Industry 4 - Automation at Work: Case Studies from the Real World

Unlocking the Business Value of Industry 4.0: Part 4
Automation at Work Automation at Work: Case Studies from the Real World

Case studies from the real world

Audi: From Assembly Line to Modular Assembly

Audi shifts the paradigm of the assembly line in manufacturing by introducing modular assembly. A large series with a high degree of standardization can hardly be produced more efficiently than with Henry Ford's assembly-line approach. But in times of ever greater individualization, the classic assembly line interlinked with a conveyor system may soon be obsolete.

“Today, buyers can choose between a combustion engine or electric drive, and soon perhaps also a fuel cell system,” according to spotlightmetal.com. “Whether white or red paint, automatic air-conditioning or a premium sound system, no car is like another. In the future, seats will be adapted precisely to the respective driver using a body scanner. The hour-long holiday journeys will then no longer cause back pain. This freedom of choice for the buyer leads to more work for the car manufacturers and demands flexible production solutions.”²⁰

With the modular assembly method, small, separate workstations allow highly flexible working routines — in terms of both time and space. Between those workstations, driverless transport systems move the car bodies as well as the parts required for production. A central computer precisely controls the driverless transport systems, recognizing the needs of each individual station, and each individual car being made, and thus ensuring a smooth workflow.

In times of ever greater individualization, the classic assembly line in auto factories may soon be obsolete as companies like Audi require more flexible working routines.

20 Silvano Böni, “Smart Factory: Audi’s Car Production of the Future,” Spotlightmetal, September 24, 2018

Caterpillar: Driving Digital Transformation at the Job Site

“We’re seeing everything as a service; everything is a subscription. [This model] is part of our strategy at Caterpillar,” says Fred Rio, worldwide product manager at Caterpillar’s Construction, Digital, and Technology division.²¹

The digital technology team is responsible for delivering site-level technologies, which include software and digital tools that help customers with equipment management, productivity, and remote command. Next-generation excavators have a “digital heart” that governs every aspect of the machines’ performance. One of the available options ties the machine into satellite positioning and digital site plans, while another adds in-field design capabilities through a touch-screen monitor. Swing and tilt body sensors create an “e-fence” and “e-ceiling,” which prevent the bucket or boom from inadvertently hitting an overhead power line.

Caterpillar is also working on autonomy at the job site. In 2021, a customer operating a Cat 320 excavator on a high wall in the quarry (a dangerous job with material spilling over the machine) will be able to use the command remote control station and perform the operation remotely, taking the human away from the risk zone, says Rio. Rolling out a few more years, an excavator program will allow the machine to dig a trench automatically while the user controls other machines with the control station. It will automatically go back to manual operation to download and discharge the pipe into the trench, a type of operation that will not be automated anytime soon. Looking ahead, Caterpillar is working on developing satellite technology to allow an operator sitting in the U.S. to remotely communicate with machines on job sites in far-flung areas of the world.

engines — **Caterpillar** is working on developing technology to allow an operator sitting in the U.S. to remotely communicate with machines on job sites across the world.

21 Fred Rio, “Getting in Front of the New Industrial Revolution,” The Caterpillar Podcast: Beyond the Iron — The Power of Yes!, 2020

Energy: Tomorrow’s Power Grid Will Be Autonomous

In a neighborhood in western Colorado, inhabitants of 27 houses share energy thanks to their connection to a microgrid, which in turn connects to the main grid. So far, utility bills are about 85% lower than typical electric bills in the state. The microgrid, the result of a partnership between local electrical utility Holy Cross Energy and the National Renewable Energy Laboratory, is described in a recent article in IEEE Spectrum magazine.²²

Within each home, every smart appliance and energy resource — be it a storage battery, water heater, or solar photovoltaic (PV) system — is controlled to maximize energy efficiency. All of those assets are monitored and can be controlled by an autonomous energy grid (AEG). Houses within the neighborhood can rapidly share power, creating reliable electricity for everyone, since solar energy generated at one house can be used to charge the electric car next door.

The AEG is a vision for how the future of energy can be defined by resilience and efficiency. In theory, power systems of any size could be covered in a patchwork of microgrids, layering regions and even an entire country in smart grids to automatically manage energy production and use across millions of controllable distributed energy resources. AEGs will create at least as many benefits for utilities as they do for customers, argues the IEEE Spectrum article. With AEGs monitoring distributed energy resources such as rooftop solar and household storage batteries, a utility’s control room will become more like a highly automated air traffic control center. The result is that energy generated within an AEG is used more efficiently — it’s either consumed immediately or stored.

Benjamin Kroposki, director of the Power Systems Engineering Center at the National Renewable Energy Laboratory in Golden, Colorado, and the article’s coauthor, argues that NREL’s research shows that even a network as complicated and large as a national power grid can operate in a decentralized and automated way.

solar fields — **Autonomous energy** grids are part of a vision of efficient and reliable power sharing through intelligent systems. Solar energy can be generated, stored, and shared across entire neighborhoods.

