent years.

Hyalomma scupense (Acari, Ixodidae) in northeast Tunisia:
Seasonal population dynamics of nymphs and adults on field cattle

Mohamed Gharbi, Mohamed Ettaїeb Hayouni, Limam Sassi, Walid Dridi, and Mohamed Aziz Darghouth
Laboratoire de Parasitologie, École Nationale de Médecine Vétérinaire, Université de la Manouba,
2020 Sidi Thabet, Tunisia

Hyalomma scupense is a two-host tick infesting mainly cattle representing in North Africa the vector of tropical theileriosis (Theileria annulata infection), a major tick-borne disease affecting cattle. In this study, three cattle farms in northeast Tunisia were surveyed during the activity seasons for adult and nymphs of Hyalomma scupense. Several indicators were studied, including chronological indicators, infestation prevalence, infestation intensity and feeding predilection sites of the ticks. When the preferential sites of attachment are known, the effectiveness of manual removal of ticks can be improved. During the study, when parameters were related to the host, they were estimated in both adult and young animals of each sex and both nymphs and adults (male and female). The curves of infestation degrees (ID) were fitted using BestFit 4.5.2.

Please note that BestFit is no longer available as a stand-alone product. Its features and functionality have been wholly integrated into @RISK as the distribution fitting feature.

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.

A Case Study of Fall versus Spring Calving for the Rocky Mountain West
by By Brian A. Strauch, Dannele E. Peck, and Larry J. Held, 2010 Journal of the ASFMRA
Feeder cattle prices are generally lower in the fall, when the volume of calves for sale is highest. Most ranches in the Rocky Mountains calve in March or April, which results in the sale of weaned calves in October, when feeder cattle prices tend to be lowest. This study was initiated with the idea that a rancher might improve profitability by switching to fall calving, which would enable them to sell calves in April at a higher price. Monte Carlo simulation with @RISK was used to determine if business risk, associated with random variation in prices, differs between the two calving systems.

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.

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.

Risk and Return for Bioenergy Crops under Alternative Contracting Arrangements
by James A. Larson, Burton C. English, Lixia He Department of Agricultural Economics, The University of Tennessee, February 2008
Farmers, agribusiness, policymakers, and others have shown considerable interest in the potential for on-farm production of biomass for ethanol production. As part of this study, a 99 year set of real, de-trended, and correlated prices for corn, soybeans, wheat, wheat straw, corn stover, switch grass, nitrogen fertilizer, and diesel fuel were simulated using @RISK.

A participatory approach for integrating risk assessment into rural decision-making:
A case study in Santa Catarina, Brazil

Ivan Luiz Zilli Bacic, Arnold K. Bregt, David G. Rossiter, Agricultural Systems, Volume 87, Issue 2, February 2006, Pages 229–244
Incomplete information is one of the main constraints for decision-making, which are then by definition risky. In this study, formal risk concepts were introduced in decision-makers’ meetings according to local demands and following a participatory approach, as a first step towards integrating risk assessment into rural decision-making in Santa Catarina, Brazil. This paper also investigates decision-makers’ attitudes towards risk, and how these were influenced by objective information. @RISK was used in the course of the 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.

## Construction/Engineering

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.

## Energy/Utilities

Financial Risk Inherent in Oil Fracking
by Roy L. Nersesian
The purpose of this paper is to analyze real-life free market risks in the oil and fracking industry through simulations and optimizations using Palisade's @RISK, Evolver, and RISKOptimizer software.

Model: Nersesian_FrackedOil.xls

For more on this topic, see Roy Nersesian's in-depth treatment in the book Energy Risk Modeling.

Integrating Renewables with Electricity Storage
by Roy L. Nersesian
The purpose of this paper is to illustrate how the inherent uncertainty of renewables can be handled relying on @RISK simulation software to model the output of a system of solar and wind farms located in different sites. System output is then compared to uncertain demand to obtain a probability distribution of the mismatch between supply and demand. This is then used to size a gravity battery to compensate for the vagaries in supply and demand, thus transforming uncertain supply into controllable supply to meet changes in demand. Sizing a utility-sized battery would follow the same general format.

For more on this topic, see Roy Nersesian's in-depth treatment in the book Energy Risk Modelling.

