Digital twins have great promise — the ability to simulate and improve the performance of systems, machines, facilities, and even entire ecosystems, at relatively low cost with software. "In today's world where it seems every day there's a new surprise, having that added insight to mimic your real world and make decisions based on the information and the data that's collected is highly valuable and important," Ara Surenian, vice president of product management for Plex by Rockwell Automation, told ZDNET.
However, there are potential roadblocks to digital twin deployment and management. Accuracy, complexity, costs, and skills availability may make it difficult to get the most out of these applications, and even potentially misrepresent or miss actual changes in the status of systems or facilities.
Also: How AI can help businesses design for positive externalities
Issues that may be encountered with digital twins — with measures suggested by industry leaders to help address those issues — include the following:
1. Complexity
Building and maintaining digital twins can be a complex process. "A big mistake companies make is allowing their desire for perfect to get in the way of good enough," Christine Bush, director of the Robotic Center of Excellence for Schneider Electric, told ZDNET. "Like any digital transformation, it all starts with data. And in most every case, at the onset of the transformation, the data is rarely good enough. However, good enough is where the process needs to start because the transformation is a journey and needs to start in order to realize the downstream benefit."
For this reason, industry leaders advocate moving cautiously when establishing digital twins. "Begin with pilot projects to showcase tangible ROI in controlled settings," Bush said. "This approach not only validates the technology but also helps secure budget approvals and organizational support."
To properly scope digital twins, "focus on a specific location versus the entire end-to-end supply chain," Surenian agreed. "Find the location where the data is perceived to be the most readily available and accurate. From there, determine what questions and issues you wish to tackle with the digital twin. Ask yourself if it's easy to understand capacity, inventory, ability to meet demand, and other relevant questions."
2. Incomplete networks
An organization adopting digital twins needs to be well-networked. "The biggest roadblock to digital systems is connectivity, at the network and human levels," Thierry Klein, president of Nokia Bell Labs Solutions Research, told ZDNET. "Digital twins are most effective when multiple digital twins are integrated, but this requires collaboration among stakeholders, a robust digital network, and systems that can be connected to the digital twin."
Also: 5.5G touted as the network to bring improved enterprise connectivity
Well-developed networks are "critical to ensure seamless data integration, real-time transmission and anywhere access supporting the scalability of digital twin implementations," Klein pointed out.
Artificial intelligence (AI) can act as a booster to overcome such challenges, Klein added. An AI model integrated into digital twins can "analyze data collected from physical systems, renders the digital twin, recommends next-step actions and simulates multiple future scenarios and optimizations. It can also analyze data, enabling more sophisticated data analysis and network and process automation."
3. Data velocity
The ability to represent physical environments in real time also presents challenges to digital twin environments. "With digital twins, you're generally relying on your model to run parallel with some real-life physical system so you can understand certain effects that might be impacting the system," Naveen Rao, vice president of AI for Databricks, told ZDNET.
Also: If AI is the 'gas guzzler' of data, how do we get better mileage?
"Take, for example, a digital twin of an aircraft jet engine. You could be using the digital twin to understand the efficiencies at different parts of the engine or to look for potential issues. If you don't have data being processed at a high enough velocity, you might give an alert too late when maintenance would be more expensive. Also, if your models aren't accurate, you could give poor recommendations and lose the trust of your maintenance team."
4. Real-time user interfaces not real-time enough
The need for real-time connectivity also extends to end-users' abilities to view what's going on within a system or facility. Extended reality (XR) and virtual reality (VR) "offer innovative ways to visualize and simulate complex systems and processes, which is particularly valuable in industries like manufacturing, construction, and healthcare," said Bush of Schneider Electric. "However, their adoption faces practical challenges, especially in environments with high-speed machinery where safety concerns arise."
At Schneider, the emphasis is on "implementing XR and VR primarily in controlled environments to mitigate risks such as physical collisions or distractions," Bush continued. "Despite the exciting possibilities XR and VR present for enhancing digital twins, ensuring operational safety remains paramount as we continue to innovate in this space."
5. Inconsistent standards
"The lack of open, interoperable data standards presents another significant roadblock. "Antiquated technology, legacy proprietary data formats, and analog processes create silos of 'dark data' — or data that's inaccessible to teams across the asset lifecycle," Shelly Nooner, vice president of innovation and platform for Trimble, told ZDNET. "These data bottlenecks cause inefficiencies that can result in higher capital expenses and higher operational costs."
Also: Three ways to create the right data culture in your business
This is where industry groups need to step up and formulate common standards that enable greater digital connectivity. In the construction industry, for example, BuildingSmart is an organization dedicated to creating and adopting open, international standards and solutions for infrastructure and buildings. BuildingSmart, according to Nooner, "is addressing the civil infrastructure open data standard challenge. They have already proven their value within the building industry and continue to gain momentum with infrastructure construction."
6. Managing diverse data inputs
"Accurate data inputs from sensors and IoT devices are essential, but successful implementation also demands a well-organized approach and adequate resources," Robert Bunger, innovation product owner in the chief technology office for Schneider Electric, told ZDNET. The key is to "integrate diverse data sources and maintain synchronized models, which can be complex and resource intensive."
Governance will play a role in managing such diversity. "Digital twin systems will need robust MLOps [machine learning operations] to ensure the latest and most accurate models can be constantly re-trained and deployed," said Rao. This includes "strong governance to ensure only the right people have access and audits can be done easily, and real-time observability so you know the moment your model, or source data, might be drifting or losing accuracy."
7. Lack of skills
As with many advanced technology projects that require engagement with systems across and outside the enterprise, digital twins require expertise in data integration, artificial intelligence, and software development. "Skill shortages and technology illiteracy are also roadblocks to digital twin deployment, "contributing to the lack of data accuracy and resistance to change," Ryan Hamze, principal consultant with ISG, told ZDNET.
Also: Ready to upskill? Look to the edge (where it's not all about AI)
Hamze advised "targeting investments for local workforce training and upskilling." Another option is "partnerships with third-party industry leaders will also help fill gaps."
Conclusion
It's always important to keep the business front and center within any digital twin initiatives. "We are helping our clients overcome many of these roadblocks by engineering human-centric digital twins that function at the business process and business execution layers," Jason Noel, executive director of emerging technology for EY Consulting, told ZDNET. "Intelligent twins are designed to be used by business and operations roles, versus only technical engineers."
A business-first strategy "will help propel the next generation of enterprise business applications where digital twins orchestrate insights, decision-making, and execution of both core and ancillary business functions," Noel added.