There is a fundamental disconnect between the wealth of digital data available to us and the physical world in which we apply it. While reality is three-dimensional, the rich data we now have to inform our decisions and actions remains trapped on two-dimensional pages and screens. This gulf between the real and digital worlds limits our ability to take advantage of the torrent of information and insights produced by billions of smart, connected products (SCPs) worldwide. Augmented reality, a set of technologies that superimposes digital data and images on the physical world, promises to close this gap and release untapped and uniquely human capabilities. Though still in its infancy, AR is poised to enter the mainstream - according to one estimate, spending on AR technology will hit $60 billion in 2020. AR will affect companies in every industry and many other types of organizations, from universities to social enterprises. In the coming months and years, it will transform how we learn, make decisions, and interact with the physical world. It will also change how enterprises serve customers, train employees, design and create products, and manage their value chains, and, ultimately, how they compete. In this article we describe what AR is, its evolving technology and applications, and why it is so important. Its significance will grow exponentially as SCPs proliferate, because it amplifies their power to create value and reshape competition. AR will become the new interface between humans and machines, bridging the digital and physical worlds. While challenges in deploying it remain, pioneering organizations, such as Amazon, Facebook, General Electric, Mayo Clinic, and the U.S. Navy, are already implementing AR and seeing a major impact on quality and productivity. Here we provide a road map for how companies should deploy AR and explain the critical choices they will face in integrating it into strategy and operations.
Whether you call it the Digital Twin or hybrid twin, the concept of copying your physical assets in the digital world is sweeping the computer-aided engineering (CAE) and Internet of Things (IoT) industries. In a panel of experts at the Analysis, Simulation and Whether you call it the Digital Twin or hybrid twin, the concept of copying your physical assets in the digital world is sweeping the computer-aided engineering (CAE) and Internet of Things (IoT) industries. In a panel of experts at the Analysis, Simulation and Systems Engineering Software Summit (ASSESS) Congress, engineers debated the definition of the Digital Twin as well as the role simulation and IoT will play in its inevitable expansion.
The digital twin is a burning topic within manufacturing industries. While it is often included in lists of today’s most strategic technologies, it has yet to be widely adopted in practice. Matti Kemppainen, Director of Research and Innovation at Konecranes, discusses the implications for manufacturers of the rolling out of digital twins. According to Kemppainen, digital twins are set to be a new standard for industry.
As it is today, many of product lifecycle processes, from design, to process planning and engineering, manufacturing are siloed because different software tools, models and data representations are used, and often by many different teams across different organizations and geographic locations. To achieve the goals of smart manufacturing, these product lifecycle processes and manufacturing functions need to be connected and integrated to increase process automation, responsiveness and efficiency, and to reduce human errors. Furthermore, because of connected smart products, this lifecycle is now being extended beyond the four-wall of the factories, into customers’ operation environment. Digital thread refers to the communication framework for integrating production functions across the product chain and integrating product data for digital models. It does so by enabling data flow and integrated view of the product’s data throughout its lifecycle across different stages, from design, to manufacturing, and now to operation, and even to end-of-life and recycling of the product
IIoT and Smart manufacturing – a twin-movement of digitalization: The Industrial Internet of Things (IIoT) and smart manufacturing are two parallel developments driven by the same core technology advances – the ubiquitous connectedness and widespread computation – that drive and are driven by the internet, and the seamless information sharing and optimal decision-making they enable.
As manufacturing processes become increasingly digital, the digital twin is now within reach. By providing companies with a complete digital footprint of products, the digital twin enables companies to detect physical issues sooner, predict outcomes more accurately, and build better products.
While the concept of a digital twin has been around since 2002, it’s only thanks to the Internet of Things (IoT) that it has become cost-effective to implement. And, it is so imperative to business today, it was named one of Gartner’s Top 10 Strategic Technology Trends for 2017. Quite simply, a digital twin is a virtual model of a process, product or service. This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations.
The Internet of Things (IoT) has been a long time coming, but as with so many software and cloud-driven markets today, the curve from hand-waving to pervasive adoption is set to be remarkably steep. Network-driven markets increasingly tend to be pretty close to winner takes all (think Google in Search, Apple in phones, Facebook in social, Snapchat in dogear-driven Augmented Reality) which makes timing and effective, community-driven execution all the more important. Which brings us to IBM. Wait. What? IBM? OK bear with me here.
A digital twin is a virtual equivalent of an actual physical product or service. Businesses from GE to Siemens are presently using digital twins to monitor the conditions of wind turbines and manufacturing equipment in real time, analyzing changes in key parameters and taking measures to perform conditional or predictive maintenance based on the slightest deviations.