22 Benjamin Kroposki, Andrey Bernstein, Jennifer King, Fei Ding, “Tomorrow’s Power Grid Will Be Autonomous,” IEEE Spectrum, November 23, 2020

Industry 3 - Unlocking the Value of Adaptive Manufacturing

Unlocking the Business Value of Industry 4.0: Part 3
Unlocking the Value of
Adaptive Manufacturing Unlocking the Value of Adaptive Manufacturing

For many organizations, intelligent adaptive manufacturing has proven challenging to implement. McKinsey’s report “Digital Manufacturing — Escaping Pilot Purgatory” reveals that while digital manufacturing pilots are common, company-wide rollouts are still rare. For instance, while around two-thirds of industrial companies have piloted connectivity, intelligence, or flexible automation initiatives, less than a third of such initiatives have been implemented.¹⁶

Transitioning to digital manufacturing is a tall order, since most organizations are updating existing assets and processes rather than starting from scratch. Industrial manufacturing is full of legacy systems, many 20 or 30 years old, reaching the end of their lifecycles. Some parts need to be swapped, but the newer technologies must be able to function within the legacy asset. A Wind River survey confirms that among the biggest barriers to deploying needed technologies are “need to upgrade or reengineer legacy systems” (35%) and “need to retrofit devices” (31%).¹⁷

Software-driven factories create risks. OT is part of a real-life, physical environment. An interruption could be catastrophic, so new technologies must be implemented while the whole system is operational. A mistake on a production line can lead to expensive downtime, or, worse yet, harm to a human being. “The only way to maintain the integrity of the devices generating the interactions and the exchange of data is to build security in at each stage of the product lifecycle: design, development, deployment, and operations,” according to a report by Wind River.¹⁸

Finding the right technology partners and systematically addressing legacy assets, inoperability, and security concerns up front is key to transitioning to digital manufacturing. McKinsey has identified the following principles for forming the comprehensive technology stack and working with technology partners.

16 Digital Manufacturing – Escaping Pilot Purgatory, Digital/McKinsey, 2018

17 “5G and Industry 4.0: Where Promise Meets Reality” (web seminar), Wind River, November 17, 2020

18 “Secure Through The Lifecycle,” Wind River

Form the Comprehensive Target-State Technology Stack¹⁹

Digital manufacturing is, by definition, technology driven. In determining the optimal technology stack, manufacturing companies should keep five characteristics in mind:

1 / Comprehensive: The definition should include a look at all five layers: collection, connectivity, data, analytics, and applications. It should also be specific to your operational model.

2 / Scalable: A critical element for scalability is the data ingestion pipeline complemented by analytic capabilities.

3 / Analytics enabled: Systems (software and infrastructure) provide the material, but analytics provide the insights that, ultimately, generate the value.

4 / Integrated: Digital manufacturing implementation requires that the relevant information from operational (OT) and information technology (IT) be integrated. Successful IT/OT convergence creates the delivery engine that will develop use cases that meet a manufacturer’s business needs.

5 / Secure: Cybersecurity must be actively addressed by, for example, analyzing the connections and adaptability between legacy and future systems.

19 "Digital Manufacturing – Escaping Pilot Purgatory," Digital/McKinsey, 2018

Industry 2 - The Backbone of Industry 4.0: Edge Computing

Unlocking the Business Value of Industry 4.0: Part 2
The Backbone of Industry 4.0:
Edge Computing The Backbone of Industry 4.0: Edge Computing

An edge computing infrastructure, which enables data collection and processing at the device level, is the backbone of IoT-driven value in adaptive manufacturing. If an adversary can control a power grid, an industrial line, or a nuclear submarine by hacking software or reverse-engineering a device, the potential physical damage could be just as lethal as many acts of conventional warfare. The number of connected devices and the amount of data they generate is increasing exponentially, thus increasing the need for processing capacity.

The full potential of edge computing is not yet fully recognized. According to Deloitte’s 2020 Industry Readiness Report, 72% of executives globally see IoT as the technology that will have the most profound impact on their organization, and only 6% say the same about edge computing.⁸ But for some executives this perception of IoT in relation to edge computing may be like the tail wagging the dog. “The popular view is that edge computing is the Internet of Things (IoT). The contrarian view is that the edge is bigger than the IoT. In my opinion, edge computing incorporates the IoT but goes beyond simply interacting with clustered sensors. The edge has the potential to offer a smart compute model that’s more grid-like than any traditional compute model we’ve seen or operated in the past,” writes Tom Fisher, SVP of business development at SAS, in Forbes.⁹

Ron Breault, director for industrial solutions business development at Wind River^®, also sees the edge infrastructure as central. “Once you have an edge system in place, you can start thinking about what you can use it for — collecting data, doing analytics, vision systems, or 5G,” he says.

“Because it uses higher frequencies and a much broader chunk of the radio spectrum than previous generations, 5G can open up new business models, in which more wirelessly connected data-gathering sensors and intelligence are deployed in the field,” according to The Wall Street Journal. Volkswagen has said that it operates around 5,000 robots at its Wolfsburg plant in Germany and will require 5G’s capabilities to control these and other internet-connected machinery, such as autonomous vehicles.¹⁰

Telecommunications equipment provider Ericsson’s factory in Texas is among the earliest examples of a 5G-connected factory. It uses 5G to enable large-scale automation in warehousing and production-line operations, and the network allows the use of video and radio sensors in all parts of the manufacturing process for tasks such as fault detection, according to The Wall Street Journal.

