AC Immune SA is a clinical-stage biopharmaceutical company leveraging their two proprietary technology platforms to discover, design and develop novel proprietary medicines and diagnostics for prevention and treatment of neurodegenerative diseases (NDD) associated with protein misfolding.

Misfolded proteins are generally recognized as the leading cause of NDD, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), with common mechanisms and drug targets, such as amyloid beta (Abeta), Tau, alpha-synuclein (a-syn) and TDP-43. AC Immune’s corporate strategy is founded upon a three-pillar approach that targets (i) AD, (ii) focused non-AD NDD including Parkinson’s disease, ALS and NeuroOrphan indications and (iii) diagnostics. They use their two unique proprietary platform technologies, SupraAntigen and Morphomer to discover, design and develop novel medicines and diagnostics to target misfolded proteins.

Using Lumivero Products

AC Immune uses @RISK to assess the Company’s enterprise value, calculating risk-adjusted net present values (rNPVs) for certain preclinical and clinical product candidates. The company then combines each respective value and determines the ultimate “Sum of the Parts” for an overall indication of the company’s price per share. The company uses this internally generated value and bridges to potential variances in their share price (Nasdaq: ACIU) or price targets published by their covering analysts.

Each product candidate that AC Immune elects to value includes many uncertain variables which impact the projected net cash flows in the development and potential commercialization period for the product candidate. The typical inputs AC Immune uses in their @RISK models include, but are not limited to:

• Clinical development timelines
• Epidemiological data
• Market share
• Estimated price per region
• Launch uptake and period
• Probability of success through development
• Discount rate

“Certain of these variables can be material and are difficult to derive a point estimate for, or can be difficult to otherwise source,” explains Julian Snow, AVP of Financial Reporting.

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Typically, Snow uses a PERT distribution for his @RISK models for the risk-adjusted NPV. These inform AC Immune of the 90% prediction interval range, given the assumptions. In this example, the mean rNPV is expected to be around CHF 523m (illustrative example only).

AC Immune also uses tornado charts to showcase the impact of certain assumptions on the resultant value, all other variables held constant. “The company can then decide how best to minimize the impact of certain factors via additional research into the assumption or other potential adjustments,” says Snow.

In addition to @RISK, AC Immune also uses PrecisionTree. “PrecisionTree allows us to set up dynamic decision trees linked to underlying cash flows to understand the risk/return at a specific point in time along the development timeline,” says Snow. “Additionally, it helps us weight a decision such as to partner or not partner a potential product candidate.”

Other decisions include the expansion of a program, addition of a second indication for research, or assistance in license and collaboration deal structuring. “Assessing the value of one decision or another is valuable for the company,” Snow says.

Benefits of Lumivero Products

For AC Immune, our products significantly improve the quality of the decision-making process, particularly with regard to allocation of resources and improving understanding of the magnitude of uncertainty on key assumptions.

“Palisade software allows our company to sensitize key variables using various distribution methods, as well as convey the sensitivity in impact on the ultimate risk-adjusted net present value,” says Snow. “The software also conveys results in clear output graphics for easy reporting to relevant stakeholders.”

Prior to using our products, Snow and his team relied on Excel functionalities to calculate the relevant data. “We viewed this process as static and more cumbersome to maintain,” says Snow. “Therefore, with Palisade, AC Immune was able to enhance its internal valuation and reporting capabilities.”

A Competitive Edge

According to Snow, other companies in this space do not typically leverage deterministic analysis to their valuation approaches. “Most peers use more static excel models that cannot capture or answer a more robust set of questions that arise over a long development timeline,” says Snow.

In addition, when comparing the cost-benefit of programs, assessing internal funding needs, assessing potential licensing and collaboration terms and other matters relevant to understanding the potential financial return from a product candidate, “AC Immune is able to ask and answer more questions than peers as a result of the use of the software,” Snow says.

Thanks to Lumivero's products, AC Immune has seen both tangible and intangible benefits, including:

• Managing risk via Monte Carlo simulations
• Defining prediction intervals
• Conveying information more clearly to internal stakeholders
• Allows asking of more “what if” questions based on the set up of the model
• Assess impact on rNPV when ranging discrete values

Thanks to data-driven, deterministic analysis, AC Immune’s cutting-edge drug discovery technologies are better enabled to potentially help patients around the world.

