Simulation is a powerful tool. With it, you can analyze, design, and operate complex systems. You use simulation models to assess real-world processes too complex to analyze via spreadsheets or flowcharts, testing hypotheses at a fraction of the cost of undertaking the actual activities. An efficient communication tool, modeling shows how an operation works and stimulates creative thinking about how to improve it. Models in industry, government, and educational institutions shorten design cycles, reduce costs, and enhance knowledge.

Simulation is important

Simulation involves designing a model of a system and carrying out experiments on it as it progresses through time. Models enable you to see how a real-world activity will perform under different conditions and test various hypotheses at a fraction of the cost of performing the actual activity.

One of the principal benefits of a model is that you can begin with a simple approximation of a process and gradually refine the model as your understanding of the process improves. This "step-wise refinement" enables you to achieve good approximations of very complex problems surprisingly quickly. As you add refinements, the model more closely imitates the real-life process.

Systems, models, and simulation

All professions use models of one form or another. But the word "model" does not always have the same meaning to business professionals, managers, scientists, and engineers.


The real world can be viewed as being composed of systems. A system is a set of related components or entities that interact with each other based on the rules or operating policies of the system:

Entities are the internal components of the system. Entities are involved in processes - activities in which they interact with each other.

Operating policies - the types of controls and availability of resources - are the external inputs to the system. They govern how the system operates and thus how the entities interact.

Over time, the activities and interactions of entities cause changes to the state of the system; this is called system behavior or dynamics. Systems can be mathematically straightforward, such as a flower growing in the soil and turning towards the sun to maximize photosynthesis. Or they can be more complex, such as supply chain operations composed of planning, selling, distribution, production, and sourcing subsystems.


A model is an abstracted and simplified representation of a system at one point in time. Models are an abstraction because they attempt to capture the realism of the system. They are a simplification because, for efficiency, reliability, and ease of analysis, a model should capture only the most important aspects of the real system. Most models can be classified into four basic types:

A scaled representation of a physical object, such as a 1:18 diecast model of a Ferrari, a clay model of a proposed packaging bottle, or a scale model of the solar system.

A graphical or symbolic visualization, such as a flow chart of office procedures, the board game Monopoly (which represents the hotels and facilities of Atlantic City), or an architect's plans for a building.

An analytical or mathematical formula that yields a static, quantitative solution. For instance, an analytic model might consist of several independent sample observations that have been transformed according to the rules of the model. Common examples of analytic models are spreadsheet models or linear programming models.

A mathematical description that incorporates data and assumptions to logically describe the behavior of a system. This type of model is typically dynamic - it has a time component and shows how the system evolves over time. ExtendSim products are tools for building mathematically-based, dynamic models of systems.

Dynamic modeling is the foundation for computer modeling. Thus, the word "model" will be used to mean a description of the dynamic behavior of a system or process.

ExtendSim models typically have a time component and can show cause and effect and the flow of entities throughout a system (you can also create ExtendSim animations that show spatial relationships.)


The Merriam-Webster OnLine Dictionary defines simulation as "the imitative representation of the functioning of one system or process by the functioning of another". This means that to determine how an actual system functions, you would build a model of the system and see how the model functions.

Simulations run in simulation time, an abstraction of real time. As the simulation clock advances, the model determines if there have been changes, recalculates its values, and outputs the results. If the model is valid, the outputs of the simulation will be reflective of the performance or behavior of the real system.

Simulation with ExtendSim means that instead of interacting with a real system you create a logical model that corresponds to the real system in certain aspects. You simulate the operations or dynamics of the system, then analyze one or more areas of interest. You do this in order to reduce risk and uncertainty so that you can make informed, timely decisions.

The current focus of the simulation industry is to increase the usability of simulation software while maintaining or increasing flexibility and accuracy. Borrowing successful features from other desktop applications, simulation software is developing into a more user-friendly tool. Advances such as graphical user-interfaces, object oriented programming, template models, and interfaces with other tools will continue to make it easier to build and maintain useful simulation models that are more intuitive.

For most complex problems, simulation is only one part of the total solution. A variety of applications can be used to help the users define their processes, acquire the necessary empirical data, model and simulate the process, analyze and optimize the process, and present the results. Improvements in the interface between different software applications helps provide a seamless integration of tools, therefore reducing unnecessary rework and user errors.

Advances in simulation software will never eliminate the need for users to have a good understanding of their processes. However, reducing the amount of effort required to build and maintain a simulation model helps users spend less time on the details and overhead of modeling and more time solving the problem at hand.

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