A digital twin is a digital replica of a living or non-living physical entity. By bridging the physical and the virtual world, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity. Digital twin refers to a digital replica of physical assets (physical twin), processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things device operates and lives throughout its life cycle. Definitions of digital twin technology used in prior research emphasize two important characteristics. Firstly, each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart. Secondly, this connection is established by generating real time data using sensors. Digital twins integrate internet of things, artificial intelligence, machine learning and software analytics with spatial network graphs to create living digital simulation models that update and change as their physical counterparts change.
Digital transformation is rapidly disrupting current industry models. The adoption of new technologies is particularly accelerating in the logistics and manufacturing sector due to the benefits it offers enterprises, and is resulting in wider implementation of smart industry solutions. As the manufacturing and logistics sectors undergo major transformation, digital twins, artificial intelligence, the industrial internet of things, and warehouse robotization rank among the leading smart industry trends for 2019 and the coming years. Digital transformation of industry continues to move forward. A study by the German branch of the company PwC indicates that 91% of the industrial companies that participated in the research are investing or plan to invest in digital factories in Europe.
I grew up in a small manufacturing town in Northeast Iowa. The factory in my hometown made tractors (no surprise given that it was Iowa), but eventually the economics of cheap foreign labor and an interconnected global economy caught up with that factory – as it did with many US-based manufacturers – and soon the factory closed, and many people were laid off. But the technology world continues to evolve – especially with respect to IoT, Data Science and AI/ML – and so comes an opportunity for manufacturing to make a big return to the US. However, tomorrow's manufacturing battles won't be fought with cheap labor. In fact, measuring a country's manufacturing strength by the number of manufacturing jobs is fighting yesteryear's battle.
SAN FRANCISCO – November 15, 2016 – Today at Minds Machines, GE (NYSE: GE) announced new products, acquisitions and partner programs to enable further adoption of Predix, the operating system for the Industrial Internet. The platform enhancements, acquisitions and new ISV partner program further complement the Predix technology stack and make it easier for industrial companies to execute a strategic digital transformation to drive internal productivity. In 2016, orders from GE's portfolio of software solutions are on track to climb 25% to more than $7 billion – making GE the fastest growing digital industrial company in the world. Demonstrating the strength of Predix within GE, digital thread productivity will exceed $600 million, accelerating into 2017. "The opportunity for industry is now," said Bill Ruh, Chief Digital Officer of GE and CEO, GE Digital.
A digital twin is a digital representation that provides the elements and dynamics of how a device or ecosystem operates and lives throughout its life cycle. Digital twins are useful for simulating the capabilities of machine tools in a safe and cost-effective way, as well as identifying the root causes of problems occurring in physical tools or infrastructure. If a physical machine tool breaks down or malfunctions, engineers can evaluate the digital traces of the digital twins' virtual machines for diagnosis and prognosis. The digitization of nearly every industry type is helping to fuel the demand for twinning platforms, as is the desire to monitor, control, and model the future behavior of real-world equipment, systems, and environments. However, like any technology, digital twins must be understood and accepted by several different stakeholders, from the operations workers up to the C-suite.