Innovation leader case study: Telefónica Tech AI of Things

The origins of Telefónica Tech AI of Things

Telefónica LUCA was set up in 2016 to “enable corporate clients to understand their data and encourage a transparent and responsible use of that data”.

Before the creation of LUCA, Telefónica’s focus had been on developing assets and making acquisitions (e.g. Synergic Partners) to build strong internal capabilities around data and analytics – with some data monetisation capabilities housed within their Telefónica Digital unit (a global business unit selling products beyond connectivity, which was disbanded in 2016). Typical projects the team undertook related to using network data to make better decisioning for the network and marketing teams, and providing Telefónica Digital with external monetisation opportunities such as Smart Steps (aggregated, anonymised data for creation of vertical products) and Smart Digits (provision of consent-based data to the advertising industry).

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Creating the autonomous LUCA unit made a statement that Telefónica was serious about its strategy to offer data products to enterprise customers. Quoting from the original press release, “LUCA offered three lines of products and services:

The Business Insights area brings the value of anonymous and aggregated data on Telefónica’s networks for a wide range of clients. This includes Smart Steps, which is focused on mobility analysis solutions for more efficient planning. For example, to optimise transport networks and tourist management in cities, or in the case of a health emergency, in helping to better understand population movements and in limiting the spread of pandemics.

The analytical and external consultancy services for national and international clients will be provided by Synergic Partners, a company specialized in Big Data and Data Science which was acquired by Telefónica at the end of 2015.

Furthermore, LUCA will help its clients by providing BDaaS (Big Data as a Service) to empower clients to get the most out of their own data, using the Telefónica cloud infrastructure.”

The following table shows a timeline from the origins of LUCA in the Telefónica Digital business unit through to its merger into the Telefónica Tech AI of Things business in 2019 – illustrating the progression of its products and other major activities.

Timeline of Telefónica’s data monetisation business

Telefonica-data-monetisation-luca-AI-IoT

Source: STL Partners, Charlotte Patrick Consult

Points to note on the timeline above:

  • Telefónica stood out from its peers with the purchase of Synergic Partners in 2015 (bringing in 120 consultancy headcount). This provided not only another leg to the business with consulting capabilities, but also additional headcount to scope and sell their existing product sets.
  • Looking at the timeline, it took Telefónica two years from this purchase and the establishment LUCA to expand its portfolio. In 2018, a range of new, mainly IoT-related capabilities, were launched, built up from existing projects with individual customers.
  • Telefónica has added machine learning to its products across the timeframe, but in 2019 the development of NLP capability for use in Telefónica’s existing products, and an internal data science platform, were then productised for customers (see below discussion about its Aura product set).
  • As the number of products has expanded, the number of partnerships has also expanded, bringing specific platforms and capabilities which can be combined with Telefónica’s own data capabilities to provide added value (examples include CARTO which creates geographic visualisations of Telefónica’s data).
  • Looking at changing vertical priorities:
    • Telefónica has always been strong in the advertising sector, starting with products from O2 UK in 2012. The exact nature of what it has offered has changed over time and some capabilities have been sold, however, it still has a strong mobile marketing business and expects it data to become of more interest to brands/media agencies as the use of cookies diminishes across the next few years.
    • The retail sector offers opportunity, but has been challenging to target over the years. Although Telefónica has interesting data for retail companies, creating replicable products is challenging as the large retailers each have differing requirements and working with small cell data in-store can be expensive. The product set is therefore currently being simplified, as the pandemic has also reduced demand from retailers.

One of Telefónica’s key capabilities which is not clearly displayed in the timeline is the provision of services to the marketing teams of the various verticals it targets. These include analytics products which Telefónica has developed from its internal capabilities and other functionality such as pricing tools.

The formation of Telefónica Tech

In 2019, Telefónica LUCA became part of the newly formed, autonomous Telefónica Tech business unit. The organisation is split into two business areas: cybersecurity & cloud, and the assets from Telefónica LUCA combined with the IoT unit. The goal of Telefónica Tech is to:

  • Enable the financial markets to clearly see revenue progression. Telefónica’s stated aim is for sustained double digit growth, which it achieved with year-on-year growth of 13.6% in 2020, although the IoT and Big Data segment only grew 0.8% y-o-y in 2020, due to the impact of COVID-19 on IoT deployments, especially in retail. Showing signs of recovery, in H121 revenue growth in the IoT and Big Data segment rose to 8.1% y-o-y, and to 26% y-o-y for the whole of Telefónica Tech.
  • Coordinate innovation, particularly around post-pandemic opportunities such as remote working, e-health, e-commerce and digital transformation
  • Take advantage of global synergies and leveraging existing assets
  • Ease M&A and partnerships activity (it already has 300 partners to better reach new markets, including relations with 60 start-ups across products)
  • Build relationships with cloud providers (it has existing relationships with Microsoft, Google and SAP).

To better leverage existing assets, Telefónica LUCA was integrated with Telefónica’s IoT capabilities to create a more unified set of capabilities:

  1. IoT is seen as an enabling opportunity for AI, which can bring added value to Telefónica’s 10,000 IoT customers (with 35 million live IoT SIMs worldwide). Opportunities include provision of intelligence around “things” (for example, products to analyse sensor data) and then the addition of Business Insight services (i.e. analysis of aggregated, anonymised Telefónica data which adds further insight alongside the data coming from IoT devices).
  2. AI is now often a commodity discussion with C-Level prospects and Telefónica wishes to be seen as a strategic partner. Telefónica’s AI of Things proposition offers an execution layer and integration experts with security-by-design capabilities.
  3. Combining capabilities provides sales teams with an end-to-end value proposition, as the addition of AI is often complimentary to cloud transformation projects and the implementation of digital platforms.

There is a growing ecosystem in IoT and data which will generate more opportunities as both IoT solutions and ML/AI solutions mature, although it is not a straightforward decision for Telefónica on how to compete within this ecosystem.