Communications service providers (CSPs) quite literally supply a networking backbone for their customers. A recent Wind River study revealed that the COVID-19 pandemic has pushed over 70% of telco 5G projects onto a fast development and deployment track.¹¹

In the summer of 2020, Verizon announced that it successfully conducted the world’s first end-to-end fully virtualized 5G data session in a live network, a technology milestone that paves the way for wide-scale mobile edge computing. “Massive-scale IOT solutions, more robust consumer devices and solutions, AR/VR, remote healthcare, autonomous robotics in manufacturing environments, and ubiquitous smart-city solutions are only some of the ways we will be able to deliver the promise of the digital world,” says Adam Koeppe, SVP of technology and planning for Verizon.¹²

What Are Your Biggest Security Threats?

13 “What Edge Computing Means for Infrastructure and Operations Leaders,” Smarter with Gartner, October 3, 2018

14 "Edge Computing Market Worth $61.14 Billion by 2028," Grand View Research, May 2021

15 Eden Estopace, “IDC Forecasts Connected IoT Devices to Generate 79.4ZB of Data in 2025,” FutureIoT, June 22, 2019

8 The Fourth Industrial Revolution: At the Intersection of Readiness and Responsibility, Deloitte Insights, 2020

9 Tom Fisher, “Five Things You Need at the Edge For Real-Time Intelligence,” Forbes, June 26, 2020

10 James Rundle, “5G’s Early Adopters Explore New Generation of Wireless Applications,” The Wall Street Journal, November 10, 2020

11 Kevin Dallas, “Wind River Proud to Play Instrumental Role in World’s First Successful End-to-End Virtualized 5G Data Session,” Wind River Blog Network, August 25, 2020

12 “Verizon Paves Way for Wide-Scale 5G Mobile Edge Compute with First Successful End-to-End Fully Virtualized 5G Data Session in the World” (press release), Verizon Communications, August 25, 2020

Industry 1 - Unlocking the Business Value of Industry 4.0

Unlocking the Business
Value of Industry 4.0
Unlocking the Business Value of Industry 4.0

Adaptive Manufacturing Changes the Paradigm

The Fourth Industrial Revolution signals a business shift

Manufacturing is increasingly software driven, blurring the lines between the physical world of machines and the cyber world of data and disrupting traditional manufacturing paradigms.

“While previous industrial revolutions have been around a new source of energy, the Fourth Industrial Revolution is going to be about fundamental shifts in business models,” says Fred Rio, worldwide product manager at Caterpillar’s Construction, Digital, and Technology division.¹

These paradigm shifts are due to the digital transformation of the industrial sector. After years of digital transformation associated with IT — focusing mostly on information flow in the digital ether — the focus has now turned to IoT. McKinsey’s Digital Manufacturing Global Expert survey reveals that most manufacturing companies (68%) consider Industry 4.0 manufacturing initiatives to be their top priority.² The global industrial automation market is expected to reach US$326.14 billion by 2027 after a decade of CAGR at 8.9%, according to Fortune Business Insights.³

panels — **Consumption and production converge.** As the world turns increasingly to alternative energy sources, autonomous, AI-driven power grids automatically manage production and use across multiple, distributed resources.

In the era of software-driven manufacturing, products are no longer simply physical things: They become services as they continuously generate data and provide insights for performance management, remote control or equipment, predictive maintenance, and many other uses. Consumption and production converge as autonomous, AI-driven energy power grids automatically manage production and use across multiple, distributed energy resources. Mass and custom production become increasingly meaningless differentiators as software-driven manufacturing enables mass customization and assembly lines give way to modular assembly.

McKinsey puts the value creation potential of manufacturers and suppliers implementing Industry 4.0 in their operations at $3.7 trillion by 2025.⁴ This economic value will be unlocked by manufacturers who replace traditional manufacturing paradigms with adaptive manufacturing by embedding their production facilities with advanced data-generating and processing software. The insights gathered from a network of connected devices can be harnessed to increase productivity and develop value-add, outcome-based services — creating new, more profitable business models.

Almost a third (30%) of global factory budgeting is earmarked for smart factory initiatives, ranging from quality sensing and detecting through asset intelligence and performance management to command centers, factory synchronization, and real-time asset tracking.⁵ Investment in smart factory initiatives pays off. “Trailblazers are pioneering factory-related innovation, dedicating a whopping 65% of their global factory budget to smart factory initiatives,” according to The Wall Street Journal.⁶ “Trailblazers report 20% average improvement in production output, factory capacity utilization, and employee productivity over the past three years.”

Consumption and production converge. As the world turns increasingly to alternative energy sources, autonomous, AI-driven power grids automatically manage production and use across multiple, distributed resources.

