A digital twin offers organizations a way to test and explore sustainability strategies. But that insight comes at a cost.
Digital twins are capable of modeling thousands upon thousands of scenarios based on a multitude of variables, often in mere seconds or even near real time, to determine the environmental impacts of the different options. Those insights aid scientists, sustainability chiefs and other executives in making decisions on how best to go green. But digital twin technology is energy intensive, can increase e-waste and poses other issues.
What digital twins are
A digital twin is a software model of a real-world entity and data to link the two.
In reality, digital twin technology is an almost umbrella term, given that it typically relies on a multitude of enabling technologies, most notably IoT devices such as sensors, to gather data from real-world assets, and edge computing to facilitate that. Digital twins also require the internet or other networking technologies to transport data between them and their real-world counterparts, and vice versa. Digital twins also rely on analytics, typically supported by AI and machine learning, to process and assess data. A digital twin provides a model of something in the real world and serves as its proxy, enabling users to manipulate scenarios via software to learn something about the real world.
"Digital twins make sense of the huge amounts of data we are generating every day," said Nick Napp, a senior member with the technical professional organization IEEE.
Ways digital twins can support sustainability
Although a nascent use, organizations use digital twins to support sustainability efforts in a number of ways. Here are a few of those.
Gain visibility into a physical asset's environmental impact
Two of the most promising uses of digital twin technology stem from its support of sustainable design and circularity.
Organizations are already using digital twins to build and test prototypes of real-world assets, Napp said, thereby reducing the need for raw materials to build multiple test versions.
Digital twins make sense of the huge amounts of data we are generating every day.
Nick NappSenior member, IEEE
The ability to model across an entire lifecycle is especially helpful.
As organizations consolidate data from not only their own systems but from partners and other stakeholders, they can use a digital twin to analyze the full spectrum of data for a physical asset -- from the origin of its component parts to the end of its life, said Erik Terjesen, a partner at Silicon Foundry, a Kearney-owned innovation advisory firm that helps its C-level enterprise executive members navigate the world of emerging tech. That enables them to truly see the environmental impact of the entire product lifecycle.
Organizations could next use a digital twin to analyze which changes, at what points and in what combination can make the asset more sustainable while also considering other factors -- such as the costs of those changes, the long-term availability of materials and geopolitical issues that could impact operations, Terjesen said. They can discover what changes are feasible under what conditions.
Analyze sustainability scenarios
Through scenario modeling, digital twins offer organizations a number of potential sustainability benefits. These include the following:
Enterprise leaders can use digital twins to specifically explore what changes would yield the best environmental gains, said Anand Rao, distinguished service professor of applied data science and AI at Carnegie Mellon University's Heinz College of Information Systems and Public Policy.
"They could do forward projections, look at interventions, introduce a specific change to see the impact; they could [ask] whether doing X leads to reaching their sustainability goals," Rao said. "That's something industries are just beginning to use digital twins to do."
Good sustainability data is key, however.
A digital twin's ability to consider at once all the factors that could impact a sustainability outcome -- assuming they're identified, with good data for each factor available -- gives this technology an edge over older modeling techniques, said Jonathan Colehower, managing director of global operations and supply chain practice at UST, a company that focuses on digital transformation.
"A big benefit of a digital twin is the ability to model additional variables so you can consider multiple outcomes," Colehower said.
A digital twin is composed of a real-world entity, a digital representation and a data connection. The technology could have wide applicability for improving sustainability.
Model complex ecosystems
Digital twins' applicability to scenario modeling is especially useful in complex systems or processes.
For example, city officials could use a digital twin to better understand traffic patterns and identify traffic light sequencing, and the digital twin can consider emissions from idling cars not just at a red light but from vehicles throughout traffic flow based on numerous factors including various traffic light patterns and congestion, Rao said.
These types of complex scenarios require IT to do extensive interoperability work.
In the case of a digital twin modeling transportation within a city, the data comes from multiple systems -- individual vehicles, mass transit and commercial operations as well as traffic control, weather forecasts and more, Napp said. Although the software applications that control each of these individual systems were not designed to interact with each other, a digital twin could pull data from each one and make sense of it, assuming IT created interoperability.
