"The seamless integration of Palisade products into the Excel
development environment is a huge advantage... This in turn
reduces training and system development costs, because risk
departments are not investing in expensive/extensive
proprietary system solutions." Darren O'Connell, Researcher, Curtin University of Technology
The Greenup Locks and Dam system is critical to a number of companies in the coal, petroleum, iron and steel companies that transport their materials along the Ohio River. In basic terms, a locks and dam system traps and contains water in a mechanism that transports large vessels past an area where the river is unnavigable. More than 62 million tons of materials pass through the Greenup Locks and Dam annually, with that number expected to increase to more than 113 million tons by 2030.
In 2011, a study utilizing Monte Carlo simulation and @RISK was commissioned to analyze the internal stability of the system’s middle wall, which forms the water barrier separating two lock chambers. The study set out to answer three specific questions:
What is the probability of unsatisfactory performance throughout the planning horizon, through the year 2070?
When is rehabilitation work anticipated to be necessary?
What should be done to insure continued safe operation of the locks?
The results from the study allowed the owners of Greenup Locks and Dam to accurately forecast when certain monoliths would need repair, based on their current level of stability. Having that critical insight ensured the creation of a repair timetable that would still allow the system to remain open, and keep the flow of materials—and the economies of critical industries—moving.
Each March, office productivity is greatly impacted by the NCAA Men’s Basketball Tournament. Just about everyone—regardless of their passion for college basketball—fills out a bracket in hopes of winning their respective pool (or pools). Some rely strictly on their knowledge of the teams involved, while others choose winners based on uniform color, where they’d rather live or other arbitrary strategies.
Wayne Winston might suggest utilizing Monte Carlo simulation to enhance your chances of racking up bracket points. Winston, who is a Professor of Decision Sciences at Indiana University, spends a great deal of his free time crunching numbers and producing probabilities of how teams will perform. This time of year, he determines the probability that schools in the 68-team field have of losing in the first round, winning the national championship and everything in between, with his use of @RISK. His methods were recently mentioned in a Wall Street Journal article.
Using power rankings from USA Today’s Jeff Sagarin (who just happens to be his best friend) and the tournament seedings, Winston simulates the tournament 10,000 times to get his results. Before the tournament started, he found Louisville to have a 21.6% chance of winning the national title. After winning their first two games against North Carolina A & T and Colorado St. and advancing to the Sweet 16, Louisville’s probability to win it all rose to 26%.
From time-to-time, the most unlikely statistical situations become reality. Before 2013, no 15-seed had ever advanced further than the first round of the NCAA Men’s Basketball Tournament, so it was no surprise that Florida Gulf Coast University had only a 1.5% chance of making it to the Sweet 16, and virtually no chance of winning the national championship. The Eagles’ statistical defiance changed the face of many brackets when they upset both Georgetown and San Diego St. to advance to the Sweet 16. Despite their two magic moments, Florida Gulf Coast University still has mere a one one-thousandth of a percent chance of cutting down the nets in Atlanta on April 8. It’s a statistical long shot, but one we—and Wayne Winston—will be watching with great anticipation.
To see more of Wayne Winston’s sports probability work, visit his website:
What will be the effect of a new health policy in a country?
How do we quantify and mitigate health risks in our food supply?
Should we allow imports of a certain agricultural product?
Quantitative risk analysis techniques can help you answer these and many more questions. A new postgraduate certificate program in Risk Analysis in Health and Food Safety is being offered by EpiX Analytics and the Royal Veterinary College (RVC), University of London. “People interested in risk analysis in health and food safety come from a variety of backgrounds, so it was important for us to use software that is not only statistically sound, but also user friendly. This is why we chose @RISK as the primary teaching software for this course,” said Dr. Francisco Zagmutt, managing partner of EpiX Analytics and one of the course instructors.
This course will use many examples and case studies to provide participants with the skills for cutting-edge quantitative risk analysis in health and food safety. The course is designed to accommodate the work schedules of professionals from research institutions, governmental and international agencies; agricultural, food, pharmaceutical and related industry; and academic staff and post graduate students.
PricewaterhouseCoopers put together a beautiful video about the Corridor Budgeting program, developed by Tobias Flath and Michael Hofmann. Corridor Budgeting is a methodology combining planning, business management, and risk management in order to prepare for a range of possible outcomes, and thereby generate a more realistic picture of the future.
