Palisade Corporation

Dr. Jose A. Briones of SpyroTek Performance Solutions recently gave a Palisade webcast presentation, using the success of Apple’s iPad as a working example, in "Use of @RISK for Quantifying Uncertainty in Innovation Project Management."

Product innovation has been described as the way out of today’s difficult business environment. However, the rate of success of development projects — in particular white space or disruptive innovation projects — remains too low.

The analysts at SpyroTek believes that a reason for the low success rate of development projects is the erroneous application of analysis methods designed for incremental innovation, such as NPV and DCF, to projects with high levels of uncertainty.

In the presentation, Jose discusses the use of @RISK and Probabilistic Decision Analysis in the management of innovation projects with high levels of uncertainty. Probabilistic decision analysis, when combined with the right management processes like Discovery Driven Planning, is a very effective approach to evaluate and manage the risk and potential of innovation projects.

» Watch "Use of @RISK for Quantifying Uncertainty in Innovation Project Management"
» View related slides from Dr. Briones

Q. I administer my company's concurrent network license. How can I tell who has a license checked out?

A. On the server, run LMTools and click the Config Services tab. Select the Palisade service and click the View Log button.  After the startup messages, you'll see a series of lines starting OUT or IN.

Each time a user opens the software, a line is written to the log with OUT and the user and machine IDs.  When that user closes Excel, the license is released and an IN line is written in the log. Anybody with an OUT line and no IN line still has Excel open.





We have a Knowledge Base article with more details: 
http://helpdesk.palisade.com/kb/article.asp?ID=1104

Join us this Thursday, Janurary 12, 2012, for a free live webcast entitled, "Refining the Business Case for Sustainable Energy Projects Using @RISK and PrecisionTree: A Biofuel Plant Case Study" to be presented by Scott Mongeau.

The sustainable energy industry sits at the nexus of growth and change: the popular groundswell for ‘green initiatives’, ongoing debates concerning global warming / climate change, fickle government incentives, the quest for renewable and alternative sources, expansion in developing economies, and the rapid emergence of new technologies. Sustainable energy industry sectors such as biofuel, solar, wind power each have unique selling points as well as practical challenges.  Across the board, profit margins are uncertain and tight, demanding detailed analysis and complex business cases.  Palisade's DecisionTools Suite is an ideal vehicle for conducting the deep risk analysis needed to separate the hype and ‘wishful vibes’ from the real risks and tangible profit cases needed to ‘green light’ sustainability projects.

Sustainable energy’s central competitor and sometimes partner, the petroleum majors, have distinct advantages, having established, streamlined supply chains and being embedded into the global economy.  However, traditional petroleum exploration is going to increasingly extreme and risky lengths to locate and exploit new reserves (i.e. Athabasca Oil Sands, deep sea drilling, project development in politically unstable regions).  The petroleum majors are dedicated users of the Palisade DecisionTools Suite to make their increasingly complex and risky business cases.

This free live webcast asserts that an energy development ‘risk / reward parity’ level is growing between new petroleum exploration and production and sustainable energy initiatives.  The presentation uses a biofuel plant case study as an example of how a profitable business case can be made for a sustainable energy project using techniques commonly applied in petroleum exploration and engineering initiatives.  The biofuel industry is expected to multiply its production by a factor of 50 by 2020.  The uncertainties of government subsidy, tax credits, and loan guarantees are crucial to meeting biofuel profit margins. Stochastic statistical analysis greatly improves the ability to pinpoint risk and to identify mitigation strategies. The case study uses @RISK to model biofuel project NPV, Evolver to suggest plant optimization strategies, and PrecisionTree to guide strategic decision making. The approaches presented have promise as a due-diligence tool for prospective sustainability entrepreneurs, investors, project managers, and firms. 

