Enterprise Risk Management at Russian telecoms giant, MegaFon

Every year, the eight regional branches of Russian mobile network operator MegaFon are required to undertake a major planning and accounting exercise for the 12 months ahead. Each branch states the risks it faces, such as competition, changes in legislation that will require it to operate differently, price increases and changes to staffing costs. They also calculate how much each budget will be over or under the forecast.  

The risk management team at MegaFon’s headquarters amalgamates the information from each of its offices and simulates possible scenarios using a model using @RISK. The variables within these models are analysed in order to identify and mitigate against the five critical factors most likely to significantly affect the company’s gross revenue.

In addition, @RISK shows realistic minimum, best case and median budget figures and the probability of their occurrence. These are compared to the budget plans to determine whether the forecast is too aggressive or not ambitious enough. Overall, the management team can see whether their desired revenue is achievable.

At the same time, MegaFon needs to plan for the continuous upgrading of its network. Projects include building new antenna, installing the latest equipment and laying fibre optic cable. In 2012, MegaFon took the decision to invest in a large data centre construction project.

Two potential locations were shortlisted and the management team used the DecisionTools Suite to make an informed decision on the optimal one. TopRank was used to perform sensitivity analysis to identify the factors in each location that would have the most influence over the total cost of the project. @RISK was then applied to forecast how these critical factors might change. This allowed MegaFon to understand the most likely Net Present Values (NPVs) for each possible location and identify the risks for building or not building (i.e. opportunity cost) each data centre.

Using the DecisionTools Suite enables MegaFon to integrate risk management within its budgeting and investment planning processes. This provides the company’s management team with transparency and understanding about the risks involved in planning, and therefore facilitates decision-making without guesswork. As a result, the company can maximise capital expenditure efficiency.

» Read the case study: Enterprise Risk Management programme at Russian telecoms giant MegaFon

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Custom Solutions: Using @RISK for Oil Field Development Decisions

Oil companies need to assess new fields or prospects where very little hard data exists. Based on seismic data, analysts can estimate the probability distribution of the reserve size. With little actual data available, companies still must quantify and optimize the Net Present Value (NPV) of this asset. The number of wells to drill, the size of the processing facility, and the plateau rate of the field must all be optimized. The following example is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel.

It is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel.  - See more at: http://blog.palisade.com/blog/decision-making-under-uncertainty-2/how-to-create-a-custom-application-for-stock-portfolio-optimization-right-in-your-spreadsheet#sthash.0by9ZU0n.dpuf
It is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel.  - See more at: http://blog.palisade.com/blog/decision-making-under-uncertainty-2/how-to-create-a-custom-application-for-stock-portfolio-optimization-right-in-your-spreadsheet#sthash.0by9ZU0n.dpuf
It is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel.  - See more at: http://blog.palisade.com/blog/decision-making-under-uncertainty-2/how-to-create-a-custom-application-for-stock-portfolio-optimization-right-in-your-spreadsheet#sthash.0by9ZU0n.dpuf

Oil Field Development Screenshots

This analysis can be simplified by representing the production profile by three phases:

  1. Build up: The period when wells are drilled to gain enough production to fill the facilities.
  2. Plateau: After reaching the desired production rate (plateau), the period when production is continued at that rate as long as the reservoir pressure is constant and until a certain fraction of the reserves is produced. In the early stages of development, this fraction can only be estimated, and production above a certain rate influences plateau duration.
  3. Decline: The period when production rates, P, decline by the same proportion in each time step, leading to an exponential function: P(t) = P(0) exp(-c*t), where t is the time since the plateau phase began and c is some constant.

With only estimates for the total Stock Tank Oil Initially In Place (STOIIP = reserve size) and percent recovery amounts, the objective is to select a production rate, a facility size, and well numbers to maximize some financial measure. In this example, the measure used is the P10 of the NPV distribution. In other words, the oil company wants to optimize an NPV value which they are 90% confident of achieving or exceeding.

As described, the problem is neither trivial nor overly complex. A high plateau rate doesn’t lose any reserves, but it does increase costs with extra wells and larger facilities. However, facility costs per unit decrease with a larger throughput, so choosing the largest allowed rate and selecting a facility and number of wells to match might be appropriate.

