Project Risk Management Strategies

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.

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Social media companies have to do a lot of decision making under uncertainty, a whole lot of uncertainty, because every risk assessment focuses on a brand-new kind of product with no history in its marketplace.  They have to bet fearlessly on a whole lot of unknown taking place on the immaterial Internet.  But as one who has enjoyed a brief two years experience working in the blogosphere, I'll bet that, as ephemeral as that realm may seem, there likely is some there there, some value when we figure out how to calculate it.  

Part 2 of this blog ended with me very quickly stating that the MotoGP tipping comp optimiser was identical in structure to a portfolio optimisation problem, where the portfolio could contain stock or other assets, or even projects. Let’s look at this in a little more detail as I’m sure you’re reading this to find how to optimise your own decisions rather than wondering how I went in the tipping competition!

In my model there was a fixed budget (though less could be spent if desired) to spend on riders, with the aim of maximising their total points haul. In the real world you may have a total budget of say $100m to invest in a range of projects perhaps many hundreds of millions of dollars in total value each of which have certain expected returns. At its simplest this decision evaluation will find the most (expected) profitable portfolio of the projects. This is an inclusion/exclusion grouping model, but it is very simple to optimise assets with a continuous level e.g. the amount of money invested in various shares etc. Another real example I have seen when working with an investment company here in Australia was a model whose goal was simply to find the portfolio mix that came closest to the total allowable spend without exceeding it.

Further realism can be included by using constraints should there be the need. A resource constraint may mean there has to be a limit to the number of projects that can be run simultaneously. There may also be a minimum number of projects determined by management as a mitigation strategy. Such constraints are very simple to employ using Evolver and add value to the decision analysis without the need to provide specific risk analysis/Monte Carlo simulation information for the model.

A slightly more sophisticated method of turning an optimisation into a useful portfolio risk management tool where uncertainty hasn’t been specifically modelled is to estimate the possible downside of each asset and include it in the calculation of the portfolio’s ‘score’. The Evolver software comes standard with over twenty example spreadsheets for your educational pleasure, of which “Portfolio Mix.xls” gives one method for doing just this.
In the next (and final) instalment of the Making Optimal Choices blog I will explore the idea that not all optimisations no matter how mathematically correct will produce the same results in good time, and that elegant modelling should always be the goal prior to firing up Evolver.

And so you know, I came second in the competition. Next year I’m hoping to go one better!

» Making Optimal Choices, Part 1
» Making Optimal Choices, Part 2

Rishi Prabhakar
Trainer/Consultant

When performing a cost risk analysis study, one of the key results is the amount of extra monetary resources that is to be added to the project cost baseline to guarantee that the budget is not exceeded at a certain confidence level. Good project risk management strategies must take this into account.

After defining the uncertain variables and risk events that affect the cost performance of the project, we can run a Monte Carlo simulation with @RISK to find out what the range of the total project cost is.  Simulation results can help us to explain the risk exposure that we have in the total cost of the project. The most popular statistics are the mean (average cost), the most likely cost, and the 10th and 90th percentiles.




To determine the contingency to be allocated to the project, we need to define what confidence level we would like to achieve: The higher the contingency level, the larger amount of contingency needed. For example, in the figure above, we are reporting the total cost of the project. Here we can observe that we are showing the 85th percentile that corresponds to a total cost of $7.8M (right delimiter).  We can say that there is only a 15% chance that we will exceed $7.8M, or alternatively, we have an 85% chance that the total cost will be less than or equal to $7.8M.  In the same figure we can also see that the 90th percentile of the total project cost is $8.02M.  We can say then that in order to increase our confidence level from 85% to 90%, we will need to add $220,000 to the total cost.

The calculation of the contingency is then accomplished by using the base cost estimate (BE) before the risk analysis was implemented, and the expected cost (EC) of the simulated results.

Some practitioners separate the contingency into two components: engineering allowance, and management contingency.

