Determining what a machine as complex as an aircraft engine or a wind turbine is doing at any given moment is far from easy - predicting how it will behave in the future is even harder. If you were to build an exact digital replica of such a machine, however, and feed it with sufficient data so that it behaves like its real-life counterpart, you can indeed learn how it would behave under varying circumstances, and even predict what it will do next. So, how can you achieve this? With Digital Twins.
Building digital replicas of physical assets, processes, and systems, digital twins interact with real systems and mimic any changes that occur - as they occur. Digital twins were initially used for critical jet engine components.
Digital twins go mainstream
The concept of the digital twin has existed since 2002, but it is only now seeing real momentum due to the low cost, high storage, and compute capacity enabled by the recent widespread adoption of Internet of Things (IoT) and cloud technologies. Such is the pace of its momentum, in fact, IDC forecast that, within the next two years, 60 percent of manufacturers will use digital twins to monitor the performance of products and assets, and that a similar number of global companies will use the technology to deliver a more exceptional customer experience.
The concept is not limited to self-contained objects such as jet engine components, though. According to Gartner, around half of enterprises using IoT technologies are either using or are planning to use a digital twin by the end of the year.
Evidence of the growing adoption of digital twin can be seen in recent moves into the marketplace by enterprise technology players including IBM, SAP, and Oracle, each of which has launched its own digital twin solution.
At the same time, software providers have also made acquisitions to strengthen their positions. Indeed, Deloitte expects that the global market for digital twin technologies will be worth $16 billion within the next five years, with digital twins now being utilized by businesses across a range of industries, including aerospace, healthcare, and retail in a bid to increase efficiency, reduce costs, and build better products.
A range of industries
By using data from IoT-enabled shelves and sales systems, for example, French supermarket chain Intermarché can create digital twins of its brick-and-mortar stores, which provide its managers with real-time insights on stock levels and test out different store layouts. Elsewhere, healthcare company Dassault Systems is building a library of realistic digital twins of human hearts which will allow consultants to better understand the condition of a particular patient at any given time.
Digital twins can be used to mirror and optimize business processes too. Software AG, for example, recently launched an enterprise digital twin framework that replicates all of an organization's processes, employees, and assets to provide an holistic visualization of its operations that will enable it to evaluate and amend its business processes.
According to a recent Deloitte report, "Digital twins can profoundly enhance an enterprise's ability to make proactive, data-driven decisions, increasing efficiency and avoiding potential issues. And they can make it possible to "experiment with the future" by exploring what-if scenarios safely and economically."
A strategy for digital twins
Digital twins is not a technology itself; rather it is the confluence of many different technologies, each designed to deliver beneficial business outcomes. It is a combination of assets from many different technology vendors, equipment providers, and systems integrators. Think of digital twins as bridges between the physical world and the digital and have largely been made possible by the introduction of IoT and predictive analytics.
Attaching IoT sensors to products and assets will provide organizations with access to the huge volumes of data which underpin a digital twin. Predictive analytics helps forward-looking organizations that can make sense of that data, turning information into real, actionable insight.
Smart components, integrated with a physical item, will use sensors to gather data on that item’s real-time status, working condition, or position. It can transmit this to a cloud-based system where it can be processed and analyzed against business and other contextual data. Lessons can be learned, and potential issues uncovered within the virtual environment, and solutions can then be applied to the physical world.
The combination of assets - sensors and analytics platforms - will bring together all the intelligence, insights and powers of visualization that businesses need to improve the ongoing efficiency of their operations. A strong partner ecosystem is required, therefore. With this in place, organizations across a range of industries will be able to capitalize on the insight and intelligence that a digital twin provides into their products, assets, and processes, ultimately enabling them to transform their business.