Deployment of digital twins by telcos runs significantly behind some verticals as they have less compelling use cases. However, they are now going live in multiple network-related areas. What are the key drivers and barriers of digital twin adoption in telecoms?
Definition of a digital twin
Digital twin is a familiar term with a well-known definition in industrial settings. However, in a telco setting it is useful to define what it is and how it differs from a standard piece of modelling. This research discusses the definition of a digital twin and concludes with a detailed taxonomy.
An archetypical digital twin:
- models a single entity/system (for example, a cell site).
- creates a digital representation of this entity/system, which can be either a physical object, process, organisation, person or abstraction (details of the cell-site topology or the part numbers of components that make up the site).
- has exactly one twin per thing (each cell site can be modelled separately).
- updates (either continuously, intermittently or as needed) to mirror the current state of this thing. For example, the cell sitescurrent performance given customer behavior.
- multiple digital twins can be aggregated to form a composite view (the impact of network changes on cell sitesin an area).
- the data coming into the digital twin can drive various types of analytics (typically digital simulations and models) within the twin itself – or could transit from one or multiple digital twins to a third-party application (for example, capacity management analytics).
- the resulting analysis has a range of immediate uses, such as feeding into downstream actuators, or it can be stored for future use, for instance mimicking scenarios for testingwithout affecting any live applications.
- a digital twin is directly linked to the original, which means it can enable a two-way interaction. Not only can a twin allow others to read its own data, but it can transmit questions or commands back to the original asset.
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What is the purpose of a digital twin?
This research uses the phrase “archetypical twin” to describe the most mature twin category, which can be found in manufacturing, operations, construction, maintenance and operating environments. These have been around in different levels of sophistication for the last 10 years or so and are expected to be widely available and mature in the next five years. Their main purpose is to act as a proxy for an asset, so that applications wanting data about the asset can connect directly to the digital twin rather than having to connect directly with the asset. In these environments, digital twins tend to be deployed for expensive and complex equipment which needs to operate efficiently and without significant down time. For example, jet engines or other complex equipment. In the telco, the most immediate use case for an archetypical twin is to model the cell tower and associated Radio Access Network (RAN) electronics and supporting equipment.
The adoption of digital twins should be seen as an evolution from today’s AI models
*See report for detailed graphic.
Source: STL Partners
At the other end of the maturity curve from the archetypical twin, is the “digital twin of the organisation” (DTO). This is a virtual model of a department, business unit, organisation or whole enterprise that management can use to support specific financial or other decision-making processes. It uses the same design pattern and thinking of a twin of a physical object but brings in a variety of operational or contextual data to model a “non-physical” thing. In interviews for this research, the consensus was that these were not an initial priority for telcos and, indeed, conceptually it was not totally clear whether the benefits make them a must-have for telcos in the mid-term either.
As the telecoms industry is still in the exploratory and trial phase with digital twins, there are a series of initial deployments which, when looked at, raise a somewhat semantic question about whether a digital representation of an asset (for example, a network function) or a system (for example, a core network) is really a digital twin or actually just an organic development of AI models that have been used in telcos for some time. Referring to this as the “digital twin/model” continuum, the graphic above shows the characteristics of an archetypical twin compared to that of a typical model.
The most important takeaway from this graphic are the factors on the right-hand side that make a digital twin potentially much more complex and resource hungry than a model. How important it is to distinguish an archetypical twin from a hybrid digital twin/model may come down to “marketing creep”, where deployments tend to get described as digital twins whether they exhibit many of the features of the archtypical twin or not. This creep will be exacerbated by telcos’ needs, which are not primarily focused on emulating physical assets such as engines or robots but on monitoring complex processes (for example, networks), which have individual assets (for example, network functions, physical equipment) that may not need as much detailed monitoring as individual components in an airplane engine. As a result, the telecoms industry could deploy digital twin/models far more extensively than full digital twins.
Table of contents
- Executive Summary
- Choosing where to start
- Complexity: The biggest short-term barrier
- Building an early-days digital twin portfolio
- Definition of a digital twin
- What is the purpose of a digital twin?
- A digital twin taxonomy
- Planning a digital twin deployment
- Network testing
- Radio and network planning
- Cell site management
- KPIs for network management
- Fraud prediction
- Product catalogue
- Digital twins within partner ecosystems
- Digital twins of services
- Data for customer digital twins
- Customer experience messaging
- Vertical-specific digital twins
- Drivers and barriers to uptake of digital twins
- Conclusion: Creating a digital twin strategy
- Immediate strategy for day 1 deployment
- Long-term strategy