Linking Mineral Systems Models to Quantitative Risk Analysis and Decision-Making in Exploration
by Dr. Oliver Kreuzer et al., X-plore Geoconsulting
The authors' approach integrates critical mineralization processes and conditions with concepts of probability theory, decision analysis and financial modeling. A case study, based on an actual porphyry copper project, illustrates how the probabilistic mineral systems model can generate a measure of the probability of ore occurrence as an input for exploration decision trees and simulations to calculate the expected value (EV) of an exploration project and the probability distribution of all possible surrounding NPV values within a minimum and maximum range. Formulation of the probabilistic model closely follows and combines principles of the well-established petroleum and mineral systems approaches and makes use of Excel™-based model templates and PrecisionTree.

Ideal Pairing for Electricity Supply in SIC (Disponible en Español)
by Elio Cuneo H., Electrical Engineer, Diploma in Finance, MBA in Finance, professor who holds the chair of Energy Management and Administration of the MA in Energy Economics at the Universidad Santa Maria, and guest lecturer for the Economic Assessment Risk Analysis course for the MA in Energy Development at the Universidad de Antofagasta.

In July 2008 a solar measurement station with a tracking system was installed in the north of Chile. The objective of these measurements was to research the potential of global and direct radiation for a possible use of the solar power in the north of Chile for energy efficient production. From the results given by @RISK, the positive effect of having solar power generation in the SIC system whose generation capacity strongly depends on hydrological conditions as they present themselves is clear.

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.

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.

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.

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.

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.

## Environment

Targeted Health Risk Assessment Following the Deep Water Horizon
Oil Spill: Polycyclic Aromatic Hydrocarbon Exposure in Vietnamese-American Shrimp Consumers

by Mark J. Wilson, Scott Frickel, Daniel Nguyen, Tap Bui, Stephen Echsner, Bridget R. Simon, Jessi L. Howard, Kent Miller, and Jeffrey K. Wickliffe
The authors use @RISK to conduct population-specific probabilistic health risk assessments based on consumption of locally harvested white shrimp (Litopenaeus setiferus) among Vietnamese-Americans in Southeast Louisiana. The study was motivated by health concerns about seafood contaminated with polycyclic aromatic hydrocarbons (PAHs) after the Deep Water Horizon oil spill of 2010. Monte Carlo simulations were used to generate hazard quotient distributions for non-cancer and cancer health risks. The risk assessment results show no acute health risks or excess cancer risk associated with consumption of shrimp containing levels of PAHs detected in our study, even among frequent shrimp consumers.

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.

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.

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.

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.

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.

Optimising adaptation investment - Coastal inundation at Narrabeen Lagoon
by AECOM for the Australian Department of Climate Change, March 2012
AECOM was engaged by the Australian Department of Climate Change to undertake an economic analysis of climate change impacts on infrastructure through the development of a series of case studies. These studies, which analyse the benefits and costs of adaptation in response to risks of climate change, will be used to inform the Australian Government‘s discussion on policy responses to the risk that climate change will increase infrastructure investment and maintenance costs. This pioneering study estimates the social benefits of adaptation to climate change in terms of willingness to pay, rather than just costs avoided. It also employs Monte Carlo analysis using @RISK to generate more realistic probabilities of overall costs and benefits, as well as modelling the expected future values of variables such as rainfall using extreme value analysis rather than just taking averages.

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.

@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.

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.

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.

## Finance/Banking

On the Use of Ensemble Models for Credit Evaluation
by Albert Fensterstock, Jim Salters and Ryan Willging
The Credit and Financial Management Review – Fourth Quarter 2013

As described in this paper, Ensemble Models represent a very powerful method for determining credit risk. And, given the fact that they can be built in Excel, makes them a very attractive alternative to other methods of credit evaluation, and in particular to judgment-based models. This class of models can aid in providing an easy to implement solution to the associated problems of initial credit evaluation, credit line determination and ongoing debt tracking.

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.

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.

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.

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.

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.

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).

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.

## Government/Defense

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.

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.

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.

## Healthcare/Pharmaceutical

Estimating the Cost of Preventive Services in Mental Health and Substance Abuse Under Managed Care
by Anthony Broskowski, PhD, and Shelagh Smith, MPH, CHES, Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services

This document presents cost estimates for six preventive interventions previously identified through a literature review and analysis of peer-reviewed, published research in mental health or substance abuse services. The models are spreadsheet-based and include the various factors (input variables) that drive the costs of each intervention: professional and clerical labor, supplies and materials, and general and administrative (G&A) overhead as well as profit margin.