Table of contents

  • Executive Summary
    • How successful has Telefónica been in data monetisation?
    • Learnings from Telefónica’s experience
    • Key success factors
    • Telefónica’s future strategy
  • Introduction
    • The origins of Telefónica Tech AI of Things
    • The formation of Telefónica Tech
  • Vision, mission and strategy
    • Scaling the business
    • Building a product set
    • Learnings from Telefónica Tech AI of Things
  • Organisational strategy
    • Where should the data monetisation team live?
    • Structure of Telefónica Tech AI of Things Team
    • External partnerships
    • Future plans
  • Data portfolio strategy
    • Tools and infrastructure
    • AI Suite
    • Vertical strategy
    • Product development beyond analytics
  • Conclusion and future moves

Related research

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A3 for enterprise: Where should telcos focus?

A3 capabilities operators can offer enterprise customers

In this research we explore the potential enterprise solutions leveraging analytics, AI and automation (A3) that telcos can offer their enterprise customers. Our research builds on a previous STL Partners report Telco data monetisation: What’s it worth? which modelled the financial opportunity for telco data monetisation – i.e. purely the machine learning (ML) and analytics component of A3 – for 200+ use cases across 13 verticals.

In this report, we expand our analysis to include the importance of different types of AI and automation in implementing the 200+ use cases for enterprises and assess the feasibility for telcos to acquire and integrate those capabilities into their enterprise services.

We identified eight different types of A3 capabilities required to implement our 200+ use cases.

These capability types are organised below roughly in order of the number of use cases for which they are relevant (i.e. people analytics is required in the most use cases, and human learning is needed in the fewest).

The ninth category, Data provision, does not actually require any AI or automation skills beyond ML for data management, so we include it in the list primarily because it remains an opportunity for telcos that do not develop additional A3 capabilities for enterprise.

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Most relevant A3 capabilities across 200+ use cases

9-types-of-A3-analytics-AI-automation

Most relevant A3 capabilities for leveraging enterprise solutions

People analytics: This is the strongest opportunity for telcos as it uses their comprehensive customer data. Analytics and machine learning are required for segmentation and personalisation of messaging or action. Any telco with a statistically-relevant market share can create products – although specialist sales capabilities are still essential.

IoT analytics: Although telcos offering IoT products do not immediately have access to the payload data from devices, the largest telcos are offering a range of products which use analytics/ML to detect patterns or spot anomalies from connected sensors and other devices.

Other analytics: Similar to IoT, the majority of other analytics A3 use cases are around pattern or anomaly detection, where integration of telco data can increase the accuracy and success of A3 solutions. Many of the use cases here are very specific to the vertical. For example, risk management in financial services or tracking of electronic prescriptions in healthcare – which means that a telco will need to have existing products and sales capability in these verticals to make it worthwhile adding in new analytics or ML capabilities.

Real time: These use cases mainly need A3 to understand and act on triggers coming from customer behaviour and have mixed appeal to telcos. Telcos already play a significant role in a small number of uses cases, such as mobile marketing. Some telcos are also active in less mature use cases such as patient messaging in healthcare settings (e.g. real-time reminders to take medication or remote monitoring of vulnerable adults). Of the rest of the use cases that require real time automation, a subset could be enhanced with messaging. This would primarily be attractive to mobile operators, especially if they offer broader relevant enterprise solutions – for example, if a telco was involved in a connected public transport solution, then it could also offer passenger messaging.

Remote monitoring/control: Solutions track both things and people and use A3 to spot issues, do diagnostic analysis and prescribe solutions to the problems identified. The larger telcos already have solutions in some verticals, and 5G may bring more opportunities, such as monitoring of remote sites or traffic congestion monitoring.

Video analytics: Where telcos have CCTV implementations or video, there is opportunity to add in analytics solutions (potentially at the edge).

Human interactions: The majority of telco opportunities here relate to the provision of chatbots into enterprise contact centres.

Human learning: A group of low feasibility use cases around training (for example, an engineer on a manufacturing floor who uses a heads-up augmented/virtual reality (AR/VR) display to understand the resolution to a problem in front of them) or information provision (for example, providing retail customers with information via AR applications).

 

Table of Contents

  • Executive Summary
    • Which A3 capabilities should telcos prioritise?
    • What makes an investment worthwhile?
    • Next steps
  • Introduction
  • Vertical opportunities
    • Key takeaways
  • A3 technology: Where should telcos focus?
    • Key takeaways
    • Assessing the telco opportunity for nine A3 capabilities
  • Verizon case study
  • Details of vertical opportunities
  • Conclusion
  • Appendix 1 – full list of 200 use cases

 

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Recovering from COVID: 5G to stimulate growth and drive productivity

For the accompanying PPT chart pack download the additional file on the left

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Related webinar: How will 5G transform transport and logistics?

In this webinar, we share learnings from 100+ interviews and surveys with industry professionals. During the presentation we will look to answer:

  • How will 5G accelerate digital transformation of the transport and logistics industry?
  • What are the key 5G-enabled use cases and what benefits could these deliver?
  • What must change within the industry to unlock this transformation?
  • What is the role for telcos – how can they work with industry leaders to increase adoption of 5G and build new revenues beyond core communication services?

Date: Thursday 10th September 2020
Time: 4pm BST

View the webinar recording

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The 5G opportunity and value to verticals

In October 2019, STL Partners published research highlighting the benefits 5G-enabled use cases could unlock for industries. Our forecast predicted a potential $1.4 trillion increase in global GDP by 2030 across eight key industries.

In this short paper we look to update these numbers and explore new insights and conclusions based on two key factors:

  1. STL Partners has produced new research on the impact of 5G on the transport and logistics industry. This has led to more granular insight on the unique benefits and use cases for this vertical.
  2. COVID has changed the global landscape. It has increased demand for some 5G use cases, such as remote patient monitoring or video analytics solutions that determine if the public are respecting social distancing, but has also brought about economic uncertainty. We reflect these nuances in our updated figures.

5G enabled use cases could increase GDP by $1.5 trillion by 2030 – an increase from our original forecast

Source: STL Partners

5G’s impact on transport and logistics: Fresh analysis and new use cases

In 2019, we deep-dived into the 5G opportunity within two key verticals: healthcare and manufacturing. We have since performed a similar deep-dive on the transport and logistics industry, consisting of primary research with experts in the industry. We interviewed 10 enterprises, solutions providers, and members of 5G testbeds who were focused on transport and logistics, as well as surveying 100+ individuals who work in the industry to test the impact they predicted for three key 5G use cases. We will shortly be publishing a full report on these findings in detail.

