Digital Twin Technology Helping Organizations Reach Sustainability Goals

The widespread adoption of digital twins across industries is having a transformative business impact in product development, risk assessment, and sustainability, according to an Altair survey of more than 2,000 professionals in IoT and software engineering, among other IT roles.

Respondents said digital twin technology would help them reach their sustainability goals in the efficient use of resources (76%), energy savings and saving on operating costs (74%), and waste reduction (60%).

The majority (68%) of management responses indicated their company is utilizing digital twins to meet sustainability goals, compared with just 43% of user-level responses.

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"The main drivers for any manufacturer are to save time and money," said Keshav Sundaresh, global director of product management of digital twin for Altair. "Manufacturers are always looking for efficient ways in which products can be tracked and monitored to improve reliability and performance."

Physical Prototyping's Impact on Sustainability

On the sustainability front, less physical prototyping equals less waste, Sundaresh said.

According to the Altair survey, about two out of three respondents expect digital twins to make physical prototypes obsolete within the next few years.

"This alone makes a significant impact on sustainability," Sundaresh said.

Digital twins let users holistically understand and measure what's happening in a part, subsystem, product, or process (combined with the environment in which its operating in) by combining multiple data sources and can also be used as a predictive guide to the future and run several what-if studies virtually.

"This results in a significant reduction in building operating costs through reduction in energy consumption, maintenance, planning, and commissioning costs," he said.

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Digital twins in the banking, financial services, and insurance (BFSI) space, for example, has been able to drastically reduce the number of printers in the organization, let alone save millions of reams of paper — and trees.

"Digital twins can help reduce raw material usage costs, reduce product development costs, and cut CO2 emissions reduction from physical prototypes and test vehicles," Sundaresh added.

Digital Twins Also Improve Quality, Accuracy

Equally important are improved quality and connectivity gained from using digital twins, he said.

"Digital twins infused by AI algorithms provide the potential for highly efficient simulations with greater accuracy compared to traditional virtual prototyping methods," he explained.

Furthermore, digital twin models can be deployed on live data from physical sensors to predict the current and future performance of parts, subsystems, system-of-systems, or processes.

Of the 2,007 respondents surveyed, while 64% of upper-management respondents said that they were "highly knowledgeable" about digital twin technology, just 35% of user-level respondents said they were "highly knowledgeable" about the technology.

Sundaresh called this finding "surprising," as it shows the disconnect between upper management in an enterprise that's responsible for driving the overall vision, strategy, and value forward and the expert users who are responsible for execution/implementation of the strategy.

From his perspective, building alignment across the organization on digital twin applications and use cases is key to increasing stickiness (aka better capturing the voice of customers), value, and scale. 

"Customers are going to demand more and seamless integration to the cloud and all the HPC resources that it has to offer, but also balance that with security concerns," Sundaresh said.

He predicts that many customers will have a hybrid environment where simulation is happening on premises and in the cloud, causing more problems with where the data is coming from.

"Additionally, we will continue to see more simulations being used more often and earlier in the design cycle, and using all the data that is generated by those simulations to build and train AI/ML models so future decisions can be made quicker," he said.

Finally, Sundaresh said there will be more and more unique digital twins that correspond to unique assets in the field — and have real streaming data feeding those twins — so precautionary measures can be taken to ensure they are preforming optimally and don't fail.

"At the end of the day, digital twins are the catalyst to converging simulation, high performance computing, data analytics, and AI," he said.