Scott Mongeau is Lead Consultant and Founder of Biomatica BV (biomatica.com), a niche consultancy specializing in biotechnology industry risk management. Scott has over a decade of experience in biotech, including key positions at Genentech Inc. related to risk management. He currently consults for several biofuel start-up initiatives and completed his thesis on biofuel project risk management. In addition to consulting, Scott is a part-time PhD researcher in the Executive Doctorate Program at Nyenrode Business University in the Netherlands. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management (MFM) from the Erasmus Rotterdam School of Management (RSM). Additionally, he holds a Certificate in Finance from University of California at Berkeley, a Masters in Communication from the University of Texas at Austin, and a Graduate Degree in Applied Information Systems Management from the Royal Melbourne Institute of Technology as a Rotary Ambassadorial Scholar. Having lived and worked in a number of countries, Scott is an American citizen and currently consults and conducts research from his office in Leiden, Netherlands.

» Register now (FREE)
» View archived webcasts

Creating a feasible budget is never easy, but it’s even more challenging during questionable economic times. That was the case for the Arc of Yates, an amazing organization in New York State that provides a wide array of services for individuals with developmental disabilities in Yates County.  For many non-profit organizations, funding often comes from local, state and federal sources. Given the current economic climate, Arc of Yates was faced with the prospect of slashed budgets on every governmental level, effectively leaving the organization with a fraction of the funding they have enjoyed previously.

With uncertainties as to where that funding would originate, Arc of Yates utilized risk analysis software @RISK to explore which areas of funding were most likely to be affected. Using probability distributions, Arc of Yates could forecast what portion of the current budget stream may not be available over the next three years. Subsequently, the organization could develop strategies to explore alternative means of funding. Now Arc of Yates has a clear plan of action to meet upcoming budgets for the foreseeable future.

We think Arc of Yates’ use of @RISK is a great example of how Monte Carlo Simulation can empower organizations and lessen the concerns and uncertainties that accompany a struggling economy. Knowing where potential shortfalls may occur offers decision-makers the foresight and flexibility to stay in front of budgetary gaps. On a personal level, it’s great to know that we were able---in some small way---to further the effort of a truly fantastic organization.

» Arc of Yates case study
» Take a look at the great work Arc of Yates is doing.

Randy Heffernan
VP, Palisade Corporation

Can averages be a dangerous measurement on which to base your decisions?

According to Britt Calloway, Research and Development Engineer for Bastian Solutions, they can be. In Britt’s case, he used a toy robot to model target throughput of an actual robot in a palletizing operation, and his concerns over uncertainty in this test lead him to @RISK.

Britt discovered that errors in picking up pieces of puzzle parts with his robot arm could drastically affect the cycle time he was measuring. For example, he would bungle the grab for a part, and it would slide away from the robot. He initially thought he would reject the mistakes in the test, but then realized those types of anomalies happen all of the time in the real world, and they would need to be represented as uncertainty in a model using Monte Carlo Simulation.

Britt elaborated . . . “In a robotic palletizing system, there can be box flaps missing, vacuum cups that my need to be replaced, human error, maintenance downtime, and other factors that affect throughput . . . Monte Carlo methods are a way to use engineering insight and more qualitative assessments of your inputs to define a quantitative output.”

Fortunately for Britt, the robot was a toy, but as you can see in his blog, Monte Carlo simulation is no toy when time and money are concerned.

See Britt’s blog here: http://www.bastiansolutions.com/blog/index.php/2011/11/23/the-sensitive-engineer-using-monte-carlo-simulation-to-understand-the-sensitivity-of-a-complex-system/

Join us this Thursday, November 10, 2011, for a free live webcast entitled, "Petroleum Resource Evaluation Using @RISK" to be presented by Dr. Ronald Brimhall.

This free live webcast contains instructions and demonstrations for using @RISK risk simulation software to examine net present value economic analyses for a petroleum resource. In this case, the asset is a low pressure gas reservoir. The main applications of @RISK cover in detail the spectrum of petroleum engineering analyses – rock and fluid properties, reservoir volumetrics, material balance, analogy, decline curve, and net present value. Microsoft Excel statistics spreadsheets with @RISK are the primary analysis tools. Basic principles are emphasized with the understanding that fundamentals may be applied to the entire spectrum of reservoir oil and natural gas assets in cases where variability and uncertainty in all relevant parameters are important.

Variability in rock properties are demonstrated by analysis of electric logs, Variability in original gas is place in demonstrated by comparing volumetric analysis and material balance for generalized reservoir (includes water influx and water production). Application of decline curve analysis with uncertainty in decline rate is applied to NPV analysis. A result of reserves determinations and NPV is compared with an alternative investment opportunity.

