Advantages, Disadvantages, and Pitfalls of simulation and modeling

Advantages of simulation and modeling

• Easy to understand - Simulation helps to learn about real system, without having the system at all or without working on real-time systems. Simulation is a very good tool of training and has advantageously been used for training in the operation of complex system. Space engineers simulate space flights in laboratories to train the future astronauts for working in weightless environment. Airline pilots are given extensive training on flight simulators, before they are allowed to handle real planes.

• Easy to test - Allows to make changes into the system and their effect on the output without working on real-time systems. In the real system, the changes we want to study may take place too slowly or too fast to be observed conveniently. Computer simulation can compress the performance of a system over years into a few minutes of computer running time.

• Easy to upgrade - Allows to determine the system requirements by applying different configurations. Simulation Models are comparatively flexible and can be modified to accommodate the changing environment to the real situation.

• Easy to identifying constraints - Allows to foresee the difficulties and perform bottleneck analysis which may come up due to the introduction of new machines, equipment and processes. It thus eliminates the need of costly trial and error method of trying out the new concepts. And reduce delay in the work process, information, etc. in real system.

• Easy to diagnose problems - Certain systems are so complex that it is not easy to understand their interaction at a time. However, Modelling & Simulation allows to understand all the interactions and analyze their effect. Additionally, new policies, operations, and procedures can be explored without affecting the real system.

Disadvantages of simulation and modeling

• Designing a model is an art that is learned over time and through experience and which requires domain knowledge and training. Furthermore, if two models are constructed by two competent individuals, they may have similarities, but it is highly unlike that they will be the same.

• Operations are performed on the system using random number, hence difficult to predict the result.

• Simulation results are difficult to interpret. Since most simulation outputs are essentially random variables, it may be hard to determine whether an observation is a result of system interrelations or randomness. So it requires experts to understand.

• Simulation is used in some cases when an analytical solution is possible, or even preferable.

• Simulation requires manpower and the analysis process can be time consuming and expensive.

Pitfalls in Simulation

Typical reasons why simulation projects fail include the following:

• Failure to state clear objectives at the outset.

• Failure to involve individuals affected by the outcome.

• Overrunning budget and time constraints.

• Failure to document and get a consensus on input data.

• Including more detail than is needed.

• Including variables that have little or no impact on system behavior.

• Failure to verify and validate the model.

• Basing decisions on a single run observation.

• Basing decisions on average statistics when the output is actually random.

• Being too technical and detailed in presenting the results to management.