With modeling and simulation, a problem-solving process is transferred from reality to an abstract image and is solved using this image. A model of the reality of a system, of an object, etc. is needed as the basis for a simulation. Therefore, a model must be made in advance. If a new model is being developed, we speak of modeling. If an existing model is being adapted so that statements can be made about the problem that is to be solved, the parameters of the model need only to be set to either the actual situation or the desired target situation and varied as appropriate. The model and/or the simulation results then can be used to draw conclusions as to the problem and its solution. Static evaluations then can follow, provided that stochastic processes have been simulated.

Simulation can provide insights on systems, for which real experiments are either impossible or would result in considerably higher expenses. The reasons for this are

• System behavior too slow or too fast (e.g., continental drift, nuclear reactions).
• System too large or too small (e.g., galaxies, atoms).
• A real system is unavailable (e.g., fusion process).
• A real system is nonexistent (e.g., product to be developed).
• A real system would be severely disrupted (e.g., stock market) or destroyed (e.g., crash test).
• A real system would be too expensive (e.g., aviation and space technology).
• The experiment is too dangerous (e.g., danger to humans and the ecosystem).

In an actual simulation, experiments are carried out on a model in order to gain knowledge about the real system, or the simulation results then are transferred to the real situation. In the context of simulation, we talk of the system that is to be simulated and of a simulator as the implementation or realization of a simulation model. This latter represents an abstraction of the system to be simulated.

Simulation is also becoming increasingly important in the area of automation technology. As we will see in Chaps. 4 and 6, it plays an important role in production planning and control. For example, there could be a considerable impact if an existing manufacturing plan is changed in the short term by a new order of higher priority. In such a case, it is helpful for those responsible for production if an MES is not only able to generate an optimal production plan but also can simulate scenarios and their effects. Thus the impact (e.g., what order is postponed, changes in cycle time, etc.) of introducing a priority order can be assessed in advance.