Dr. Brimhall’s experience covers 50 years in industry and in academia. He was part of the Petroleum Engineering Faculty at Texas A&M University, and maintained a professional practice related to formation evaluations, resource evaluations, log and pressure transient analyses, production operations for oil, natural gas and groundwater, as well as environmental and resource assessments for subsurface operations in energy and groundwater resources. His past project management experience includes business development as well as proper utilization of environment & natural resources.

» Register now (FREE)
» View archived webcasts

Join us this Wednesday, October 19, 2011, for a free live webcast entitled "Modeling Oil & Gas Risk Problems using The DecisionTools Suite" to be presented by Rishi Prabhakar.

In today’s industry getting oil and gas safely and efficiently to the surface is a significant challenge. Oil & Gas problems and decisions are beset by uncertainty in all areas: production forecasting, reserves estimation, calculating exponential decline, scheduling, etc. There are many complex options for strategy and operations. The DecisionTools Suite has been used successfully throughout the entire value chain in the Oil & Gas industry.  This webcast will feature some of the common problems and solutions addressed effectively by the DecisionTools Suite.

Rishi brings a broad range of experience and expertise to the Palisade team. He has worked in and consulted to the energy industry, telecommunications, scientific research, banking and finance with an emphasis on operational risk and Basel II. Rishi has expert skills in the areas of statistical analysis, simulation, time series forecasting, risk/capital modelling, extreme value theory, survey design and analysis. He holds a BSc Mathematics from the University of Technology, Sydney.

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While it is accepted wisdom that the world needs to increase its focus on sustainable energy, there is still much dispute about whether initiatives of this kind are financially viable.

Sark7 is a specialist consultancy that aims to bridge the gap between populist enthusiasm surrounding ‘green’ initiatives and the intensive financial risk analysis demanded by capital investors and participants. It uses Palisade’s DecisionTools Suite to develop profitable business cases for sustainable energy projects. In effect, these act as due-diligence tools for prospective sustainability entrepreneurs, investors, project managers, providers and organisations.

Taking a biofuel plant as an example (but stressing that the techniques used are equally applicable to sustainable energy initiatives such as wind or solar energy), Sark7 uses various components of Palisade’s DecisionTools Suite to demonstrate viability. @RISK models the biofuel project’s Net Present Value (NPV), PrecisionTree informs strategic scenario decision-making and Evolver suggests plant optimisation strategies.

The premise is simple: Sark7 believes that environmental experts and enthusiasts need to be realistic about whether their proposal is financially viable if they are going to attract the funds that will make it a reality. Palisade’s risk analysis tools provide the language that is familiar to the financial world, as a result of which the project is more likely to receive funding. Sustainability criteria can therefore be met at the same time as providing investors with a risk-balanced return - so everyone wins.

» Read the full case study

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.”

» More about Professor Asher Drory
» More about @RISK

For their capstone design projects, undergraduate mining engineering students at Missouri University of Science and Technology develop “real-world” solutions. So, Dr. Samuel Frimpong provides his students with real-world tools, including Palisade’s @RISK software.

Similarly, he uses @RISK to help graduate students undertake research projects in geology and geological engineering, mining and petroleum engineering.

Dr. Frimpong says the risk analysis software helps students with risk modeling for everything from investment to production. Projects might include production forecasting, reserve estimation, exponential decline, and other key areas. @RISK also helps students understand stochastic processes – how random events can affect engineering phenomena over time.

“@RISK offers a comprehensive package for simulating stochastic processes defined by parametric probability and statistics,” says Dr. Frimpong, who has been teaching at the Rolla, Mo.-based university for more than 6 years. “The Excel environment also makes @RISK user-friendly.”

He also praised the software’s “efficient pre-simulation definition of input variables and post-simulation results.”

Dr. Frimpong says he has been using @RISK since his time at University of Alberta, Edmonton, Canada, where he completed his PhD in 1992 and later served as an associate professor of mining engineering.

A native of Ghana, Dr. Frimpong holds several patents in the area of oil sands extraction and is a noted expert in mine design, mineral economics, modeling methods and operations research.

