We are all aware to some extent of the importance of simulation models in our world. Boeing Corporation and Airbus Industries, for example, commonly build simulation models of their proposed jet aircraft and then test the aerodynamic properties of the models. Your local civil defense organization may carry out rescue and evacuation practices as it simulates the natural disaster conditions of a hurricane or tornado. The U.S. Army simulates enemy attacks and defense strategies in war games played on a computer. Business students take courses that use management games to simulate realistic competitive business situations. And thousands of business, government, and service organizations develop simulation models to assist in making decisions concerning inventory control, maintenance scheduling, plant layout, investments, and sales forecasting.

As a matter of fact, simulation is one of the most widely used quantitative analysis tools. Various surveys of the largest U.S. corporations reveal that over half use simulation in corporate planning.

Simulation sounds like it may be the solution to all management problems. This is, unfortunately, by no means true. Yet we think you may find it one of the most flexible and fascinating of the quantitative techniques in your studies. Let’s begin our discussion of simulation with a simple definition.

To simulate is to try to duplicate the features, appearance, and characteristics of a real system. In this chapter, we show how to simulate a business or management system by building a mathematical model that comes as close as possible to representing the reality of the system. We won’t build any physical models, as might be used in airplane wind tunnel simulation tests. But just as physical model airplanes are tested and modified under experimental conditions, our mathematical models are used to experiment and to estimate the effects of various actions. The idea behind simulation is to imitate a real-world situation mathematically, then to study its properties and operating characteristics, and, finally, to draw conclusions and make action decisions based on the results of the simulation. In this way, the real-life system is not touched until the advantages and disadvantages of what may be a major policy decision are first measured on the system’s model.

Using simulation, a manager should (1) define a problem, (2) introduce the variables associated with the problem, (3) construct a simulation model, (4) set up possible courses of action for testing, (5) run the simulation experiment, (6) consider the results (possibly deciding to modify the model or change data inputs), and (7) decide what course of action to take. These steps are illustrated in Figure 13.1.

The problems tackled by simulation can range from very simple to extremely complex, from bank teller lines to an analysis of the U.S. economy. Although very small simulations can be conducted by hand, effective use of this technique requires some automated means of calculation—namely, a computer. Even large-scale models, simulating perhaps years of business decisions, can be handled in a reasonable amount of time by computer. Though simulation is one of the oldest quantitative analysis tools (see the History box that follows), it was not until the introduction of computers in the mid-1940s and early 1950s that it became a practical means of solving management and military problems.

We begin this chapter with a presentation of the advantages and disadvantages of simulation. An explanation of the Monte Carlo method of simulation follows. Three sample simulations, in the areas of inventory control, queuing, and maintenance planning, are presented. Other simulation models besides the Monte Carlo approach are also discussed briefly. Finally, the important role of computers in simulation is illustrated.