Question

Discussion - Crystal Ball Simulation Review the description regarding Crystal Ball simulation and the Futura Apartments file (in this weeks unit) and discuss how more sophisticated simulation tools such as Crystal Ball can be used to address complex business issues.

Discuss how simulation could be used in your organization. What simulations do you think would be of value and do you have enough data to draw upon? If you can't think of any uses of simulation, do some research on simulations that some organizations have used and tell us what you learned.

Answer 1

Crystal Ball is the leading spreadsheet-based application for predictive modeling, forecasting, simulation, and optimization. It gives you unparalleled insight into the critical factors affecting risk.

When we use the word simulation, we refer to any analytical method meant to imitate a real-life system, especially when other analyses are too mathematically complex or too difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and simulation to automatically analyze the effect of varying inputs on outputs of the modeled system.

Each time you perform a Crystal Ball simulation, you gain a richer understanding of the inherent risks.With a Crystal Ball analysis, you know what your competitor does not: the probability of a particular outcome. Because Crystal Ball lets you quantify your risks, it can be a crucial tool for a successful negotiation.With Crystal Ball's Sensitivity Analysis you will know which factors really drive results. You can focus your energies on the right problem and complete the analysis sooner with less effort.

USING OF MONTE CARLO SIMULATION:

Monte Carlo simulation is especially helpful when there are several different sources of uncertainty that interact to produce an outcome. For example, if we're dealing with uncertain market demand, competitors' pricing, and variable production and raw materials costs at the same time, it can be very difficult to estimate the impacts of these factors -- in combination -- on Net Profit. Monte Carlo simulation can quickly analyze thousands of 'what-if' scenarios, often yielding surprising insights into what can go right, what can go wrong, and what we can do about it.

EXAMPLE:

As a simple example of a Monte Carlo simulation, consider calculating the probability of a particular sum of the throw of two dice (with each die having values one through six). In this particular case, there are 36 combinations of dice rolls. Based on this, you can manually compute the probability of a particular outcome. For example, there are six different ways that the dice could sum to seven. Hence, the probability of rolling seven is equal to 6 divided by 36 = 0.167.

Instead of computing the probability in this way, however, we could instead throw the dice a hundred times and record how many times each outcome occurs. If the dice totaled seven 18 times (out of 100 rolls), we would conclude that the probability of rolling seven is approximately 0.18 (18%). Obviously, the more times we rolled the dice, the less approximate our result would be. Better than rolling dice a hundred times, we can easily use a computer to simulate rolling the dice 10,000 times (or more). Because we know the probability of a particular outcome for one die (1 in 6 for all six numbers), this is simple. The output of 10,000 realizations.