This is just one example of how Palisade can provide personalized risk solutions for your business needs. We offer custom software development services as well as software developer kits to create your own applications integrating @RISK, RISKOptimizer, and other Palisade technology. We can also help automate Palisade software using VBA in Microsoft Excel or Project.

» Learn more about Palisade's Custom Development

You may also be interested in the following:

Free Webcast this Thursday: "Exploring Oil & Gas Applications of @RISK and the DecisionTools Suite" - See more at: http://blog.palisade.com/blog/risk-and-decision-analysis-news/free-webcast-this-thursday-exploring-oil-and-gas-applications-of-risk-and-the-decisiontools-suite#sthash.sev3ym2s.dpuf

Cost Risk Analysis Example Movie: Palisade's Custom Development Team uses @RISK's XDK for this custom application
 

 

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Free Webcast this Thursday: "Exploring Oil & Gas Applications of @RISK and the DecisionTools Suite"

Register now for a free webcast to be presented by Rafael Hartke, Palisade's Oil and Energy Industry Consultant. "Exploring Oil and Gas Applications of @RISK and the DecisionTools Suite: Examples from Petrobras and Others" will demonstrate how the DecisionTools Suite can be used in oil and gas exploration, production, and project decisions.

JOIN US THIS THURSDAY - December 19, 2013 - 11:00am ESTRegister Now
"Exploring Oil and Gas Applications of
@RISK and the DecisionTools Suite:
Examples from Petrobras and Others"

For decades, @RISK has helped engineers and finance managers estimate unknown reserves, value new projects against each other, and craft optimal strategies. PrecisionTree, another tool in the DecisionTools Suite, is commonly used for drilling strategy discussions, production siting problems, and other multi-stage, sequential decisions. RISKOptimizer comes into play when companies need to determine the best “mix” of projects in their portfolio in order to maximize overall returns.

For many years, Rafael Hartke solved these types of problems at Petrobras, the state oil company of Brazil and one of the world’s largest producers. In this free live webcast, he will draw on his experiences to demonstrate how @RISK and the DecisionTools Suite can be applied to common types of issues faced by oil and gas producers, such as: estimation of unknown reserves, analysis of multi-play concessions, structure of complex partnership agreements, optimization of uncertain project portfolios, and more.

Rafael Hartke is also a contributing writer for Oil & Gas Monitor. Here are a few of his previous articles:

 

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Palisade's Risk Analysis Solutions at Work in China

Palisade is establishing a robust presence in China. Recently, Palisade published two new case studies from the country; one details how the Shanghai Food and Drug Administration uses @RISK software for risk and decision analysis in food safety  and risk assessment. Specifically,  @RISK has helped the Shanghai FDA carry out exposure assessments of chemical and biological contaminates, as well as analyzing surveys of data on residents’ expenditure on various foods. In one case, @RISK helped asses nitrite contamination risk for cooked meats, in another, it aided in evaluating the likelihood of vomitoxin contamination of wheat products. With these and many other successes, the Shanghai FDA has been able to implement effective risk management recommendations.

The other Chinese case study details how @RISK and the DecisionTools Suite have been used in Professor Li Mian's research and his graduate-level engineering course at Shanghai Jiao Tong University. Prof. Li 's research includes complex engineering system design and optimization, decision-making and optimization theory.

Both of these reports also appear in Chinese on the Palisade website:

Read the case studies in English here and here.

See also: Palisade's Risk Analysis Software Ensures Shanghai Food Supply is Safer Palisade's Decision Tools Used At Shanghai's Top University

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Palisade's Decision Tools Used At Shanghai's Top University

Palisade prides itself of software tools that can be easy to use and multi-use. Professor Li, of the University of Michigan – Shanghai Jiao Tong University Joint Institute, provided some enthusiastic feedback on Palisade products in a recent case study.

Prof. Li 's research includes complex engineering system design and optimization, decision-making and optimization theory. Li uses Palisade's @RISK tools into his research and courses at Jiao Tong University. In his research, Li routinely uses @RISK, PrecisionTree, TopRank, and StatTools to do specific calculations in system design process, enabling him to analyze his ability to guarantee original design performance while changing different variables. He also uses the tools to perform decision analyses for system users.