Palisade's DecisionTools Suite is an integral part of this program. As explained in the video: "We identify the factors underlying variables and ranges that impact on financial results. The Corridor is regularly analyzed and validated, giving visibility into any gaps or delta between target bandwidths and actual figures. Specially designed software then aggregates these bandwidths and event risks to paint an accurate picture of possible scenarios."
The video depicts modeling with Monte Carlo simulation: "A simulation models the total risk exposure. The width of the curve indicates the level of uncertainty. The height shows the likelihood that an event will occur, giving senior executives insights into the full spectrum of future possibilities, and making it easier to make the right decisions."
The video concludes, "Corridor Budgeting allows businesses to forecast more realistically, and prepare more effectively for what's to come, equipping them to face the future, however it turns out." It's a simple and beautiful explanation of how risk and decision analysis are a central part of business decisions today.
Palisade is pleased to announce that version 6.1 of @RISK and the DecisionTools Suite is coming soon. This new version will feature an enhanced, faster engine for the simulation of Project schedules, as well as compatibility with Excel 2013, Windows 8, and 64-bit versions of Microsoft Project. It has also been translated into Spanish, Portuguese, German, French, Japanese, and Chinese.
We encourage you to try a special Release Preview of version 6.1 available now, in all seven languages.
Operational risk management has been a key part of the LEGO Group’s strategy for many years. The approach is used to handle issues including supply disruptions, demand volatility, currencies, employee health and safety, and product quality and safety.
Today, the sophistication of LEGO’s Enterprise Risk Management (ERM) framework is widely recognised. It is one of the foremost companies to use Monte Carlo simulation to quantify risk and present key risk information to its board of directors for decision-making purposes. @RISK from Palisade helps the LEGO Group to manage much of the risk it faces. This gives the company a true understanding of the volatility that it believes is inherent in its activities, in order that it can act to pre-empt it.
The LEGO Group uses @RISK to consolidate its risk portfolio. The standard way to calculate overall risk is to multiply the likelihood with the impact for each risk, and add up the total for the portfolio giving an average loss. The LEGO Group however takes a more strategic approach, in the belief that risk management is about extremes, not averages. It wants to know what will happen in the 10% chance that something does occur, not the 90% chance that it doesn’t.
LEGO Risk Management therefore identifies all risks it faces which include factors such as competitor infringements, currency risk and vendor breakdown (if a packaging vendor suffers a strike or a fire, for example, this impacts LEGO’s ability to do business). It then quantifies the risks with @RISK so that each risk is assessed in terms of the chance of it happening and the cost / impact if it does.
Once management has defined figures for all risk factors, it adds up the total exposure that it faces using a Monte Carlo simulation. From this the 5% worst-case exposure is looked at, and compared to the risk tolerance that the company has set itself, based on its earnings, to see if this is acceptable. If it is above the risk exposure, risk handling needs to be addressed to reduce the risk. If it is below, the company is allowed to follow a more aggressive – or ‘risky’ - policy.
Knowing its risk tolerance and consolidated exposure against its actual tolerance enables management to make informed and intelligent decisions about its capacity to take on more risks or be more cautious.
Defining and addressing strategic and operational risks enable the company’s managers to take prudent and proactive mitigating actions, which result in better performance.
Executive Insight serves C-level executives in the healthcare field, focusing on the latest strategies, protocols and products.
Heffernan's article explores the benefits of risk analysis technology for healthcare organizations.
"How can healthcare organizations accurately assess risk?" asks Heffernan. "For many healthcare providers, risk analysis technology, which utilizes a computational method called Monte Carlo simulation, has proven to be a valuable asset in understanding risk factors and making crucial decisions that address real-world healthcare concerns, such as determining optimal staffing, patient capacity and facility expansion."
Several applications of risk analysis technology for healthcare are explored, including avoiding staffing shortages, patient capacity projections, and facility expansion.
It’s estimated that the human population will be nine billion by 2030. The Food and Agriculture Organisation believes that aquaculture, which currently provides around half of the fish and shellfish eaten around the world, is the only agricultural industry with the potential to meet the protein requirements of this size of population.
