How Digital Twins foster innovation in IoT-enabled environments

Dynamic digital representations, or Digital Twins, are rapidly changing the way industries design, build and operate their products and processes. Gartner predicts, “by 2021, half of large industrial companies will use Digital Twins, resulting in those organizations gaining a 10 percent improvement in effectiveness.”

Powered by the Cloud, IoT, and AI, Digital Twins enrich complex systems like cars, wind turbines and buildings across their entire life cycles. A Digital Twin combines design, production and operational data. It allows assets to be tested before, during and after production, and across a wide range of environments.

IBM Research – Ireland is developing different Digital Twin technologies. These include:

  • A virtual platform for testing of complex IoT systems with live and simulated data.
  • Forming a knowledge graph for IoT that combines reasoning with machine learning to allow the system to autonomously analyze and understand life cycle data.

Utilizing a virtual testing platform for IoT systems

In order to test complex IoT Systems, our researchers are using a virtual platform. This allows designers and developers of transportation services to investigate large-scale connected car services. They achieve this by merging simulations of large-scale automotive IoT deployments with proof-of-concept capabilities provided from real world vehicles. This platform is helping automotive partners design their services at scale while accelerating time to market.

The platform also allows drivers of actual vehicles to experience a large-scale connected scenario first hand. This combination of simulated and real-world data generates valuable insights. These insights are critical to user-centric development, resulting in reliable systems that are ready for the market. By embedding the data from actual vehicles into the digital environment, we can test the effects of assisted and autonomous driving in large-scale traffic simulations, in real time.

For example …

In collaboration with University College Dublin (UCD), we are using our virtual testing platform to evaluate a number of new mobility concepts. For example, we are testing a new car sharing mobility service that dynamically adapts to user preferences. This then allows a group of users to meet based on changeable traffic conditions and their variable pick-up time arrangements.

We are also investigating using IoT services to maximize air quality intake for pedestrians and cyclists by reducing their exposure to pollution. Imagine an electric bike using IoT devices, such as mobile phones and sensors. These IoT devices detect and automatically assist the cyclists when traveling through areas of high pollution. In those areas, the engine of the e-bike would be automatically triggered into operation. When that happens, it reduces the cyclist’s pedaling effort, resulting in a lower breathing rate and lower pollution intake. The virtual testing platform can also be used to connect to the e-bike and monitor how the cyclist would actually react to this new service, investigating the interactions between the cyclist and the bike.

Another service solution we are evaluating would reduce a pedestrian’s exposure to car exhaust pollution. How? The AI controls of a hybrid car to automatically switch between combustion and electric mode when the vehicle is in close proximity to pedestrians and cyclists.

These examples illustrate how a virtual testing platform can help accelerate the development of new services. At the same time, it also helps the transportation industry respond to the ever-increasing demands for environmental accountability.

Automating Insights with a knowledge graph for IoT

At IBM Research – Ireland, we are developing AI technologies to connect and understand IoT data in new ways. We’re combining machine learning with knowledge graph reasoning to enhance data being extracted from an IoT network. And we’re also adding layers of semantic meaning to create new insights within the network. This technology is the Digital Thread at the core of each Digital Twin. It connects information along the lifecycle stages into a knowledge graph. This graph then enables new informed decisions and automation of processes.

By using a knowledge graph, we are able to organize data and its variables being extracted into groups and establish the relationships between the data sets and their variables. The knowledge graph provides a shared vocabulary of information that can be used to create a model of a domain, the types of data within it, their properties and the relationships between the data–and we are using natural language to do all of this.

As a result, our AI solution understands the meaning and the relationships between the different types of data within a network or system. This gives our research teams new ways to derive innovative insights from an IoT system and present them as new knowledge and information to end users.

Self-diagnosing problems

For example, take an IoT temperature sensor in a building. The temperature sensor has data readings, the type of data that it is recording and its location. Our AI system understands general concepts of physics and how temperature is influenced by heating or cooling, such as environmental factors, heat system controls and so on. This allows our system to form a knowledge graph to understand the temperature settings within the building and the multiple factors that impact the temperature within its operating environment. This allows for the self-diagnosis of problems within the system while enabling it to learn and understand this relationship over time. It is also scalable and works across industries such as retail and automotive.

Our virtual testing platform and knowledge graph for IoT demonstrate the value of Digital Twin. We’re enabling industries to create better informed designs, optimize production, and manage efficient operation. The virtual testing platform can simulate these large-scale environments and networks while providing a way to perform controlled user-acceptance tests.

This combination of simulated and real-world data generates valuable insights that are critical to systems development. Our knowledge graph for IoT is a scalable solution that enables IoT to learn system behaviors, to understand management operations and to self­-diagnose problems. And all while making human­-machine interaction more natural and intuitive.