Li says that he compared @RISK to other research software on the market, including Matlab. While Matlab and similar software offer some of the risk and decision analysis functionality found in Palisade’s software, researchers have to add their own code to access this level of functionality, taking considerable effort and time. In contrast, Palisade risk and decision analysis software simplifies these functions so that they are easy to use, and allows the creation of comprehensive data models.

Using DecisionTools for Product Pricing and Engineering Design
Prof. Li gives two examples on how the software is used in his teaching and research. The first involves a study of pricing risk and decision-making when introducing a new product to the market. Many factors affect the price of a new product, such as raw material and production labor costs. Li wanted to determine how large an impact these factors would have on a product's final price. Using Monte Carlo simulation, Li was able to find the relative influence of each variable. The whole approach was simple and intuitive. In another example, Li used the software to determine the influences of various factors on tolerances in car engine design. By using Palisade’s DecisionTools Suite, Li was able to save a lot of time, completing the procedures with just two or three commands.

Short Learning Curve
Prof. Li has found that the DecisionTools Suite enables students to perform highly flexible case analysis and decision analysis of any issues that may be encountered in a system. Students are able to grasp a number of related concepts with greater ease, while gaining powerful tools to facilitate solving practical problems. In Prof. Li’s research, Palisade has provided excellent support for the calculations and optimization required in areas like engine design and decision-making support theory, thus saving huge amounts of time and effort. Says Prof. Li, "You can now complete what originally took you 20 hours to do in as little as a single hour!"

Read the original case study here.

This case study is also available in Chinese: 上海交大开设DecisionTools 和@RISK软件课程并获得成果

See also: Some Uses of Decision Support Software

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Palisade Publishes New Book: Energy Risk Modeling, by Professor Roy Nersesian

One of Palisade’s most vocal supporters in the higher education sector is Professor Roy Nersesian, who teaches energy courses at Columbia University's School of International and Public Affairs. Roy not only utilizes Palisade solutions in the classroom, but he has written a series of books published by Palisade on risk modeling. His latest offering, Energy Risk Modeling, examines how @RISK, PrecisionTree and Evolver are utilized by a wide range of traditional and alternative energy industries.  A sampling of topics covered include:

A sampling of topics include:

  • Modeling payoffs of oil drilling, using both PrecisionTree and @RISK
     
  • Analyzing the economics of a Liquefied Natural Gas (LNG) export project where uncertain variables (cost of natural gas extraction, cost of liquefaction, cost of transportation and the price of LNG in the intended foreign market) are modeled and simulated in @RISK.
     
  • Optimizing an oil refinery to maximize profits and valuing a real option of purchasing a coal-fired plant using Evolver, shown to have a greater predictive efficacy than Excel’s built-in Solver.
     
  • Modeling solar panel and wind turbine power outputs by factoring cloud cover, temperature, time of day and wind speed, while optimizing said uncontrollable energy sources with uncontrollable demand to closely match daily energy demand with power generated.
     
  • Projecting hydropower output in terms of percent capacity using rainfall, evaporation and dam leakage as probabilistic variables.

There is an almost endless stream of potential risk factors to be considered by decision-makers in the energy sector, and Energy Risk Modeling clearly demonstrates how Palisade solutions can assist in planning for and avoiding potentially disastrous obstacles. More real-life examples of Palisade’s utilization in the energy sector can be found on Palisade’s case study page.

» Join us on September 19th for a complimentary live webcast with Roy Nersesian about Energy Risk Modeling
 
» Read more about Energy Risk Modeling

» More about Professor Nersesian, his courses, and publications

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Free Webcast this Thursday: "Energy Risk Modeling as a Teaching Tool, and Overview of the Book" with Roy Nersesian

Register now for a free webinar to be presented by risk modeling expert Roy Nersesian.

"Energy Risk Modeling as a Teaching Tool, and Overview of the Book"

Free webcast this Thursday, 19 September 2013
11am EDT

Energy Risk Modeling is a new book from Roy Nersesian for those looking for simulation, decision trees, and optimization techniques for energy applications.

Much of Energy Risk Modeling is a result of Professor Nersesian’s course Energy Modeling at School of International and Public Affairs (SIPA) at Columbia University.

