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Bridging the Physical and Digital

Digital Twins IoT IIoT

Bridging the Physical and Digital – Digital twins are multiplying as their capabilities and sophistication grow. But realizing their full promise may require integrating systems and data across entire organizational ecosystems.

IMAGINE that you had a perfect digital copy of the physical world: a digital twin. This twin would enable you to collaborate virtually, intake sensor data and simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and output instructions to manipulate the physical world.

Today, companies are using digital twin capabilities in a variety of ways. In the automotive1 and aircraft2 sectors, they are becoming essential tools for optimizing entire manufacturing value chains and innovating new products. In the energy sector, oil field service operators are capturing and analyzing massive amounts of in-hole data that they use to build digital models that guide drilling efforts in real time.3 In health care, cardiovascular researchers are creating highly accurate digital twins of the human heart for clinical diagnoses, education, and training.4 And in a remarkable feat of smart-city management, Singapore uses a detailed virtual model of itself in urban planning, maintenance, and disaster readiness projects.5

Digital twins can simulate any aspect of a physical object or process. They can represent a new product’s engineering drawings and dimensions, or represent all the subcomponents and corresponding lineage in the broader supply chain from the design table all the way to the consumer—the “as built” digital twin. They may also take an “as maintained” form—a physical representation of equipment on the production floor. The simulation captures how the equipment operates, how engineers maintain it, or even how the goods this equipment manufactures relates to customers. Digital twins may take many forms, but they all capture and utilize data that represents the physical world.

Recent MarketsandMarkets research suggests that such efforts are already underway: The digital twins market—worth US$3.8 billion in 2019—is projected to reach US$35.8 billion in value by 2025.6

What accounts for this kind of growth? And why now? After all, digital twin capabilities are not new. Since the early 2000s, pioneering companies have explored ways to use digital models to improve their products and processes.7 While digital twins’ potential was clear even then, many other companies found that the connectivity, computing, data storage, and bandwidth required to process massive volumes of data involved in creating digital twins were cost-prohibitive.8

The digital twins trend is gaining momentum thanks to rapidly evolving simulation and modeling capabilities, better interoperability and IoT sensors, and more availability of tools and computing infrastructure. As a result, digital twins’ capabilities are more accessible to organizations large and small, across industries. IDC projects that by 2022, 40 percent of IoT platform vendors will integrate simulation platforms, systems, and capabilities to create digital twins, with 70 percent of manufacturers using the technology to conduct process simulations and scenario evaluations.9

At the same time, access to larger volumes of data is making it possible to create simulations that are more detailed and dynamic than ever.10 For longtime digital twins users, it is like moving from fuzzy, black-and-white snapshots to colorful, high-definition digital pictures. The more information they add from digital sources, the more vivid—and revealing—the pictures become.

Models + data = insights and real value

Digital twin capabilities began as a tool of choice in the engineer’s toolbox because they can streamline the design process and eliminate many aspects of prototype testing. Using 3D simulations and human-computer interfaces such as augmented reality and virtual reality,11 engineers can determine a product’s specifications, how it will be built and with what materials, and how the design measures against relevant policies, standards, and regulations. It helps engineers identify potential manufacturability, quality, and durability issues—all before the designs are finalized. Thus, traditional prototyping accelerates, with products moving into production more efficiently and at a lower cost.

Beyond design, digital twins are poised to transform the way companies perform predictive maintenance of products and machinery in the field. Sensors embedded in the machines feed performance data into a digital twin in real time, making it possible not only to identify and address malfunctions before they happen but to tailor service and maintenance plans to better meet unique customer needs. Recently, Royal Dutch Shell launched a two-year digital twin initiative to help oil and gas operators manage offshore assets more effectively, increase worker safety, and explore predictive maintenance opportunities.12

Digital twins can help optimize supply chains, distribution and fulfillment operations, and even the individual performance of the workers involved in each. As an example of this in action, global consumer products manufacturer Unilever has launched a digital twin project that aims to create virtual models of dozens of its factories. At each location, IoT sensors embedded in factory machines feed performance data into AI and machine learning applications for analysis. The analyzed operational information is to be fed into the digital twin simulations, which can identify opportunities for workers to perform predictive maintenance, optimize output, and limit waste from substandard products.13

Smart city initiatives are also using digital twins for applications addressing traffic congestion remediation, urban planning, and much more. Singapore’s ambitious Virtual Singapore initiative enables everything from planning for cell towers and solar cells to simulating traffic patterns and foot traffic. One potential use may be to enable emergency evacuation planning and routing during the city’s annual street closures for Formula 1 racing.14

What’s new?

