Digital Twins and Agent-based Modeling and Simulation

Digital Twins Types

There are presently three types of digital twins: those for individual assets, operations and predictions. In this article, we will focus on individual assets. Examples of these assets include drilling machines in the oil and gas industry or assembly line equipment. Each type of digital twin creates a three-dimensional simulation of the real-world features it models based on relationships of IoT data. The simulated models capture and contextualize this low-latent data about each asset for vital visibility into its performance. This real-time data provides a blueprint for diminishing downtime, scheduling maintenance and monitoring other factors that impact overall asset productivity and ROI. At scale, each factor translates into significant savings, increased performance and greater chances for optimization.

Bringing Digital Twins To Life

Digital twins also rely on a number of different simulation techniques to furnish their three-dimensional models. Hybrid simulation engines are pivotal for implementing the three-dimensional models of data for IoT assets. Simulation technologies should ideally support discrete event modeling, agent-based modeling and system dynamics. Agent-based modeling encompasses the various things people may or may not do to affect models, while system dynamics deliver overarching umbrellas of the impact of different factors (like those in a supply chain) on one another.

Further Reading

Jans Aasman, Three Necessities For Maximizing Your Digital Twins Approach, Forbes, 04/11/2019.