Asher Drory of the University of Toronto’s Rotman School of Management uses @RISK in his graduate-level Financial Management course.

Understanding how to use Monte Carlo simulation to account for risk in decision-making is quickly becoming a required skill for today’s business leaders, says Asher Drory, Adjunct Professor of Finance at University of Toronto’s Rotman School of Management.

“Many leading corporations are now using Monte Carlo simulation in their business cases,” Professor Drory says. “Students who want a leg up with such corporations should seek out all opportunities to get experience in working with Monte Carlo simulation.”

In his Financial Management course, Drory uses @RISK to teach some 200 graduate students each year how to use Monte Carlo simulation in analyzing working capital and capital budgeting decisions. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities that will occur for any choice of action.

For example, Drory’s classes use @RISK and Monte Carlo simulation to look at:

*How forecasts of financial statements are needed to determine future funding requirements in working capital decisions. *How forecasts of future free cash flows are required and risk must be assessed in capital budgeting analysis.

Separately, Drory and his students use Palisade’s PrecisionTree software in modeling decision tree analysis for new product development. The students have access to the entire DecisionTools Suite which is loaded on all of the computers in the Rotman Finance Laboratory.

“All key financial decisions such as investing, operating and financing decisions can benefit from Monte Carlo simulation,” says Prof. Drory, who has taught at the University of Toronto for 21 years. “I ran across @RISK about 5 years ago when I was looking for PC-based Monte Carlo simulation tools. @RISK has a straightforward and easy-to-use interface.”

A consultant with Triangle Economic Research, an Arcadis company, Tim Havranek works with Fortune 500 clients to identify and quantify their potential environmental liabilities and to simulate the least costly routes to meeting their responsibilities.

Large industrial companies operating out of many different locations and facilities often have numerous actual or potential environmental legacies that linger for decades as financial liabilities. Longtime DecisionTools® user Tim Havranek has made a successful career out of helping companies manage their “environmental risk portfolios” cost-effectively. A consultant with Triangle Economic Research, an Arcadis company, Havranek works with Fortune 500 clients to identify and quantify their potential environmental liabilities and to simulate the least costly routes to meeting their responsibilities. Many of the complex cases that he and his associates at TER work on involve hundreds of millions of dollars, multiple stakeholders, and a powerful amount of modeling. As he has for years, Havranek relies on @RISK and PrecisionTree® to compare the scenarios and the decision paths that guide his clients’ decisions.

A Typically Complicated Case

In one recent case, a major industrial manufacturer sold approximately 15 of its active plants to another manufacturer. The terms of these sales included the provision that the original corporate owner would retain responsibility for historical environmental impacts. As time passed, environmental claims against the original corporate owner continued, and the corporation sought appropriate means of reducing cost and risks, such as receiving regulatory closure and/or selling the properties and liabilities to other parties.

Also, the historical environmental impacts at times potentially limited the ways that the new owners could manage and expand the properties. This often led to disagreements. Such disagreements were anticipated during the sale, and the purchase agreement included an arbitration clause to address issues as they arose.

Three Routes to Resolution

The corporation identified three possible solutions:

1. Pay for the transfer of liabilities to the current owners of the plants (cash out)
2. Buy back the properties
3. Continue under the asset purchase agreement and the system of arbitration it provided.

Havranek used Triangle’s time-tested procedure for framing the model. He met with all the stakeholders to identify all known cost elements, inherent uncertainties, and future potential liabilities for each of the three alternatives. The model included more than 100 unknowns. In order to pinpoint those issues on which the company would need to prevail in arbitration, Havranek and his team performed sensitivity analyses on the cost drivers identified by the framing meeting participants. The model was then run using @RISK and PrecisionTree.

Outside Verification

The model had three output cells, one for each alternative. The outcome was intriguing: the least costly alternative was to stay with the asset purchase agreement and arbitrate as needed. The model indicated an expected value savings of more than $30 million. An outside actuarial group verified and validated the model using proprietary actuarial software. In the end, the actuarial group’s projections agreed not only with Triangle’s inputs and assumptions but also with its findings.