In this free live webcast, the presentation will be on how he utilized the contents of his book to build a course. His presentation will also be an overview of the book for individuals in the field of energy who may want to utilize @RISK products in their work.

The book has been written in a way to spur ideas on how @RISK products can be used in various aspects of energy from fossil fuels to renewables. At the end of his presentation, he will address matters on the minds of attendees on how @RISK may be applied to their work.

» Register now for the free webcast "Energy Risk Modeling as a Teaching Tool, and Overview of the Book"

About the Presenter

Roy Nersesian is a professor at the Leon Hess School of Business at Monmouth University and an adjunct professor at the School of International and Public Affairs at Columbia University. Professor Nersesian holds a BS in physics from Rensselaer Polytechnic Institute and a MBA from Harvard Business School. His time is committed to education and writing. In addition to Energy Risk Modeling, he has also written for Palisade @RISK Bank Credit and Financial Analysis, Evolver Solutions for Business, and RISKOptimizer for Business Applications. He has written several books among them the 2nd edition of Energy in the 21st Century published by M. E. Sharpe in 2010. He is currently under contract with M.E. Sharpe to write a book on energy and economics.

» View the complete webcast schedule, and see past presentations in the Archive

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Cost Risk Analysis Example Movie: Palisade's Custom Development Team uses @RISK's XDK for this custom application

Here is an example of a custom application written by Palisade Custom Development using @RISK's XDK in Excel. In this example, @RISK is used for cost risk analysis and estimation.  The application prompts the user for a three-point estimate for each cost item in the project as a way to recognize uncertainty in these cost elements.  A risk register is created using a simply colored grid interface.  Next, because in real life costs are seldom independent of each other, the user is able to set up correlations between related cost elements.  Finally, the user can define external risk events that will affect the total cost of the project. Automation takes the shape of an Excel add-in, which is shown to the user as a new Excel ribbon.

 

 

Custom Development in Excel

Palisade Custom Development has written applications for insurance, cost estimation, retirement planning, oil and gas prospecting, portfolio risk management, schedule-cost risk analysis and more – all utilizing @RISK technology in Excel. This means we can create risk analysis solutions for you using a range of powerful analytics, including Monte Carlo simulation, decision trees, statistics, neural networks, and optimization. In each case, the interface is customized to include only what the users need, hiding unused @RISK functionality and preventing user access to the underlying model logic. You can also automate processes like reporting, generating only the charts and data you want. The result is a tailored application ready to roll out to your workgroup. 
 

New XDK Functionality and Documentation in version 6.2

Excel Developer’s Kits (XDK) automatically come as part of the DecisionTools software which includes, @RISK, PrecisionTree, Evolver, StatTools, and NeuralTools. XDKs allow you to automate and customize the tool within Excel using Excel’s built-in VBA programming language. In @RISK 6.2, the XDK has been updated to include new functionality for the automation of @RISK graphs and simulation filters, as well as several additional improvements. For most  products, the XDK now includes a new “Automation Guide” to help you get started quickly. In addition, new videos and example files have been added to the XDKs to help you use this powerful feature.

» See XDK videos

 
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See how a customized Retirement Saving template assesses the performance of a portfolio in future years

Here is an example of a custom application written by Palisade Custom Development using @RISK's XDK in Excel. In this example, @RISK is used to analyze the investment of funds for retirement planning.  The application prompts the user for profile characteristics of the client and portfolio parameters. 

Once the information is entered, the application runs an @RISK simulation to assess the performance of the portfolio in future years as well as the effects of various withdrawal rates after retirement.  Results are presented in tables and graphs.

This video demonstrates how easy it is to utilize this Custom Application:
  1. Define Profile Parameters
    The user inputs the profile characteristics of the individual whose profile is to be modeled. 
     
  2. Define Model Parameters
    The next step is to add the probability distributions for the return of each of the investments. 
     
  3. Simulate and View Results
    Observe multiple simulations and view the custom reports that are provided.
     
Automation takes the shape of an Excel add-in, which is shown to the user as a new Excel ribbon. 
 

