4 min read
Digital twins have moved from an exciting concept to reality. More companies than ever are connecting assets and production networks with sensors and using analytics to optimize operations across machinery, plants, and industrial networks. As exact virtual representations of the physical environment, digital twins incorporate historical and real-time data to enable sophisticated spatial analysis of key relationships. Teams can use digital twins to model the impact of process changes before putting them into production, reducing time, cost, and risk.
For the second year in a row, Gartner has identified digital twins as one of the top 10 strategic technology trends. According to Gartner, while 13 percent of organizations that are implementing IoT have already adopted digital twins, 62 percent are in the process or plan to do so. Gartner predicts a tipping point in 2022 when two out of three companies will have deployed at least one digital twin to optimize some facet of their business processes.
This is why we’re excited by the great work of ANSYS, a Microsoft partner working to extend the value of the Microsoft Azure Digital Twins platform for our joint customers. The ANSYS Twin Builder combines the power of physics-based simulations and analytics-driven digital twins to provide real-time data transfer, reusable components, ultrafast modeling, and other tools that enable teams to perform myriad “what-if” analyses, and build, validate, and deploy complex systems more easily.
“Collaborating with ANSYS to create an advanced IoT digital twins framework provides our customers with an unprecedented understanding of their deployed assets’ performance by leveraging physics and simulation-based analytics.” — Sam George, corporate vice president of Azure IoT, Microsoft
Digital twins model key relationships, simplifying design
Digital twins will be first and most widely adopted in manufacturing, as industrial companies invest millions to build, maintain, and track the performance of remotely deployed IoT-enabled assets, machinery, and vehicles. Operators depend on near-continuous asset uptime to achieve production goals, meaning supply-chain bottlenecks, machine failures, or other unexpected downtime can hamper production output and reduce revenue recognition for the company and its customers. The use of digital twins, analytics, business rules, and automation helps companies avoid many of these issues by guiding decision-making and enabling instant informed action.
Digital twins can also simulate a multidimensional view of asset performance that can be endlessly manipulated and perfected prior to producing new systems or devices, ending not just the guesswork of manually predicting new processes, but also the cost of developing multiple prototypes. Digital twins, analytics-based tools, and automation also equip companies to avoid unnecessary costs by prioritizing issues for investment and resolution.
Digital twins can optimize production across networks
Longer-term, companies can more easily operate global supply chains, production networks, and digital ecosystems through the use of IoT, digital twins, and other tools. Enterprise teams and their partners will be able to pivot from sensing and reacting to changes to predicting them and responding immediately based on predetermined business rules. Utilities will be better prepared to predict and prevent accidents, companies poised to address infrastructure issues before customers complain, and stores more strategically set up to maintain adequate inventories.
Simulations increase digital twins’ effectiveness
ANSYS’ engineering simulation software enables customers to model the design of nearly every physical product or process. The simulations are then compiled into runtime modules that can execute in a docker container and integrate automatically into IoT processing systems, reducing the heavy lift of IoT customization.
With the combined Microsoft Azure Digital Twins-ANSYS physics-based simulation capabilities, customers can now:
- Simulate baseline and failure data resulting in accurate, physics-based digital twins models.
- Use physics-based predictive models to increase accuracy and improve ROI from predictive maintenance programs.
- Leverage “what-if analyses” to simulate different solutions before selecting the best one.
- Use virtual sensors to estimate critical quantities through simulation.
In addition, companies can use physics-based simulations within the Microsoft-ANSYS platform to pursue high-value use cases such as these:
- Optimize asset performance: Teams can use digital twins to model asset performance to evaluate current performance versus targets, identifying, resolving, and prioritizing issues for resolution based on the value they create.
- Manage systems across their lifecycle: Teams can take a systems approach to managing complex and costly assets, driving throughput and retiring systems at the ideal time to avoid over-investing in market-lagging capabilities.
- Perform predictive maintenance: Teams can use analytics to determine and schedule maintenance, reduce unplanned downtime and costly break-fix repairs, and perform repairs in order of importance, which frees team members from unnecessary work.
- Orchestrate systems: Companies will eventually create systems of intelligence by linking their equipment, systems, and networks to orchestrate production across plants, campuses, and regions, attaining new levels of visibility and efficiency.
- Fuel product innovation: With rapid virtual prototyping, teams will be able to explore myriad product versions, reducing the time and cost required to innovate products, decreasing product failures, and enabling the development of customized products.
- Enhance employee training: Companies can use digital twins to conduct training with employees, improving their effectiveness on the job while reducing production design errors due to human error.
- Eliminate physical constraints: Digital twins eliminate the physical barriers to experimentation, meaning users can simulate tests and conditions for remote assets, such as equipment in other plants, regions, or space.
Opening up new opportunities for partners
According to Gartner, more than 20 billion connected devices are projected by 2020 and adoption of IoT and digital twins is only going to accelerate—in fact, MarketsandMarkets™ estimates that the digital twins market will reach a value of $3.8 billion in 2019 and grow to $35.8 billion by 2025. Our recent IoT Signals research found that 85 percent of decision-makers have already adopted IoT, 74 percent have projects in the “use” phase, and businesses expect to achieve 30 percent ROI on their IoT projects going forward. The top use case participants want to pursue is operations optimization (56 percent), to reap more value from the assets and processes they already possess. That’s why digital twins is so important right now because it provides a framework to accomplish this goal with greater accuracy than was possible before.
“As industrial companies require comprehensive field data and actionable insights to further optimize deployed asset performance, ecosystem partners must collaborate to form business solutions. ANSYS Twins Builder’s complementary simulation data stream augments Azure IoT Services and greatly enhances its customers’ understanding of asset performance.”—Eric Bantegnie, vice president and general manager at ANSYS
Thanks to Microsoft partners like ANSYS, companies are better equipped to unlock productivity and efficiency gains by removing critical constraints, including physical barriers, from process modeling. With tools like digital twins, companies will be limited only by their own creativity, creating a more intelligent and connected world where all have more opportunities to flourish.