Simplifying the Complex

Although other companies may turn to proprietary software to parse environmental risks, Havranek sees no reason to use custom software to accommodate the many complex inputs he includes in his models. He likes the convenience of working in Excel and being able to share his results with clients. But most important, he says, “I am always trying to streamline my models. To simplify simulations you need the flexibility that proprietary tools don’t always offer. These tools have that flexibility without any sacrifice of power. @RISK and PrecisionTree have all the power you need.”

P&G trained over a thousand people throughout the company on @RISK for modeling its entire range of investment decisions including new products, geographical expansions, manufacturing projects, and production siting.

“We’ve trained well over a thousand people throughout the company on @RISK,” says Procter & Gamble’s Bob Hunt, and now he’s rolling out another Decision Tool, PrecisionTree. In fact, Hunt, who is Associate Director for Investment Analysis in P&G’s Corporate Finance organization, and serves as a resource to the business units, was on his way to China and Japan to introduce PrecisionTree to P&G Finance managers in those countries.

P&G has been using @RISK since 1993 when Hunt first introduced it for modeling production siting decisions. The company was evaluating some cross-border siting options, and these decisions required them to take into account not only uncertainties involving the capital and cost aspects of plant location but fluctuations in exchange rates as well. The company has since come to rely on @RISK for its “entire range of investment decisions” including new products, extensions of product lines, geographical expansions into new countries, manufacturing savings projects, and production siting.

More recently Hunt and his colleagues have been working with PrecisionTree. “Its attraction is its capacity to value complex decisions, which often involve multiple, sequential decision steps”. They find it particularly valuable in evaluating “real options”. “We considered using financial option calculators to analyze the real options that are embedded in our complex decisions, but we found that they simply can’t solve for the real option value in projects with multiple, sequential investment decisions. Decision trees are really the only tool that can correctly value multiple sequential decisions where uncertainty is private risk.” Last spring, Hunt taught three (3) business units how to use PrecisionTree to test it as a tool for valuing complex decisions made under uncertainty. After a successful test, Procter & Gamble is now in the process of rolling out PrecisionTree to all of its major business units around the world.

Business units are evaluating investment options based on their impact on shareholder value, and PrecisionTree helps them make good choices and better decisions. “It has been very useful in helping us break complex projects down into individual decision options, helping us understand the uncertainties, and ultimately helping us make superior decisions.” He also notes that a lot of the value derived from using PrecisionTree is realized during the process that the staff goes through in determining the probabilities, and laying out the decision sequence and the criteria for making those decisions. The combination of the different approach required to frame decisions, and the ease and effectiveness of the PrecisionTree software, says Hunt, “is really powerful for our company.”

Unilever selected Palisade’s DecisionTools Suite as the principle analysis software to support its Decision Making Under Uncertainty process and decision-focused culture due to its flexibility and ability to do Monte Carlo and decision tree analysis.

In recognition that the decisions it needs to make around business-critical innovation are highly complex, global fast moving consumer goods supplier Unilever developed its Decision-Making Under Uncertainty (DMUU) approach. Combining a structured method with Palisade’s DecisionTools Suite software ensures that project teams fully understand the scope of their decisions, and have the tools and the knowledge to make informed and high-quality choices. This prevents opportunities and threats being overlooked, and increases Unilever’s agility in the market place.

Background

Unilever is one of the world’s largest suppliers of fast moving consumer goods in the refreshment, foods, home and personal care sectors. With a portfolio of over 400 brands, it has consistently ambitious growth targets. The company has an extensive annual budget for cutting-edge research and development, and thousands of projects in its innovation pipeline at any one time. This means that in order to make informed decisions on how to manage this portfolio, it needs absolute clarity around the risks and opportunities it faces.

However, like any large, dynamic organisation, complexity has a large impact on Unilever’s decision-making process. Many parties are involved in the process, often with conflicting values, motivations, perspectives, personalities and power bases. These organisational complexities are reinforced with analytical complexities such as the large number of interrelated inputs that must be factored in to the decision, the high level of uncertainty inherent in early-stage developments and potentially conflicting decision criteria.

A Structured Approach to Decision Making

For business-critical innovation, Unilever recognised the inherent complexity of its decisions and the need to maintain a dual internal and external focus to prevent important opportunities and threats from being overlooked. It understood that incorporating these factors into an effective decision making process would improve decision quality, facilitate faster decision making and ultimately increase Unilever’s agility in the market place.

