Across various industries and disciplines, simulation modeling proffers valuable solutions by giving clear insights to complex problems.

One significant advantage that sets simulation models apart from other models is analyzing a model without altering the real-life sequence.

Let’s consider a couple of things you need to know about simulation models.


A simulation is an attempt to reproduce a real-life event or process in an isolated environment.

So you can understand better, let’s consider this example. Think about building a model about the cycle of events that play out in a retail store. You begin by first deciding the rules.

How will people interact? When will supplies be delivered? You’ll also consider things like Rush-hour, downtime, and every other thing you’ll need to design an accurate model.

The next step will then be to test the model in simulation software. Simulation software will show you the results of implementing the rules against certain variables. Some of these variables may include a black Friday surge or an unavoidable late night shipment.

Three primary situations would necessitate simulation software. They include;

  • When you have insufficient data, you will typically encounter this when studying historical or ancient events.
  • When you have complicated business processes, such that you cannot analyze them with traditional methods.
  •  When you need to experiment with low risk, low-cost environment.

Depending on the problem and nature of the variables involved, you can employ different simulation models to analyze the situation.

Types of Simulation Model?

There is a wide range of simulation models that you can choose from. Your selection, however, should depend on the nature of the real-life event, your intended outcome, and your requirements.

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Let’s consider four types of simulation models below.

1. Monte Carlo/Risk Analysis Simulation

In straightforward terms, a Monte Carlo simulation is a method of analyzing business risk. Most businesses use this model before implementing any major project or initiating a change in a routine.

The Monte Carlo simulation model is mathematically inclined and uses empirical data of real inputs and outputs. It further identifies potential risks and uncertainties through probability distributions.

Businesses use this simulation model because it provides a thorough understanding of the market. This model goes very well with any industry or field.

2. Agent-based Modeling & Simulation

The Agent-based simulation model analyzes the impact of an ‘agent’ on the ‘environment’ or ‘system.’ It analyzes a cause and effect situation. For example, consider the effect of new factory equipment on a manufacturing line.

The agent in this model could be any factor that the business environment responds to. It could be equipment, people, and practically anything else. In designing the simulation model, rules must be prescribed to act within the system. You’ll then observe how the system responds to those rules.

And of course, you shouldn’t come up with these rules in abstraction. You should base regulations should be based on real-life world data.

3. Discrete Event Simulation

A discrete event simulation model allows you to observe specific events that trigger your business processes. Take, for example, the technical support process that involves the user calling your company, your system receives and assigns the call, and your agent picks up the call.

You can use the discrete simulation model to study different systems to give you a wide range of outcomes. Some typical plans include healthcare, manufacturing, technical procedures, and others.

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4. Systems Dynamics Simulation Solutions

Unlike its counterparts, this simulation model is very abstract and discrete. The Systems Dynamics Simulation Solutions does not encourage specific details. So in analyzing a manufacturing company, this model does not account for data like labor and machinery.

How then does the Systems Dynamics Simulation Solutions operate?

Well, the model simulates for the long term and strategic level overall view of the system. Its priority is to get a comprehensive insight into the entire system about action in basic terms. Some examples of situations where this model may apply are deciding to end a product line, reducing CAPEX, etc.


The simulation model uses vary, and it is often used when it is impossible to conduct experiments on real-life scenarios. Here are some important uses of simulation models;

1. It provides a risk-free environment.

Simulation models provide the safest way to explore and test different scenarios without having to risk anything. With a simulation model, you get to make the right decision before effecting the actual changes.

2. You save money and time

Experiments are less costly and require less time when using simulation models. You also do not have to worry about alerting competition unnecessarily as your activities are conducted more covertly.

3. A higher level of accuracy

With simulation models, you can capture more details than an analytical model. As a result, this type of model’s outcomes is more accurate, leading to precise forecasting.