Understanding Simulation Theory
UNDER STANDING SIMULATION THEORY
Simulation Theory is a depiction of a real-world event happening in an environment that is isolated or disconnected from its real-world counterpart. The basic idea behind simulation training method is to help professionals understand how the real system functions with a set of new conditions or rules.
There is a wide range of simulation models to choose from. One can select the kind of simulation they wish to opt for depending upon their nature of work & the kind of real-world situations they are willing to deal with depending upon their requirements.
1. Monte Carlo / Risk Analysis Simulation
Monte Carlo simulation is a method of analyzing risk in business. Organizations use Monte Carlo Theory prior the implementation of a major change in a process, such as a manufacturing assembly line. Built on the basis of mathematical models, Monte Carlo Theory analyses the usage of the pragmatic data collected from the inputs and outputs of the real system. The theory then identifies the potential risks & uncertainties involved through probability distributions.
The biggest advantage of applying Monte Carlo-based simulation is that it helps the origination to be aware of the potential risks involved in a particular project or situation & how the organization can protect itself from the market risk.
Monte Carlo simulations can be implemented to practically any industry, inclusive of diversified industries like oil and gas, supply chain management, manufacturing, engineering and many others.
2. Agent-Based Modeling & Simulation
An agent-based simulation is a theory that helps an organization scrutinize and understand the impact of an ‘agent’ on the process. To explain it further in simpler term, just think of the impact of new equipment or resource on your overall manufacturing line or the processing system.
The term ‘agent’ in this theory could be a person, or equipment or practically anything new in the system. The agent-based simulation theory includes the agent’s ‘behavior,’ which serve as rules of how those agents must act in the system. You then look at how the system responds to those rules.
However, you must draw your rules from real-world data — otherwise, you will not generate accurate insights. In a way, it serves as a means to examine a proposed change and identify potential risks and opportunities.
3. Discrete Event Simulation
A discrete event simulation model facilitates the organization to observe the specific events that impact your business growth directly. For example, the process of typical technical support, wherein the end-user calls you, your system receives the call and assigns the same further, and ends with your agent picking up the call. You can utilize a discrete event simulation model to moderate the process of technical support.
4. System Dynamics Simulation Solutions
This module is an abstract form of simulation theory. Unlike other simulations, this theory system dynamics does not include specific details about the system. So for a manufacturing facility, this model will not factor in data about the machinery and labor.
Rather, businesses would use system dynamics models to simulate for a long-term, strategic-level view of the overall system. In other words, the priority of using this model is to get an aggregate-level of insights about the functioning of an entire system in response to their actions.