However, one of the biggest constraints to achieving this is the depletion of stock levels through disease. Biosecurity measures, which aim to prevent, control and ideally eradicate disease are regarded as essential. However, encouraging the adoption of these practices are often difficult due to aquatic farmers’ levels of education, training, responsibility and perceived economic benefits. In addition, global estimates of disease losses may appear remote and irrelevant to farmers and producers.
Having seen Palisade’s risk analysis tool @RISK being demonstrated, Dr Chris Walster, a qualified veterinary surgeon and the secretary of the World Aquatic Veterinary Medical Association (WAVMA), started using the program to calculate the realistic risk of aquatic disease to farms, with a focus on cases where data inputs were limited. The capacity of @RISK to present data in an easy to understand way meant farmers could more easily understand the disease risk probabilities, review the cost/benefit of disease prevention and make informed choices about whether to put controls in place.
“@RISK enables farmers to reduce the risk of disease spreading amongst their animals whilst minimising additional costs,” Dr Walster explains. “For aquatic vets, the key is the graphs which allow us to demonstrate a complex probability problem quickly and simply in a way that is easy to understand and trust. These inform decision-making, thereby helping to boost the world’s aquatic stock whilst safeguarding farmers’ livelihoods.”
“This technique also potentially offers an economical method of assisting in the control of many diseases. Farmers undertake their own tests, with each of these providing incremental inputs so that the macro picture can be developed and acted upon,” concludes Walster.
All DecisionTools Suite products now include revised and updated example files, as well as all-new examples in a range of industries. All examples are clearly organized and easy to navigate. These examples have been designed and written by leading MBA professor and author Dr. Chris Albright of Indiana University. They provide step-by-step guidance on setting up and running models to address a wide range of industry applications. In addition, many examples offer short, built-in videos that walk you through the model.
Furthermore, new video tutorials have been developed by Dr. Albright to help new users get started quickly, and to help experienced users get more out of the software.
Quick Start models files and videos
These short, interactive tutorials are designed to teach you how to use DecisionTools software by walking you through an actual model – all under 30 minutes! Build a model step by step, following the simple video instructions provided.
Guided Tour Videos
Guided Tours explain the functionality of each part of the software ribbon in detail. Learn how the features work, improve your efficiency, and avoid errors in your models.
Example Videos
Follow along as experts demonstrate the powerful analytical features of the DecisionTools Suite, and then open the actual risk and decision analysis models. Models are available here, and linked directly from your software.
Like the inner workings of an intricate timepiece, major projects are made up of a number of parts—often moving in different directions—that must all work in concert to keep accurate time. The functioning interconnectivity of the smaller projects is greatly impacted by an organization’s ability to accurately forecast the risk, both major and detailed. For management consulting firm Value Management Strategies (VMS), Monte Carlo simulation has proven to be an effective method of risk management for many multi-level projects.
Value Management Strategies, Inc., based in Escondido, Ca. is a management consulting firm specializing in value analysis, value engineering and risk management for organizations in both the public and private sectors. VMS clients include state and federal government agencies, private engineering and architectural firms and local and international manufacturing firms.
Many of VMS’ clients are looking to deliver projects, implement processes or produce innovative products in specific verticals. To successfully complete these projects, they turn to VMS to analyze the seemingly never-ending maze of uncertainty and risk that exists in various projects and to develop risk response strategies and action plans that maximize opportunities and minimize threats. In this capacity, the VMS’ utilization of Monte Carlo simulation through @RISK helps to support the decision-making process, as well as offer a more granular understanding of the nature of uncertainties being characterized. Clarity of the likelihood and exposures of risks can help to minimize delays and maximize efficient deployment of capital resources.
We’re excited that @RISK is able to help VMS to keep its clients “moving parts” moving in the right direction. Time is money…tick-tock, tick-tock.
@RISK can be very effectively used to forecast the production of oil and gas reserves about which little is known. This is a simple model forecasting production for a particular oil well. The estimated reserves within the well are uncertain and are represented with a Lognormal distribution function. The mean is 500,000 STB and the standard deviation is 50,000 STB.
The output in the model is the NPV of the reserves for the first 10 years of production. Other factors considered include the decline rate, the gas-oil ratio (GOR), the prices of oil and gas, as well as the rate of increase of the prices of oil and gas. The only input factor that contains an @RISK probability distribution function is reserves, but you could make the model more realistic by using distribution functions to describe the decline rate, GOR, price of oil, etc.
