BUSINESS MODELING

Business Modeling for Decision Makers

Business Modeling for Decision Makers

For decades we have used analytical tools such as probabilistic modeling for dealing with uncertainty. However, outside the financial services industry only a few of companies have embraced them. Instead, executives often make big strategic decisions just “from the gut.” Some of the reasons for this include the perceived complexity of modeling techniques, lack of transparency, and the “garbage in, garbage out” syndrome. Indeed, numerous examples, including the current credit crisis, suggest decision makers rely too much on the seemingly sophisticated outputs of probabilistic models, while reality behaves quite differently.

In our opinion, the problem is not with the tools, but rather in a misunderstanding about their purpose and use. Due to the complex dynamics of real strategic business problems, expectations that the likelihood of all possible outcomes can be accurately estimated (or even imagined) are obviously unrealistic. Claims that a strategic decision is safe or optimal to some highly quantified confidence level (e.g., 95 percent) or that it carries a specific 95 or 99 percent value at risk, are usually inappropriate. Such claims might be meaningful where risk arises solely from the aggregate of a long sequence of micro-risks whose behavior can be predicted adequately from extensive historical data (for instance - and with caveats - risks of default in a truly diversified credit portfolio). But they are rarely meaningful when managers face one-of-kind business decisions involving strategic, execution, or macroeconomic risks.

Even in these cases, however, and perhaps especially in these cases, probabilistic and similar modeling methods can be tremendously useful as a structuring device to organize and combine all available insights about the relevant uncertainties and their impact. Used as an exploratory decision making tool, they improve decision makers' understanding of the key business value drivers, the importance and interdependencies of the most relevant uncertainties, and how sensitive a decision is to the assumptions that have gone into making it. Used correctly, the methods offer essential support to the process of making risk-informed decisions. They can also help managers explore the potential value of managerial flexibility.

As recent events have shown, the mere existence of a probabilistic model is a poor reason to feel that your risks are under control and that you can sleep well at night. In situations where there is significant uncertainty, however, the process of building a probabilistic model and interpreting the results can give you the confidence of having understood the key risks in as robust a way as possible.

Business executives cannot escape the need to make strategic decisions in the face of uncertainty and the study of this process has long been at the forefront of management science. However, there is an intriguing paradox. Key analytical tools, such as probabilistic modeling using Monte Carlo simulation, real options, game theory and others, have been around for decades, and since 1969 eight Nobel Prizes have been awarded for related cutting edge thinking in economics, finance, risk and uncertainty, and decision ...