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.
Issues that may be encountered with digital twins—with measures suggested by industry leaders to help address those issues—include the following:
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 [return on investment] 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."
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."
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."