Custom Development in Excel

Palisade Custom Development has written applications for cost estimation, retirement planning, oil and gas prospecting, portfolio risk management, and more – all utilizing @RISK technology in Excel. In each case, the interface is customized to include only what the users need, hiding unused @RISK functionality and preventing user access to the underlying model logic. You can also automate processes like reporting, generating only the charts and data you want. The result is a tailored application ready to roll out to your workgroup. Because the application is in Excel, the training required for users is minimal. XDKs come with the DecisionTools software PrecisionTree, StatTools, NeuralTools, RISKOptimizer, and Evolver as well as @RISK. This means we can create risk analysis solutions for you using a range of powerful analytics, including Monte Carlo simulation, decision trees, statistics, neural networks, and optimization. 
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Why Use Decision Tree Analysis?

Life is full of tough choices. Most of us muddle through them using best guesses and gut feelings. But have you ever wondered if there might be a more sophisticated way to make decisions? Many businesses, researchers, and organizations have asked the same question, and turned to decision tree analysis.

Decision trees for decision analysis, in PrecisionTreeDecision trees are quantitative diagrams with nodes and branches representing different possible decision paths and chance events. This helps you identify and calculate the value of all possible alternatives, so you can choose the best option with confidence. This technique applies to almost any industry and field; it can help oil companies determine optimal testing and drilling strategies, help medical researchers determine the best tests and procedures to maximize a patient's recovery, or help a law firm decide on the best litigation strategy in a legal dispute.

How does this tool work? Decision trees let you visually map out complex, multi-layered decisions in a sequential, organized manner. This helps you identify all possible alternatives and choose the best option. This formal structure represents decisions and chance events that are linked in sequence from left to right. Decisions, chance events, and end results are represented by nodes and connected by branches. The result is a tree structure with the "root" on the left and various payoffs on the right. Probabilities of events occurring and payoffs for events and decisions are added to each node in the tree. Palisade's  PrecisionTree software creates decision trees  and influence diagrams in Mircosoft Excel, allowing you to  identify all possible alternatives and choose the best option.

PrecisionTree is a part of Palisade's DecisionTools Suite, an integrated set of programs for risk analysis and decision making under uncertainty that runs in Microsoft Excel. The DecisionTools Suite includes @RISK for Monte Carlo simulation, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, the DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISK Optimizer for optimization. All programs work together better than ever before, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.

See also: Some Uses of Decision Support Software

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Modeling Today and Tomorrow's Risk: One Insurance Company's Strategy

In a recent white paper from Government Entities Mutual, (GEM) Inc., which writes Liability, Workers’ Compensation, and Property reinsurance coverage, underwriting manager Joel Kress posed the question, “how risky are we?” 
 
To answer this question, Kress and his team decided to simply model the most detrimental and most quantifiable risks: Underwriting Risk and Reserve Development Risk. For Underwriting Risk, they sought to quantify their annual risk transfer contracts. For Reserve Development Risk, they outlined and measured the risk associated with all past contracts they had written. Since GEM is almost 10 years old, they knew there would be years (and decades) of further Incurred But Not Reported (IBNR) development on GEM’s balance sheet. This type of risk accumulates geometrically as the years move on.
 
Since GEM’s loss experience alone was limited and thus statistically non-credible, Kress and his team supplemented this data with loss experience from other industry reinsurance data. With this combination, they were able to create a single loss distribution, which statistically estimates the company’s predictability of loss.
 
Using @RISK's Monte Carlo simulation, GEM then created a profile for each contract written in the  most recent policy year (2011), and distilled all the information from each contract into exposure to loss, which is simply frequency x severity, that GEM held as the risk bearing captive. Kress and GEM actuaries then estimated the risk for the historical policy periods by using the selected loss distribution to measure the variability around the expected loss reserves. This variability or, of greater concern, the variability of losses costing more than expected, was the third piece to GEM’s risk metric. GEM’s selected loss distribution looked like many other (re)insurance loss distributions--skewed towards the  right, indicating a chance, albeit slim, of a large, calamitous loss. 
 
The majority of this risk came from contracts currently being written, since the insurable events have not yet occurred. Turning to @RISK again, Kress and his team used the  input variables to estimate GEM’s  losses for the current policy year’s contracts, and then ran the algorithm for 10,000 hypothetical policy years. From this tome of data, they were able to determine key statistical metrics. 
 