1 Fred Rio, “Getting in Front of the New Industrial Revolution,” The Caterpillar Podcast: Beyond the Iron — the Power of Yes!, 2020

2 “The Next Economic Growth Engine: Scaling Fourth Industrial Revolution Technologies in Production,” World Economic Forum/McKinsey & Company, January 2018

3 “Industrial Automation Market Size, Share & COVID-19 Impact Analysis, by Component, by Industry, and Regional Forecast, 2020—2027,” Fortune Business Insights, August 2020

4 “The Next Economic Growth Engine: Scaling Fourth Industrial Revolution Technologies in Production,” World Economic Forum/McKinsey & Company, January 2018

5 “2019 Smart Factory Study: Capturing Value Through the Digital Journey,” Deloitte and the Manufacturers Alliance for Productivity and Innovation (MAPI)

6 “How Smart Factories Can Ignite Productivity,” The Wall Street Journal, CIO Journal/Deloitte, February 3, 2020

Build and lead a focused ecosystem of technology partners

For cohesion and seamlessness, the entire technology stack process, from development to rollout, must be tightly managed. Manufacturers should track three aspects of the process:⁷

Architecture complexity: Challenges arise in navigating the complex landscape of solution providers. When building in the technology stack’s needed components, machinery manufacturers should fully leverage industry standards to ensure interoperability across different organizations.

Partnerships: Select a few partners with deep functional and integrative expertise, and codevelop when possible. More than 40% of respondents say they prefer to build their IT/OT systems in-house or tailor them based on external sources. This adds to the need to bridge a wide range of in-house, purchased, and codeveloped systems.

Agile execution: Manufacturing companies should bring an agile mindset to software and analytics in their digital initiatives. In addition to forming reliable external partnerships, they need to support internal collaboration across functions and break down organizational silos.

7 Digital Manufacturing – Escaping Pilot Purgatory, Digital/McKinsey, 2018

Automotive Intelligent Systems

Intelligent systems research

Automotive

The automotive industry is undergoing its most significant revolution in 100 years. Executives and leaders told us that intelligent systems sit at the center of a vision for a 30% growth in revenue opportunities.

68% of the impact on intelligent systems success in this sector will come from characteristics built in the next three years.

Intelligent systems research

Automotive

The Need to Act

Getting the sequence right and focusing on building the right sets of intelligent systems characteristics are both key to success. McKinsey believes that these new business models could expand automotive revenue pools by about 30%, adding up to an additional $1.5 trillion to the industry. In a sector currently seeing single-digit revenue growth, this is a light at the end of the tunnel, promoting innovation and change.

McKinsey’s recent roundup of the auto industry of 2030 states:

“Today’s economies are dramatically changing, triggered by development in emerging markets, the accelerated rise of new technologies, sustainability policies, and changing consumer preferences around ownership. Digitization and new business models have revolutionized other industries, and automotive will be no exception. In the the automotive sector, these forces are giving rise to four disruptive technology-driven trends: diverse mobility, autonomous driving, electrification, and connectivity.”¹

All three of the key (first-tier impact) intelligent systems characteristics need to be built for success in the next three to five years: the ability to have true compute on the far edge, the ability to predict stresses and failures and resolve them, and the ability to customize device experience in the cloud. Telecommunications is the only other industry with a focus on investment in these three pillars of intelligent systems success. They will be the pillars for a range of capabilities that will help automotive vendors more readily tap into future opportunities, such as consumer personalization and near-latency-free service performance.

The importance of the far edge is clear for most automotive leaders: 80% say half or more of their embedded products and services will be designed to be used on the far edge cloud. At the same time, however, 51% of these companies are still in the experimental stage (meaning they believe “somewhat” in the value of intelligent systems). And only 16% are thriving across the key metrics.

The most important capability for success, according to 56%, is being able to build, develop, and operate using simulations or emulations to increase productivity and/or reduce time-to-market. The power of a collaborative workflow platform will become more valuable over time as intelligent capabilities are increasingly infused into products and services.

The automotive industry is in the midst of its largest revolution in more than a hundred years.

In five years' time, many of the core needs for intelligent systems in the automotive industry will need to be in place. This is the DNA for future success in a software-led industry.

Related resources

>> Forbes Report: Characteristics of an Intelligent Systems Future >> Forbes Insight's "Futures in Focus" Podcast with Cadillac's Melissa Grady-Dias >> Wind River Automotive Solutions

According to 56% of automotive leaders, the most important intelligent systems characteristic is using simulations or emulations during development and operation to increase productivity and reduce time-to-market. The next most important characteristics are protecting data and guarding against cyberattacks (46%) and being able to sense and react based on goal-based algorithms (44%).

What really matters to executives building intelligent systems?

While there are 13 key characteristics of intelligent systems, not all characteristics deliver the same positive impact. Your peers across all sectors told us the far edge is vital for success, especially when 65%+ rely on embedded devices for business success.

These stacks represent the magnitude of impact each intelligent systems characteristic has on such systems. The larger the block, the greater the impact. You can view this data for companies grouped by momentum and success, or by specific industry.

Source: Characteristics of an Intelligent Systems Future, Forbes, 2021

Three Facts About Intelligent Systems in the Automotive Industry

68%

of the total impact is generated by characteristics that need to be created in the next three years.

39%

of leaders told us that cybersecurity and security in products and services are critical building blocks for all business success.

38%

of leaders believe embedded devices are mission critical for the future of the company.