The Unilever response was to develop a unique approach known as Decision Making Under Uncertainty (DMUU). This is a disciplined, methodical and structured approach to decision-making, with probabilistic analysis at the heart of its logical reasoning. It combines framing and structuring tools with leading-edge analytical software – Palisade’s DecisionTools Suite. The DecisionTools Suite is an integrated package of seven risk, decision, and data analysis tools that run in Microsoft Excel. This approach ensures that project teams fully understand the scope of the decision, that they have the tools and the knowledge to make high-quality decisions, and the insight to understand the consequences of taking one course of action over another.

Overall, DMUU helps to provide the required clarity, insights and commitment to action.

DecisionTools Suite Guides Decisions on Innovation

Unilever selected Palisade’s DecisionTools Suite as the principle analysis software to support its DMUU process and decision-focused culture due to its flexibility and ability to do Monte Carlo and decision tree analysis using component products @RISK and PrecisionTree, respectively. Today, the DecisionTools Suite enables Unilever to develop probabilistic business cases for its biggest innovations, as well as its major strategic decisions.

DMUU and the use of the DecisionTools Suite is now a standard part of Unilever’s innovation process and probabilistic business cases are required for all big and complex projects. For example, a typical use for @RISK, the risk analysis element of the suite, is in evaluating alternative strategies for a new product launch or a major capital investment.

Unilever teams also use PrecisionTree, the decision analysis tool, to evaluate early stage projects where decisions and uncertainties will occur at various times in the future. This approach, using decision trees in PrecisionTree, is used to evaluate the current value of a project and also to understand the risks and benefits of internal versus external development routes.

In recognition of the importance of the DMUU, Unilever has an internal consultancy function to provide decision support and software expertise when required.

In addition, Palisade’s software is used to support other business areas including supply chain, safety, regulatory, as well as additional complex one-off decisions. All of these have the common features of multiple compelling alternatives, significant contradictions on how to proceed and high stakes should the ‘wrong’ decision be made.

“Strategic decisions require a process that addresses all the elements of decision quality,” explains Andrew Evans, decision analyst at Unilever. “However, an integral part of that process is powerful and flexible software that informs the debate on which direction should be taken. We evaluated various options and Palisade’s DecisionTools Suite was the tool that best met our business requirements. As a result it has played a key role in increasing the quality of decision-making and helping project teams to think clearly, act decisively and feel confident.”

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Additional Information

Key software / features useful to Unilever: @RISK is the most commonly used application of the tools available in the DecisionTools Suite. Decision-makers at Unilever are now used to seeing insights from business cases described using histograms and advanced sensitivity tornados. Box-and-whisker diagrams (box plots) are also very useful when alternatives or projects need to be compared. Sensitivity and scenario analysis are used to understand the key drivers of uncertainty. In addition, analysts help to draw insights from the models using summary graphs and scenario analyses.

Distributions Used

Pert and Triang are the distributions used most often when Unilever is deploying @RISK to evaluate business cases, as they are good for describing distributions when data is elicited from experts. The discrete distribution is used to simulate alternative futures, such as competitor action, or different levels of success in a product launch. However, when good quality historic data is available, or when the ‘tails’ (eg in safety studies) are of interest, Unilever uses the wider set of distributions and tools such as distribution fitting feature available within @RISK.

Dave MacAdam, Senior Manager of Innovation Strategy at Novelis, relies on the DecisionTools Suite of products to value high-risk research and development (R&D) projects. His process brings a new level of rigor and precision to the company’s business-as-usual approach to evaluating R&D risk. Through working with different stake-holders and experts across Novelis, MacAdam has started to introduce quantitative analysis to their R&D process, providing more opportunity to optimize their R&D project portfolio.

Novelis, the world’s largest aluminum rolling and recycling company, is a global leader in rolled aluminum production, beverage can recycling and automotive sheet production. The company produces aluminum products for packaging materials, automotive and transportation companies, as well as architecture and building. Novelis has 25 plants throughout 11 countries, employing 10,900 people, and has research and technology (R&T) offices in the USA, Germany, Switzerland and South Korea. Although a successful global innovator, Novelis’ process for evaluating new R&D projects previously did not incorporate a quantitative system for risk assessment.