We will demonstrate the knowledge graph for IoT at the IEEE flagship IoT conference World Forum IoT, February 5-8th in Singapore.  A prototype of the virtual testing platform will be shown at the ENABLE-S3 consortium General Assembly, Review and Marketplace event. This is scheduled at our Research lab in Dublin on July 4, 2018.

For deeper research on Digital Twins and related topics, see:

Joern Ploennigs, Amadou Ba, Michael Barry, Materializing the Promises of Cognitive IoT: How Cognitive Buildings are Shaping the Way,  IEEE Internet of Things Journal, 2017

Wynita Griggs, Giovanni Russo, Robert Shorten, “Leader and Leaderless Multi-Layer Consensus With State Obfuscation: An Application to Distributed Speed Advisory Systems”,  IEEE Transactions on Intelligent Transportation Systems, 2017

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Leveraging Digital Twins To Breathe New Life Into Your Products And Services

Are you familiar with the concept of the twin paradox? In physics, the twin paradox is a thought experiment in which one twin stays on Earth while the other travels in a spaceship at a high speed for a period of time. According to the special theory of relativity, the second twin will return home measurably younger than the first.

In a similar way, the concept of the digital twin can accelerate your business and breathe new life into your products and services.

But the digital twin isn’t just a thought experiment. Gartner lists digital twins as a Top 10 strategic trend for 2017. It’s part of a broader digital transformation on which IDC says companies will invest $ 2.1 trillion a year by 2019.

Already, smart companies are using digital twins to better understand operations, get closer to customers, and transform their business.

Connecting real and virtual

A digital twin is a virtual representation of a real-world product or service. That could be anything from a toaster to industrial machinery to complex processes. The virtual representation combines three types of information: business data, contextual data, and sensor data.

Business data covers information such as customer name, location, and service-level agreements. Contextual data includes details such as ambient temperature, humidity, and weather events. Sensor data involves things like machine speed, operating temperature, and vibration.

Sensor data is key because, while companies have been using digital twins for years, it’s only with the Internet of Things (IoT) that they’ve become cost-effective. Gartner predicts that 6.4 billion things will be connected this year, a 30% jump over 2015. By 2020, at least half of all new business processes will incorporate IoT – transforming live data into new value.

Drilling down on digital twins

How does a digital twin work? Let’s say you manufacture industrial drills. A digital twin can help you understand how customers use your drill. The goal is to continuously improve the product to increase customer satisfaction and identify opportunities for new products and services.

For example, you might discover that your drill malfunctions in certain situations. That can enable you to improve product design. Or it can let you help customers modify the way they use the drill to avoid problems.

Or, you might discover that customers use your drill not only to make holes but also to cut materials. That might lead you to develop a new product that’s purpose-built for cutting.

Or, maybe you discover that while customers want holes made, they don’t necessarily want to purchase and operate a drill. So rather than sell drills, you might offer a hole-drilling service. In other words, instead of charging customers for machinery they operate, you charge them for holes drilled by machinery you operate for them. Some SAP customers have been quite successful in making this kind of leap from products to services.

Digital twins across industries

Digital twins aren’t just for manufacturers. Insurers can apply digital twins in offerings like usage-based car insurance. Retailers can track how customers navigate the store and interact with products on the shelves. Cities can model areas for things like smart lighting. Ports can monitor weather, shipping traffic, containers, and trains and trucks entering and leaving.

Digital twins cover the entire lifecycle of an asset or process. In fact, they can form a foundation for an end-to-end, closed-loop value chain for smart, connected products and services, from design to production, from deployment to continuous improvement.

The promise of continuous improvement is why it’s increasingly important to integrate digital technologies into all products. As you leverage your digital twin to identify opportunities for new or better features, you can implement those improvements quickly and cost-effectively through firmware updates.

Implementing digital twins involves four steps:

  1. Integrate smart components such as sensors, software, computing power, or data storage into new or existing products.
  1. Connect the product to a central location where you can capture sensor data and enrich that sensor data with business and contextual data.
  1. Analyze that data on an ongoing basis to identify opportunities for product improvements, new products, or even new business models.
  1. Leverage these digital insights to transform your company — for example, by reducing costs through proactive avoidance of business interruptions, or by creating new business opportunities.

Of course, while those steps are easy to list, they can require significant effort to achieve. But digital twins are becoming a business imperative. Companies that fail to respond will be left behind. Those that embrace digital twins have the opportunity to better understand customer needs, continuously improve their products and services, and even identify new business models that give them competitive advantage.

Consumer demand for virtual reality is changing how businesses manage and operate. Learn how to transition From E-Business to V-Business.

Internet of Things – Digitalist Magazine

Digital Twins: Eclipse Ditto

The concept of a “digital twin” has been making popping up in the industry thanks to industrial giant GE. The concept is pretty simple – basically a twin is referring to a cloud-based model of a real-world physical asset.