Over the course of the last decade, deployment of digital twin capabilities has accelerated due to a number of factors:

  • Simulation. The tools for building digital twins are growing in power and sophistication. It is now possible to design complex what-if simulations, backtrack from detected real-world conditions, and perform millions of simulation processes without overwhelming systems. Further, with the number of vendors increasing, the range of options continues to grow and expand. Finally, machine learning functionality is enhancing the depth and usefulness of insights.
  • New sources of data. Data from real-time asset monitoring technologies such as LIDAR (light detection and ranging) and FLIR (forward-looking infrared) can now be incorporated into digital twin simulations. Likewise, IoT sensors embedded in machinery or throughout supply chains can feed operational data directly into simulations, enabling continuous real-time monitoring.
  • Interoperability. Over the past decade, the ability to integrate digital technology with the real world has improved dramatically. Much of this improvement can be attributed to enhanced industry standards for communications between IoT sensors, operational technology hardware, and vendor efforts to integrate with diverse platforms.
  • Visualization. The sheer volume of data required to create digital twin simulations can complicate analysis and make efforts to gain meaningful insights challenging. Advanced data visualization can help meet this challenge by filtering and distilling information in real time. The latest data visualization tools go far beyond basic dashboards and standard visualization capabilities to include interactive 3D, VR and AR-based visualizations, AI-enabled visualizations, and real-time streaming.
  • Instrumentation. IoT sensors, both embedded and external, are becoming smaller, more accurate, cheaper, and more powerful. With improvements in networking technology and security, traditional control systems can be leveraged to have more granular, timely, and accurate information on real-world conditions to integrate with the virtual models.
  • Platform. Increased availability of and access to powerful and inexpensive computing power, network, and storage are key enablers of digital twins. Some software companies are making significant investments in cloud-based platforms, IoT, and analytics capabilities that will enable them to capitalize on the digital twins trend. Some of these investments are part of an ongoing effort to streamline the development of industry-specific digital twin use cases.

Costs versus benefits

The AI and machine learning algorithms that power digital twins require large volumes of data, and in many cases, data from the sensors on the production floor may have been corrupted, lost, or simply not collected consistently in the first place. So teams should begin collecting data now, particularly in areas with the largest number of issues and the highest outage costs. Taking steps to develop the necessary infrastructure and data management approach now can help shorten your time to benefit.

Even in cases where digital twin simulations are being created for new processes, systems, and devices, it’s not always possible to perfectly instrument the process. For chemical and biological reactions or extreme conditions, it may not be possible to directly measure the process itself; in some cases, it may not be cost-effective or practical to instrument the physical objects. As a result, organizations need to look to proxies (for example, relying on the instrumentation and sensors in a vehicle rather than putting sensors into tires) or things that are possible to detect (for example, heat or light coming from chemical or biological reactions).

And with the cost of sensors dropping, how many sensors is enough? Balancing the cost/benefit analysis is critical. Modern aircraft engines can have thousands or tens of thousands of sensors, generating terabytes of data every second. Combined with digital twins, machine learning, and predictive models, manufacturers are providing recommendations to help pilots optimize fuel consumption, help maintenance be proactive, and help fleets manage costs.15 Most use cases, however, require only a modest number of strategically placed sensors to detect key inputs, outputs, and stages within the process.

Models beyond

In the coming years, we expect to see digital twins deployed broadly across industries for multiple use cases. For logistics, manufacturing, and supply chains, digital twins combined with machine learning and advanced network connectivity such as 5G will increasingly track, monitor, route, and optimize the flow of goods throughout factories and around the world. Real-time visibility into locations and conditions (temperature, humidity, etc.) will be taken for granted. And without human intervention, the “control towers” will be able to take corrective actions by directing inventory transfers, adjusting process steps on an assembly line, or rerouting containers.

Organizations making the transition from selling products to selling bundled products and services, or selling as-a-service, are pioneering new digital twin use cases. Connecting a digital twin to embedded sensors and using it for financial analysis and projections enables better refinement and optimization of projections, pricing, and upsell opportunities.

For example, companies could monitor for higher wear-and-tear usage and offer additional warranty or maintenance options. Or organizations could sell output or throughput as-a-service in industries as varied as farming, transportation, and smart buildings. As capabilities and sophistication grow, expect to see more companies seeking new monetization strategies for products and services, modeled on digital twins.

Modeling the digital future

As the digital twins trend accelerates in the coming years, more organizations may explore opportunities to use digital twins to optimize processes, make data-driven decision in real time, and design new products, services, and business models. Sectors that have capital-intensive assets and processes like manufacturing, utilities, and energy are pioneering digital twin use cases already. Others will follow as early adopters demonstrate first-mover advantage in their respective sectors.

Longer term, realizing digital twins’ full promise may require integrating systems and data across entire ecosystems. Creating a digital simulation of the complete customer life cycle or of a supply chain that includes not only first-tier suppliers but their suppliers, may provide an insight-rich macro view of operations, but it would also require incorporating external entities into internal digital ecosystems. Today, few organizations seem comfortable with external integration beyond point-to-point connections. Overcoming this hesitation could be an ongoing challenge but, ultimately, one that is worth the effort. In the future, expect to see companies use blockchain to break down information silos, and then validate and feed that information into digital twin simulations. This could free up previously inaccessible data in volumes sufficient to make simulations more detailed, dynamic, and potentially valuable than ever.

It’s time to transition your digital organization from black-and-white to color. Are you ready?

The insight is from DeloitteInsights.com. You can read the full article by clicking here