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System Dynamics

System Dynamics is a simulation modelling methodology that represents the dynamic behavior of complex systems in casual loop or stock and flow diagrams. The elements of these diagrams are feedback, accumulation of flows into stocks, and time delays.

However, System Dynamics ignores the complexities of a system – for example, the individual properties of people, products or events – and its representation of complex systems is rather general. This is why it is predominantly used for problems of a strategic nature, for example market adoption rates and social process dependency.

Discrete-event Modelling

Discrete-event simulation focuses on the processes in a system, thus it is often used in manufacturing, logistics, and healthcare sectors. However, this methodology is still relatively abstract, since the specific, physical details of the processes are not represented.
It is advised to use discrete-event modelling techniques when a system under analysis can naturally be described as a sequence of operations.

Agent-based Modelling

Agent-based modelling is one of the most preferred approaches to simulation modelling, particularly for supply chain optimization and epidemiology. This is because it is able to model more complex structures, without knowing any global interdependencies; all it requires is some knowledge of the individual participants’ behavior.
Agent based models are also easier to maintain since model refinements typically result in local changes, rather than global.

Multi-method Modelling

Multi-method modelling is the combination of the three simulation modelling methodologies: system dynamics, discrete-event, and agent-based. The primary benefit of this approach is that it creates a hybrid model, making the most of each methodology’s advantageous properties and deviating each of their drawbacks.

Using a single method can make modelling at an appropriate abstraction level challenging, since real-life cases are often complex; thus, it is useful to attribute different methods to different parts of a system. The ability to truly capture a business system’s complexity is significantly limited by only using one method: either some system elements will have to be excluded, or a workaround must be developed. Therefore, multi-method modelling produces efficient and manageable models without the need to use workarounds.