The industrialized world is under constant transformations, with various technological developments that have enhanced facilities & features in the supply chain to make the overall production & manufacturing experience better. More & more industries are incorporating advanced technologies in their manufacturing facilities & shop floor activities due to their increasing focus on maintaining the asset health of heavy industrial equipment and reduced operational efficiencies associated with the aging infrastructure.
Moreover, there is a significant amount of risk associated with the operational activities in industries, such as oil & gas, aviation, automotive, and healthcare. Consequently, there are monetary & human losses with the failure of that equipment when applied in real-time. Hence, across various industries, there is a surging need for a technology that can virtually analyze the behavior of assets before they get implemented in real-time.
The Global Digital Twin Market is anticipated to grow at a CAGR of around 58% during the forecast period, i.e., 2021-26, says MarkNtel Advisors.
Get a sample copy of the market research: https://www.marknteladvisors.com/query/request-sample/global-digital-twin-market.html
The digital twin technology, if fully implemented, industries can not only observe the behavior of an asset virtually with the real-time data through IoT but also predict the failure time and asset health through AI to reduce the process downtime. Through hardware & software platforms, this technology helps accumulate the complete value chain within a single operating virtual model, thereby addressing specific client-centric challenges, along with an improvement in the overall performance of the system.
The replica of an actual asset embedded with all features to virtually understand its operational characteristics is gradually driving the acceptance of the digital twin technology across various industries. Access to real-time information across remote locations can be achieved using IoT-enabled sensor data that brings extraordinary insights on the process performance into the digital twin model.
AI utilizes advanced algorithms & stacks of data accumulated from the source to provide systems & machines with the ability to perceive, think, calculate, and analyze information similar to a human brain. The concept of digital twins is applicable for different product/system lifecycle stages like ideation, realization, & utilization. Thus, it becomes the backbone of product development, thereby delivering greater product insights, enhancing efficiency, and surging market agility.
Top Benefits Associated With the Digital Twin Technology
· Enhanced Performance and Less Risks: The digital twin technology comprises all the necessary information required to predict the product’s performance and forecast the probability of product or system success. It can also predict system response to critical events before they become serious. Hence, the technology is well-capable of mitigating damage or degradation.
· Minimized Product & Operational Costs: The technology can help reduce design tolerance while taking care of manufacturing uncertainties, which, in turn, leads to cost reduction in the manufacturing process.
· Reduced Development Time- Digital twins provide an integrated view of the product’s physical & behavioral aspects. It provides insights regarding how systems will interact and their probability of failure in certain conditions. This eliminates the need for several tests, thereby minimizing the time needed to have a product ready for production.
Over the coming years, the deployment of digital twins is expected to bring many industries various benefits like asset optimization, enhanced operations, and added value to the business. Hence, these solutions are well-focused on helping companies across the value chain mitigate production backlogs by ensuring high yields from machines and, in turn, succeed in the market.