The implications of this is that you are able to expose and interact with the inner workings of the device and its surrounding systems in real-time. This also provides the ability to introduce parameters against the model to improve its efficiency, etc before you deploy the changes in the real world.

Eclipse along with the support of Bosch SI are launching a new project called Ditto under a Eclipse Public License 1.0 to provide functionality to manage the state of Digital Twins.

Ditto addresses the following aspects in its scope:

  • Device-as-a-Service
    By exposing a unified resource-based API to interact with devices, the complexity of different device types and how devices are connected can be abstracted away. The device can then be turned inot a service and used in other services.
  • State management for Digital Twins
    Differ between reported (last known), desired (target) and current state (live) of devices, including support for synchronization and publishing of state changes.
  • Organize your set of Digital Twins
    Support finding and selecting sets of Digital Twins by providing search functionality on meta data and state data. That data is automatically indexed by Ditto which leads to fast search responses provided by the search API, even when there are millions of devices to search in.

Ditto Architecture

The Ditto project augments several of the other Eclipse IoT projects.

  • Eclipse Hono for the message exchange with devices
  • Eclipse Vorto for the modeling of device structures reflected by the Digital Twins
  • Eclipse Hawkbit for rolling out software updates based on meta-data of the Digital Twins
  • Eclipse Kapua as integration framework as easy quick-start for end-to-end IoT solutions leveraging the Digital Twins approach

Eclipse Ecosystem

The initial project contributions are set to go live in Q2 of this year with several microservices bundled as Docker images..

More details can be found on the Ditto project page.

Postscapes: Tracking the Internet of Things

Coffee Machines Brew Industry Disruption: Digital Twins Emerge In 2017

How fast can your coffee machine accelerate business growth? Of all the demos I saw at SAP TechEd Barcelona, digital twins was among the most fascinating.

Opportunities explode and industries implode when everyday items like coffee machines power a direct conversation between customers, companies and suppliers, not only crunching high-volume, actionable data in real time, but also looking into the future.

It’s not surprising that digital twins made it into Gartner Research’s top 10 trends for 2017. Those analysts predict hundreds of millions of things will be represented by digital twins within three to five years.

Above, Thomas Kaiser, senior vice president of IoT at SAP, talked with me about how the smartest companies are using digital twin technology to shake up the status quo. Featured is a clip of Ian Kimball of SAP demonstrating the amazing power of digital twins using a connected coffee machine at SAP TechEd Barcelona.

How digital twins disrupt

A digital twin is a virtual representation of a process, product, or service. While companies have been using digital twins for years, it’s only with the Internet of Things (IoT) that they’ve become cost-effective.

Using software on a cloud-based platform, digital twins pull together and analyze data companies can use to monitor and head off repairs and other problems before they occur. They can look into the future, simulating scenarios to uncover new opportunities for delighting customers. The data is deep and broad, encompassing business content like the customer’s name, exact street location of their coffee machines, and service level agreements. Information is also contextual and, of course, from sensors. The digital twin replicates everything about the machine’s operation history, from how many cups and what type of coffee people are drinking, to the precise temperature of the milk and amount of steam pressure used to brew each pour.

Think of digital twins as a combination of your smartest product technician coupled with advanced machine monitoring capabilities plus predictive and preemptive analytics. The measurable gains for companies are astounding. By 2018, IDC predicts companies investing in IoT-based operational sensing and cognitive-based situational awareness will see 30 percent improvements in the cycle times of impacted critical processes.

Four steps to get started

When I talked with SAP’s Thomas Kaiser, senior vice president of IoT, at SAP TechEd, he told me about the hottest industries using digital twins, and what companies can realistically expect. After the event, he added these thoughts to what we covered in my video interview.

“Digital twins are becoming a business imperative, covering the entire life cycle of an asset or process and forming the foundation for connected products and services,” said Kaiser. “Companies that fail to respond will be left behind. Those that embrace digital twins have the opportunity to better understand customer needs, continuously improve their products and services, and even identify new business models that give them competitive advantage.”

Digital twins are becoming a business imperative, forming the foundation for connected products and services.

Kaiser recommended four steps to get started with digital twins, noting that while these steps are easy to list, they can require significant effort to achieve. First, integrate smart components into new or existing products. Second, connect the products/services to a central (cloud-based) location with streaming, Big Data, in-memory, and analytics capabilities to capture sensor data and enrich it with business and contextual data. Third, constantly analyze the data to identify areas for improvements, new products or even new business models. Fourth, use digital insights to create new services that transform the company — disrupt before your business is disrupted.

The coffee machine on stage at SAP TechEd may have looked like every other one, but quietly brewing behind it is a world of innovative difference. As for that question about how fast your coffee machines can fuel growth, using digital twin technology, it’s a potent brew of fresh insights fueling innovation with tremendous business outcomes.