We have revised our estimation on the impact of 5G on the transport and logistics industry. In 2019, we predicted 5G enabled use cases could increase the GDP value of the transport and logistics industry by 3.5% in 2030. We now believe the impact could be as high as 6%, though importantly some of these benefits are indirect rather than direct.

New forecasts show a bigger impact to the transport and logistics industry

Source: STL Partners

The three 5G-enabled solutions newly explored in detail in our study were:

  • Real-time routing and optimisation: Sensors collect data throughout the supply chain to improve visibility and optimise processes through real-time dynamic routing and scheduling;
  • Automated last 100 metres delivery: Using drones or automated delivery vehicles for the last ‘hundred yards’ of delivery, where the delivery van acts as a mobile final distribution point;
  • Connected traffic infrastructure: Smart sensors or cameras are integrated into traffic infrastructure to collect data about oncoming traffic and trigger real-time actions such as rerouting vehicles or changing traffic lights.

Benefits from these use cases include fewer traffic jams, more efficient supply chains, less fuel required and fewer accidents on the roads.

COVID has changed the landscape and appetite for 5G services

COVID-19 has caused a global economic slowdown. There has been a widespread fall in output across services, production, and construction in all major economies. Social distancing and nationwide lockdowns have led to a significant fall in consumer demand, to business and factory closures, and to supply chain disruptions. The pandemic’s interruption to international trade has far exceeded the impact of the US-China trade war and had a major impact on national economies. Lower international trade, coupled with a precipitous fall in passenger air travel, has also caused the air industry to enter a tailspin.

Table of Contents

  • Preface
  • The 5G opportunity: Updated forecast on value to verticals
  • 5G’s impact on transport and logistics: Fresh analysis and new use cases
    • Increased productivity through more efficient roads: An impact beyond transport and logistics
  • COVID has changed the landscape and appetite for 5G services
    • COVID has impacted the GDP of every country – and outlook for recovery is still unclear
    • Operators’ 5G strategies and roll out have also been impacted
    • Appetite for 5G-enabled healthcare services has been accelerated
  • Conclusion: Where next for the industry?

Reliance Unlimit: How to build a successful IoT ecosystem

Reliance Unlimit’s success so far

Unlimit, Reliance Jio’s standalone IoT business in India, established in 2016, understood from the start that the problem with the IoT wasn’t the availability of technology, but how to quickly pull it all together into a clear, affordable solutions for the end customer. The result is that less than four years later, it has deployed more than 35,000 end-to-end IoT projects for a prestigious portfolio of customers, including Nissan Motor, MG Motor, Bata, DHL, GSK and Unilever. To meet their varying and evolving needs, Unlimit had built a IoT ecosystem of almost 600 partner companies by the end of 2019. Of these, nearly 100 are fully certified partners, with which Unlimit co-innovates solutions tailored to the Indian market.

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The state of the IoT: Balancing cost and complexity

In 1968, Theodore Paraskevakos, a Greek American inventor and businessman, explored the idea of making two machines communicate to each other. He first developed a system for transmitting the caller’s number to the receiver’s device. Building on this experiment, in 1977 he founded Metretek Inc, a company that conducted commercial automatic meter reading, which is essentially today’s commercial smart meter. From then, the world of machine to machine communications (M2M) developed rapidly. The objective was mainly to remotely monitor devices in order to understand conditions and performance. The M2M world was strongly telecommunications-oriented and focused on solving specific business problems. Given this narrow focus, there was little diversity in devices, data sets were specific to one or two measurements, and the communications protocols were well known. Given this context, it is fair to describe first-generation M2M solutions as a siloed, with little – if any – interaction with other data and solutions.

The benefits and challenges of the IoT

The purpose of the Internet of Things (IoT) is to open those silos and incorporate solution designers and developers into the operating environment. In this evolved environment, there might be several applications and solutions, each delivering a unique operational benefit. Each of those solutions require different devices, which produce different data. And those devices require life cycle management, the data needs to be analysed to inform better decisions, and automation integrated to improve efficiency in the operational environment. The communication methods between those devices can also vary significantly, depending on the environment, where the data is, and the type of applications and intelligence required. Finally, all this needs to run securely.

Therefore, the IoT has opened the silos, but it has brought complexity. The question is then whether this complexity is worth it for the operational benefits.

There are several studies highlighting the advantages of IoT solutions. The recent Microsoft IoT Signals publication, which surveys over 3000 decision makers in companies operating across different sectors, clearly demonstrates the value that IoT is bringing to organisations. The top three benefits are:

  • 91% of respondents claim that the IoT has increased efficiency
  • 91% of respondents claim that the IoT has increased yield
  • 85% of respondents claim that the IoT has increased quality.

The sectors leading IoT adoption

The same study highlights how these benefits are materialising in different business sectors. According to this study – and many others – manufacturing is seen as a top adopter of IoT solutions, as also highlighted in STL Partners research on the Industrial IoT.

Automotive, supply chain and logistics are other sectors that have widely adopted the IoT. Their leadership comes from a long M2M heritage, since telematics was a core application of M2M, and is an important part of the supply chain and logistics process.

The automotive sector’s early adoption of IoT was also driven by regulatory initiatives in different parts of the world, for instance to support remotely monitored emergency services in case of accidents (e.g. EU eCall). To enable this, M2M SIMs were embedded in cars, and only activated in the case of an accident, sending a message to an emergency centre. From there, the automotive industry and mobile network operators gradually developed a broader range of applications, culminating in the concept of connected cars. The connected car is much more sophisticated than a single emergency SIM – it is an IoT environment in which an array of sensors is gathering different data, sharing that data externally in various forms of V2X settings, supporting in-vehicle infotainment, and also enabling semiautonomous mobility. Sometime in the future, this will mature into fully autonomous mobility.

The complexity of an IoT solution

The connected car clearly represents the evolution from siloed M2M solutions to the IoT with multiple interdependent data sources and solutions. Achieving this has required the integration of various technologies into an IoT architecture, as well as the move towards automation and prediction of events, which requires embedding advanced analytics and AI technology frameworks into the IoT stack.

