The Digital Twin concept was first published in 2002 by Dr Michael Grieves (then of the University of Michigan). Still, it was anticipated years earlier in David Gelernter's 1991 book, Mirror Worlds.Long before the phrase, Digital Twin emerged, it was widely recognised that one of the first examples of a Digital Twin ever being used was by NASA during the Apollo 13 mission. The Apollo 13 mission to the moon didn't succeed due to a failure in an oxygen tank. That failure set off a chain of unforeseen and never even considered events, and over the space of 3 days, NASA engineers back on earth worked day and night to find a way to bring the astronauts back safely. NASA used sensor data and multiple simulators to evaluate the failure and extended a physical model of the vehicle to include digital components and real-time data in what is widely regarded as the first Digital Twin ever used. This first use of Digital Twin is also depicted in the movie Apollo 13. 
But what is Digital Twin? Is it something we should be excited about, or is it just another blip in the technology landscape? Real-world objects have existed in digital form for almost as long as we have had computers. Simulations are widely used across industries, government and academia daily; computer games have become hyper-realistic, incorporating real-world locations and objects. The emergence of AR, VR and the Metaverse is becoming a melting pot between the physical & digital world. Considering all that, what is a Digital Twin, and how can it be useful. 
What makes a Digital Twin a digital twin and not just a powerful model or simulation? A Digital Twin is personalised for a unique physical asset, process, or both. Digital Twin is the ability to take a virtual representation of the elements and the dynamics of real-world objects. It is much more than a simulation, more than a virtual replica of a real-world entity. It can connect the natural world and the virtual world by using real-time and near real-time data from sensors, probes and databases. 
The data can be either locally decentralised or stored centrally in the cloud. The inclusion of a vast amount of data relating to the real-world object sets a Digital Twin apart. Furthermore, the use of such data makes Digital Twin a living computational model instead of a static computational model, and a Digital Twin can be dynamically updated with data from its physical twin throughout its lifecycle. This ongoing relationship between the natural and virtual worlds sets Digital Twin apart from statistical and computational models or simulations. There are already multiple use cases for this relatively new technology, ranging from Manufacturing, Supply Chain to Healthcare & Retail, but let's take a closer look at how telcos can utilise Digital Twin. From a network perspective, telcos can use Digital Twin to help make accelerated decisions on where to invest their capital, resulting in an acceleration of the rate of investment and reducing the construction time of a network. 
Digital Twin can also be used in network planning and simulations; for example, many regulations govern electromagnetic field emissions and radiating power. With the deployment of Massive MIMO and mmWave technology, phased array antennas have become a standard feature in wireless networks. As a result, we have to manage a much more complex radiating environment with multiple directional beams, beamforming and beam sweeping making power optimisation much more challenging. We can test and adjust radiating power levels using a Network Digital Twin containing data about the landscape, buildings, towers, network equipment, antennas, foliage, people, and traffic. We can even visually see what effect adjustments to radiating power will have on the real-world network. Doing so enables us to find the best radiating power solution quickly and safely without adjusting and measuring in the physical network, which could negatively impact customers and even temporarily exceed regulations. 
People could even have a Digital Twin of their homes, including information about building schematics and where pipes and electrical conduits are running. Also, information about appliances when they were purchased, warranty and repair information, how many times an appliance has been used, and instruction manuals are all in one digital place, constantly being kept up to date from sensors and smart home devices. 
Today the Digital Twin Market is worth approximately USD 3-5 billion and is forecasted to grow by 10 times to almost USD 50 billion by 2026, making it one of the fastest-growing technologies. Digital Twins will become more advanced. The only limitation on how advanced a Digital Twin can become is the imagination of its creator and the amount of data and data sources being fed into the model. 
Even though Digital Twin Models are getting more accurate, sharing or reusing models across applications or industries is far trickier. Like any technology that spans different vendors, sectors and use cases, integration and standardisation will play a key role in its future success. Organisations like the International Standards Organization (ISO) are already working on developing various standards for digital twins, and we can already find ecosystems of tools starting to emerge. 
Digital Twin is not yet an off-the-shelf technology, and so far, we have only scratched the surface of this technology. However, one thing is clear the potential is endless, and Digital Twin technology will become an integral part of enabling us to achieve sustainability and enabling a circular economy.