@RISK for Developing Healthcare Model That Cuts Costs
"Reduced use of erythropoiesis-stimulating agents and intravenous iron with ferric citrate: a managed care cost-offset model," by Rich Mutell, Jaime Rubin, T. Christopher Bond, and Tracy Mayne, International Journal of Nephrology and Renovascular Disease, April 2013

The researchers at the DaVita Clinical Research in Minneapolis, MN are using @RISK to develop a new healthcare model that introduces pharmacological innovation to a healthcare system desperate for cost reduction. @RISK’s Monte Carlo simulation allowed the researchers to model the cost savings generated when end-stage renal disease patients with electrolyte disturbances are given ferric citrate. Their research forms the basis of a proposed new healthcare model that could save money for hospitals, patients, and healthcare insurers.

Cost Analysis of Various Low Pathogenic Avian Influenza Surveillance Systems in the Dutch Egg Layer Sector
by Niels Rutten, José L. Gonzales, Armin R. W. Elbers, and Annet G. J. Velthuis for the PLoS ONE, April 2012
As low pathogenic avian influenza viruses can mutate into high pathogenic viruses the Dutch poultry sector implemented a surveillance system for low pathogenic avian influenza (LPAI) based on blood samples. It has been suggested that egg yolk samples could be sampled instead of blood samples to survey egg layer farms. To support future decision making about AI surveillance economic criteria are important. Therefore a cost analysis is performed on systems that use either blood or eggs as sampled material. A sensitivity analysis was performed to assess and identify the inputs that influence the total costs the most. This analysis was carried out using the add-in software TopRank for Excel from Palisade Corporation’s the DecisionTools Suite.

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.

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.

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.

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.

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.

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.

Trial Application of a Model of Resource Utilization, Costs, and
Outcomes for Stroke (MORUCOS) to Assist Priority Setting in Stroke

Marjory L. Moodie, DrPH et al., Stroke, 2004;35:1041-1046., May 2004
Stroke is a major cause of mortality and morbidity in Australia, accounting for 5.4% of the total disability adjusted life-year (DALY) disease burden. Costs associated with the disease are high; total first-year costs of first-ever strokes in Australia in 1997 were estimated at US \$427 million and the present value of lifetime costs were US \$1 billion. Researchers used @RISK to perform probabilistic uncertainty analysis using Latin Hypercube simulation as part of this study.

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.

»

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

## Insurance/Reinsurance

Assessing Your Own Risk and Solvency Or, Said Differently… “How Risky are We?”
By Joel Kress, Underwriting manager Government Entities Mutual, Inc. PCC, July 2012
Joel Kress, underwriting manager at Government Entities Mutual, Inc. PCC, asks the question, “How risky is our captive insurance company?” @RISK's Monte Carlo simulation allowed him to look at how exposure to risk changed over time in his company, and then simulate hypothetical future policy years. The model helped create a policy around risk, so that the Board of Directors and Regulators could feel comfortable that management's day-to-day decisions will not exceed the predetermined desired risk levels.

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.

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.

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.

## Manufacturing

Identifying Key Risks to the Development of a Pellet Manufacturing Plant Analysis
of a 50,000 ton per year pellet manufacturing facility

by William (Bill) Strauss, FutureMetrics, LLC., August 2012
This analysis contains detailed financial and risk modeling for the construction and operation of a 50,000 ton per year nameplate capacity wood pellet fuel project valued at approximately \$11 million.

The modeling incorporates the uncertainty of some of the key inputs to both the capital cost and the operating cost models. Monte Carlo simulations reveal the expected distributions of key cash flow metrics and the sensitivity of the key cash flow metrics to changes in inputs. The analysis shows the risk of insufficient cash flows to the project developers and identifies those cost inputs and revenue generating assumptions whose changes generate the greatest risk of project failure.

The analysis is presented by FutureMetrics. FutureMetrics is a globally recognized consultancy in bioenergy project development.

Ceramic coatings for jet engine turbine blades
by David Parsons and Julia Chatterton, Carbon Brainpoint Case Study, July 2011
Ceramic thermal barrier coatings (TBCs) are applied to jet turbine blades to protect them from the high temperature gases leaving the combustion chamber and to increase the efficiency of the engine. @RISK was used to estimate emissions reductions due to improved coatings.