High level view of an IoT architecture

Overview of IoT architecture

Source: Saverio Romeo, STL Partners

There are five levels on an IoT architecture:

  1. The hardware level includes devices, sensors, gateways and hardware development components such as microcontrollers.
  2. The communication level includes the different types of IoT connectivity (cellular, LP-WAN, fixed, satellite, short-range wireless and others) and the communication protocols used in those forms of connectivity.
  3. The middleware software backend level is a set of software layers that are traditionally called an IoT platform. A high-level breakdown of the IoT platform includes a connectivity management layer, a device management layer, and data management and orchestration, data analytics and visualisations layers.
  4. The application level includes application development enablement tools and the applications themselves. Those tools enable the development of applications using machine-generated data and various other sources of data –all integrated by the IoT platform. It also includes applications that use results of these analytics to enable remote and automated actions on IoT devices.
  5. Vertically across these levels, there is a security layer. Although this is simplified into a single vertical layer, in practice there are separate security features integrated into IoT solutions at each layer of the architecture. Those features work together to offer layer-to-layer and end-to-end security. This is a complex process that required a detailed use of security-by-design methodology.

The IoT architecture is therefore composed of different technological parts that need to be integrated in order to work correctly in the different circumstances of potential deployment. The IoT architecture also needs to enable scalability supporting the expansion of a solution in terms of number of devices and volume and types of data. Each architectural layer is essential for the IoT solution to work, and they must interact with each other harmoniously, but each requires different technological expertise and skills.

An organisation that wants to offer end-to-end IoT solutions must therefore make a strategic choice between “in-house” IoT architecture development, or form strategic partnerships with existing IoT technology platform providers, and integrate their solutions into a coherent architecture to support an IoT ecosystem.

In the following sections of this report, we discuss Unlimit’s decision to take an ecosystem approach to building its IoT business, and the steps it took to get where it is today.

Table of contents

  • Executive Summary
    • Four lessons from Unlimit on building IoT ecosystems
    • How Unlimit built a successful IoT ecosystem
    • What next?
  • The state of the IoT: Balancing cost and complexity
    • The benefits and challenges of the IoT
    • The sectors leading IoT adoption
    • The complexity of an IoT solution
    • The nature of business ecosystems
  • How Unlimit built a successful IoT business
    • So far, Unlimit looks like a success
    • How will Unlimit sustain leadership and growth?
  • Lessons from Unlimit’s experience

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Telco data monetisation: What is it worth?

Data revenue opportunities are variable

Monetisation of telco data has been an area of activity for the last six years. However, telcos’ interest levels have varied over time due to the complexity of delivering and selling such a diverse range of products, as well as highly variable revenue opportunities depending on the vertical. Telcos’ appetite to pursue data monetisation has also been heavily impacted by the fortunes of other new telco products, in particular IoT, owing to the link between many data/analytics products and IoT solutions.

This report assesses the opportunity for telcos to monetise their data and provide associated data analytics products in two parts:

  1. First, we look at the range of products and services a telco needs to create in order to deliver financial value.
  2. Then, we explore the main use cases and actual financial value of telco data analytics products across 12 verticals, plus horizontal solutions that apply to multiple verticals.

Telco data monetisation: Calculation methodology

The methodology used to model the financial value of telco data analytics is outlined in the figure below.

  • The starting point for this analysis is 210 data or data analytics use cases, spread across 12 verticals and the horizontal solutions applicable to multiple verticals.
  • We then assess how difficult it is for a telco to address each use case, based on pre-requisite supporting platforms and solutions, regulatory constraints, etc. (shown in red). This evaluation enables us to assess how likely telcos are to develop products for each use case.
  • Thirdly, we assess which types of telco are able to develop the use case (in yellow). For example, telcos in a market with particularly restrictive regulation around use of personal data are simply not able to create certain products.
  • Finally, it is necessary to understand whether the data/analytics products created for a use case can be offered as an independent, standalone product, or more likely to be provided as a bolt-on service to another, pre-existing solution. This question is primarily pertinent in the IoT space where basic data/analytics are likely to be included in the price of the IoT service.
    • For products that we expect to be sold independently, we calculate the potential revenue based on estimated pricing for the type of data product, where known, and likely volumes that a telco will sell in a year.
    • For data analytics products closely linked to IoT, we attach no monetary value.

Calculation methodology for the feasibility and value of telco data monetisation use cases

Rationale behind data monetisation potential

Source: STL Partners, Charlotte Patrick Consult

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Viewing the data

Underlying the analysis in this report is a database tool including a detailed assessment of each of the 210 data monetisation use cases we have identified, with numerical analysis and charting capabilities. We know many of our readers will be interested to explore the detailed data, and so have made it available for download on the website in the form of an Excel spreadsheet.

Full use case database and analysis available on our website

Source: STL Partners

Table of Contents

  • Executive Summary
  • Introduction
    • Calculation methodology
  • What is this market worth to telcos?
  • Creating products for data monetisation
    • Telco products for the ecosystem
    • Data and analytics for IoT
    • Use of location in data monetisation
  • Maximising value in different verticals
    • Advertising and market research
    • Agriculture
    • Finance
    • Government
    • Insurance
    • Healthcare
    • Manufacturing
    • Real estate and construction
    • Retail
    • Telecom, media and technology
    • Transportation
    • Utilities
    • Horizontal solutions for all verticals
  • Conclusion and recommendations
    • How to pick a winning project
  • Index

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$1.4tn of benefits in 2030: 5G’s impact on industry verticals

Understanding the 5G opportunity in other industries

The aim of this report is to highlight the impact that 5G will have on global GDP between 2020 and 2030. To do this, we have focused on eight industries where we feel 5G will have the largest impact. Often when 5G is discussed, the focus is on the impact it will have on the consumer market. Here, we argue that 5G will unlock significant new revenue opportunities in the enterprise space, enabling innovative use cases that are currently impossible to scale commercially (with existing technologies).

Insight from this report is explored further in the following publications:

The document was researched and written independently by STL Partners, supported by Huawei. STL’s conclusions are entirely independent and built on ongoing research into the future of telecoms. STL Partners has written widely on the topic of 5G, including a recent two-part series into the short- and long-term opportunities unlocked by 5G, and lessons that can be learnt from early movers.

Comparing apples with apples: How to compare nascent 5G with established 4G

If you compare the technological specifications for 3GPP release 14 and 3GPP release 15 (the first 5G release), you might be underwhelmed. Despite the hype that 5G will be transformative, it does not appear to be delivering much more than incremental increases in speed and reliability. But, of course, 4G is now a mature form of connectivity (having been in-life for 6+ years) whereas 5G is still nascent.

