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Forestalling Healthcare Center Foreclosure: Using @RISK to Analyze Debt Capacity and Find a Strategy to Avoid Mortgage Default Steven E. Slezak This case illustrates how even a simple @RISK simulation can provide valuable insights into a complicated financial issue and lead to a strategic solution. A medical center in rural California secured a 30-year mortgage from a lending agency backed by the U.S. Government. Combined annual principal and interest payments became a burden shortly after the loan was issued due to an unexpected decrease in the center’s net income, leading the borrower to suspend mortgage payments and the lender to begin the default process. The parties to the loan requested FinEx Company, LLC, provide an analysis of the debt situation, with particular focus on the borrower’s debt capacity from 2010 through the maturity of the mortgage. What is the best strategy for the borrower to follow to avoid default? Should the center refinance? If the current mortgage is to be salvaged, how likely is it the borrower will be able to resume mortgage payments within the next 12 months? The study aimed to identify a minimum level of net income for the center to target, a level which would allow it to maintain sufficient debt capacity to pay off the mortgage. The study also looked at how changing the terms of the loan – interest rate and maturity – by refinancing might contribute a solution. Could refinancing help the center manage its debt? The goal of the study was to determine the best course of action to enable the center to resume debt payments. Three possibilities existed: repayment of the existing debt, refinancing, or default. Since repaying the mortgage was in the best interests of both the borrower and lender, and since default was not a desirable option, emphasis was placed on a work out strategy that would allow the existing mortgage to be serviced. A simple Excel spreadsheet was used to determine target the minimum net income level need to support debt capacity for the mortgage at various points of time in the maturity of the mortgage. A uncomplicated @RISK analysis was then performed to determine the probability that the center would be able to meet that minimum net income level within the next 12 months, and to find out if refinancing would be a viable solution under the circumstances. The @RISK simulation helped to determine an optimal strategy.
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Optimizing Global Clinical Trial Investments: Palisade’s @RISK Software used to optimize Patient Enrollment Forecasting Todd D. Clark In this presentation, Value of Insight Consulting, Inc. (VOI), a life-sciences advisory firm, discusses the results of a recent project employing risk-modeling for a global clinical trial. Specifically, VOI was hired by a major pharmaceutical company to determine the relative suitability of 23 countries as locations for a phase III oncology clinical study. The objective of our analysis was to determine the best countries in which to place this trial in order to ensure that a minimum of 690 patients would be enrolled within the sponsor’s 26-month timeline. To accomplish the task, VOI employed Palisade Software’s @RISK software to conduct Monte Carlo Analysis. The results of our forecasts allowed our client to considerably reduce the number of countries in which the trial was held, thus lowering the cost of the clinical trial by millions of dollars without sacrificing the on-time enrollment goal. As this presentation shows, in an industry that is arguably facing the most challenging period in its history, companies that incorporate risk-modeling into their decision-making process can provide themselves with a distinct and affordable advantage over their competitors.
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The use of the DecisionTools Suite in Biotechnology Project and Portfolio Decision Making Svetlana A. Sigalova 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 model using @RISK to enhance its portfolio process. 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. Top Rank 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. 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, the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.
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Using @RISK for Opportunity Analyses to Estimate Likely Health Program Outcomes Gordon K. Norman, M.D., M.B.A. Alere and its parent company, Inverness Medical Innovations, offer a health management program for PT/INR home testing for patients on long term warfarin anticoagulation to adjust their medication dosage within the narrow safe range for PT/INR. Health plans are interested in purchasing this program to the extent they are convinced it can help them save costs, but have trouble extrapolating the expected financial impact of deploying such a program. Using @RISK to model the key uncertain variables that influence cost impact, Alere has been able to demonstrate a likelihood of significant cost savings which has helped us sell this new program.
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The Use of @RISK for Project in Biopharmaceutical Schedule Risk Assessment, Team Building and Strategic Planning Michael Huston Biopharmaceutical projects are a complexity of disciplines with technical challenges in manufacture, formulation, clinical development, and regulatory approvals. The average time to approval from identifying lead compounds is about twelve years. Only 2% of drugs that enter nonclinical animal testing make it to clinical testing. Historically, only 20% of those compounds show acceptable benefit/risk to be approved. Working with teams to identify and model timeline or schedule risks can benefit the project financially and operationally. Identifying critical path sensitivities illuminates the importance of factors identified by @RISK for MS Project. In a case study, this information led to manufacturing decisions that took years and considerable cost to implement. Operationally, the team members from various disciplines contribute actively in the assumptions and uncertainties used to build the model with the project scheduler. In practice this exercise can build team cohesion and appreciation of each other’s technical challenges Huston Associates has worked with teams in start-ups and mid-tier pharmaceutical companies to establish development plans for investors, senior management, and federal agencies. Examples will be presented that will illustrate the value of the @RISK for Project for project teams and to these stakeholders.
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Valuing Life Science Investments Using Simulation: Calculating the odds and playing to win Robert Ameo, PhD The basics of high-stakes biotech betting is the subject of this presentation. Whether you’re a buyer, investor, or seller, you have to know at what value an investment is a good bet. To do that you need to calculate the odds like a championship poker player. Simulations can produce histograms that lay out the range of possible outcomes from any venture, but there are just so many histograms senior executives will look at before their eyes glaze over. These individuals are used to making decisions on a handful of metrics from a point forecast (e.g., NPV, IRR). The data overload from a simulation forecast can be both overwhelming and unwelcome. In his presentation Dr. Ameo describes how to use the simple, straightforward language of wagering to effectively communicate the relative risks and opportunities presented by specific investments. The derivation of key gambling metrics—chances of winning, value of winning, cost of a loss, and “the odds”—will be presented along with the appropriate language and graphics to present and discuss the findings. Implications for portfolio planning and decision-making will be discussed.
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Introduction to the DecisionTools Suite 5.5 Thompson Terry This session will show you how to use the elements of the new DecisionTools Suite 5.5 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. Each of the products in the suite, @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools, will be presented, showing how they can be used to solve practical problems in the real-world.
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Thompson Terry This introduction to @RISK 5.5 will walk you through a risk analysis using various example models. Key features of @RISK will be highlighted, and new enhancements in version 5.5 will be pointed out along the way. You will experience the intuitive interface of @RISK 5.5 as you define distributions, correlations, and other model components. During simulation you will be able to see all charts, thumbnails, and reports update in real time. View results with a variety of graphing options, including new cumulative-histogram overlays, scatter plots in scenario analysis, and more. There’s so much to see, we’ll cover as much as time permits.
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Introduction to PrecisionTree 5.5 Thompson Terry This presentation combines an introduction of the enhanced user interface, tighter Excel integration, and new features of PrecisionTree with demonstrations of how PrecisionTree can be used to analyze various problems in decision analysis.
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Introduction to RISKOptimizer 5.5 and Thompson Terry RISKOptimizer and Evolver use powerful genetic algorithms to perform optimization in Microsoft Excel. RISKOptimizer builds on traditional optimization by adding Monte Carlo simulation to account for uncertain (stochastic), uncontrollable factors in your optimization problem. This session introduces you to these powerful tools, showing you how to set up a model, define constraints within the model, and ultimately arrive at the optimal outcome. Examples of resource allocation, budgeting, and scheduling will be included.
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