Characteristics of Intelligent Systems Success in the Automotive Industry

Knowing when to invest in each characteristic requires a blueprint for building critical infrastructure, delivering core foundational needs, and much more.

Plot Your Intelligent Systems Journey in the Automotive Sector

Download “Plotting Your intelligent Systems Journey,” a free 30-page report developed in partnership by Wind River and Forbes.

Our research is based on more than 200 points of comparison across companies building and deploying intelligent systems.

This report shows:

How your peers are thinking about the barriers to and drivers for adoption of intelligent systems
What factors would accelerate the adoption of intelligent systems in your sector
The relative importance of all intelligent systems characteristics, to help you prioritize your investments
How your peers see the roles and importance of 5G, AI, ML, and cybersecurity in their decision-making
The key components for the mission-critical success of intelligent systems
What the future of embedded devices and solutions looks like in an intelligent systems world
Where digital feedback loops are crucial for success
What the key metrics for success are
Where your peers see extensive value for intelligent systems in addressing wider societal issues

1 "Automotive Revolution: Perspective Towards 2030," McKinsey & Company, January 2016

Aerospace and Defense Intelligent Systems

Intelligent systems research

AEROSPACE AND DEFENSE

The aerospace and defense sector has the highest urgent focus on building the most complete intelligent systems now.

Intelligent systems success in the aerospace and defense sector requires the most advanced design compared to other major industries.

Intelligent systems research

AEROSPACE AND DEFENSE

The Need to Act

The aerospace and defense sector has a unique blend of intelligent systems characteristics, whereby nine of the key characteristics are seen as essential foundational and infrastructure needs in the next three years. (In contrast, in the automotive sector only four characteristics have a similar level of immediate impact for success.) 71% of the impact value of intelligent systems will be created in that time frame.

CB Insights states, “In the aerospace and defense industry, supporting software has to make quick decisions in high-risk scenarios. Artificial intelligence is becoming integral to the $8.7T space as companies and government agencies explore using technologies from robotics and autonomous systems to cybersecurity and telecommunication for national security.”¹ These are just small parts of an intelligent systems world functioning in near real time with mission-critical and cybersecurity as fundamental, invariably on the far edge.

True compute on the far edge and the ability to emulate and simulate in real time are the two most important characteristics for intelligent systems success in aerospace and defense. The inherent complexity and volume of mission-critical capabilities mean there are unique needs to have systems compute, sense, and connect in near real time.

ability to emulate and simulate in real time are the two most important characteristics for intelligent systems success in aerospace and defense

The key characteristics needed now are the ability to synthesize workflows with one process for all parties involved and the use of tools such as digital feedback loops in new product and service development. This illustrates the importance of bringing people, data, and new collaborative work processes to the forefront in these organizations’ intelligent systems. Ideas such as Air Force Platform One are based on the concept of the constant development, deployment, and operation of complex systems responding in near real time to digital feedback loops. Commercial aviation companies equally understand this need, as they look to bring vast arrays of near real-time data into business operations to benefit customers and improve product and service delivery and development.

The two most impactful needs for five years from now are the capacity to benefit from automated learning and machine learning and the ability to deliver total automation. The AL/ML characteristic, considered the more important of the two, can only occur after eight other characteristics have been developed and delivered.

Aerospace and defense have the highest urgent
focus on building the most complete intelligent systems now.

There is an underlying paradox that leaders need to resolve. 86% of the companies that see themselves as A&D industry leaders believe that half or more of their embedded products and services will be designed for use on the far edge. Still, only 14% are both committed to and succeeding at implementing the needed intelligent systems characteristics.

To see the future as heavily focused on computing on the far edge and to have only 14% reporting success in doing so now means that most of these companies still have not implemented the necessary components of intelligent systems success to deliver on their own vision of an intelligent systems future.

Related resources

>> Forbes Report: Characteristics of an Intelligent Systems Future >> Forbes "Futures in Focus" Podcast with Airmap's Ben Marcus >> The Cyber-Digital Battlefield >> Accelerate Change Or Lose: Gaining The OODA Edge

Sequencing for the A&D sector starts with building capabilities for true, mission-critical compute on the edge; a real-time workflow platform for people, applications, and operational capabilities; and real-time digital feedback loops for developing products and services. All this must combine with the ability to customize devices on the cloud and simulate and emulate possible outcomes in near real time.

What really matters to executives building intelligent systems?

Source: Characteristics of an Intelligent Systems Future, Forbes, 2021

Three Facts About Intelligent Systems in the Aerospace and Defense Sectors

55%

of leaders, when asked what would accelerate investment, named evidence of improved employee satisfaction.

37%

believe that underlying technology needs, such as 5G for the intelligent edge, are not appropriately deployed or likely to be available to their industry within the next five years.

59%

believe that ROI will outweigh the level of effort needed.

Characteristics of Intelligent Systems Success in the Aerospace and Defense Sector

Knowing when to invest in each characteristic requires a blueprint for building critical infrastructure, delivering core foundational needs, and much more.

Characteristics of Success in The Aerospace and Defense Sectors

Plot Your Intelligent Systems Journey in the Aerospace & Defense Sector

Download “Plotting Your intelligent Systems Journey,” a free 30-page report developed in partnership by Wind River and Forbes.