Once all the simulations were finished, it was time to measure the results. GEM used Surplus as a measuring stick since it is easily understood, readily calculable, and of concern to most interested parties. GEM found that at a 60% Confidence Level, their Surplus would need to make up a $965,000 shortfall in losses. Thus with this risk they modeled, the amount of extra money from GEM’s current and historical contracts will cost beyond what is expected.
 
The last step in this process was to use five statistical benchmarks of ruin to measure themselves against. These benchmarks included the total Captive’s Contributed Capital, Company Action Level, Regulatory Action Level, Authorized Control Level, and Mandatory Control Level. GEM was able to assign chance percentages to all these potential risks, ranging from 17.2% and 0.4%.
 
Thus, using @RISK, Joe Kress and GEM were able to assess their risk for current and future books of business. According to Kress, “None of this minutia would be possible without the power of computers. It is one thing to program an algorithm to do a set of tasks, as outlined above. It is another thing entirely to make the computer work for you.”
 
 
 
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How to Create a Custom Application for Stock Portfolio Optimization, Right in Your Spreadsheet

This is an example of the use of @RISK automation applied to stock portfolio optimization. It is a custom application written by Palisade Custom Development using @RISK’s XDK in Excel. 

The steps outlined in the Stock Portfolio Optimization example movie:
  1. Obtain Price & Weight Data
    The user will first define a portolfio of stocks. 
     
  2. Run Analysis & Review
    The next step is to run the analysis for the returns of each security. 
     
  3. Fit Data & Simulate
    The third step is to obtain the distributions that fit the historical return of the portoflio. Distributions of 10 will be used as a base for prediting gains or losses using a simulation. 
     
  4. Optimize & Generate Efficient Frontier
    The last step is to run the optimization process and maximize the portfolio's mean return, given certain constraints. Another feature available is to get the Efficient Frontier of the portfolio.
Automation takes the shape of an Excel add-in, which is shown to the user as a new Excel ribbon. 
 

Custom Development in Excel

Palisade Custom Development has written applications for cost estimation, retirement planning, oil and gas prospecting, portfolio risk management, and more – all utilizing @RISK technology in Excel. In each case, the interface is customized to include only what the users need, hiding unused @RISK functionality and preventing user access to the underlying model logic. You can also automate processes like reporting, generating only the charts and data you want. The result is a tailored application ready to roll out to your workgroup. Because the application is in Excel, the training required for users is minimal. XDKs come with the DecisionTools software PrecisionTree, StatTools, NeuralTools, RISKOptimizer, and Evolver as well as @RISK. This means we can create risk analysis solutions for you using a range of powerful analytics, including Monte Carlo simulation, decision trees, statistics, neural networks, and optimization. 
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Free Webcast this Thursday: "Modeling Multi-Staged Investments with @RISK" with Eric Torkia

Register now for a free webinar to be presented by Eric Torkia. Don't miss this opportunity for inside tips from a successful consultant who uses Palisade risk and decision analysis software solutions to address current problems in financial risk analysis.

"Modeling Multi-Staged Investments with @RISK"

Free webcast this Thursday, 25 July 2013
11am EDT

When planning a multi-staged investment such as new product development or the deployment of a new asset consisting of multiple stage gates, the consequences on analyzing NPV risk are substantial. This free live webcast will show you how to model multiple gates as well as their impact on financial performance. Also presented in this model are how to assess completion stages and probabilities of investment success:

  • Overview of traditional discounted cash flows versus expanded NPV
  • Understanding and modeling product pipelines
  • Integrating expert judgment/opinion into your predictions
  • How to combine multiple staged investments into an optimized investment portfolio

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

» Register now for the free webcast "Modeling Multi-Staged Investments with @RISK"

About the Presenter

Eric Torkia, MASc is a senior management consultant/trainer and business analyst. He has collaborated with some of the world's most recognized organizations to ensure the optimal design and delivery of enterprise systems, analytics as well as new forecasting and decision making processes.

Eric combines a unique set of skills and competencies revolving around performance, risk and change management to bring about durable business performance improvements: Financial and project based valuations, Project Risk Analysis on 1+ billion dollar projects, Performance Management business analysis and consulting, Spreadsheet Modeling and VBA automation for simulation, forecasting and optimization, Change Management consulting and training and instructional design relating to the adoption and implementation of enterprise analytics.