Engineering allowance (EA) is the difference between the expected cost and the base estimate:

EA = EC – BE

Management contingency (MC) is calculated using the difference between the cost at certain confidence lever (Cp) and the base estimate:

MC = Cp – EC

In our example, our BE = $6.5M; therefore, engineering allowance EA = EC – BE = 6.86M – 6.5M = $0.36M. 

For the calculation of management contingency, we use a confidence level of 85% so Cp(85%) = $7.8M; therefore, MC = Cp – EC = 7.80M – 6.86M = $0.94M.

In many situations, the suggested contingency might be excessive, so the need for a mitigation study is necessary. We can use the sensitivity analysis tool in @RISK to detect the key drivers affecting our total cost. This is valuable information so that we can concentrate our efforts in reducing the impact of risk events and uncertainties to the total cost. Below, we see a tornado graph with the most important drivers. The analyst will then explore the appropriate mitigation strategies and assess their implementation cost. A second simulation can be run to assess the effectiveness of the proposed mitigation plan, and compare the pre-mitigated and post-mitigated cost distributions.




In following blog posts, I will explain how to distribute the assessed contingency to cost elements and identified risk events in project risk management models.

Javier Ordóñez, Ph.D
Director of Custom Solutions

An item from the Department of More Things Change, the More They Stay the Same.

Last week, speaking at a conference on managing retirement income, an executive with a U.S. division of Deutsche Bank announced that with the "failure" of diversified investing strategies, Modern Portfolio Theory was dead.  R.I.P. balanced portfolios.  R.I.P. the Nobel Prize-winning work of Harry Markowitz.  R.I.P. Monte Carlo simulation projections.

Instead, announced Phillip Hensler, "Advisors who offer predictability will prevail"-- isn't predictability the goal of all those portfolio managers who rely on statistical analysis techniques for risk assessment?  And he foresees that we will enter a new era of "Outcome Driven Investing"--isn't outcome what drives all investment activity?

In this new era financial planners will help their clients match their "health risks, market risks, and longevity risks with specific guaranteed and non-guaranteed" investment products.  Two questions: What else have financial planners been doing for the past decade?  And just how are they going to measure that risk?  

Maybe in this new era, sound investment advice won't be based on Modern Portfolio Theory and risk evaluation won't be the work of Monte Carlo software.  But just exactly what will be the era's guiding principles and analytical techniques?  Post-Modern Portfolio Theory and Las Vegas computational tools?

When considering decision-making under uncertainty, one may need to evaluate which of several decision possibilities to select, and then conduct a detailed risk analysis of that decision. Palisade Corporation’s Decision support software (such as the PrecisionTree decision tree software and the @RISK Monte Carlo simulation software) can be used in a wide range of decision analysis contexts to support the selection and detailed analysis in these situations. In addition, one may need to calibrate models or explore and analyse existing data sets, and the statistics software StatTools can facilitate certain forms of statistical analysis that may not be possible when using Excel statistics functionality. (Palisade’s DecisionTools Suite also contains other software products, including RISKOptimizer and Evolver to deal with optimisation problems, the neural network software NeuralTools, and TopRank to support model auditing and sensitivity analysis).

Frequent applications of the DecisionTools Suite include cost estimation (project cost estimation, construction cost estimation, cost budgeting and contingency planning), discounted cash flow analysis and financial forecasting, risk registers (event risk modelling and operational risk), options valuation and real options analysis, Six Sigma analysis, product strategy, environmental risk analysis, veterinary risk assessments, operations management, retirement planning and so on. Indeed, the range and flexibility of the DecisionTools Suite means that the number of applications is vast, and really only limited by a user’s ability to appropriately formulate their own situation in a way that is suitable for quantitative analysis.

Whilst the software can be used essentially in applications in all industries and functions, the oil and gas sector is a very active one. The increasing cost of discovery and recovery of oil from more remote and hostile environments means that an effective resource allocation and a rigorous decision evaluation are key to business success in these contexts. Decision tree software and Monte Carlo simulation software are therefore widely used in exploration and production (e.g. seismic testing decisions, reserves estimation, and production forecasting using exponential decline curves or other methods) and for other aspects of risk assessment for large projects.