SimTex, a pharmaceutical company, is in the early stages of developing a new drug called Biathnon. As with most new drugs, the future of Biathnon is highly uncertain. For example, its introduction...

SimTex, a pharmaceutical company, is in the early stages of developing a new drug called Biathnon. As with most new drugs, the future of Biathnon is highly uncertain. For example, its introduction into the market could be delayed, pending tests by the Food and Drug Administration (FDA). Also, its market could be diminished by a potential rival product from SimTex’s competition. SimTex has identified the following key inputs that will affect Biathnon’s future profitability:

These are the inputs to a profitability model for Biathnon. A natural question is how changes in the inputs affect the key output—the NPV of Biathnon over its lifetime. How can SimTex use TopRank and @RISK to analyze this NPV?

Objective To use TopRank to identify the inputs that affect NPV most, and then to use @RISK to model these inputs with probability distributions.