Risk Assessment

Have you ever wanted to take Palisade's @RISK software for a guided test drive?

On Tuesday, September 14th, Palisade will host a complimentary open house event in Long Beach where you will be able to follow a product demonstration of the new @RISK 5.5.1.

Andrew Sich will show several example models that illustrate how Monte Carlo simulation can be used for applications in risk analysis. You’ll learn how to add uncertainty to your own models, correlate inputs, use historical data to create best fit distributions, run simulations and sensitivity analyses, and then fundamentally transform decision making discussions by communicating  in “ranges”, “probabilities”, and “likelihood of occurrence” – leaving single point estimates, as well as best-case, worst-case, and most-likely estimates to historians.  Complimentary trial versions of @RISK and the full DecisionTools Suite software will be provided.

The event will be held from Tuesday, September 14th, 11:00AM to 1:00PM at:

Renaissance Long Beach Hotel
111 East Ocean Boulevard
Long Beach, CA 90802
Phone: (562)437-5900


There is no charge for the event, and light refreshments will be served. To register, please contact Andrew at asich@palisade.com or 607-277-8000 x 330.

Separate and private meetings at your facility to discuss licensing and training options, pricing, and perhaps a shorter demo of @RISK for your colleagues are also available.

For more information:

Andrew Sich
Regional Sales Manager
Palisade Corporation
www.palisade.com

Email: asich@palisade.com
Phone: 1-800-432-7475 (ext. 330)
or 1-607-277-8000 (ext. 330)
Cell: 1-607-279-6266
Fax: 1-607-277-8001

Modeling Uncertain Number of Events, Each with Uncertain Parameters
Often in contract negotiations, a supplier of a product or service may agree with customers to pay a penalty if certain performance targets have not been met. In many cases, the supplier does not really have a good idea of the true possible impact of this clause, for example in terms of the average penalty that should be expected.

In this model, we use Monte Carlo simulation to assess such penalties depending on various performance levels.

The model could be extended (and explored in more detail in Palisade training courses) to evaluating whether one or more investment proposals that would improve performance (e.g. by reducing the variability or improving the average performance) should be accepted. The costs and benefits of each these projects could be compared in a single simulation using the Simtable feature.

» Download the model: PenaltyClause.xls

When doing risk analysis simulation modeling, it’s critical to represent the fact that many variables are related. Very rarely are all variables completely independent of one another.  For example, when interest rates are low, housing starts tend to go up. This relationship is represented by a correlation coefficient, a number between -1 and 1. -1 means the two variables are perfectly negatively correlated;  that is, when interest rates go up 1 unit, housing starts go down 1 unit. A coefficient of 1 is exactly the opposite, and 0 means there is no relationship between the variables at all. Typical coefficients are between these extremes. This correlation between input variables can and must be captured in your financial risk analysis models. Even if you are using expert opinion, estimating these relationships is better than ignoring them completely.

@RISK provides an easy way to define correlation coefficients. Through the use of a slider bar, you can dynamically update coefficients between variables as well as scatter plots representing those variables. This helps you visualize and more easily define these relationships.

For a quick video demonstrating this, see:



» View short videos on recently added @RISK risk software features

Presented at the Palisade Risk & Decision Analysis Conference New York City
Chris Albright, author of the book VBA for Modelers, presented a number of examples of how to automate @RISK, RISKOptimizer, and StatTools in Excel using Excel's VBA and Palisade's built-in object-oriented Excel Developer Kit. These examples include production applications, scheduling applications, World Series simulation, and more. All examples include macros written by Dr. Albright, so you'll need to enable macros when you open them.

» Download the examples
» Order Dr. Albright's book "VBA for Modelers"

On Thursday, September 2, 2010, Svetlana A. Sigalova will present a free live webcast entitled. "The Use of the DecisionTools Suite in Biotechnology Project and Portfolio Decision Making "

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

» Register now (FREE)
» View archived webcasts

Vertex Pharmaceuticals, Inc. is a global biotechnology company based out of Cambridge, MA. The Company's strategy is to commercialize its products both independently and in collaboration with major pharmaceutical companies. Vertex's product pipeline is focused on viral diseases, cystic fibrosis, inflammation, autoimmune diseases, cancer, and pain.