Eric’s academic background includes a Master’s degree in information systems management from the University of Québec in Montreal as well as a BBA in international marketing and management from Northwood University Florida.

» View the complete webcast schedule, and see past presentations in the Archive

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Palisade Global Risk Conferences Advance Best Practices in Risk Management

Join us next week in London and Frankfurt.

In the last few months, nearly 1,000 professionals from around the world have gathered to push the boundaries of risk analysis through the Palisade Risk Conference series. Since March, Palisade Risk Conferences have been held in Santiago, Chile; São Paulo, Brazil; Johannesburg, South Africa; Tokyo, Japan; and Shanghai, China. Decision makers from industry and academia have presented dozens of real-life case studies, applying sophisticated quantitative techniques in new and exciting ways. These innovative approaches use Monte Carlo simulation, optimization, decision trees, and other techniques found in Palisade’s @RISK and DecisionTools Suite software to solve a wide range of pressing problems.

2013 Palisade Risk ConferencesSome examples include:

  • Using stochastic modeling for financial analysis in the mining industry
    Presented by Collahuasi Mining Co
  • Assessing the financial viability of new projects
    Presented by the Brazil National Development Bank
  • Risk assessment of capital projects in the power industry
    Presented by PricewaterhouseCoopers
  • Achieving optimal inventory levels to minimize cost
    Presented by Mitsubishi Chemical Company
  • Risk assessment of food safety imports
    Presented by the Shanghai Food and Drug Administration
  • Probabilistic risk analysis of cross-border oil projects
    Presented by Sinopec

Join us next week in London and Frankfurt! Upcoming dates:

 » 2013 Palisade Risk Conference Series

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"Using Technology to Navigate Risk," in Executive Insight magazine

The December issue of Executive Insight magazine features an article by Palisade's Randy Heffernan, entitled "Using Technology to Navigate Risk."

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.

» Read the complete article: "Using Technology to Navigate Risk"

 

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Free Webcast this Thursday: “Good Practices and Common Mistakes”

Join us this Thursday, December 6, 2012, for a free live webcast entitled, "Good Practices and Common Mistakes" to be presented by Dr. Huybert Groenendaal. This was one of the favorite sessions last month at the Palisade Risk Conference in Las Vegas.

Ever question if your Monte Carlo model is correct? Ever wondered how other organizations use @RISK, how to get the most value out of @RISK, and what are some of the most important best practices? Then this is the right free live webcast for you!

An increasing number of organizations are using analytical techniques such as quantitative risk analysis, value at risk (VaR), and risked NPV to help them improve decision making. However, all too often, these techniques may not be used optimally or accurately and their full value may not be realized.

During this presentation, Dr. Huybert Groenendaal will share his hands-on experience through hundreds of projects, and will discuss the following topics:

  1. Good practices for the use of @RISK and risk modeling to support decision-making
  2. Common mistakes in Monte Carlo simulation and how to prevent them.

All good practices and common mistakes will be discussed with the use of real-life risk modeling case studies and models based on EpiX consulting work.

Dr. Huybert Groenendaal is a managing partner at EpiX Analytics, a consultancy that helps clients use Monte Carlo simulation and probabilistic modeling in a broad range of industries and fields, ranging from financial risk analysis, business development, marketing, budgeting, inventory optimization, and pricing, to risk analysis in health. Dr. Groenendaal has extensive experience in risk modeling and analysis for business development, financial valuation, R&D portfolios and portfolio evaluations in pharmaceuticals and medical devices. Dr. Groenendaal also teaches a variety of risk analysis training courses including Financial Risk Modeling for Pharmaceuticals, Quantitative Risk Analysis, and Corporate Finance Risk Analysis and customized on-site courses. He also lectures on the use of risks modeling in business at the executive MBA program at the Leeds School of Business, University of Colorado and teaches two online risk analysis courses at Statistics.com. Dr. Groenendaal has an MBA in Finance from the Wharton School of Business and PhD from Wageningen University and is an adjunct faculty at Colorado State University.

