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Why use simulation modeling?

Simulation modeling solves real-world problems safely and efficiently. It provides 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 enables 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. Simulation software provides a dynamic environment for the analysis of computer models while they are running, including the possibility to view them in 2D or 3D.

The uses of simulation in business are varied and it is often utilized 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.

To learn how simulation is different from traditional mathematical modeling and check if it is applicable to your challenges, try AnyLogic yourself — download the Personal Learning Edition for free. This version is specially designed for self-learning, so you can freely explore the world of simulation!

Alternatively, start with our white paper based on the presentation by Lyle Wallis, director at PwC. It compares different approaches for modeling and analyzing business strategies and demonstrates the commercial use of simulation with case studies from world-famous companies.

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The Use of Simulation with 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 simulation 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. Simulation 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.

Flowchart for the simulation model

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.

Simulation model data
Simulation model results

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 of the modeling and simulation, therefore, give confidence and clarity for analysts, engineers, and managers alike.


Frequently Asked Questions

What is meant by modeling and simulation?

Modeling and simulation involve creating a digital representation of a real system to study its behavior over time. The model outlines the system’s logic, rules, resources, constraints, and interactions. By running the model under various conditions, you can test scenarios, compare decisions, and reduce uncertainty before implementing real-world changes.

What is the difference between agent-based, discrete-event, and system dynamics approaches?

Agent-based modeling shows how individual agents behave and interact. Discrete-event simulation shows how processes, queues, and events happen over time. System dynamics shows high-level trends, feedback loops, and long-term system behavior.

What is a hybrid simulation?

Hybrid simulation means combining several simulation methods in one model. In AnyLogic, we usually call this multimethod simulation. For example, one model can use discrete event simulation for processes, agent-based modeling for individual behavior, and system dynamics for long-term trends. This helps represent complex systems more accurately because each part is modeled with the method that fits it best.

How does AnyLogic’s multimethod approach compare with using separate tools like Simulink, SimEvents, and an agent-based platform?

AnyLogic integrates discrete-event simulation, agent-based modeling, and system dynamics into a single environment. Teams can model the entire system in one place, simplifying the building, testing, visualization, and explanation of complex business systems. This leads to a more connected workflow and decisions based on the entire system, not individual parts.

What are the latest advancements in simulation modeling?

The main advancements in simulation modeling are connected to AI, digital twins, cloud deployment, and easier experimentation. Simulation models are increasingly connected to real-time data, used with AI and reinforcement learning, shared through cloud platforms, and integrated into enterprise decision-making workflows.

Another important trend is better visualization, including 3D environments. This helps teams validate models, explain results, and make simulation more accessible to business users, not only technical specialists.

Simulation is the best approach for addressing business challenges

Learn why in the white paper Developing Disruptive Business Strategies with Simulation

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