Thu, 31 May 2018
Industry 4.0 is changing the face of technology.
Without a doubt, its advances will continue to shape our relationship with the digital world for many decades to come. It is precisely developments in IoT, facilitated by Industry 4.0, that has allowed one of the most exciting concepts of the last 20 years to become a cost-effective reality. Now, advances in technology allow businesses, governments, and individuals to create extensive and accurate virtual models of products, processes, and services that are identical to a real-life counterpart. Users can operate these complex systems and hypothesize various scenarios and challenges, without the need for expensive production cost and time associated with building products, thus drastically reducing costs and improving production efficiency.
How and Why They Work
While the idea of this disruptive technology has been around since the start of the century, they owe their recent successes to the growth of IoT, Artificial Intelligence, Big Data and Machine Learning. Digital Twins are born through the synthesization of a number of sources, such as operational data, physical data, manufacturing data and insights from analytics. Once this is all integrated with AI algorithms, we have a virtual product that we can modify and analyze. It makes sense, then, to recognize the way the advancements of Industry 4.0 are directly influencing our ability to use such technology. In particular, as the industry grows, the scale of economies naturally reduces the cost of incorporating IoT technology. As IoT sensors decline in cost, so does the collection of operational data. Furthermore, one of the most attractive features of this concept is the rich insights it can provide. Cloud offerings and advancements in machine learning allow ever-higher amounts of data to be provided and analyzed from the models.
Applications of Digital Twins on Cities
We have heard a lot about the future being full of ‘Smart Cities’, saturated with connected systems that allow for the smooth running of the city’s infrastructure. Such implementation allows for a more efficient management of resources while increasing the quality of the inhabitants’ lives. By incorporating Digital Twinning into the development of Smart Cities, governments and city planners can determine which policies will have the most social benefit and plan accordingly.
While the operational aspect of Digital Twinning can allow for the monitoring and tracking of supply-chains, environmental data is also incorporated into the models, which allows one to predict potential problems and identify at which stage they may occur.
With the ability to incorporate large amounts of data into Digital Twin models, institutions can monitor performance based on the real-life situations presented. An example of this is in hospitals, where managers can identify the busiest and most congested times of day, and then simulate a number of potential methods for easing the stress on staff and equipment, ultimately increasing both patient and worker experience. A further application within healthcare is already a reality, with Dassault Systems building a database of simulations based on the human heart. By doing this, doctors can interpret the efficacy of certain treatments over others.
Retailers are always looking for the most in-depth model of potential customer they can find. The synthesization of many different sources of data allows a more complete model of customer than ever before. By harnessing this data, firms can predict how customers will behave. Companies like Amazon are already using this technology, by creating models of each individual’s consumption based on behavioral data. By implementing analytics, Amazon is able to learn from the consumption actions that are taken and produce new business outcomes. With the consolidation of this type of modeling and analytics into one platform, companies can then modify them to apply to a new service they may want to offer.
Transport and Auto Motives
Significantly for companies involved in these sectors, Digital Twins can offer huge efficiency and cost savings. By requirement, firms may need to overhaul an engine or piece of machinery every 24 months. However, by proving through a digital twin that such an object does not require service until 36 months, that machinery can be in use for 50% more time, without needing to undergo substantial change. This approach is already being embraced by the U.S. Air Force, and it isn’t hard to see why. The alternative, where maintenance is done based on averages derived from field experience is open to inefficiencies.
The Future of Digital Twins
Digital Twinning is not a concept that will go away any time soon. From the basic idea that a digital version of something is nearly always cheaper than producing a physical one, advancements in technology mean that companies no longer have to be wary of their simulations lacking external validity. Indeed, the International Data Corporation predicts that, by the end of 2018, companies who invest in digital twin technology will see a 30% improvement in cycle times of critical processes. Furthermore, Gartner predicts that by 2021, half of large industrial companies will use digital twins, gaining them a 10% improvement in overall effectiveness. The Digital Twin produces, at its core, a map from which you can plan and predict future performance of a product, process or service. Part of its beauty is its adaptability to range of objects, and as the technology and data collection around it develops, the potential for direct benefits to consumers, and society generally, will continue to grow.