Modeling the competitive market efficiency of Egyptian companies: A probabilistic neural network analysis
by Dr. Mohamed M. Mostafa, Expert Systems with Applications, 2009
Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial neural networks (NN) and a traditional statistical classification method to classify the relative efficiency of top listed Egyptian companies. “Because of its extensive capabilities for building networks based on a variety of training and learning methods, NeuralTools Professional package (Palisade Corporation, 2005) was chosen in this study.” Results indicate that the NN models are superior to the traditional statistical methods. The study shows that the NN models have a great potential for the classification of companies' relative efficiency due to their robustness and flexibility of modeling algorithms.

## Project Management

Integrated Cost-Schedule Risk Analysis
by David T. Hulett, Ph.D, Hulett & Associates, LLC Michael R. Nosbisch, CCC, PSP. Project Time & Cost, Inc., February 2012
This paper describes an improvement in cost risk analysis over the traditional methods that address cost risk without explicit reference or, indeed, any reference at all to the project schedule and its risk. We now know how to represent the role that schedule risk has in driving project cost, because the longer some resources such as engineering or construction work the more they cost. @RISK and Monte Carlo simulation is the most commonly used approach to analyzing the impact of multiple risks on the overall project schedule or cost risk. Simulating a resource-loaded project schedule derives consistent results for the project schedule and cost in each iteration. The results can be used to conduct a risk mitigation exercise to improve the project’s prospects for success.

Project Schedule Risk is Key to Understanding Cost Risk
by David T. Hulett, Ph.D., Hulett & Associates
We have always heard that “time is money.” That is true for projects that are conducted, in part, using resources like labor or rented equipment that will cost more if they work longer. However, this fact has not been well implemented in project cost estimating until recently. In fact, many cost estimates assume that the project schedule is engraved in stone, whereas the schedule may be the most risky component of the project.

In this article, a risk analysis of the cost estimate is conducted using the resource-loaded project schedule where the project budget (without any embedded contingency amounts) is assigned to the schedule activities they support. Then, the schedule is simulated using Monte Carlo techniques.

Project Cost and Schedule Modelling
by Stephen Grey, Broadleaf Capital, As presented at the 2012 Palisade Risk Conference, Sydney, May 31, 2012.
For over twenty years, the use of Monte Carlo simulation to analyze risk in project cost and schedule forecasts has been open to anyone with a PC. The basic idea seems simple but, while twenty years is long enough for many people to have tried it there is still a lot of variation in how analyses are carried out and in the quality of the results they produce. This paper describes how, using @RISK, you can avoid reinventing the wheel by taking advantage of good ideas that have only really matured in the last few years.

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.

The Effectiveness of Using Project Management Tools and Techniques for Delivering Projects
by Mufeed Hajjaji, Paul Denton, Steve Jackson, Computing and Engineering Researchers' Conference, University of Huddersfield, Dec 2010
The outcome of this work is to demonstrate how the adoption of project management tools and specific risk management software, if successfully implemented, can be important tools in enabling project managers to achieve higher levels of success. The authors use @RISK in the study.

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.

Risk Analysis and Contingency Determination Using Range Estimating   También traducido al español
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.

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.

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.

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.

## Six Sigma

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.

## Transportation

@RISK for the Highway Contractor
by Thomas W. Townley, TEAM Consulting, April 2012
Uncertainty in construction is a daily reality. Measuring processes to determine stability, capability and predictability is the first step to sustain success. Applying @RISK simulations can then provide solutions for managing processes such as scheduling, estimating and bid strategy. This paper will illustrate strategies for management to success.

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.

Assessing Traffic and Revenue Studies for Tolled Facilities Using a Toll Viability Screen Tool built with @RISK
Don R. Smith, Carlos Chang-Albitres, Wm. R. Stockton, and Craig Smith, Texas Transportation Institute of The Texas A&M University, October 2004
In an era where agencies are looking to invest hundreds of millions – and even billions – of dollars into a single transportation facility, the feasibility and investment-grade studies for such projects must instill a high confidence to the financial markets and the public. Texas Transportation Institute (TTI) assesses the reasonableness of toll road and managed lane traffic and revenue (T&R) studies by using simulation techniques to examine the rationality of base assumptions and study the unique and simultaneous risks associated with those variables.

Because toll road and managed lane projects can vary greatly in their complexity, TTI needed to devise a way to review their corresponding T&R studies in a consistent and direct method. With federal and state funding, TTI created the “Toll Viability Screening Tool” (TVST) using @RISK. studies in a consistent and direct method. With federal and state funding, TTI created the “Toll Viability Screening Tool” (TVST) using @RISK.

## Other

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.

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).

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