Opinion
electrical power. For such uncertainties, very sophisticated and complicated price prediction models exist. But if businesses aim for less complexity,
Investments with the highest-expected NPV may also provide the highest downside potential.
the project evaluation & review techniques (PERT) distribution provides a robust but easy to use tool that allows a fair balance between accuracy and model complexity. Historic data of the price development can then be used to derive the required
complicated mathematical constructs. In addition, the approach is easy to communicate when explaining the basis of the business case. Most of today’s business cases are based on large and often unmanageable numbers of assumptions. Integrating uncertainties into each assumption and combining all factors into a fi nal result is diffi cult.
The Monte Carlo analysis A proven method is the Monte Carlo analysis. Based on uncertain input data, the Monte Carlo method generates a large number of scenarios to approximate the entire range of potential outcomes. Stanislaw Ulam invented the modern
Using the presented approach, an additional 30% in NPV could be achieved compared to the previously planned investment.
input variables for the distribution: minimum value expected, maximum value expected as well as most likely value to occur. Accordingly, uncertainty models can easily be designed for other infl uential factors (e.g. engineering costs). T e PERT distribution off ers the possibility of asking an expert about how they think certain factors will develop (including most likely, minimum and maximum values). T is information is suffi cient to adequately model uncertainties without having to burden experts with
version of the Monte Carlo method in the late 1940s while he was working on nuclear weapons projects at the Los Alamos National Laboratory in the USA. After Ulam’s breakthrough, John von Neumann understood its potential importance and programmed the ENIAC computer to carry out Monte Carlo calculations. T e method is named after Monte Carlo in Monaco, famous for its casinos and the gambling on games of chance that take place within them. T e gambling games – roulette, dice and slot machines – all exhibit random behaviour. Monte Carlo methods vary, but tend
to follow a pattern: defi ne a domain of possible inputs; generate inputs randomly from a probability distribution over the domain; perform a deterministic computation on the inputs; aggregate the results. And even though the method is an
approximation, the result is precise enough to give the decision-maker a decent understanding of the uncertainty level associated with an investment. Furthermore, a better comparison of alternative investments is possible. An example from a power plant
evaluation provides a good example of the advantage of risk assessments. Based on a simple NPV analysis, ‘Investment C’ was considered the best investment opportunity. But after taking the associated risks into account, huge downside potential was revealed. In this example, ‘Investment B’ presented the most reasonable choice, having a moderate expected return but a very low spread of potential outcomes.
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