How do you operate, maintain, or repair systems when you aren’t in their physical proximity? That was the challenge for NASA when it had to monitor its rockets and vehicles on space missions. So it created mirrored systems or digital twins, still on earth that allowed engineers and astronauts to determine how they could control space mission vehicles. The concept of digital twin helps to control Chandrayaan-2, India’s lunar mission. A digital twin can create huge value in logistics and provide greater insight into and visibility of the current and future state of logistics.
It was in the late 1990s that Michael Grieves began thinking about the idea of the digital twin. Grieves was then pursuing his executive management doctorate program at Case Western Reserve University in Cleveland, Ohio. However, the term he used then for the concept was “Doubleganger”. In 2002 Grieves introduced the concept as part of his product lifecycle management (PLM) research at the University of Michigan. The slide that contained the revolutionary idea showed a simple graphic with the sober title “Conceptual Ideal for PLM”.
While the term changed over and over again in the years to follow, this graphic already contained everything that still makes up the digital twin: a virtual image that contains all the information of a physical product and reflects it throughout the entire product lifecycle – an idea so visionary that it could not be achieved for many years.
While Grieves was toying with the idea years ago and was not in a position to implement a comprehensive digital twin, he was firmly convinced that the computer would be powerful enough someday to bring his ideas to life. Today, machine intelligence and connectivity to the cloud allows us an unprecedented potential for large-scale implementation of digital twin technology for companies in a variety of industries.
Digital twins are today coming of age. Fuelled by the confluence of progress in the internet of things, big data, cloud computing, open APIs, artificial intelligence, and virtual reality, once static digital models and simulations can now truly come alive in real-time to help predict future situations, the state of physical things, and even the world around us.
Today the evolution of sensors and network technologies enables us to link previously offline physical assets to digital models. In this way, changes experienced by the physical object are reflected in the digital model, and insights derived from the model allow decisions to be made about the physical object, which can also be controlled with unprecedented precision.
Digital twins can ultimately represent any physical thing, from nanomaterials all the way to entire cities. Even human beings and their behaviors have been modeled by digital twins in some cases. Organizations in multiple sectors are developing, testing, and utilizing digital twins within their operations. The following examples show how digital twins have the potential to solve a broad range of business challenges and to unlock many different sources of value.
The central goal of using digital twins today is to accurately predict and prevent problems before they occur and to plan for the future efficiently.
Digital twins enable logistics providers and other supply chain enterprises to offer the increased transparency partners and consumers demand. And in doing so, companies earn one of the most valuable, hard-to-come-by commodities in business: trust.
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