Why use simulation modeling?

Simulation modeling solves real-world problems safely and efficiently. It is an important method of analysis which is easily verified, communicated, and understood. Across industries and disciplines, simulation modeling provides valuable solutions by giving clear insights into complex systems.

Bits not atoms. Simulation modeling is experimentation on a valid digital representation of a system. Unlike physical modeling, such as making a scale copy of a building, simulation modeling is computer based and uses algorithms and equations. The model of the system is dynamic and can be analyzed while it is running, even viewed in 2D or 3D.

Computer simulation is used in business when conducting experiments on a real system is impossible or impractical, often because of cost or time.

The ability to analyze the model as it runs sets simulation modeling apart from other methods, such as those using Excel or linear programming. By being able to inspect processes and interact with a simulation model in action, both understanding and trust are quickly built.

An example: Simulation Modeling for Efficient Customer Service

This specific example may also be applicable to the more general problem of human and technical resource management, where companies naturally seek to lower the cost of underutilized resources, technical experts, or equipment, for example.

Finding the optimum number of staff to deliver a predefined quality of service to customers visiting a bank

Firstly, for the bank, the level of service was defined as the average queue size. Relevant system measures were then selected to set the parameters of the model - the number and frequency of customer arrivals, the time a teller takes to attend a customer, and the natural variations which can occur in all of these, in particular, lunch hour rushes and complex requests.

A flowchart corresponding to the structure and processes of the department was then created. Models only need to consider those factors which impact the problem being analyzed. For example, the availability of office services for corporate accounts, or the credit department have no effect on those for individuals, because they are physically and functionally separate.


Finally, after feeding the model data, the simulation could be run and its operation seen over time, allowing refinement and analysis of the results. If the average queue size exceeded the specified limit, the number of available staff was increased and a new experiment was done. It is possible for this to happen automatically until an optimal solution is found.


Overall, multiple scenarios may be explored very quickly by varying parameters. They can be inspected and queried while in action and compared against each other. The results, therefore, give confidence and clarity for analysts, engineers, and managers alike.

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