» More about Dr. Samuel Frimpong
» More about @RISK

Join us this Thursday, October 6, 2011, for a free live webcast entitled, "Modeling Behavior Using @RISK and PrecisionTree: Why probability estimates aren’t always what they seem," to be presented by Christopher Brand.

Over the past three decades, behavioral economists and psychologists have gathered a significant amount of evidence suggesting that most people find it surprisingly difficult to make accurate judgments about probabilities. This is a cause for concern in real-world decisions, which normally involve at least some degree of risk and uncertainty. Even more troubling are the consequences for long-term decisions, in which it is necessary to account for multiple possible sequences of events; if each event includes an erroneous prior probability judgment, this will have the effect of multiplying the overall error in the decision.

This free live webcast will discuss some examples of probabilistic decision making where intuitions and judgments are regularly incorrect -- such as the Monty Hall problem, the base rate fallacy, and the conjunction fallacy -- and demonstrate these cases via implementation within @RISK risk analysis software, and PrecisionTree decision trees modeling software. Furthermore, the presentation will also explain how models of behavior during decision making can be developed using Palisade software.

Chris Brand is an Associate at Captum Capital Limited, where he provides consulting and training services to early stage life science companies in the behavioral aspects of business development. He is also a PhD student at Birkbeck, London University where he is active in the psychology of decision making. Chris holds an MSc in Cognitive and Decision Sciences from University College, London, an MA in Philosophy from the University of York and a BSc in Philosophy and Psychology from the University of Keele.

» Register now (FREE)
» View archived webcasts

Calum Turvey, W.I. Myers Professor of Agricultural Finance, uses @RISK in his Risk Simulation and Optimization course. Offered by the Charles H. Dyson School of Applied Economics and Management at Cornell University, Risk Simulation and Optimization is in its second year and has now become a regular course offering, with roughly 45 students per semester. @RISK is used to evaluate problems of finance, including capital budgeting, investments, random walks, derivatives pricing and real options. Dr. Turvey was first exposed to Palisade’s tools when they were in their infancy: “If I recall, I was customer number 100 in 1987 when @RISK was introduced as a Lotus 1-2-3 application, and have used it ever since.”

On the importance of exposing his students to @RISK, Dr. Turvey explains, “Students of finance are largely taught finance from the view of certainty. Adjustments are made in the standard investment model to adjust for risk, but these are generally not insightful. Risk assessment using probabilities is confined to simple decision trees. What we do in this course is take the standard textbook in finance, and chapter by chapter we convert everything to a probability model, starting with coordinated financial statements to investigate cash flow risk and on to NPV applications. On the derivatives side, I show how Monte Carlo simulation can be used to replicate options prices, and how Monte Carlo simulation can be used to price exotic options.”

Calum Turvey received his PhD from Purdue University in 1988, after which he joined the faculty of Agricultural Economics and Business at the University of Guelph, obtaining the rank of professor, until 2002. In 2002, he joined the faculty of Cornell’s Department of Agricultural, Food and Resource Economics as professor and director of the Food Policy Institute and Chair from 2003-2005. In 2005, he joined the Department of Applied Economics and Management as the W.I. Myers Professor of Agricultural Finance. He is the editor of “Agricultural Finance Review” and conducts research in the area of agricultural finance, risk management and agricultural policy.

» Dr. Calum Turvey’s work, on SSRN
» More about @RISK

The @RISK — Excel Reports command selects reports to be generated on the active simulation results, or the current model definition.

A variety of different pre-built simulation reports are available directly in Excel at the end of a simulation. The Quick Report is a report on simulation results designed for printing. This report contains a single page report for each output in a simulation. The other available reports, starting with Input Results Summary, contain the same information as the equivalent report in the Results Summary Window or other Report windows.

You can also use template sheets to create your own custom simulation report. Simulation statistics and graphs are placed in a template using @RISK statistics functions (such as RiskMean) or the graphing function RiskResultsGraph When a statistics function or graphing function is located in a template sheet, the desired statistics and graphs are then generated at the end of a simulation in a copy of the template sheet when you choose the Template Sheets option in the Excel Reports dialog. The original template sheet with the @RISK functions remains intact for use in generating reports from your next simulation.