To compare apples with apples, it makes sense to compare 5G release 16, where capabilities such as ultra-reliable low-latency and network slicing are being added, with LTE today.

Mature 5G benchmarked against the capabilities of mature 4G

Mature 5G benchmarked against mature 4G

Source: ITU, Nokia, ublox, gps world

Of course, these figures represent a best-case scenario occurring in a laboratory environment. This is true for both the 4G and 5G numbers. It’s also true that, in reality, it will take time before we see commercialised rollout of enhanced mobile broadband (“pure 5G”) rather than enhanced mobile broadband with 4G fall-back alongside fixed wireless access. Despite this, these figures make clear that when 5G reaches maturity, it will far outstrip the capabilities of 4G, and unlock new use cases.

Our assumption is that by 2025 5G technology will be mature, enabling massive M2M / IoT use cases as well as those that require ultra-reliable low-latency communications. Several of the 5G use cases we’ll go on to explore in more detail are reliant on this technology, so it is important to acknowledge that their commercialisation is only likely to start from around 2023 and in many markets they still won’t be fully deployed in 2030.

It’s not all about LTE: 5G must be compared to all available technology

Mobile is not the only form of connectivity used by enterprises. Plenty of industries are also making use of Wi-Fi, LPWAN, Zigbee, Bluetooth and fixed connectivity as part of their overall connectivity solution. When 5G is rolled out, in some cases, it will need to integrate with these existing technologies rather than replace them. The table below summarises some of the key benefits and shortcomings of current technologies, including highlighting the sorts of situations in which industries are making use of them.

Current technologies will not be entirely replaced by 5G, but it can address some of they key shortcomings

current technologies will not be entirely replaced by 5G, but it can address some of their key shortcomings

There are clear scenarios where 5G will be superior to existing technologies and bring significant benefits to industrial users. Ultimately, in particular, 5G will enable:

  1. Low latency and high bandwidth requirements for wireless connectivity
  2. Massive IoT through ability to handle high cell density
  3. Ultra-reliable and secure connectivity.

Table of Contents

  • Preface
  • Executive Summary
    • 5G enabled solutions are estimated to add c.$1.4 trillion to global GDP in 2030
    • Operators must embrace new business models to unlock significant revenues with 5G
    • Recommendations for operators: how to capitalise on the 5G opportunity
  • Introduction
    • Background
    • Comparing apples with apples: how to compare nascent 5G with established 4G
    • It’s not all about LTE: 5G must be compared to all available technology
    • 5G deployment: 5G will mature over the next ten years
  • 5G will add more than $1.4 trillion to the global economy by 2030
  • Mobile network operator strategic options with 5G
    • 5G alone will not change the game for operators
    • Strategic options for operators to add more value with 5G
  • 5G-enabled digital transformation in healthcare
    • Example 5G use case: Remote patient monitoring
    • Implications for telcos
  • 5G-enabled digital transformation in manufacturing
    • 5G can create $740bn in additional GDP by 2030
    • Example 5G use case: Advanced predictive maintenance
    • Implications for telcos
  • Conclusions for operators: how to capitalise on the 5G opportunity

Table of Figures

  • Figure 1: Mature 5G benchmarked against the capabilities of mature 4G
  • Figure 2: Current technologies will not be entirely replaced by 5G, but it can address some of their key shortcomings
  • Figure 3: Forecast of 5G deployment in major regions
  • Figure 4: Responses from industry surveys
  • Figure 5: 5G will contribute ~$1.4 trillion to global GDP by 2030
  • Figure 6: Manufacturing, energy & extractives and media, sports & entertainment industries will see the largest upticks to their industry thanks to 5G use cases
  • Figure 7: In 2030, manufacturing and construction will be the largest industry sectors (in 2030)
  • Figure 8: High income countries will see almost 75% of the benefit of 5G in 2025, but the share is more even across all geographies by 2030
  • Figure 9: 4G rollout did not produce sustainable revenue increase
  • Figure 10: What should telcos’ role be in 5G B2B?
  • Figure 11: As telcos move beyond just connectivity, they can increase their share of the wallet
  • Figure 12: Telcos must focus efforts in specific verticals – some are already doing this
  • Figure 13: Global impact of 5G on healthcare across four key contact points
  • Figure 14: Remote patient monitoring enables wearables to send data about the patient to the hospital for monitoring
  • Figure 15: Estimated impact of 5G-enabled remote patient monitoring
  • Figure 16: The potential roles for telcos can within healthcare
  • Figure 17: The TELUS Health Exchange as a point of coordination
  • Figure 18: There is opportunity for telcos’ to play multiple roles higher up the value chain in healthcare
  • Figure 19: Estimated impact of 5G on manufacturing GDP (USD Billions) by use case
  • Figure 20: Advanced predictive maintenance enables many sensors to send data about machinery for monitoring and optimisation

5G regulation: Ensuring successful industrial transformation

How should governments regulate 5G?

The old regulatory models are less relevant for 5G

Regulators in different markets around the world have a tried and tested formula for making spectrum available for new networks and for regulating the operators that run those networks. They have successfully used this formula for 2G, 3G, and 4G.

However, 5G is different and may require a different approach for both licensing spectrum and for regulating mobile network operators’ services. As we outline in the section 5G benefits industry and society, unlike its predecessors, 5G is not simply a faster pipe which therefore benefits individual end-users. Instead, it has been designed with new capabilities that can have a profound effect on enterprises and entire industries.

These capabilities and how they compare to LTE and to other wireless technologies are outlined in the Appendix. Because 5G can create so much value to all constituents of society, STL Partners contends that the focus of governments and regulators should be in ensuring that:

  1. It is rolled out as quickly as possible;
  2. Regulation is sufficiently flexible and focussed to reflect the needs of different industries and of consumers;
  3. Mobile network operators are encouraged to deliver more value to their existing customers and potentially new ones by contributing to cross-industry activity that benefit governments, enterprises, and consumers.

Put simply, our analysis suggests that the upside from rapid 5G deployment far outweighs the short-term benefits of high spectrum licensing fees. From a pure economic perspective, 5G should contribute an additional $1.4 trillion of Gross Domestic Product globally in 20301. The higher rates of employment, corporate profits, and consumer spending associated with this increased GDP will translate into significant increases in receipts of corporation and income taxes, national insurance contributions, and sales tax for governments as well as enhanced national competitiveness.

