Get answers to common questions about how to leverage your data to gain the full benefits of virtual twins, combining the science-based representation of the product/factory/company through modeling and simulation with the intelligence brought by data science on a collaborative platform.
According to the Aberdeen Strategy & Research report “The Four Building Blocks to Unleashing Continuous Innovation,” “Successful companies gain the full benefits of digital twins by combining the science-based representation of the product/factory/company through modeling and simulation with the prediction brought by real-world data science.”
Following are answers to a few questions you may have about how data can enrich digital twins.
What challenges are businesses facing around data?
“Businesses are drowning in their data,” according to Aberdeen Strategy & Research in its report The Four Building Blocks to Unleashing Continuous Innovation. Aberdeen has found that: “37% of organizations find that more data is available than is used for meaningful analysis, and 35% of them find that data needed for reporting is spread across different functions“. Engineers, analysts, and executives all have unique perspectives and unique data, so when information is overloaded in separate silos, it makes collaboration and sharing across departments difficult. In isolation, the data is not sufficiently rich, meaningful, or accessible and manual data manipulations are time-intensive and error-prone. Without communication across requisite departments, data has no chance of realizing its full potential of being turned into valuable, actionable insights. Unfortunately, 41% of organizations have insufficient or poor-quality data, and they are unable to make optimal – or even trusted – decisions when it comes to innovation, whether it be for processes, products, or manufacturing.”
Why is it important to enable access to data?
Data collected is often found in silos. Engineers work with engineering service data, managers work with management data, and accountants work with accounting data. However, the manager may need the accountant’s data or the engineer’s data in order to make better and more pertinent decisions. The engineer may need marketing service data to learn about the next trends and have guidelines to develop new products. Enabling data access removes silos inside the organization and between departments. When the challenges of managing, connecting and collaborating around data are addressed, data can be used as a trusted foundation for more holistic, meaningful, and multi-faceted insights.
Why is data context important?
If engineers, analysts, and executives apply their unique perspectives to real-time data, they can achieve alignment that drives ongoing cycles of decisions, actions, and innovation at all levels of the enterprise. Everyone can work together, effectively collaborate, and capture ideas using the same information powered by analytics. Engineers and manufacturers can map data to 3D models and visualize it in the context of the models to gain product design insights. Managers can track key business metrics, while analysts can evaluate the results of virtual product or process innovation. In the end, breaking data silos gives context to every organization. Enabling data consumption and people collaboration gives context to an organization and the context is an essential foundation for a digital twin.
What is a digital twin and what is it used for?
A digital twin is assimilated to what is called “the mirrored world”. It is a highly detailed representation of a physical object such as a plane or a factory but also, at a larger scale, an airport or a city. Despite what one may think, this is not a recent innovation. NASA used something close to a digital twin in 1970 when it modeled the Apollo 13 spacecraft to test and refine the solution that would bring the stricken craft home. Nowadays, it is commonplace to develop a digital twin, since the digital world makes each part of a product or an organization more accessible than it would be in the real world. The development of a digital twin is often a first step before the addition of an artificial intelligence layer allowing predictive tests that would not be possible without loss of money and time in the real world.
What is the difference between digital twin and virtual twin?
As explained above, a digital twin is a highly detailed digital representation of an asset, whether a product, infrastructure like an airport or a port or even a city. If you only have a digital twin and you exchange e-mails or pictures of the twin to make decisions, you are not capable of executing. As Morgan Zimmermann, NETVIBES Chief Executive Officer, explains: “A virtual twin is not only a representation; it is model-based, which means that it is possible to create “what-if scenarios”. In other words, what will happen if this material replaces this material or if this design replaces this design? It allows people to test and predict what may happen in the real world. A virtual twin allows collaboration between people with the aim of finding solutions, building plans together in the same place on which the model and the data are, for continuous execution.”
How does data science on the 3DEXPERIENCE platform set virtual twin experiences apart from digital twins?
Businesses are connecting tens, if not hundreds, of different data sources that they want to project on the twin they created thanks to our technologies. These data are about everything and anything, from pictures to videos, structured data to sensors, and IoT data to unstructured data. To cope with that, a few things have been developed. First, a data science infrastructure supports all of this diversity of models and data representation. Second, the 3DEXPERIENCE platform provides access to semantic and ontology functionalities. An ontology is the understanding of the meaning of human languages such as slang or clichés, for example. It allows the system not only to understand the meaning of isolated words, but also to understand the meaning of all the words together and to have a human reasoning. You need ontology because whenever you want to connect all of those pieces of data together, an abstraction is needed, and that abstraction is an ontology. NETVIBES allows the user not only to be assisted by AI to generate the ontology of the industry, but it also allows interpreting human language to match what people into that ontology say.
How is the virtual twin optimizing day-to-day performance?
The virtual twin allows organizations to better understand their customers and partners and to optimize day-to-day performance by leveraging AI and science. By unifying data, users are able to share more efficiently and to save time on their daily tasks. By visualizing customer requests via their virtual twins, companies can rapidly explore and test the feasibility of new ideas that come directly from their customers. The twin allows a constant feedback loop by sending and receiving data. That enables users to access predictive analysis and hypothetical scenarios with the aim of making the best decisions to optimize performance. Leveraging AI and science, a virtual twin allows organizations to better understand their customers and partners. By unifying data and visualizing customer requests via their virtual twins, companies can rapidly explore and test the feasibility of new ideas that come directly from their customers. The virtual twin facilitates a constant feedback loop, sending and receiving data, thus enabling users to access predictive analysis and hypothetical scenarios with the aim of making the best decisions to optimize performance.
Discover more in the Aberdeen Strategy & Research report HERE.
Discover Morgan Zimmermann’s interview HERE.
Learn more about virtual twin experiences HERE.