» Read more about generating @RISK reports in Excel
» View a short video demonstrating reports in Excel  
» Download the example file Template.xls to see how to set up your own report

What do banks, bond-rating agencies and homeowners in places like Las Vegas have in common? They all grossly misjudged risk and, as a result, made bad decisions during the recent housing bubble.

That’s why Dr. Arnoldo Camacho, a professor at the highly regarded INCAE Business School in Alajuela, Costa Rica, incorporates Palisade’s @RISK software in his MBA courses – Finance I, and Financial Institutions and Capital Markets.

“The Finance course focuses on the creation of value through efficient decision making, which involves risk analysis,” says Dr. Camacho, who has taught at INCAE for 22 years. “In the Financial Institutions and Capital Markets courses, an in depth analysis of credit risk requires the estimation of the probability of default of issuers of debt.

“In both courses, @RISK is used for simulation and sensitivity analysis.”

Camacho was introduced to @RISK through INCAE, where his courses attract typically attract 60 to 65 students.

When asked why it is important to expose his students to @RISK, Camacho says, “It is easy to handle, and it allows students to move from uncertainty to risk analysis, which requires critical thinking.”

» More about Dr. Arnoldo Camacho
» More about @RISK

You can easily flag any cell that contains @RISK input distribution functions or @RISK output functions using the Application Settings window of @RISK. Application Settings is located under the Utilities menu in the @RISK ribbon:


 
Here you can apply default cell formats to cells in your workbook where @RISK inputs and outputs are located. You can select a color for cell font, border or background.


 
Using the Application Settings Dialog, a wide variety of @RISK settings can be set at default values that will be used each time the program runs. These include graph color, displayed statistics, coloring of @RISK cells in Excel, and others.

@RISK is an add-in to Microsoft Excel. @RISK performs risk analysis using Monte Carlo simulation to show you many possible outcomes in your Microsoft Excel spreadsheet—and tells you how likely they are to occur. This means you can judge which risks to take and which ones to avoid, allowing for the best decision making under uncertainty.

» Read more about Application Settings in @RISK

Join us this Friday, September 9, 2011, for a free live webcast entitled "Modeling Time Series Forecasts with @RISK," to be presented by Eric Torkia. 

Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.

This free live webcast is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm.

We will discuss these topics as well as present practical risk analysis models and applications using @RISK.

Eric Torkia has practical experience as a consultant and as a coach with organizations implementing technological and strategic change, including Analytics and Enterprise Systems.

» Register now (FREE)
» View archived webcasts

Join us this Thursday, September 8, 2011, for a free live webcast entitled, "Project Management Risk Modeling Methods using The DecisionTools Suite," to be presented by Rishi Prabhakar.

In the field of Project Management, risk exists in scheduling, costing, contracted risk allocation, maintaining, and delivering. Options and uncertainty play a significant role in the success of project engagements. With so much uncertainty, project managers require sophisticated methods for evaluating risk. This webcast will highlight a few primary applications of The DecisionTools Suite for addressing risk and uncertainty in Project Management.

Rishi Prabhakar is a Consultant and Trainer at Palisade Asia-Pacific, based in the Sydney, Australia. Rishi brings a broad range of experience and expertise to the Palisade team. He has worked in and consulted to the energy industry, telecommunications, scientific research, banking and finance with an emphasis on operational risk and Basel II. Rishi has expert skills in the areas of statistical analysis, simulation, time series forecasting, risk/capital modelling, extreme value theory, survey design and analysis. He holds a BSc Mathematics from the University of Technology, Sydney.

» Register now (FREE)
» View archived webcasts

A key issue facing organisations operating in newly-deregulated markets is that they must become competitive if they are to ensure their longterm survival.

In Eastern Europe, the unbundling of Romania’s power monopoly resulted in the generation of a ‘market’ for electricity for the first time, and with that the introduction of competition. This saw power companies exposed to market risks.  As a result, they are now required by the country’s energy regulation agency to undertake risk analysis to ensure that they can identify and measure risk, as well as factor in a source of finance that will mitigate this.