Our research is based on more than 200 points of comparison across companies building and deploying intelligent systems.

This report shows:

How your peers are thinking about the barriers to and drivers for adoption of intelligent systems
What factors would accelerate the adoption of intelligent systems in your sector
The relative importance of all intelligent systems characteristics, to help you prioritize your investments
How your peers see the roles and importance of 5G, AI, ML, and cybersecurity in their decision-making
The key components for the mission-critical success of intelligent systems
What the future of embedded devices and solutions looks like in an intelligent systems world
Where digital feedback loops are crucial for success
What the key metrics for success are
Where your peers see extensive value for intelligent systems in addressing wider societal issues

1 "6 Ways AI Is Transforming the Aerospace & Defense Industry," CB Insights, September 30, 2020

Telecom Intelligent Systems

Intelligent systems research

Telecommunications

The future goes far beyond 5G in the telecommunications industry, as leaders turn toward becoming complete intelligent systems companies.

The key characteristic that needs to be built now for intelligent systems success revolves around the need for a real-time collaborative workflow process along with the ability to customize devices on the edge.

Intelligent systems research

Telecommunications

The Need to Act

33% of carrier leadership in the U.S. believe their organizations are committed to the idea of intelligent systems as core to their futures. Yet the chances of success, even within that group, run at only 54%.

The commonly held view is that, within three years, this sector will:

Have a clear focus on creating an intelligent systems future
Have core business practices in place that contribute to an intelligent systems future
Have their embedded products/services completely developed for intelligent systems

This is an aggressive short-term view compared to that of the other sectors.

All three of the key (first-tier impact) intelligent systems characteristics need to be built for success in the next three to five years.

Only the automotive sector has equal focus on the key intelligent characteristics needed now: true compute at the edge, the ability to simulate and emulate in near real time, and digital feedback loops that connect data to products and services. These characteristics represent the foundation for success and are the most important in terms of impact. Leaders and executives in the telecommunications industry clearly understand the core layers needed for success now, in three years, and in five years, stating that these foundational needs account for 54% of all intelligent systems impact.

The impact of the characteristics that need to be created in the first three years accounts for 70% of the total impact. This sense of urgency, in a world where 5G is seen as a key technology for widening business opportunities, illustrates how telecommunications leaders see intelligent systems as the platform for expanding the capabilities available to them.

The future of intelligent systems goes far beyond 5G for the telecommunications industry, as leaders are turning their companies into complete intelligent systems organizations.

90% of these leaders believe that over 50% of their embedded products and services will need to work on the far-edge cloud, within less than three years.

63% believe that their products and services will be infused with AI and ML in the next three years. Unlike the other sectors, telecommunications and carrier executives lead in the vision that intelligent systems will create new business models and opportunities (43%), will support technically advanced machines that react based on goal-based algorithms (40%), and will be able to react to unplanned situations via machine learning.

In five years, two key characteristics can become realities: total automation and the ability to act based on sensory data and algorithms. Building key infrastructure now, including a common workflow platform and the ability to customize devices in the cloud, will enable the addition of all first-tier impact characteristics needed for the next three and five years. This focus on the key requirements of the next five years is critical to the vision needed to become an intelligent systems carrier or telecommunications leader. Srini Kalapala, Verizon’s leader of infrastructure, thoughtfully summarized the future world he wants to service and the sequencing of intelligent systems in a recent interview on the Forbes Futures in Focus podcast about the world of 2030:

Related resources

>> Forbes Report: Characteristics of an Intelligent Systems Future >> Forbes "Futures in Focus" podcast with Verizon's Srini Kalapala >> Wind River Telecommunications Solutions >> Open RAN for a New World Powered by 5G

“To think about the infrastructure of 2030, we’ve got to put ourselves into [the mindset of] the users of 2030. If you look into what we’re going to see in 2030, it’s mass sensorization. So everything that we can imagine is going to be embedded with sensors. You will have drones, autonomous vehicles, all kinds of things that are managing on their own, embedded with cameras and all kinds of sensors. Ambient computing will be prevalent, which means that we would be able to get answers to everything we’re looking for — in some cases, predicted and proactively delivered to us before we even require or we even think about it. [That] requires a mass number of compute there: AI being extremely prevalent, lots of decisions being made, lots of proactive conclusions being drawn based on the amount of data available.”

What really matters to executives building intelligent systems?

Source: Characteristics of an Intelligent Systems Future, Forbes, 2021

Three Facts About Intelligent Systems in the Telecommunications and Carrier Sector

Intelligent systems priorities do not seem to change in the next three to five years. The number-one priority is connecting existing business processes with new forms of sensors, and the second is the ability to predict system failures and take preemptive action against them.

29%

As many as 29% of these leaders see themselves as behind their peers on the idea that the intelligent edge and 5G are the future. Additionally, 28% believe that cybersecurity is critical and 25% believe that AL/ML are critical for success.

42%

When asked about the wider societal impacts of intelligent systems, 42% of telecommunications leaders say they will impact how we govern society, as real-time information and systems give quick feedback. Another 40% believe intelligent systems will inform our views about adaptive and efficient infrastructures.

