Simulation is a tool that has become widely accepted by managers for several reasons:

1. It is relatively straightforward and flexible. It can be used to compare many different scenarios side-by-side.

2. Recent advances in software make some simulation models very easy to develop.

3. It can be used to analyze large and complex real-world situations that cannot be solved by conventional quantitative analysis models. For example, it may not be possible to build and solve a mathematical model of a city government system that incorporates important economic, social, environmental, and political factors. Simulation has been used successfully to model urban systems, hospitals, educational systems, national and state economies, and even world food systems.

4. Simulation allows what-if types of questions. Managers like to know in advance what options are attractive. With a computer, a manager can try out several policy decisions within a matter of minutes.

5. Simulations do not interfere with the real-world system. It may be too disruptive, for example, to experiment with new policies or ideas in a hospital, school, or manufacturing plant. With simulation, experiments are done with the model, not on the system itself.

6. Simulation allows us to study the interactive effect of individual components or variables to determine which ones are important.

7. “Time compression” is possible with simulation. The effect of ordering, advertising, or other policies over many months or years can be obtained by computer simulation in a short time.

8. Simulation allows for the inclusion of real-world complications that most quantitative analysis models cannot permit. For example, some queuing models require exponential or Poisson distributions; some inventory and network models require normality. But simulation can use any probability distribution that the user defines; it does not require any particular distribution.

The main disadvantages of simulation are as follows:

1. Good simulation models for complex situations can be very expensive. It is often a long, complicated process to develop a model. A corporate planning model, for example, may take months or even years to develop.

2. Simulation does not generate optimal solutions to problems as do other quantitative analysis techniques such as economic order quantity, linear programming, or PERT. It is a trial-and-error approach that can produce different solutions in repeated runs.

3. Managers must generate all of the conditions and constraints for solutions that they want to examine. The simulation model does not produce answers by itself.

4. Each simulation model is unique. Its solutions and inferences are not usually transferable to other problems.