These annual inflows to the public purse will be far bigger than the one-off payment from licensing spectrum. But these benefits only accrue if 5G is deployed quickly and effectively so that its full potential is realised by industry in each market. A slow 5G rollout risks enterprises investing in workaround solutions that do not require 5G and do not generate the same value.

Spectrum licensing: Auctions vs beauty contests

A focus on short-term auction fees could be counter-productive as it may inhibit operators’ ability to invest aggressively in rolling out 5G. But as we show in graphic below, it is easy to administer, shows the regulator is ‘doing a good job’, and results in higher short-term economic benefits. We believe that governments need to look at the longer-term sustainable benefits of 5G deployment and, potentially, opt for spectrum licensing ‘beauty contests’ – in which spectrum is allocated on a detailed raft of requirements such as rollout speed and network performance – rather than auctions. Such an approach may require input beyond the telecommunications regulator. For example, the treasury, health and social welfare, business, transport and energy ministries might also be needed to evaluate whether a spectrum beauty contest offers a better social and economic return than a spectrum auction.

And it’s not all about money, globally 5G could result in 1 billion patients with improved access to healthcare globally in 20302. Governments, therefore, need to evaluate the social benefits of 5G as well as the economic ones. But managing a regulatory approach for 5G via input from different government departments is complex and may require management at the highest level.

The 5G spectrum licensing conundrum

5G spectrum licensing conundrum

Recognising that not all markets are the same

While we have outlined above a bias towards spectrum beauty contests over auctions for 5G, it is important to note that the right approach will vary by country. Based on what we have already seen from the 5G deployments and announcements in 2018 and 2019, there is a clear delineation between countries in their 5G rollout speed. We have segmented markets into three types in Figure 2:

  1. Leaders: countries where all operators are pushing ahead aggressively with 5G deployment (in part owing to the role of the regulator in the way they have managed spectrum licensing);
  2. Followers: countries where 5G rollout is patchier and many operators are reluctant to deploy 5G and are essentially doing it under duress, in other words, they worry that they will suffer if they don’t deploy and their competitors do so they do enough to be seen to be ‘keeping up’;
  3. Laggards: (developing) countries where the current network deployment focus of operators is LTE rather than 5G.

The table below outlines reasons why segments are operating at different 5G rollout speeds and offers suggestions for how governments might wish to stimulate operator 5G investment in each segment.

5G spectrum licensing country segmentation5G spectrum licensing segmentation

Table of contents

  • Preface
  • Executive Summary
  • How should governments regulate 5G?
  • The old regulatory models are less relevant for 5G
    • Spectrum licensing: auctions vs beauty contests
    • Recognising that not all markets are the same
    • Local vs national regulatory issues
    • Principles and options for 5G regulation relating to industrial IoT
  • 5G benefits industry and society
    • Introduction: 5G is estimated to add c.$1.4 trillion to global GDP in 2030
    • Healthcare benefits
    • Manufacturing benefits
    • Telecoms industry energy efficiency benefits
  • Telcos (may) need encouragement to invest in 5G
    • Lower revenues, lower profits
    • 5G per se won’t change the game for operators
    • Fast 5G network deployment needs to be encouraged
  • Appendix
    • Comparing apples with apples: how to compare nascent 5G with established 4G
    • It’s not all about LTE: 5G must be compared to all available technology
    • 5G deployment: 5G will mature over the next ten years

Table of Figures

  • Figure 1: The 5G spectrum licensing conundrum
  • Figure 2: 5G spectrum licensing country segmentation
  • Figure 3: Managing national and local mobile networks and services
  • Figure 4: 5G will contribute around USD1.4 trillion to global GDP by 2030
  • Figure 5: Global impact of 5G on healthcare (annual cost savings USD Billions)
  • Figure 6: Benefits from 5G to global manufacturing (USD Billions) by use case
  • Figure 7: Annual global emissions from mobile networks under 4 scenarios (metric tonnes of CO2)
  • Figure 8: Global mobile services revenues 2009-2022 (USD Trillions)
  • Figure 9: Global mobile operators EBITDA margins 2007-2017
  • Figure 10: 4G rollout did not produce sustainable revenue increase
  • Figure 11: Mature 5G benchmarked against the capabilities of mature 4G
  • Figure 12: 5G can address some key shortcomings with existing technologies
  • Figure 13: Forecast of 5G deployment in major regions

The Industrial IoT: What’s the telco opportunity?

The Industrial IoT is a confusing world

This report is the final report in a mini-series about the Internet for Things (I4T), which we see as the next stage of evolution from today’s IoT.

The first report, The IoT is dead: Long live the Internet for Things, outlines why today’s IoT infrastructure is insufficient for meeting businesses’ needs. The main problem with today’s IoT is that every company’s data is locked in its own silo, and one company’s solutions are likely deployed on a different platform than their partners’. So companies can optimise their internal operations, but have limited scope to use IoT to optimise operations involving multiple organisations.

The second report, Digital twins: A catalyst of disruption in the Coordination Age, provides an overview of what a digital twin is, and how they can play a role in overcoming the limitations of today’s IoT industry.

This report looks more closely at the state of development of enterprise and industrial IoT and the leading players in today’s IoT industry, which we believe is a crucial driver of the Coordination Age. In the Coordination Age, we believe the crucial socio-economic need in the world – and therefore the biggest business opportunity – is to make better use of our resources, whether that is time, money, or raw materials. Given the number of people employed in and resources going through industrial processes, figuring out what’s needed to make the industrial IoT reach its full potential is a big part of making this possible.