For more on digital twin technology, see Leveraging Digital Twins To Breathe New Life Into Your Products And Services.

Follow me @smgaler

Images via SAP

Internet of Things – Digitalist Magazine

IoB Insiders: Risk, digital twins and insurance

IoB Insiders: Risk, digital twins and insurance

IoB Insiders Could digital twins hold the key to insurance companies building a better picture of customer assets, asks Andy Yeoman, CEO of Concirrus?

With the rise in connected assets and sensors, and the ever-expanding capacity of predictive analytics and machine learning, the possibilities for insurers to know and understand more of the world around them are huge.

We’ve spoken in the past about how this will enable insurers to change their business models, moving from a model of insurance (that is, what they’ve been doing for the past few hundred years) to one of assurance (where risks are reduced or prevented, and where the insurer actively helps the customer to protect their assets).  

We’ve also spoken about how ‘the law of unintended consequences’ could mean that hyper-connectivity will bring about new, unforeseen developments. On a macro-level, this may mean that the insurance market develops in unexpected ways – individual players (insurers, customers, original equipment manufacturers, brokers and so on) may rise or fall or be sidelined entirely as each develops new technological capabilities.

But it may also have consequences on a micro level, with our understanding of device and asset behaviour becoming much more detailed. One technology affecting our understanding of devices and assets is ‘digital twin’ technology.

Read more: With connected policies, who own IoT data? 

What is a digital twin, anyway?

Digital twin technology was rated by IT market research company Gartner as one of the top ten strategic technologies of 2017. In effect, it allows companies to create a digital version of a device, machine or system, that can then be used for simulation purposes. This may include diagnosing faults, preventing downtime or generating predictive models that allow greater understanding of its physical, ‘real world’ counterpart.

An early application of digital twin technology was pioneered by NASA for space exploration, allowing engineers to understand how to diagnose and fix machine faults remotely.

As Bernard Marr of UK-based think tank and consulting organization, the Advanced Performance Institute, writes in Forbes, when disaster struck the Apollo 13 mission, “it was the innovation of mirrored systems still on earth that allowed engineers and astronauts to determine how they could rescue the mission.”

Indeed, the importance of this is difficult to overstate. As Thomas Kaiser, senior vice president of IoT at software giant SAP, states, “Digital twins are becoming a business imperative, covering the entire lifecycle of an asset or process and forming the foundation for connected products and services. Companies that fail to respond will be left behind.”

Read more: What data does a digital twin run on? 

Insurance implications

So what are the implications for insurance? We’ve gone on record many times to say that insurers that fail to adopt connected technologies will become extinct in the near future. But taking the decision to adopt connected technologies is only part of the answer – they need to be baked into that company’s business model.

Part of doing this means utilizing information generated by networks of smart devices. If you’re a marine insurer and all of your customers have vessels packed full of engine diagnostic sensors and connected technology, you could, theoretically speaking, create predictive models that tell you how likely each vessel is to have engine problems (and how likely the owner is to make a claim).

Executives at power systems company Rolls-Royce are already talking about this and applying predictive analytics to maintain efficiency and prevent downtime with the company’s engines. They are also talking about autonomous vessels by 2020, something that would result in huge amounts of digital data being made available for analysis.  

In a similar vein, industrial giant GE is applying this technology to its renewable energy business, with predictive maintenance modelling for wind farms.

Here at Concirrus, we’re already working with CHS Engineering and Heathrow Airport to monitor critical infrastructure and detect potential malfunctions before they result in downtime.

Read more: GE powers up data insights

What happens next?

So what happens to insurers when this kind of data is produced by customers? How do they ensure that they aren’t sidelined, as other industry players (brokers, for example) become powerful, data-fuelled entities with massive insight into market risks?

The answer lies in an insurer’s ability to translate raw data into behavioural insight. With greater insight into the likely behaviour of (and risk associated with) individual assets, which may in part be generated through the use of digital twins, we can start to see how they might manage risk more effectively and even work with customers to mitigate it. Closer working relationships with corporate customers’ risk management departments are likely to be key here.

This data will also help insurers to model and plan their overall risk portfolio, envisaging likely scenarios and ensuring that an optimal balance of risk is maintained. This ‘digital portfolio’ might help remove some elements of uncertainty within the business, helping it to plan more effective reinsurance policies, for example.

But in order to leverage this data, insurers need ways of bringing it all together and making sense of it. This is currently beyond the abilities of many, and will mean that third-party technology providers have a huge role to play. These companies will act as the intermediary between insurers and data providers (such as customers and manufacturers, as well as industry datasets ). The race to achieve this is now on, and the winners will enjoy a period of substantial competitive advantage over the rest of the market.

Read more: How Zurich Insurance went from pilot to project with AI, IoT and AR

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