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Simulation & Digital Twin

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.

Using a digital twin enhances insight and understanding of how your systems work and interact, helping evaluate parameters and interdependencies. As a virtual environment, a digital twin also allows low-cost and no-risk possibilities for experimentation.

Risk-free environment

Simulation modeling provides a safe way to test and explore different “what-if” scenarios. The effect of changing staffing levels in a plant may be seen without putting production at risk. Make the right decision before making real-world changes.

Save money and time

Virtual experiments with simulation models are less expensive and take less time than experiments with real assets. Marketing campaigns can be tested without alerting the competition or unnecessarily spending money.



Simulation models can be animated in 2D/3D, allowing concepts and ideas to be more easily verified, communicated, and understood. Analysts and engineers gain trust in a model by seeing it in action and can clearly demonstrate findings to management.


Insight into dynamics

Unlike spreadsheet- or solver-based analytics, simulation modeling allows the observation of system behavior over time, at any level of detail. For example, checking warehouse storage space utilization on any given date.

Increased accuracy

A simulation model can capture many more details than an analytical model, providing increased accuracy and more precise forecasting. Mining companies can significantly cut costs by optimizing asset usage and knowing their future equipment needs.


Handle uncertainty

Uncertainty in operation times and outcome can be easily represented in simulation models, allowing risk quantification, and for more robust solutions to be found. In logistics, a realistic picture can be produced using simulation, including unpredictable data, such as shipment lead times.

Machine Learning

Simulation can be used for Machine Learning applications by training Reinforcement Learning agents.

Simulation models can be used to train AI agents with reinforcement learning. Simulation models can be connected to RL agents to train them for deployable policies.

Machine Learning applications can be tested in a simulated environment by embedding them to the simulation models.

Simulation models can be used to generate unlimited amounts of relevant, clean, structured, and labeled training data to be used in machine learning applications.

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