Forty-eight percent of organizations that are implementing the Internet of Things (IoT) said they are already using, or plan to use digital twins in 2018, according to a recent IoT implementation survey* by Gartner, Inc. In addition, the number of participating organizations (202 respondents across China, U.S., Germany and Japan) using digital twins will triple by 2022.
Gartner defines a digital twin as a virtual counterpart of a real object, meaning it can be a product, structure, facility or system. Gartner predicts that, by 2020, at least 50 percent of manufacturers with annual revenues in excess of $5 billion will have at least one digital twin initiative launched for either products or assets.
“There is an increasing interest and investment in digital twins and their promise is certainly compelling, but creating and maintaining digital twins is not for the faint hearted,” said Alexander Hoeppe, research director at Gartner. “However, by structuring and executing digital twin initiatives appropriately, CIOs can address the key challenges they pose.”
Gartner has identified four best practices to tackle some of the top challenges posed by digital twins:
1- Involve the entire product value chain
Digital twins can help alleviate some key supply chain challenges. Digital twin investments should be made value chain driven to enable product and asset stakeholders to govern and manage products, or assets like industrial machinery, facilities across their supply chain in much more structured and holistic ways. Some challenges that supply-chain officers face in improving their performance are for example, a lack of cross-functional collaboration or a lack of visibility across the supply chain.
The value of digital twins can be an extensible product or asset structure that enables addition and modification of multiple models that can be connected for cross-functional collaboration. It can also be a common reference with comprehensive content for all stakeholders to access and understand the current status of the physical counterpart. When engaging the supply chain in digital twin initiatives, CIOs should incorporate access control based on the sensitivity of the content and the role of the supplier.
2- Establish well documented practices for constructing and modifying the models
Best-in-class modeling practices increase transparency on often complex digital twin designs and make it easier for multiple digital twin users to collaboratively construct and modify digital twins. They attempt to minimize the amount of effort to enable changes within the digital twin or between the digital twin and external, contextually important content. When modeling practices are standardized, one user is more likely to understand how another user created a digital twin. This enables the downstream user to modify the digital twin in less time and with less need to destroy and recreate portions of the digital twin.
3- Include data from multiple sources
It is difficult, to anticipate the nature of the simulation models, data types and data analysis of sensor data that might be necessary to support the design, introduction and service life of the digital twins’ physical counterparts. While 3D geometry is sufficient to communicate the digital twin visually and how parts fit together, the geometric model may not be able to perform simulations of the behavior of the physical counterpart in use or operation. At the same time, the geometric model may not be able to analyze data if it is not enriched with additional information. CIOs can expand the utility of digital twins by recommending that IT architects and digital twin owners define an architecture that allows access and use of data from many different sources.
4- Ensure long access life cycles
Digital twins with long life cycles include buildings, aircraft, ships, factories, trucks and industrial machinery. The life cycles of these digital twins extend well beyond the life spans of the formats for proprietary design software that most likely were used to create them and the means of storing data.
“This means that digital twins created in proprietary design software formats have a high risk of being unreadable throughout their service life,” said Mr. Hoeppe.
Additionally, the digital twin evolves and accumulates growing historical data, such as geometric models, simulation data and IoT data. As a result, the digital twin owner risks becoming increasingly locked into the vendor with the authoring tools. “CIOs can guard against this if they increase the viable life of digital twins by setting a goal for IT architects and digital twin owners to plan for the long-term evolution of data formats and data storage,” Mr. Hoeppe added.