“Within our R&T department, there was previously little to no structure put around the risk evaluation of projects,” says MacAdam. “The process was perfunctory and overly reliant on input from the commercial side. While commercial input is necessary, we found more information from the scientific side of the company was needed to accurately assess risk and more integration was needed between those two schools of thought.”

Introducing a New Approach

In his role as Senior Manager of Innovation Strategy, MacAdam set out to bring a new level of precision and accuracy to the risk evaluation process. Using @RISK and the DecisionTools Suite, MacAdam introduced a method in which models could be built that incorporated all assumptions from the different Novelis teams—commercial and scientific combined.

To start the evaluation process, MacAdam sat down with project leads, asking them to map out key decision points and key risk reduction points within each project. Through these conversations, MacAdam was able to capture what the risks were, as well as the leads’ predictions on the chances of the worst case, best case and most likely outcomes for each stage of a project.

“By going through this process, I was able to refine the assessment of the risk of each project from the project leads themselves,” says MacAdam. “This becomes an integral part of our risk assessment process and portfolio optimization.”

MacAdam also leverages what he calls an adaptive discount rate (ADR), which is a measurement developed by Mike Pellegrino at Pellegrino and Associates, a firm that specializes in intellectual property valuation.

The ADR incorporates Novelis’ target rate of return (also known as the “hurdle rate”), the holding period and the predicted success at each stage of a project. The target rate of return is usually made standard across an organization, while the holding period quantifies the period of time a company is willing to allow for that desired return to materialize.

“The holding period reflects an organization’s tolerance for risk and its patience for research,” says MacAdam.

Lastly is the success rate. MacAdam works with the technical community at Novelis to create a probability distribution for the success of each stage of a project. All these data combine to produce a risk-adjusted discount rate, which allows you to get a more accurate Net Present Value (NPV) looking forward to each stage of a project.

After determining the risks associated and the key decision points with each project lead, and identifying the different cash-flow stages of the project—such as prototyping, pilot plant and production, MacAdam then looks at the risk of each decision point and cash-flow stage of the project. He explains that the risk at each point compounds with the other points downstream, thus the importance of carefully evaluating each segment of the process within any given project.

“I’m big on risk retirement,” says MacAdam. “If you can retire risk, that makes the chance of success greater as you move forward—all your downstream risks get lower.”

If, for example, the prototype stage of a project goes through with 100 percent success, it reduces the risk of the pilot-production stage, and so on. To illustrate how this compounding works, MacAdam uses PrecisionTree to show project leads how each stage of a project influences the next.

MacAdam turned to another Palisade product, @RISK, to get probabilistic distributions of key outcomes such as NPV for a project, tailoring the output graphs to show data that would be most relevant to his stakeholders.

“These graphs make explaining the risk of a project easier,” MacAdam says. “In particular, @RISK’s tornado graph feature allows us to take the inputs from all the different participants on a project —from R&D, operations, marketing, etc.— and articulate what the biggest risks are in a hierarchical way. It’s an opportunity to challenge people’s assumptions and get at the heart of the real risks in a project.”

MacAdam gave the example of analyzing a new process being developed to enhance the efficiency of Novelis’ recycling process. After speaking with the project lead, MacAdam included several variants of scientific results from the research phase of the process. Thanks to this careful inclusion of data, the model showed that the entire success of the project hung heavily on the outcome of just a few specific technical tests.

“We realized in order to reduce risk, our researchers needed to do more testing,” says MacAdam. “We really couldn’t have much confidence in the business model without first having more confidence in the scientific measurements.”

Thanks to this software, MacAdam has been able to demonstrate to key decision makers which areas contribute most to a project’s overall risk, often revealing areas underestimated in their significance.

The Beginning of a New Risk-Aware Era

According to MacAdam, this method of parsing out and analyzing the risk of each step in a proposed project has started small, ensuring the foundation is set and kinks are worked out before embarking on a larger rollout.

“Right now, we’re implementing this process on a project-by-project basis, mainly using it to assess the risk / return of specific R&D projects,” he says. “But the vision is to ultimately have it assess the relative value of projects—to help us decide between projects and see which ones to keep active. This system is becoming an essential part of our portfolio management and we anticipate it to have a significant role in our ‘go’ / ‘no-go’ decision making in the future.”

The Palisade software has been integral to the success of this new approach.