Characteristics of Intelligent Systems Success in the Telecommunications Sector

Knowing when to invest in each characteristic requires a blueprint for building critical infrastructure, delivering core foundational needs, and much more.

Plot Your Intelligent Systems Journey in the Telecommunications Sector

Download “Plotting Your intelligent Systems Journey,” a free 30-page report developed in partnership by Wind River and Forbes.

Our research is based on more than 200 points of comparison across companies building and deploying intelligent systems.

>> Download the report now

This report shows:

How your peers are thinking about the barriers to and drivers for adoption of intelligent systems
What factors would accelerate the adoption of intelligent systems in your sector
The relative importance of all intelligent systems characteristics, to help you prioritize your investments
How your peers see the roles and importance of 5G, AI, ML, and cybersecurity in their decision-making
The key components for the mission-critical success of intelligent systems
What the future of embedded devices and solutions looks like in an intelligent systems world
Where digital feedback loops are crucial for success
What the key metrics for success are
Where your peers see extensive value for intelligent systems in addressing wider societal issues

Energy and Utilities

Intelligent systems research

ENERGY AND UTILITIES

The right infrastructure and foundational investments for the energy industry’s intelligent systems needs could shift the way the industry functions and delivers.

Intelligent systems success in the energy and utilities sector will drive a clean, affordable, reliable, and data-driven new reality. Our research found six specific characteristics of that success that will change the dynamics for design.

Intelligent systems research

ENERGY AND UTILITIES

The Need to Act

In the energy sector, six of the 13 characteristics that will drive intelligent systems success need to be designed in place now, and the sequencing of these characteristics will matter. For six out of 10 energy companies, this is not yet happening. However, 41% of the sector’s leaders believe that intelligent systems can positively transform how carefully and efficiently we use environmental resources and that we can become more adaptive.

For many of the leaders in this sector (59%), the idea of intelligent systems is very appealing. But the chances of realizing any ROI now are limited to just 26% of them.

59% of executive leaders in energy see the value of intelligent systems for their future success

In the energy sector, 81% of leaders believe that embedded devices and applications will increasingly be used in innovative ways yet to be discovered.

They are shifting their vision of what intelligent systems can and will deliver. Current goals include the ability to predict system failures and resolutions, the capacity to seamlessly link all systems between supplier and end customer, and the ability to make business decisions autonomous. The ambition for five years from now includes a much more aggressive focus on data-centric decision-making on the far edge of the cloud.

16% of energy companies already see themselves as intelligent systems digital business companies. Still, one in three are generating low returns, and the leading perceived barrier for this sector is that they will remain in strategic discussions for the next three years. Beliefs about how to achieve the ambitious new vision are not consistent across the sector.

Intelligent systems success in the energy and utilities sector will drive a clean, affordable, reliable, and data-driven new reality.

81% of energy leaders say that over 50% of their embedded products and services will be designed for use on the far edge.

The ability to compute on that far edge is the most important characteristic of intelligent systems in the energy industry, according to the executives we interviewed – not the most necessary for success, but of foundational importance for the next three to five years. Other characteristics (such as being able to find and resolve stresses and failures, or being able to customize devices in the cloud) that are also foundational are not considered to deliver the same level of overall impact.

39% of these companies see themselves in a nascent intelligent systems state. This is higher than in any other sector measured. These nascent companies need to examine the profiles of the committed and successful to understand how they sequence their specific intelligent systems characteristics.

Of all the intelligent systems characteristics that will drive impact, 63% will need to be built within the next three years. The longer-term nice-to-haves will contribute less than 20% toward the success of intelligent systems in the energy sector.

Related resources

>> Forbes Report: Characteristics of an Intelligent Systems Future >> The Engine of the Intelligent Edge >> Wind River Industrial Solutions

Two of the top three intelligent systems characteristics for success will only be realized within the five-year horizon, according to energy sector executive leadership. These two are the ability to simulate and emulate in near real time and the ability to connect data from digital feedback loops into the developmental process for new products and services — illustrating the core value of data for this sector.

Mission-critical capabilities are the number-one driver of overall success for energy sector intelligent systems, according to 51% of leaders. They particularly singled out the need for protection against cyberattacks. This matters as much for those selling embedded products and services as for those using them.

What really matters to executives building intelligent systems?

Source: Characteristics of an Intelligent Systems Future, Forbes, 2021

Three Facts About Intelligent Systems in the Energy and Utility Industries

55%

of all energy companies are experimenting with intelligent systems ideas and technologies.

ONLY 11%

of executive leaders in the energy industry see themselves as visionary.

37%

of energy company executive leaders believe they are ahead of their peers in being able to highly connect in real time with customers.

Characteristics of Intelligent Systems Success in the Energy and Utility Industries

Knowing when to invest in each characteristic requires a blueprint for building critical infrastructure, delivering core foundational needs, and much more.

Characteristics of Success in the Energy and Utility Industries

Plot Your Intelligent Systems Journey in the Energy Sector

Download “Plotting Your intelligent Systems Journey,” a free 30-page report developed in partnership by Wind River and Forbes.

Our research is based on more than 200 points of comparison across companies building and deploying intelligent systems.