The electric power grid company, Transelectrica, turned to risk analysis with @RISK to enable it to operate as a commercial organisation and meet EU standards.  Using an @RISK model enables Transelectrica to analyse the risks specific to the energy market, as well as the impact that different tariffs, aimed at mitigating these risks, will have on the organisation overall. This helps to determine the level at which the risk component in its tariff should be set to ‘insure’ Transelectrica against financial repercussions should adverse events occur.

Historical data ‘measuring’ potentially adverse events in terms of the likelihood that they will occur and the consequences if they do are used as the inputs to the @RISK model. Transelectrica can then determine the level at which it needs to set its tariffs.

For example, in 2003 a drought across eastern Europe led to a water shortage that resulted in the hydro-electric companies in the region generating less power. Factoring this into Transelectrica’s @RISK power transmission price model showed an increased risk exposure that must therefore be reflected in the tariff.

The use of risk assessment based on @RISK has resulted in a significant improvement in the regulatory framework of the Romanian power market. The coherent risk management activity at Transelectrica also enabled the grid company to attain a good financial rating, thereby ensuring it could meet the requirements of the EU Directive (114/2008) to protect critical infrastructure in Europe.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

@RISK risk analyis software gives you multiple options for saving Monte Carlo simulations you have run and comparing them with other simulations.

These include:

• Storing simulations in your Excel workbook
• Saving simulations as a separate .RSK5 file outside the workbook
• Using the @RISK Library for storing and comparing different simulations.

When you want to store simulation results and graphs, @RISK allows you to keep all data in your Excel workbook. This makes it easy for you to give simulations to others, without worrying about sharing a separate simulation file. However, if you want to store the simulation results in a separate .RSK5 file in order to reduce the file size of the Excel workbook, you have that option, too.



When a simulation is saved in your workbook, all data and graphs are stored and will be automatically opened the next time you open the workbook in Excel with @RISK running.

You can also use the Application Settings command in the @RISK Utilities menu to specify the default location where you wish to store your @RISK data. These options make it easy to manage your risk analysis models.

» Read more about Saving Simulations

Today a moderately powerful earthquake rattled Washington, D.C. and was felt as far north as Massachusetts. Sitting here, feeling the earthquake shake my desk and water glass in central New York State, hundreds of miles from the epicenter, I was reminded that we are never far from the risk of natural disaster.

The Washington Post’s Jason Samenow wrote today that aftershocks are a significant concern. Although the 5.9 magnitude quake did not appear to cause significant damage, earthquakes are rare in the region and people are ill-prepared for them. According to the Post: “McNutt, director of USGS expressed a concern that the earlier quake will precede something more powerful: ‘What the concern is, of course, is that this is a foreshock. If it’s a foreshock, then the worse is yet to come.’ If not a foreshock, Mike Blanpied, associate coordinator for the USGS earthquakes hazards program cautioned aftershocks are possible: ‘Aftershocks could go on for days, weeks, or even months. They’re most likely to be felt under the next three or four days.’”

It got me thinking to ways that risk and data analysis techniques that we use every day in business applications could be applied in this situation. After all, the use of Monte Carlo simulation and and decision trees in DecisionTools Suite software has been used to cope with natural disasters – from volcanoes to hurricanes.

A few years ago the US Geological Survey asked the same question in an interesting study on the use of Monte Carlo simulation for the prediction of aftershocks in California. The paper, published in 2008, notes the typical absence of data specific to a particular earthquake site and examines the usefulness of Monte Carlo simulation for “assessing recurrence from limited paleoearthquake records.” In the absence of data, Monte Carlo simulation can be quite effective.

In a similar situation, the use of neural networks was examined by researchers in China to get a handle on the risk of aftershocks from the enormous 2009 Sichuan province quake. In their paper, published by the Journal of Sustainable Energy and the Environment in 2009, data from initial aftershocks was provided to a neural network so that it could “learn” any patterns in the aftershocks. These patterns were then used to predict future tremors. The concentration and trend of the aftershocks was predicted “precisely,” according to researchers.

In science as well as business, quantitative risk and decision analysis techniques produce tangible benefits that directly impact many of us.

Randy Heffernan
VP, Palisade Corporation