» View the recorded webcast "Good Practices and Common Mistakes"
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Free Webcast this Thursday: “Multi-Dimensional Project Portfolio Optimization and @RISK”

Join us this Thursday, April 26, 2012, for a free live webcast entitled, "Multi-Dimensional Project Portfolio Optimization and @RISK" to be presented by Eric Torkia.

Many speak of organizational alignment, but how many tell you how to do it? Others present only the financial aspects of portfolio optimization but abstract from how this enables the organization to meets its business objectives.  We are going to present a practical method that enables organizations to quickly build and optimize a portfolio of initiatives based on multiple quantitative and qualitative dimensions: Revenue Potential, Value of Information, Financial & Operational Viability and Strategic Fit.
         
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 how this approach can dramatically improve organizational focus and overall business performance.

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

Discussion Topics

  •     Optimization Basics
    • Typical Optimization Applications
    • What is non-linear stochastic optimization
    • Objectives and constraints - things to know about optimization
  •     Quick Overview of Portfolio Theory
    • Overview of conventional portfolio methods (financial, strategic, IT...).
    • Markowitz and the efficient frontier
    • Viability/Fit method
  •     Optimizing with value of information and other critical dimensions
    • What is value of information (VOI)
    • How can VOI and portfolio methods be used to improve decision-making
    • How does the VOI impact portfolio decisions
  •     Building and running the model
    • Overview of the components of the portfolio model
    • Run a quick optimizations using different dimensions and discuss results
    • Questions and Answer Period


Eric Torkia MASc is a senior management consultant/trainer and business analyst.  He has collaborated with some of the world's most recognized organizations to ensure the optimal design and delivery of enterprise systems, analytics as well as new forecasting and decision making processes.

Eric combines a unique set of skills and competencies revolving around performance, risk and change management to bring about durable business performance improvements.

  • Financial and project based valuations
  • Project Risk Analysis on 1+ billion dollar projects
  • Performance Management business analysis and consulting
  • Spreadsheet Modeling and VBA automation for simulation, forecasting and optimization
  • Change Management consulting and training and instructional design relating to the adoption and implementation of enterprise analytics

Eric’s academic background includes a Master’s degree in information systems management from the University of Québec in Montreal as well as a BBA in international marketing and management from Northwood University Florida.

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Neural Networks Optimize Police Force Efficiency

In the August 16, 2011 edition of the New York Times there was an interesting article about the use of predictive analytics by police departments. Entitled “Sending the Police Before There’s a Crime,” the article explores how the Santa Cruz, California police department optimizes the use of limited resources by anticipating where crimes are more likely to occur so they can deploy police there in advance. How does it do this? According to the article:

“Santa Cruz’s method is more sophisticated than most. Based on models for predicting aftershocks from earthquakes, it generates projections about which areas and windows of time are at highest risk for future crimes by analyzing and detecting patterns in years of past crime data. The projections are recalibrated daily, as new crimes occur and updated data is fed into the program.”

This may sound like science fiction technology, but the model Santa Cruz is using is exactly what neural networks do.  Neural networks are an artificial intelligence data analysis technique that identifies patterns from historical data and uses those patterns to predict new outcomes when presented with current partial data.  

Neural Networks Optimize Police Force EfficiencyIt’s the same technology that shuts off your credit card when it’s stolen and someone uses it to buy ten 60-inch TVs in another country.  In that case, the credit card company has established spending patterns from your purchasing history, and when a transaction appears that falls outside that pattern, a neural network assesses the probability of fraud. If the probability is high enough, the card is blocked.

The applications for neural networks are limitless. They have been used for medical diagnosis, commodities price prediction, patient load forecasting in hospital, and much, much more. You can read examples of examples of interesting neural network applications using NeuralTools, a leading neural networks tool for Excel, here.

Santa Cruz’s efforts are being monitored and copied by other major metropolitan forces such as the Chicago and Los Angeles police departments. Such analytics could prove to be a major tool in the resource-strapped battle against crime for years to come.

» NBC Nightly News video: "New police motto: To predict and serve?"

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Free Webcast this Thursday: “Refining the Business Case for Sustainable Energy Projects Using @RISK and PrecisionTree: A Biofuel Plant Case Study”

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.

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@RISK used to evaluate capital budgeting, investments, random walks, derivatives pricing and real options at Cornell's Dyson School of Management

Students at the Dyson School use @RISK 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

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