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

Vertex’s strategy and analytics group within the corporate finance division seeks to provide the senior management with dynamic revenue and profit forecasting methodology that helps to identify types of drugs that should be developed given a finite amount of cash and resources. A traditional financial view allows the user to identify scenarios and potential outcomes, but lacks the ability to show the range of potential values within each and every outcome. Vertex’s team uses the DecisonTools Suite to establish the average outcome, the variability of outcomes and to pressure-test risk and uncertainty of a particular scenario throughout the decision process.

Vertex’s team built a complex financial risk analysis model using @RISK to enhance its portfolio process. Monte Carlo simulation and optimization are used to analyze and optimize project and portfolio decisions, given short and long-term corporate strategy. @RISK is also frequently used throughout the business development process: simulating across multiple sales forecasts provides BD team with a range of potential outcomes, making it easy to pinpoint a particular scenario on a curve, along with its probability and value. TopRank turns the sensitivity analysis into a quick and seamless exercise, answering multiple what-if questions within minutes. Franchise and program leaders can now see a dollar effect of their program being delayed or advanced, adding supplementary indications to the development plan and even addressing the price uncertainties all at the same time. The simple interface of PrecisionTree along with tornado chart outputs makes it easy to explain the effect and importance of a particular assumption / decision to an audience with no finance background.

As the company continues to grow, adding more drugs and collaborations to its development pipeline, we will see in this free live webcast how the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.

» Register now (FREE)
» View archived webcasts

Abbott suggests a compromise: streamline your plot by masking (Abbott says "removing") fields that contribute to the robustness of the analysis but involve statistical twists and turns that are distracting to decision makers who may not be fascinated with technique and just want to see how the story turns out. This, he explains, allows you to work from a model both you and the decision makers can believe in.

Your thoughts? 

The UK's new coalition government has said that, as part of its 'Green Deal', it will encourage home energy efficiency improvements paid for by savings from energy bills. It seems likely that, in the year that energy regulator Ofgem warned of 20 percent electricity price hikes by 2020, this initiative will include solar panel technology

Currently the UK still lags behind many other countries in Europe and the rest of the world when it comes to harnessing solar power. Not only do we have less hours of sunshine than many regions, but there is a lack of clarity as to the 'payback' time when it comes to users seeing a return on investment.

This is where Palisade customer, the California-based Tioga Energy, makes an interesting case study. Whilst it may seem unfair to compare the UK with the west coast of America when talking about solar-related matters, the sunnier climate does not reduce the need to prove ROI for customers with solar energy agreements.

Tioga Energy provides project financing through its solar Power Purchase Agreements (PPAs), and maintains and operates solar systems on behalf of its customers. Tioga’s offering delivers predictably priced power and enables organisations to to both 'green' their operations and reduce energy costs. To illustrate the benefits of solar, estimating future electricity prices and making comparisons by showing the savings from a new solar system, Tioga enlisted the help of @RISK for risk analysis solutions.

To forecast possible price increases, Tioga Energy inputs California's historical electricity rate data into a quantitative risk analysis model developed using @RISK. This generates a probability distribution for electricity rate rises over the 20-year PPA period, which shows that there is a 25 percent likelihood that price increases will be less than 4.8 percent, and a 25 percent chance that rate rises would be more than 8.7 percent.

The @RISK risk analysis model therefore helps Tioga Energy evaluate the likelihood that a customer will save money for a variety of PPA scenarios (i.e. the rate at which electricity would initially be charged and the amount by which it would then increase each year). It also calculates the magnitude of savings for the different combinations of first year costs and subsequent rises. Consumers are therefore able to better understand the pricing and make an informed decision about whether to sign up for a PPA.