This report shows:

How your peers are thinking about the barriers to and drivers for adoption of intelligent systems
What factors would accelerate the adoption of intelligent systems in your sector
The relative importance of all intelligent systems characteristics, to help you prioritize your investments
How your peers see the roles and importance of 5G, AI, ML, and cybersecurity in their decision-making
The key components for the mission-critical success of intelligent systems
What the future of embedded devices and solutions looks like in an intelligent systems world
Where digital feedback loops are crucial for success
What the key metrics for success are
Where your peers see extensive value for intelligent systems in addressing wider societal issues

Medical Intelligent Systems

Intelligent systems research

Medical Technology

Alan Turing’s idea of the imitation game was a precursor to current thinking about intelligent systems inside the medical industry. The application of intelligent systems for preventive maintenance and innovation opens a world of new possibilities.

This sector’s infrastructure needs for intelligent systems characteristics require more investment today than any other vertical. Just under 36% of them are needed now.

Intelligent systems research

Medical Technology

Alan Turing’s idea of the imitation game was a precursor to current thinking about intelligent systems inside the medical industry. The application of intelligent systems for preventive maintenance and innovation is extremely exciting.

This sector’s infrastructure needs for intelligent systems characteristics require more investment today than any of the other verticals. Just under 36% of them are needed now.

The Need to Act

Accenture calculates that by 2026, AI alone could save U.S. healthcare $150 billion annually. Imagine what else could be saved with automation and connected applications in devices. The opportunities to compute data in near real time on common development and operations platforms for the benefit of both patient and institution are immense.

For executive leaders in the medical technology sector, there are necessary precursors to success:

Delivering a real-time collaborative workflow platform
Connecting data in feedback loops back into product development for insights
Detecting and resolving events
Delivering total automation to key outcomes

Although these four characteristics may seem disconnected, they represent the combination of elements needed to achieve the longer-term performance that will bring together applications, functionality, and system management.

These initial-phase intelligent systems characteristics account for 36% of all the critical characteristics for intelligent systems success. No other sector scores higher than 18% for these necessary precursors.

The ability to compute on the far edge is the next most important characteristic for success in the building phase of intelligent systems capability for this sector. It joins the ability to predict stresses and failures and resolve them and the ability to customize device experiences in the cloud as the three characteristics core to success in straddling needs over the next three to five years.

Alan Turing’s idea of the imitation game was a precursor to how to think about intelligent systems inside the medical industry. The application of intelligent systems for preventative care and innovation is extremely exciting.

Most medical technology executive leaders are either committed to the idea of intelligent systems (33%) or in the experimental phase (48%). In the first group, the gap between those committed and succeeding and those committed but not succeeding is big: 20% versus 11%.

Two key characteristics for success need to be achieved in the initial five-year time frame: the application of AI and ML and the ability to deliver actions based on sensory data and algorithms. These will drive the success of longer-term objectives in the medical technology sector.

Related resources

>> Forbes Report: Characteristics of an Intelligent Systems Future >> The Engine of the Intelligent Edge >> Wind River Medical Technology Solutions

While 78% of leaders in medical technology companies are on a pathway to building intelligent systems, one third of them are not succeeding. A strong focus on building the precursor characteristics is an essential first step to increasing performance. While characteristics such as experimenting as a learning system are interesting, they are not seen as anything more than nice-to-haves.

What really matters to executives building intelligent systems?

Source: Characteristics of an Intelligent Systems Future, Forbes, 2021

Five Facts About Intelligent Systems in the Medical Technology Sector

66%

of leaders in medical technology agree that potential for ROI outweighs the level of effort.

24%

of leaders in medical technology say the key barrier for future success is the lack of skill sets.

The prime motivations for building intelligent systems are the ability to move more decision-making computing to the far edge of the cloud (more important than in other industries) and the ability to make business decisions autonomous.

The key societal value for intelligent systems is the ability to govern corporations in a more real-time and data-centric manner. This sector, compared to the other industries studied, marks this as the number-one extended value of intelligent systems for society.

52%

of leaders in this sector believe that the most important characteristic of intelligent systems for organizational success is that they can be built, developed, and operated using simulations or emulations to increase productivity and reduce time-to-market.

Characteristics of Intelligent Systems Success in the Medical Technology Sector

Knowing when to invest in each characteristic requires a blueprint for building critical infrastructure, delivering core foundational needs, and much more.

Plot Your Intelligent Systems Journey in the Medical Technology Sector

Download “Plotting Your intelligent Systems Journey,” a free 30-page report developed in partnership by Wind River and Forbes.

Our research is based on more than 200 points of comparison across companies building and deploying intelligent systems.

>> Download the report now

This report shows:

How your peers are thinking about the barriers to and drivers for adoption of intelligent systems
What factors would accelerate the adoption of intelligent systems in your sector
The relative importance of all intelligent systems characteristics, to help you prioritize your investments
How your peers see the roles and importance of 5G, AI, ML, and cybersecurity in their decision-making
The key components for the mission-critical success of intelligent systems
What the future of embedded devices and solutions looks like in an intelligent systems world
Where digital feedback loops are crucial for success
What the key metrics for success are
Where your peers see extensive value for intelligent systems in addressing wider societal issues

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