“@RISK enables us to speak directly with the information holders, have them verbalize their assumptions and then work with us to model it into a risk assessment that is truly useful—it’s an incredibly powerful tool,” MacAdam says. “Our researchers are now having conversations about their estimations, rather than being forced to enter a number into a box—which had been the previous approach to gathering and ranking risk data.”

MacAdam’s favorite aspect of using the product? How easy it made communicating results to stakeholders.

“We’re all making sausage, and no one wants to see how it gets made. The more succinct and clear you make those graphs, the more effective the decision-making process can be.”

Researchers used @RISK and PrecisionTree to model the likelihood of a successful evacuation during a volcano eruption.

University of Bristol’s Environmental Risk Research Centre (BRISK) adds new dimension to modeling volcanic risk in Guatemala

Conducting a quantitative risk assessment is often a difficult process, requiring data that is sparse or even unobtainable. With volcanoes, the effects of uncertainty are accentuated by the potentially high costs of making a wrong call.

Guatemala has many active volcanoes, but none are as close to large populations as the ‘Volcán de Fuego’, potentially one of the most dangerous volcanoes in Central America with a large population surrounding it. Many farmers live and work in its shadow due to the fertile slopes that provide the best ground for coffee growing in the region. Large eruptions in 1974 fortuitously did not lead to any deaths, but buried in the volcano’s geological history are signs of ominous behaviour.

Using Monte Carlo sampling to quantify the threat

The volcano has been very active over the last few years with many small eruptions taking place every day, and the fear that this activity could suggest the build up towards larger eruptions in the future is a worrying prospect. The “Instituto Nacional de Sismologia, Vulcanologia, Meteorologia e Hidrologia” (INSIVUMEH), regularly monitors activity at the volcano, however, despite the gallant efforts of the scientists there, no formalised risk assessments are carried out, mostly due to lack of funding and resources.

Recent work using Palisade’s The DecisionTools Suite however, is now enabling volcanologists to quantify the nature of one of the threats from the volcano to peoples’ lives. As an integrated set of programmes for risk analysis and decision making under uncertainty, The DecisionTools Suite running in Microsoft Excel, allows access to Monte Carlo simulation and other advanced analytics quickly and simply on the desktop.

A different approach to risk assessment

Conventional risk assessments attempt to model the probability of a hazard and combine that with the vulnerability of the population, to create societal risk curves and estimated values of Individual Risk per Annum (IRPA). For many of the people living on the slopes and indeed the authorities, knowing the potential number of deaths or cost from an eruption is not entirely useful, as little planning control or mitigation can be carried out. In an attempt to increase the usefulness of the risk modeling to the end-user (the authorities and people living near the volcano), BRISK has looked at the vulnerability in a different way.

Normally volcanic risk assessments assume that the whole population is present in a location when a hazard hits. However, new work by BRISK has modeled the likelihood of a successful evacuation, using both @RISK and PrecisionTree, by inputting several variables obtained through a process of structured expert judgment. These variables, which include the time taken between a possible eruption and a possible hazard hitting a location, along with communication times from authorities and evacuation times, are each estimated with an uncertainty distribution by the experts. These expert views are then weighted and pooled together. The variables are then constructed together in a logic tree within Palisade’s PrecisionTree, with the end node either being evacuation or no evacuation – and the probability of these outcomes being quantified, with their uncertainties. When fed back into the @RISK (Hazard * Vulnerability) model, the effects of a potential evacuation on the risk is very clear.

Better planning and effective mitigation strategies

When looking in more detail at the model outputs from the logic tree, it became clear where the sensitivities were within the system. For example, it may be for a given location that the amount of time between a warning and the hazard hitting is crucial, or it may be that the time taken to evacuate is crucial. This new way of modeling volcanic risk informs better planning and more effective mitigation strategies.

Jonathan Stone, a researcher at the University of Bristol, working with colleagues Prof Willy Aspinall1 and Dr Matt Watson, said “Palisade’s DecisionTools Suite has proved to be invaluable in the work we are doing with INSIVUMEH, and potentially very useful for those living and working around Volcán de Fuego.”

1 Professor Willy Aspinall has been using Palisade’s @RISK software for some time in his work analysing the risk of volcanic eruptions and earthquakes around the globe. His work was documented in the following article.