Digital twins are the "killer app" for the Internet of Things (IoT) and, more profoundly, the new IT centre of gravity for event-driven, asset-centric business processes across all industries.
Digital twins are a relatively new form of enterprise software component that produces a digital proxy for a thing, person or process. They are used to increase situation awareness, as well as to better understand and respond to a business resource’s changing state. These capabilities are then applied more broadly to drive improvements in commercial processes and other forms of business value.
Adoption is proliferating, with 13 percent of organisations that have implemented IoT claiming to have already deployed digital twins, according to a recent Gartner survey. Sixty-two percent are either implementing or planning to implement during the next year.
Few organisations today, however, fully grasp how digital twins will drive fundamental changes to many of their core business applications. Additionally, few fully understand the different types of digital twins, or the potential relationship between them. Without these insights, they’ll struggle to get the most out of their IoT investments.
The role of different digital twins
A high-impact consequence of the proliferating adoption of digital twins is that companies are wittingly — or unwittingly — beginning to produce different types of digital twins.
Discrete digital twins are used to optimise individual assets, people and other physical resources. Composite digital twins, on the other hand, can be applied to operations involving a combination of discrete digital twins and resources, such as from external data sources. For example, composite equipment such as cars or industrial turbines, and whole and partial manufacturing or industrial processes.
Then there’s digital twins of organisations (DTOs), which maximise value across specific commercial processes such as manufacturing, or across entire business operations. For example, optimising overall manufacturing capacity based on changing go-to-market strategy, or maximising business value for all corporate stakeholders.
While all three differ in their scope and the type of outcomes they produce, they all perform two vital roles for business. They increase situation awareness via analysis of IoT data, events and interactions. By adding sensors to and analysing IoT data from an industrial turbine, for example, future failure can be predicted.
They also leverage that insight to make better business decisions by triggering appropriate responses within core enterprise applications. That same industrial turbine can be repaired prior to its failure, by checking inventory management and field service management applications when it’s already scheduled to be offline.
This dual role typically means that digital twins have a key intermediary role, sitting architecturally between IoT-connected things and business entities on the one hand and core enterprise applications on the other.
Acquiring or developing digital twins
All major types of business applications— including CRM, enterprise asset management (EAM), field service management (FSM) and others — can benefit from and are directly impacted by IoT projects. Digital twins are either a part of, or a supplement to, various core business applications to achieve or improve specific desired business outcomes.
They can either be acquired as an embedded feature of newer, IoT-native enterprise applications, or acquired separately and added as an enhancement to older, pre-IoT era enterprise applications. With the IoT predicted to become so pervasive, many companies will probably combine both approaches.
Gartner predicts that more than 50 percent of discrete digital twins in use will be acquired from OEMs and manufacturers of finished goods by the end of 2024, in conjunction with new IoT-connected product or equipment purchases.
If you’re considering developing digital twins, there are a plethora of technology service providers that offer IoT-specific development and analytics tools to produce digital twins.
However, given digital twins are a complex form of enterprise software with many potential technical challenges (such as metadata models, edge computing, IoT analytics, workflow and integration), a better option might be to outsource development to an external IoT service provider. Many of these challenges may be beyond the desire or ability of many companies to implement themselves.
IoT service providers, such as Accenture, Hitachi and Infosys, increasingly offer IT services around IoT-related projects, which include digital twin design and development as part of implementing IoT. More than 65 percent of mid to large enterprises will use external IoT services for at least half of their IoT solution build effort by 2021, up from 35 percent in 2018, according to Gartner.
Regardless of which approach is used to acquire or develop digital twins, there will be inevitable technical and commercial challenges. Integration is a key one, particularly to support interoperability between different kinds of digital twins.
Benoit Lheureux is a VP analyst at Gartner. His focus is on IoT platforms, integration and architecture. Benoit will be presenting on digital twins at the Gartner Application Architecture, Development & Integration Summit in Sydney, 29-30 July.