Three types of IoT

There are three ways of dividing up the types of IoT applications. As described by IoT expert Stacey Higginbotham, each group has distinct needs and priorities based on their main purpose:

  1. Consumer IoT: A connected device, with an interactive app, that provides an additional service to the end user compared with an unconnected version of the device. The additional service is enabled by the insights and data gathered from the device. The key priority for consumer devices is low price point and ease of installation, given most users’ lack of technical expertise.
  2. Enterprise IoT: This includes all the devices and sensors that enterprises are connecting to the internet, e.g. enterprise mobility and fleet tracking. Since every device connected to an enterprise network is a potential point of vulnerability, the primary concern of enterprise IoT is security and device management. This is achieved through documentation of devices on enterprise networks, prioritisation of devices and traffic across multiple types of networks, e.g. depending on speed and security requirements, and access rights controls, to track who is sharing data with whom and when.
  3. Industrial IoT: This field is born out of industrial protocols such as SCADA, which do not currently connect to the internet but rather to an internal control and monitoring system for manufacturing equipment. More recently, enterprises have enhanced these systems with a host of devices connected to IP networks through Wi-Fi or other technologies, and linked legacy monitoring systems to gateways that feed operational data into more user-friendly, cloud-based monitoring and analytics solutions. At this point, the lines between Industrial IoT and Enterprise IoT blur. When the cloud-based systems have the ability to control connected equipment, for instance through firmware updates, security to prevent malicious or unintended risks is paramount. The primary goals in IIoT remain to control and monitor, in order to improve operational efficiency and safety, although with rising security needs.

The Internet for Things (I4T) is in large part about bridging the divide between Enterprise and Industrial IoT. The idea is to be able to share highly sensitive industrial information, such as a change in operational status that will affect a supply chain, or a fault in public infrastructure like roads, rail or electricity grid, that will affect surroundings and require repairs. This requires new solutions that can coordinate and track the movement of Industrial IoT data into Enterprise IoT insights and actions.

Understandably, enterprises are way of opening any vulnerabilities into their operations through deeper or broader connections, so finding a secure way to bring about the I4T is the primary concern.

The proliferation of IoT platforms

Almost every major player in the ICT world is pitching for a role in both Enterprise and Industrial IoT. Most largescale manufacturers and telecoms operators are also trying to carve out a role in the IoT industry.

By and large, these players have developed specific IoT solutions linked to their core businesses, and then expanded by developing some kind of “IoT platform” that brings together a broader range of capabilities across the IT stack necessary to provide end-to-end IoT solutions.
The result is a hugely complex industry with many overlapping and competing “platforms”. Because they all do something different, the term “platform” is often unhelpful in understanding what a company provides.

A company’s “IoT platform” might comprise of any combination of these four layers of the IoT stack, all of which are key components of an end-to-end solution:

  1. Hardware: This is the IoT device or sensor that is used to collect and transmit data. Larger devices may also have inbuilt compute power enabling them to run local analysis on the data collected, in order to curate which data need to be sent to a central repository or other devices.
  2. Connectivity: This is the means by which data is transmitted, including location-based connectivity (Bluetooth, Wi-Fi), to low power wide area over unlicensed spectrum (Sigfox, LoRa), and cellular (NB-IoT, LTE-M, LTE).
  3. IoT service enablement: This is the most nebulous category, because it includes anything that sits as middleware in between connectivity and the end application. The main types of enabling functions are:
    • Cloud compute capacity for storing and analysing data
    • Data management: aggregating, structuring and standardising data from multiple different sources. There are sub-categories within this geared towards specific end applications, such as product or service lifecycle management tools.
    • Device management: device onboarding, monitoring, software updates, and security. Software and security management are often broken out as separate enablement solutions.
    • Connectivity management: orchestrating IoT devices over a variety of networks
    • Data / device visualisation: This includes graphical interfaces for presenting complex data sets and insights, and 3D modelling tools for industrial equipment.
  4. Applications: These leverage tools in the IoT enablement layer to deliver specific insights or trigger actions that deliver a specific outcome to end users, such as predictive maintenance or fleet management. Applications are usually tailored to the specific needs of end users and rarely scale well across multiple industries.

Most “IoT platforms” combine at least two layers across this IoT stack

graphic of 4 layers on the IoT stack

Source: STL Partners

There are two key reasons why platforms offering end-to-end services have dominated the early development of the IoT industry:

  • Enterprises’ most immediate needs have been to have greater visibility into their own operations and make them more efficient. This means IoT initiatives have been driven primarily by business owners, rather than technology teams, who often don’t have the skills to piece together multiple different components by themselves.
  • Although the IoT as a whole is a big business, each individual component to bringing a solution together is relatively small. So companies providing IoT solutions – including telcos – have attempted to capture a larger share of the value chain in order to make it a better business.

Making sense of the confusion

It is a daunting task to work out how to bring IoT into play in any organisation. It requires a thorough re-think of how a business operates, for a start, then tinkering with (or transforming) its core systems and processes, depending on how you approach it.

That’s tricky enough even without the burgeoning market of self-proclaimed “leaders of industrial IoT” and technology players’ “IoT platforms”.

This report does not attempt to answer “what is the best way / platform” for different IoT implementations. There are many other resources available that attempt to offer comparisons to help guide users through the task of picking the right tools for the job.

The objective here is to gain a sense of what is real today, and where the opportunities and gaps are, in order to help telecoms operators and their partners understand how they can help enterprises move beyond the IoT, into the I4T.

 

Table of contents

  • Executive Summary
  • Introduction
    • Three types of IoT
    • The proliferation of IoT platforms
    • Making sense of the confusion
  • The state of the IoT industry
    • In the beginning, there was SCADA
    • Then there were specialised industrial automation systems
    • IoT providers are learning about evolving customer needs
  • Overview of IoT solution providers
    • Generalist scaled IT players
    • The Internet players (Amazon, Google and Microsoft)
    • Large-scale manufacturers
    • Transformation / IoT specialists
    • Big telco vendors
    • Telecoms operators
    • Other connectivity-led players
  • Conclusions and recommendations
    • A buyers’ eye view: Too much choice, not enough agility
    • How telcos can help – and succeed over the long term in IoT

Telcos and enterprise verticals: 5G is not the only opportunity

Introduction

This report outlines key challenges within selected industry verticals, and how telcos can help resolve them with three emerging networking technologies – 5G, IoT and edge computing.