"You can have all the data overlayed, so you know how they all interplay. That allows you to see the underlying trends that could impact sustainability," Napp said.
See problems in a new way
Few people resist falling prey to cognitive biases, for example, giving preferential treatment to what confirms their existing beliefs or automatically believing the opinion of a favored employee. The same is true when it comes to sustainability.
Organizations typically follow processes built over many years based on legacy modeling and analytics techniques or even intuition, Terjesen said. Digital twins offer a fresh analysis. They can reveal scenarios that don't seem logical at first because they don't correlate with historical perspectives and assumptions but make sense based on actual data and analysis.
Sometimes the best pathway to sustainability defies expectation.
Colehower points to his work with a company that used a digital twin to plan a warehouse and distribution center. Its leaders assumed that a smaller warehouse would be a more sustainable option, as it would limit both its footprint on the land and its energy needs. However, through use of a digital twin to model different warehouse designs, leaders found that a larger facility with more loading docks -- which would cut the amount of time trucks would be idling and, thus, their emissions -- was actually the better environmental choice.
"[The finding] surprised people," Colehower said.
Future-proof the business
Today, disruption is the norm -- in sustainability as in other areas.
Organizational leaders across industries are dealing with changing regulatory, consumer and environmental landscapes. Governments are enacting more environmental laws. More consumers are considering a brand's sustainability record as part of their buying decisions. And climate change is creating more frequent hurricanes, wildfires and other extreme weather events.
Organizational leaders can use digital twins today to consider how those factors will impact their processes, products, locations and decisions in the future so they can determine the best actions to take now to be ready, Colehower said.
Address industry-specific environmental issues
Organizations can use digital twins to help them identify the unique sustainability challenges facing their industry.
Utility companies are using digital twins to more thoroughly and more accurately assess demand and help determine how to best integrate energy from renewable sources, Terjesen said. Similarly, the agriculture sector is using digital twins to optimize yield while healthcare, pharmaceuticals and life sciences companies are using the technology to model patient populations and analyze scenarios to determine optimal outcomes.
Digital twin sustainability drawbacks
As with any technology, the use of digital twins also comes with sustainability drawbacks and challenges. Here are just a few.
Increased energy, carbon emissions, e-waste
Digital twins typically use AI technologies that require powerful new data centers responsible for rapidly increasing negative environmental impacts, including increased energy, land and water use.
Digital twins' higher energy demands could actually worsen an organization's environmental impact if it doesn't use the digital twin to improve its sustainability enough to offset that energy use, Napp said.
In other words, organizations need to create a net positive effect, or they could actually do more harm than good with their digital twin efforts.
In addition, since digital twins are typically dependent on IoT sensors, organizations might also be responsible for major upticks in e-waste.
At this time, organizations typically do not use their digital twins to become more sustainable, Colehower said. Instead, they use their digital twins to solve other problems.
Sustainability is often a byproduct of those efforts anyway.
For example, Colehower worked with an e-cigarette manufacturer that worked on ways to recover the lithium batteries built into its devices, because the batteries were so expensive. The company's existing e-cigarette design did not allow it to recover the battery, so it used a digital twin to create the optimal redesign to enable that recovery. Although the leaders' main motivation for redesigning the product wasn't sustainability, efforts nonetheless created a more sustainable product and process, Colehower said.
Operational and maturity issues
Digital twin technology poses a number of other challenges related to the fact that the tech is not a mature technology.
Creating, maintaining and running digital twins is expensive, Napp said. As a result, many companies cannot afford to adopt and use the tech for any reason, including to support their sustainability initiatives.
In addition, digital twins are complex, require a level of clean data, interoperability and standardization that, for many uses, doesn't yet exist.
Although digital twins have been in use in aerospace, defense and auto manufacturing for years, they are still in their infancy, Rao said.
The technology also poses a number of security concerns, including the concept of an evil digital twin, a malicious virtual model used to support hacks and other criminal activity.
Sustainability and IT leaders interested in exploring digital twin technology should make sure they are clear about what the digital twin concept entails, understand testing strategies and learn about associated technologies such as edge computing and AI.
Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.
