THE EVOLUTION OF SMART CONNECTED SYSTEMS

Since the beginning of computing there have essentially been three waves of technology innovations. The first wave in the 1960s to the 1980s was driven by the advent of silicon – micro-processors, and microcontrollers that enabled calculations and put computational capabilities into business, society, and professionals’ hands on a significant scale. Engineers could design products; businesses could manage orders and inventories; and scientists could model diverse realms like human disease, the weather, and natural resources.

The second wave in the 1990s and 2000s, brought us interconnectedness or the “connected economy.” Everything was linked and integrated creating a new virtual world where the Internet, telco networks, and satellites enabled shared computing resources, interconnecting machines, software, systems, processes, and people. This shift enabled digitally triggered real-world actions and execution.

The third wave of innovation, which is still evolving, is enabled by cheap pervasive networked sensors and data fusion. This is driving new innovations and enabling a whole new generation of “awareness” applications. As networks continue to invade the physical world, system developers are seeing the value that comes from the growing interactions between sensors, machines, systems, and people, and the ability to detect patterns from large-scale sensor and machine data.

Evolution Of Networking And Computing

source: Harbor Research

Applications like computer vision caused the development of algorithmic systems that could recognize a pattern anomaly and execute an action based on the result. These new capabilities revolve around real-time situational awareness and automated analysis of very large volumes of sensor data. As a result, technology has moved beyond just proposing task solutions — such as executing a work order or a sales order — to sensing what is happening in the world around it, analyzing that new information for patterns, risks, and possibilities, then presenting alternatives and automatically taking actions.

THE SHIFT TO COMPLEX ADAPTIVE SYSTEM OF SYSTEMS

These combinatorial innovations are what we call “complex adaptive systems” or “System of Systems,” where multiple technologies converge and reinforce one another. A System of Systems is an arrangement of independently operated systems that allow for collaborative interactions among them, giving rise to emerging capabilities and values while maintaining each of the individual system’s own capabilities and values.

Complex Adaptive System of Systems

source: Harbor Research

For example, the development of autonomous vehicles not only depends on advancements in robotics and artificial intelligence to operate vehicles, but also on the maturation of the Internet of Things so an array of sensors can analyze driving conditions and interact with other cars, as well as improvements in lithium and battery technologies for cars to be able to efficiently refuel themselves.

The interdependence of these technologies has no doubt contributed to their synchronous advancement. For example, many believed a limiting factor in the emergence of driverless cars was the high cost of batteries required to travel long distances. In response, however, battery producers have dramatically increased production to scale down unit costs.  Over the last ten years, EV battery prices have fallen 90 percent.

WHAT IS A DIGITAL TWIN?

The Digital Twin Consortium (DTC) defines a digital twin as “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.”

Providing virtual representations of real-world entities and processes, emerging digital twin systems offer the opportunity to study the virtual and the physical, either separately or together.

Digital twins can integrate actual operating information, as well as analytics capabilities and recommendation engines, to enable real-time decision intelligence. Whether driven by historical or live data-feeds, this built-in intelligence provides users with a dashboard view into how the entity is performing at any given moment, as well as insights into what to anticipate in the future. For businesses, this powerful technology holds the key to accelerating holistic understanding and improved decision-making.

The digital twin of a complex entity or process actually consists of many models of component parts of the whole, each one pinned to measurement data collected by sensors on the original object or process in the physical world. The complex pipeline of data that informs a digital twin is called a “digital thread,” which is made up of data signatures that can be followed from the original to the digital twin and back again. The source of all the behavior and data is shown in the digital twin, which allows for investigation without dismantling the original in the physical world.

Digital twins can offer extraordinary business advantages to both the companies that manufacture and support machines as well as the users of machines. The ability to detect patterns from aggregating data is the “holy grail” of Smart Systems. New machine data and learning technologies enable not only data patterns, but a much higher order of intelligence to emerge from large collections of ordinary sensor and machine data.

