Monte Carlo simulation

David Lengacher introduces a forecasting tool ideally suited for use in the global economic crisis. When it comes to forecasting any aspect of a companyÔÇÖs future performance, the mathematics and calculations required can get very tricky. This is especially true in cases that involve managing risk and measuring opportunitiesÔÇötwo tasks which can involve hundreds, if not thousands, of potential outcomes with different likelihoods of occurrence.┬á  In this arena a single prediction, such as the most likely outcome, is of little use to managers. They require a broader spectrum of information. When it comes to planning, managers can often benefit from having a range of revenue and expense forecasts, along with their associated likelihoods, so they can make more informed decisions. To illustrate, letÔÇÖs say that senior management at Acme Company predicts next year will bring between $500,000 and $2.5 million in new business (with the most likely case being $1 million). In addition, the company also predicts it will likely retain between $1 million to $2 million of its existing business, (with the most likely figure being $1 million). Finally, let us also assume that there are pending legal and insurance matters the company is wading through. There is consensus among management that these matters will be decided upon in the coming year and net proceeds are expected to be $1 million to $5 million, with each value in between being equally likely to occur. Although it may be great to have ranges like these agreed, what can anyone actually do with them? How can they be used in strategic planning, budgeting, or capital management? What Acme really needs is to assess the net effect of all three issues that will play a defining role in the companyÔÇÖs future. For example, if Acme requires at least $3 million in revenue next year to remain a viable business, what is the probability they will fall short? What is the 90 percent revenue floor (or minimum revenue estimate for which actual revenue has only a 10 percent probability of falling below)?┬á┬á Do not feel bad if you cannot compute the answers in your head, because it is not possible. This type of problem can only be handled via a powerful tool called Monte Carlo Simulation (MCS). The name Monte Carlo comes from the fact that during World War II, many computer simulations were built to help estimate the probability that a specific chemical chain reaction would occur, setting off an atomic bomb successfully. The scientists doing this work were avid gamblers and so they gave this type of simulation the name ÔÇ£Monte Carlo.ÔÇØ It is easy to see the parallels between chemical chain reactions and chain reactions involving business events. In fact, when it comes to calculating the likelihood with which specific situations will occur, they are effectively identical. Today many companies use MCS as an important tool for decision-making; for example, to estimate the sales of new products, to forecast net income, and to determine the ideal plant capacity for new facilities. The potential uses for MCS are so numerous that it would be futile to try to summarize them. In fact, once companies start using MCS for one purpose, they will often uncover several more opportunities to use it.┬á Fortunately, there are user-friendly MCS tools available that make these opportunities easy to exploit. One such product is @RISK (www.palisade.com), a tool that has quickly become the industry standard in this type of simulation. What makes @RISK so powerful is the fact that it harnesses managersÔÇÖ ubiquitous familiarity with MS Excel. More specifically, it shows not only what could happen, but also the likelihood of each outcome being modeled. The tool also provides graphical results which are extremely important for communicating key findings to stakeholders. Perhaps most important of all, it provides the user a vehicle through which to conduct sensitivity analysis, allowing managers to see which variables had the most significant effect on bottom-line results.By leveraging the power of MCS, companies can see the net impact of a multitude of events which have yet to come to fruition. In the current global economic crisis, where few companies are certain of their future, a tool like Monte Carlo Simulation can provide valuable insight into what reality may look like in the future. ┬á