This research builds on many previous reports:

Enterprise services evolve alongside communications and information technologies

The early days of 2G/3G

  • Basic M2M connectivity
  • Early versions of private networks, bypassing the internet for sensitive data transfer

Improving mobility and capacity with 4G and fibre

  • Better connectivity drives demand for video-conferencing and more sophisticated UCaaS
  • Mobile and fixed data connectivity is powerful enough to enable greater enterprise mobility and support the shift towards cloud-based services
  • Different verticals increasingly require bespoke solutions for unique needs – which are easier to deliver through the cloud

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Greater flexibility and frictionless experiences with 5G, IoT and edge computing

  • Increasing complexity of connectivity, IoT, cloud and IT ecosystems are driving demand for flexible yet seamless solutions:
    • Mobile connectivity across geographies without onerous roaming charges
    • Seamless mobile connectivity across multiple networks and technologies, especially in remote areas
    • Frictionless remote set-up of IoT devices shipped directly from manufacturer to live environment
    • Ability to migrate to new technologies seamlessly (e.g. public sector move from TETRA to cellular)
    • All of the above controlled and monitored on user-friendly, cloud-based dashboards
  • The shift from product to service-based business models means a growing number of enterprises want to embed connectivity into their offer to customers
    • i.e. demand for greater control over wholesale connectivity solutions
    • remote maintenance, asset-tracking, etc.

5G applications will arrive at different times…

evolution of 5G technology eMBB, URLLC, private 5G, massiv IoT

Source: STL Partners

…in the meantime, other technologies can help address enterprise needs

The interdependencies between 5G, IoT and edge computing

Source: STL Partners

The problem with 5G for enterprises

  • Most enterprises are not looking at 5G in isolation, but as one of many technologies that will help resolve pain points around efficiency and innovation. The Internet for Things (I4T) and edge computing are two other key technologies that many enterprises need, and which telcos could potentially provide
  • In the long term, STL Partners does not expect 5G connectivity on its own to deliver growth for telcos. So to grow enterprise revenues, telcos should also develop I4T and edge computing solutions
  • But developing expertise in 5G, I4T and edge computing will be expensive and complex to manage
  • Therefore, telcos should start by targeting their investments to meet specific enterprise pain points

This report helps telcos assess how to target their investments by highlighting key pain points in a selection of industry verticals, and how relevant 5G, I4T and edge computing are for solving them.

Sectors with strong demand for all three technologies hold the highest potential value, but this will be difficult for telcos to capture owing to strong competition in I4T and edge computing from other technology companies.

This report covers a selection of verticals that STL Partners has developed knowledge of through research and consulting activities: manufacturing, construction, utilities, agriculture, transport, automotive, healthcare, and sports, media and entertainment. 

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Elisa Smart Factory: How to win over industry leaders in two years

Elisa’s Smart Factory solution

As STL Partners has described in The Coordination Age: A third age of telecoms, moves are afoot in the global digital economy to improve the efficiency of resource utilisation by combining the digital and physical worlds in new and innovative ways. Elisa’s Smart Factory solution is a prime example of how telcos can address this need.

Coordinating manufacturing

In the case of manufacturing industries, understanding and managing the flow and progress of materials and goods through production processes has long been a critical component of business success.

Managing and continually improving complex processes is central to operational success on the supply-side of the manufacturing industry. This includes everything from a floor manager overseeing production, to time-and-motion studies, total quality management, just-in-time production, robotics and automation, and many other managerial and operational approaches.

A number of new concepts and practices are now emerging, driven by the same imperatives but arising to a degree independently and in different disciplines, for example:

  • Industry 4.0 ‘the fourth industrial revolution’ – the trend of automation and data exchange in manufacturing industries
  • Digital twins – a virtualised version of a real thing, a bit like an avatar but for a thing rather than a person. It can simulate the real item, interact with it, and exchange information and commands with other digital twins based on pre-defined rules
  • The Industrial Internet of Things (IIoT) – connecting industrial devices, sensors, equipment, etc., to gather and exchange information, and sometimes perform remote control

Numerous companies have embarked on the journey to incorporate and use such connected technologies. However the degree of progress made varies greatly.

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A growing industry

Connecting machinery is far from a new idea. Many industrial machines and processes are already highly connected and automated, and this goes back as far as sixty years in SCADA (Supervisory Control and Data Acquisition) systems in electricity power station control.

What is new is the ability and desire to link these systems together and allow data exchange and a degree of autonomy within managed bounds. This can optimise performance, improve productivity, and ultimately lead to new operational business models.

There are many different possible paths to achieving these ends. For instance, powerful industrial players and consortia are all trying to establish leadership in different ways. Heavyweight contenders on the industry side include GE, Bosch, Siemens, and PTC, with consortia including the somewhat mystically titled All Seeing Alliance.

STL Partners will explore the wider opportunity and main players competing in this field in an upcoming report titled ‘Why we need an Internet for Things’.

Enter Elisa, the innovative Finlander

Elisa is the leading Finnish mobile and fixed operator and No.2 player in Estonia. It has 6.2 million customers.

Yet despite its relatively small footprint compared to some of the industry giants, STL Partners regards Elisa as one of the most innovative operators in the world, and certainly in Europe. Indeed, 18% of Finnish business customers say that it is the most innovative IT actor in its market, compared to 6% for CGI and 5% for Fujitsu.

One of its notable recent innovations is a totally automated Network Operations Centre (NOC). To create this, Elisa had to go through its own journey of process engineering and automation.

Elisa now resells its Elisa Automate NOC solutions to other operators. Similarly, it has leveraged the IP and learning to create Elisa Smart Factory, a solution to help global enterprise customers achieve the levels of success Elisa has achieved itself.

Our thanks to Henri Korpi, EVP New Business Development, and Kari Terho, General Manager, Smart Factory at Elisa, who talked to us openly about the proposition, the business, and how it came into existence.

Contents:

  • Executive Summary 
  • Introduction
  • Understanding manufacturing customers’ problems
  • Unplanned downtime
  • Unstable production quality
  • Lack of visibility
  • Practical obstacles to smart manufacturing
  • How Elisa approached the solution
  • Creating a service operation centre
  • Smart Factory’s claims
  • How did Elisa get here?
  • “There’s loads of discussion of which platform is best. What you actually need is a solution”
  • Conclusions
  • Success factors and lessons for others
  • Challenges
  • Next steps

Figures:

  1. Downtime, data usage and visibility – the three dogs of manufacturing
  2. Elisa Smart Factory Schematic
  3. Elisa Smart Factory screenshot
  4. Typical business objectives of Smart Factory solutions
  5. What an Elisa 3D Digital Twin looks like
  6. A high level view from Elisa’s “End-to-End Cockpit”
  7. Results from Elisa’s automated NOC

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