Using historical data and @RISK's risk modelling software capacity, Tioga offers consumers a robust view of the potential benefits of a solar PPA. This enables them to hedge against rising electricity rates, as well as feel confident that they are playing a part in tackling global warming.

» Read the Tioga Energy case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

I recently saw an interesting @RISK cashflow model from the portable phone industry. It modeled the uncertainty in the length and decline of overall market demand for a particular technology against five strategies for getting various application products to market as soon as possible. 

Using @RISK’s Simtable function, combined with Excel’s Index function, it was possible to run multiple simulations and see which strategy could take best advantage of the potential market, given the uncertainties in the development process, the possibility of competitors, the market take-up and the margins that might be achieved.

As is often the case in all aspects of life, the simulation revealed that ‘a bird in the hand is better than two in the bush’; it’s very comforting to know that @RISK risk analysis solutions can cut through loads of detail and come back with an answer that echoes received wisdom!

Ian Wallace, ACMA
Palisade Training Team

@RISK risk analysis software using Monte Carlo simulation is used for a wide variety of applications. In this model, we have an example of a general usage to address Operational Risk.

In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions such as "What is the loss amount that will not be exceeded in 95% of cases?" @RISK simulation can be used to answer such questions.

» Download the example: EventandOperationalRisks.xls

With the New York State primaries coming up September 14 and the general election on November 2, I predict that as soon as summer turns the corner into September, we'll start hearing lots and lots about polls that predict election outcomes.  To find out if there was any early discussion of polls, polling, and outcomes, I returned to my favorite election forecast site from the 2008 presidential elections, FiveThirtyEight: Politics Done Right.

You can quickly generate a graph of any simulated output relative to a given input using the tornado graphs in @RISK risk analysis software. Just drag a bar from the tornado graph onto a blank space, and a scatter plot of that input relative to the simulated output appears. This is a great communication tool when you’re trying to assess the most important risk variables in your risk analysis model and want to “zoom in” on a particular input to see what’s going on. It can really help understand relationships in your modeling.

See this video for a quick demo:



» View "Getting Started in @RISK" video tutorials

Most people understand that @RISK and Monte Carlo simulation are designed to be an improvement on single-point estimates.  In practice, however, I often see people using @RISK as a forecasting tool to get yet another single-point estimate, such as the 90th percentile, without putting it into the context of the potential range of outcomes.

This is probably the difference between a forecasting and a decision-making.  The former tends to focus on historical or observed trends and developing specific scenarios (e.g. best, most likely, worse) based on expert opinion, while the latter is concerned with confidence ranges and likelihood.

Indeed, it’s not until you add probability, as with @RISK, that you start to measure the quality of your forecasts (i.e. your confidence level) and calculate the margin of error – something that’s crucial in all walks of life!

In my opinion, therefore, @RISK is much more of a decision-making tool than a forecasting tool.  Both involve trying to predict the future but the addition of probability gives decision-makers vital insight to a problem. 

Don’t you just love semantics!

Ian Wallace, ACMA
Palisade Training Team

A key application of @RISK is Six Sigma and quality analysis. This model demonstrates how @RISK can be used for a DOE analysis of a welding project.

Suppose you are analyzing a metallic burst cup manufactured by welding a disk onto a ring. You need to make sure weld strength is within safety limits. The model relates the weld strength to process and design factors, models the variation for each factor, and forecasts the product performance in relation to the engineering specifications. @RISK is used to model the variation in each factor, and simulate different outcomes for weld strength. Output includes After Six Sigma statistics for Cpk-Upper, Cpk-Lower, Cpk, and PPM Defects (or DPM). Standard @RISK statistical analysis functions (like RiskMean) are also used.

» Download the example: SixSigmaDOE.xls

On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

» Register now (FREE)
» View archived webcasts

On Thursday, August 19, 2010, David Roy will present a free live webcast entitled. "Assessing Your New Product, Process or Service Introduction Methodology: Is Yours Premier? Does it Enable Six Sigma Performance? "

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

» Register now (FREE)
» View archived webcasts