Because the digital twin is completely data-driven, it does something that the original can’t do on its own: it constantly evolves and improves itself, and then offers those improvements to the designers and users of real-world products and systems.

The fact that a rapidly expanding range of devices have the capability to automatically transmit information about status, performance, and usage, and can interact with systems and people anywhere in real time, points to the new values enabled by simulation tools and virtual representations. In short, the emergence of digital twins is a textbook example of a complex adaptive and autonomous system.

A TURBINE AND ITS DIGITAL TWIN

CONNECTIVITY AND INTEGRATION CHALLENGES CREATE DIGITAL ORPHANS

Before delving further into the new thinking that makes this story possible, let’s talk about why it’s necessary at all.

Whatever we call these opportunities—smart complex systems, adaptive systems, System of Systems, or digital twins—they all presuppose technologies, standards, and tools that make connecting equipment and integrating machine data cheap, easy, and seamless. How will the physical and digital integration aspects of these new opportunities actually take place? How will virtually any device – right down to a lowly light bulb – become a peer that connects at will to the global data network?

Obviously, billions of devices of wildly varying types cannot each receive individual attention and configuration, or conform to elaborate a priori specifications. If it literally takes a network engineer to screw in a smart light bulb, digital systems and twins are never going to work.

Many schemes and so-called “standards” for device connectivity and device data integration already exist. But of course, all those “solutions” add up to one big problem. We don’t want many standards; we want one.

Contrary to the growing number of papers and articles heralding the benefits of digital twins, there are still fundamental technical and development hurdles that will need to be overcome to realize the many benefits digital twins can enable.

• For digital twins to flourish we need a straightforward, vendor-agnostic, automated means to onboard devices, equipment, and machines onto networks, while maximizing their interoperability.
• Most of the available tools that users and OEMs are working with today were not designed to handle the diversity of data types and the massive volume of data generated from real-time networked machines and equipment. Understanding which data, parameters, signals and values to collect and keep, and addressing the many data normalization and management challenges that arise when integrating sensor and machine data, are significant hurdles for digital twin development.
• Network integration and data challenges aside, aggregating and integrating the different models and simulations required to create a robust digital twin necessitates purpose-built, off-the-shelf frameworks and modeling tools. This prevents every digital twin development program from becoming a one-off science project.

These challenges are diluting the ability of technical organizations to efficiently and effectively organize all of the elements required to develop digital twins. Luckily, the DTC and other innovators are making progress in addressing solutions to these hurdles.

OPEN, COMPOSABLE, AND INTEROPERABLE EVENT-DRIVEN SYSTEMS

Perhaps the most important shift we see occurring is the relationship between software technology and OEM business models, as well as the need for open systems and collaboration. Open systems must be based on a scalable, event-driven architecture that enables the cooperative development of composable solutions.

Our evolving “thesis” goes something like this: As the complexity of these systems increases, the number and diversity of stakeholders expands, and the volume and nature of their interactions grows. The systems technology and architecture will, by necessity, need to become more and more open and interoperable to foster collaboration. This openness will, in turn, inform radically new modes of collaboration, including increased sharing of system data and services.  These forces must be aligned with the corresponding business and revenue models of the players that comprise the ecosystem and must be mutually supportive without inhibiting one or the other.

However, trying to coordinate and leverage the need for open systems and collaboration creates contention. OEMs today have largely ignored the need for open interoperable systems and many of their maneuvers are symptoms of an aging OEM strategy mindset that is based on the belief that these businesses can continue to exist as “solo” portfolio and solution providers.

Of course, most OEM businesses insist that they’re not flying solo. They remind us that they depend upon many relationships that comprise complex value chains with diverse channel partners. Yes, but those excuses miss the point. Their relationships aren’t open ecosystems that encourage open innovation. “You want us to collaborate and share our software and data,” we often hear from the C-suite. “But our data’s the new path to profitability and higher multiples.” Systems suppliers that have come from a legacy of “solo” solution delivery resist fundamental change. But change they must.