It is important for an insurance company to estimate the amount of claims it will incur in a given year. This series of models assumes that there are three types of claims: auto, general liability, and worker's comp. Historical data for the company is included. The top section lists the numbers of claims for the previous 20 years. Summary measures across years are shown to the right. These include correlations, since it is conceivable that the number of claims of one type might be correlated with the number of claims of another type. The bottom section lists the claim dollar amounts for all claims in the most recent year. Summary measures for these are also listed to the right.
The base model assumes that the numbers of claims and the amounts of typical claims are known for the coming year, and that they are equal to the means of the historical data. For example, it assumes that there will be 994 auto claims, each for the amount $3,409. Then the total of all claims, shown in the red cell, is the sum of products of the values in rows 4 and 5. Of course, there is a lot of uncertainty in the values in rows 4 and 5, so later models that include this uncertainty are much more realistic than this deterministic model.
Additional models are included using the RiskCompound function to summarize claims, and resampling to deal with minimal historical data.
The July 14th issue of E-Commerce Times features an article by Palisade's Randy Heffernan about the importance of understanding risk factors in the technology sector, and planning strategies accordingly.
"Tech mainstays and startups alike now require a clearer forecast of a project's probability of success or failure," writes Heffernan. "Clearly, the margin or error has drastically shrunk, and organizations are paying greater attention to analyzing the risks associated with each project, investment and acquisition."
Heffernan gives recommendations about identifying and communicating current external and internal risk factors, and outlines how Monte Carlo simulation and computing risk can help.
The ongoing debate about the feasibility of offshore windfarms as a renewable energy source continues to generate discussion and headlines.
A key issue for potential operators is that offshore windfarms face more adverse weather conditions, such as higher wind speeds and the increased risk of being struck by lightning, than their onshore counterparts. In addition, a failed offshore turbine may take several months to repair, rather than a few days. The financial implications of repair and downtime must therefore be factored in to any calculations related to the feasibility of the operation.
This is made more difficult due to the inherent level of uncertainty involved. For example, the nascent nature of the industry means there is a shortage of historical data on the failure frequencies of the turbines. Even information that is available is subject to uncertainties – such as the prices of crane ships and access vessels (which may vary per season and even from day-to-day), the cost of spare parts, and the electricity price, as well as the lead times of spares and vessels.
However, using @RISK (Palisade's Microsoft Excel software add-in for risk analysis using Monte Carlo simulation) for probabilistic analysis, the Energy Research Centre of the Netherlands has developed an innovative approach to determine whether offshore wind farms are financially viable from an operations and maintenance perspective. The @RISK risk simulation software model produces a distribution that determines the uncertainty associated with the downtime and maintenance costs of an offshore wind farm. This enables a project developer to make an informed decision, firstly as to whether to proceed with the project and then, if this is affirmative, the best way to do so.
Measuring the uncertainty also helps to make the project more viable in terms of financing. With the help of @RISK, wind farm developers – and their potential investors – can make informed decisions about whether an offshore operation will offer a good return on investment.
In evaluating a business decision, oftentimes Net Present Value is used to make a determination of whether a opportunity is worth pursuing. In this short video, Thompson Terry looks at a new product projection analysis, where a new specialty product is being brought to market. The product has a high selling price, but a small number of sales units. The analysis is looking to determine if this is an opportunity worth pursuing, by using Net Present Value in a Cash Flow analysis.
The analysis is completed in @RISK, a Microsoft Excel add-in for risk analysis using Monte Carlo simulation.
The June Baseline magazine features an article by Stephen Johnson, chief financial officer of Arc of Yates, explaining how the nonprofit uses @RISK and Monte Carlo simulation to account for potential shortfalls in annual budget planning.
Founded in 1975, Arc of Yates is a nonprofit organization for people with developmental disabilities. Arc of Yates provides a wide range of community-based services, including service coordination, residential living, clinical services, employment opportunities, and industrial and educational development throughout Yates County in upstate New York.
