Telco digital twins: Cool tech or real value?

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.

In addition:

  • 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

digital-twins-evolution-of-todays-ai-models-stl-partners

*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
  • Introduction
    • 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
    • Drivers
    • Barriers
  • Conclusion: Creating a digital twin strategy
    • Immediate strategy for day 1 deployment
    • Long-term strategy

Related research

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Coordinating the care of the elderly

Are telcos ready to enable digital health?

The world has been talking about connected healthcare – the use of in-home and wearable systems to monitor people’s condition – for a long time. Although adoption to date has been piecemeal and limited, the rapid rise in the number of elderly people is fuelling demand for in-home and wearable monitoring systems. The rapid spread of the Covid-19 virus is putting the world’s healthcare systems under huge strain, further underlining the need to reform the way in which many medical conditions are diagnosed and treated.

This report explores whether telcos now have the appetite and the tools they need to serve this very challenging, but potentially rewarding market. With the advent of the Coordination Age (see STL Partners report: Telco 2030: New purpose, strategy and business models for the Coordination Age), telcos could play a pivotal role in enabling the world’s healthcare systems to become more sustainable and effective.

This report considers demographic trends, the forces changing healthcare and the case for greater use of digital technologies to monitor chronic conditions and elderly people. It explores various implementation options and some of the healthcare-related activities of Tele2, Vodafone, Telefónica and AT&T, before drawing conclusions and recommending some high-level actions for telcos looking to support healthcare for the elderly.

This executive briefing builds on previous STL Partners reports including:

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Why healthcare needs to change

During the twentieth century, life expectancy in most countries in the world rose dramatically.  This was down to advances in medical science and diagnostic technology, as well as rising awareness about personal and environmental hygiene, health, nutrition, and education. Average global life expectancy continues to rise, increasing from 65.3 years in 1990 to 71.5 years in 2013.  In some countries, the increase in lifespans has been dramatic. The life expectancy for a Chilean female has risen to 82 years today from 33 years in 1910, according to the World Health Organization (WHO).

Figure 1: Across the world, average life expectancy is rising towards 80

raising lift expectancy to 2050

Source: The UN

Clearly, the increase in the average lifespan is a good thing. But longer life expectancy, together with falling birth rates, means the population overall is aging rapidly, posing a major challenge for the world’s healthcare systems. According to the WHO, the proportion of the world’s population over 60 years old will double from about 11% to 22% between 2000 and 2050, equivalent to a rise in the absolute number of people over 60 from 605 million to an extraordinary two billion. Between 2012 and 2050, the number of people over 80 will almost quadruple to 395 million, according to the WHO. That represents a huge increase in the number of elderly people, many of whom will require frequent care and medical attention. For both policymakers and the healthcare industry, this demographic time bomb represents a huge challenge.

Rising demand for continuous healthcare

Of particular concern is the number of people that need continuous healthcare. About 15% of the world’s population suffers from various disabilities, with between 110 million and 190 million adults having significant functional difficulties, according to the WHO. With limited mobility and independence, it can be hard for these people to get the healthcare they need.

As the population ages, this number will rise and rise. For example, the number of Americans living with Alzheimer’s disease, which results in memory loss and other symptoms of dementia, is set to rise to 16 million by 2050 from five million today, according to the Alzheimer’s Association.

The growth in the number of older people, combined with an increase in sedentary lifestyles and diets high in sugars and fats, also means many more people are now living with heart disease, obesity, diabetes and asthma. Furthermore, poor air quality in many industrial and big cities is giving rise to cancer, cardiovascular and respiratory diseases such as asthma, and lung diseases. Around 235 million people are currently suffering from asthma and about 383,000 people died from asthma in 2015, according to the WHO.

Half of all American adults have at least one chronic condition with one in three adults suffering from multiple chronic conditions, according to the National Institutes of Health (NIH). Most other rich countries are experiencing similar trends, while middle-income countries are heading in the same direction. In cases where a patient requires medical interventions, they may have to travel to a hospital and occupy a bed, at great expense. With the growing prevalence of chronic conditions, a rising proportion of GDP is being devoted to healthcare. Only low-income countries are bucking this trend (see Figure 2).

Figure 2: Spending on healthcare is rising except in low income countries

Public health as % of government spending WHO

Public health spending as % of GDP WHO

Source: The WHO

However, there is a huge difference in absolute spending levels between high-income countries and the rest of the world (see Figure 3). High-income countries, such as the U.S., spend almost ten times as much per capita as upper middle-income countries, such as Brazil. At first glance, this suggests the potential healthcare market for telcos is going to be much bigger in Europe, North America and developed Asia, than for telcos in Latin America, developing Asia and sub-Saharan Africa. Yet these emerging economies could leapfrog their developed counterparts to adopt connected self-managed healthcare systems, as the only affordable alternative.

Figure 3: Absolute health spending in high income countries is far ahead of the rest

per capita health spending by country income levelSource: The WHO

The cost associated with healthcare services continues to rise due to the increasing prices of prescription drugs, diagnostic tools and in-clinic care. According to the U.S. Centers for Disease Control and Prevention, 90% of the nation’s US$3.3 trillion annual healthcare expenditure is spent on individuals with chronic and mental health conditions.

On top of that figure, the management of chronic conditions consumes an enormous amount of informal resources. As formal paid care services are expensive, many older people rely on the support of family, friends or volunteers calling at their homes to check on them and help them with tasks, such as laundry and shopping. In short, the societal cost of managing chronic conditions is enormous.

The particular needs of the elderly

Despite the time and money being spent on healthcare, people with chronic and age-related conditions can be vulnerable. While most elderly people want to live in their own home, there are significant risks attached to this decision, particularly if they live alone. The biggest danger is a fall, which can lead to fractures and, sometimes, lethal medical complications. In the U.S., more than one in four older people fall each year due to illness or loss of balance, according to the U.S. Centers for Disease Control and Prevention. But less than half tell their doctor. One out of five falls causes a serious injury, such as broken bones or a head injury. In 2015, the total medical costs for falls was more than US$50 billion in the U.S. Beyond falls, another key risk is that older people neglect their own health. A 2016 survey of 1,000 U.K. consumers by IT solutions company Plextek, found that 42% of 35- to 44-year-olds are concerned that their relatives aren’t telling them they feel ill.

Such concerns are driving demand for in-home and wearable systems that can monitor people in real-time and then relay real-time location and mobility information to relatives or carers. If they are perceived to be reliable and comprehensive, such systems can provide peace of mind, making home-based care a more palatable alternative for both patients and their families.

Table of contents

  • Executive Summary
    • Barriers to more in-home healthcare
  • Introduction
  • Why healthcare needs to change
    • Rising demand for continuous healthcare
    • The particular needs of the elderly
    • Shift to value-based care
    • Demands for personalised healthcare and convenience
  • How healthcare is changing
    • Barriers to more in-home healthcare
  • Implementation options
    • Working with wearables
    • Cameras and motion sensors
    • The connectivity
    • Analysing the data
  • How telcos are tackling healthcare
    • KPN: Covering most of the bases
    • Tele2 and Cuviva: Working through healthcare centres
    • Vodafone and Vision: An expensive system for Alzheimer’s
    • Telefónica’s Health Moonshot
    • AT&T: Leveraging a long-standing brand
  • Conclusions and recommendations
    • Recommendations

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