Part 2. MM4XL Tools > 1. Strategic Tools > Risk Analyst > 2. Simulation Never heard of it > Simulation? Never heard of it.

Risk Analyst

Simulation? Never heard of it.

Simulation is the process of creating a model that imitates some type of real-life, uncertain behavior. Repeating the model a number of times, each time using different input parameters, and synthesizing appropriately the results of the repetitions, produces useful information that enables you to answer important questions that in turn will reduce the uncertainty or risk associated with the model.

Basically, in a simulation model we define the objective of the analysis, such as what our sales would be with and without advertising investment, and then we create the model in MS Excel. Some variables in the model are defined in a way that makes them vary randomly within some user-defined range of values every time the model is refreshed. In our example we could have two output variables, the sales level with and without advertising, which may be the result of the interaction of several input variables, such as investment by media channel, shelf saturation level, competitors sales, and so on. The model is then repeated hundreds or thousands of times, each time saving the results.

This simulation technique is known as the Monte Carlo technique, for it works like spinning roulette wheels at a casino. It was used for the first time in the 1930s when the US government was developing the earliest atomic bomb. In the field of social science it has long been employed to study behavioral processes. And in the 80s it was discovered by business, first to study issues in operations management, such as plant efficiency, production quality and physical distribution issues, and more recently also in strategic and operational management. For years, companies like Procter & Gamble, Merck, Kodak, United Airlines, Burger King, and AT&T have used simulation models when dealing with complex and risky projects.

A lot of mind-numbing statistics lie behind simulation, and a rigorous approach to the subject can turn it into a dry, tiring, and frightening topic. From working with managers on simulation, however, we know that building models, refining them and interpreting the results is a very dynamic and involving process that managers can do well. This modeling activity often helps to reinforce team cohesion and team members awareness of risk faced in todays competitive environment. If a little study can help a company move forward, every manager should consider the investment worth making.

Risk Analyst solves the computational problem. This help chapter provides you with the information you need to get up to speed with the tool quickly. Read this material and work with the example sheets. At the end of the first reading of this help chapter you will have already learned a lot. By the time you have read it a second time, you might well have become your companys risk analysis guru! Its up to you.

It must be said, however, that a simulation is only an approximation of the reality. An analytic model is always preferable, whenever possible. Unfortunately, it is often impossible to create an analytic model, or even if it is possible, it may prove difficult to build it in mathematical notation. On the other hand, risk analysis through simulation is a rather intuitive subject that managers can grasp very well. The hurdle may be understanding the concept of probability distribution functions. Read through this help file and you will have gained that understanding as well.

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