OPEN INNOVATION CHANGES ALL OF THE RULES

Never before has open innovation been as much a part of software development as it is today. Open-source software has become central to virtually all digital strategies, influencing every layer of the tech stack, from operating systems and programming languages to middleware and development tools.

Meanwhile, commercial open-source companies like Red Hat, Docker, and many more, along with the cloud vendors, have turned open-source software into viable commercial products. Together, these forces have commoditized software development, shifting the speed, scale, and economics of innovation by offering powerful infrastructure and development capabilities without players investing enormous amounts of capital up front or swearing allegiance to an OEM. Open innovation does not work “by invitation only.” Rather, it is rapidly moving to “self-service” open collaboration.

Open innovation will force the industrial behemoths to embrace open-source software and collaborative developer communities. But sadly, most OEMs that we speak with fail to see the evolving relationship between open innovation and collaborative business models as fertile ground for innovation. OEMs need to understand that new open-source software tools and open collaboration must be interwoven and mutually supportive to effectively leverage their combined potential.

What’s required are system composition and service orchestration tools that creatively combine multiple innovations in cloud infrastructure management, workflow automation, and event-driven system development. These tools will reduce complexity and better leverage the architects, developers, and integrators across ecosystems.

Model-based system architecture is quickly becoming the foundation of every technology-driven organization — not just hardware and software companies, but every company building digital capabilities. However, relatively few players understand how dramatically the software development arena has begun to change and, along with it, the strategies they need to maximize value have evolved as well.

DESIGNING INTEROPERABLE DIGITAL TWINS

As we discover new applications of digital twin systems for the betterment of business and society, we become increasingly aware of the importance of interoperability. Interoperable systems ensure that the parts maintain their independence but work together. Ensuring that these systems’ discrete components, as well as the broader System of Systems, are interoperable is essential to unlocking their larger potential with less implementation cost, less risk of failure, and less complexity at scale.

Innovators are striving to create a framework that would enable simple USB-style compatibility for all systems connected to the Internet and private networks. Creating a framework to codify and normalize what for years has been relegated to the domain of “system integration” is, of course, a daunting challenge. Most systems were designed to perform specific tasks and typically do not inherently interoperate with entities outside of each system. System integrators would historically add a translation layer to make discrete systems work for the larger system, creating an indirect data path that cannot be reused elsewhere. In contrast, interoperable systems are able to share, interpret, and present data to outside entities without middleware and while maintaining their original context. The data path is simplified and direct, leading to less complexity and faster computing.

The Digital Twin Consortium’s Recommendations for System Centric Design

The DTC published the Digital Twin System Interoperability Framework to help unify nascent ecosystems of high-value, multi-vendor services that can seamlessly plug into a multi-dimensional, interoperable System of Systems. More specifically, it lays the groundwork for building interactive ecosystems of interoperable digital twins. The framework distills the multiple facets of system interoperability into key concepts to help frame the design considerations necessary to make independent systems interoperate at scale, including:

System-Centric Design.  Designing products and applications as systems within an ecosystem rather than stand-alone products simplifies their ability to connect and interact in real-time to form a dynamic System of Systems.

Common Metamodel.  Developing a common conceptual model for a composable and interoperable System of Systems requires a high level of abstraction utilizing top-level concepts. It requires a universal metamodel for interoperability that can be applied simply to all system models, regardless of data type, and without sacrificing any information. When all systems share a common metamodel for encapsulating capabilities, purpose, and interface, they become inherently interoperable.

This is an excerpt from our full whitepaper, “SYSTEM of SYSTEMS and DIGITAL TWINS ORCHESTRATION“. In it we dive into two critical open-source innovations driving digital twin connectivity: Metamodels and Connection Profiles. We also explore the key applications and use cases that are enabling digital twin adoption. Download it below:

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