Johnson outlines how Arc of Yates used @RISK to develop effective strategic financial contingency plans and communicate challenges to board members and other stakeholders. Johnson writes, "We also were able to quantify the magnitude and probability of risks in our funding streams that could affect our financial planning. And we could update risks monthly or even weekly to keep up with our fast-paced environment. As a result, we were able to gain crucial insights that enabled us to develop our three-year strategic financial plan."
He continues: "Using @RISK has empowered our organization to foresee potential obstacles and shortfalls in our budgetary forecast. By understanding risks, we are able to both quantify and focus on other activities that may generate replacement revenue and produce contingency plans for cost reduction."
Alternative Energy eMagazine interviewed Randy Heffernan, Vice President of Palisade, for the June/July 2012 issue. Questions addressed in the interview include:
What is risk analysis? How does it work?
How can companies in the energy industry use risk analysis?
Is there a minimum project budget that you would consider to begin to benefit from risk analysis?
What are some of the benefits of analyzing risk early on?
At what stage of a project do you recommend starting the risk analysis, who should be involved and where does the data come from?
What are the costs involved with risk analysis?
Can you share some examples to illustrate how risk analysis can be an important tool for analyzing the success of clean energy projects?
The article highlights FutureMetrics' use of @RISK to hedge wood prices in the production of burning wood pellets. Based in Bethel, Maine, FutureMetrics is recognized as one of the leading domestic experts in the economics of the production and use of renewable bioenergy.
Today, the global consumption of wood pellets is approximately 15 million tons per year. It is expected to exceed 45 million tons by 2020. The largest market for wood pellets exists in Europe, where regulations encourage the use of carbon neutral fuels, like biomass. With such a dramatic jump in global wood pellet consumption predicted in the future, FutureMetrics and INRS, via their partnership in FutureEnergy Partners developed a wood price hedging product that uses @RISK to measure its effectiveness.
“I have been using @RISK since the early 1990’s, so I have a perspective on the commitment to the users that the Palisade team has,” said Dr. William Strauss, president and founder of FutureMetrics. “Every new version has new and very useful features that often are based on feedback from users. Palisade’s @RISK software has the richest feature set and has the most relevant tools for analyzing models that contain uncertainty.”
Join us this Thursday, May 24, 2012, for a free live webcast entitled, "A Stochastic Simulation Model for Dairy Business Investment Decisions " to be presented by Dr. Jeffrey Bewley.
A dynamic, stochastic, mechanistic simulation model of a dairy enterprise was developed to evaluate the cost and benefit streams coinciding with investments in Precision Dairy Farming technologies. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user-friendly, farm-specific, decision-making tool for dairy producers or their advisers and technology manufacturers.
The basic deterministic model was created in Microsoft Excel. @RISK was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model comprised a series of modules, which synergistically provided the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical U.S. dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. The economic feasibility of investment in an automated BCS system was explored to demonstrate the utility of this model.
An expert opinion survey was conducted to obtain estimates of potential improvements from adoption of this technology. Benefits were estimated through assessment of the impact of BCS on the incidences of ketosis, milk fever, and metritis; conception rate at first service; and energy efficiency. Improvements in reproductive performance had the greatest influence on revenues followed by energy efficiency and disease reduction, in order. Stochastic variables that had the most influence on NPV were: variable cost increases after technology adoption; the odds ratios for ketosis and milk fever incidence and conception rates at first service associated with varying BCS ranges; uncertainty of the impact of ketosis, milk fever, and metritis on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percentage of cows with BCS at calving ≤ 3.25 before and after technology adoption. The deterministic inputs impacting NPV were herd size, management level, and level of milk production. Investment in this technology may be profitable; but results were very herd-specific.
Investment decisions for Precision Dairy Farming technologies can be analyzed with input of herd-specific values using this model. This free live webcast will go into detail about the model, as well.
Dr. Jeffrey Bewley, is Assistant Professor and Extension Dairy Specialist, specializing in Dairy Systems Management, Dairy Decision Making, and Precision Dairy Farming, at the University of Kentucky. He received his BS from the University of Kentucky, MS from the University of Wisconsin-Madison, and PhD from Purdue University. Jeffrey has a keen appreciation for the opportunities automation provides for more precise management. His own work is in the technical and economic assessment of Precision Dairy Farming technologies, body condition scoring, and temperature monitoring. His outreach program is focused on developing tools and strategies for improved decision-making on dairy farms.
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