Digital Twins: Testing and Optimizing Without Disruption in the Real World

With a digital twin – of a device, a supply chain or even a human – companies can confidently test things, adjust things and optimize processes. At the moment it’s still something for large companies, but wider adoption is fast approaching.

Imagine that a product, a company or even an entire city has an online, real time, digital copy of itself. The more data entered into the replica, the more the replica resembles its real “twin” or “sister”.

Digital twins provide insight

How this replica behaves when the components and processes are changed, the environment changes or the supply chain is disrupted gives companies detailed insight into the activities and effects of decision-making without affecting the real-world counterpart. This is commonly referred to as a ‘digital twin’: a technology phenomenon made more accessible by cloud providers, among others.

In its simplest form, building a digital twin can be a business real time provide insight into current processes or equipment. A step further, it allows them to run scenarios and test products or plant operations. It combines existing data systems in a 3D view to find ways to optimize efficiency, increase production and improve performance.

Smart use of digital twins can bring engineering functions closer to production and operational realities, uncovering efficiency and responsiveness in, for example, the line production of wind turbines or racing car parts. The business case with significant profit i time to market for new products, product quality and sales increase are strong convincers.

Complex challenges

But digital twins also provide global businesses, lawmakers and other critical stakeholders with the data they need to solve complex real-world challenges across industries as diverse as manufacturing, health, infrastructure, sports and aerospace.

To date, the first digital twin applications are highly tailored to their specific purpose and only make economic sense for high-value use cases, such as the operation of jet engines, power plants and industrial plants.

In the coming years, we expect wider availability of this technology, which will drive adoption in a wide range of everyday contextual visualization uses. It’s certainly a growing market, with MarketsandMarkets analysts suggesting it will be worth $48.2 billion by 2026.

Groundbreaking

The concept of twins is not new and dates back to the early days of the US space program. The Apollo 13 mission in the 1960s is an early use of twins to model the condition of the damaged spacecraft and solve the problems necessary to return the astronaut crew safely to Earth.

Customers today are interested in implementing digital twins for a wide range of applications, including complex equipment engineering, 3D environments, robotics, preventive maintenance, industrial plant operations, precision medicine, digital agriculture, manufacturing, urban planning, and – more recently – metaverse-related applications. Technology is pushing the boundaries of what is possible in a wide range of industries, including science, medicine, engineering work and pharmacy. At the height of their ambition, some projects aim to clone the human brain and even an entire human.

Today, many companies are early in their digital twin journey. IDC’s EMEA Manufacturing Insights Team research shows that these are the top three challenges manufacturers face in data management and analytics: lack of real time data collection, appropriate tools and platforms and expertise in data science, including artificial intelligence and machine learning to be.

The path to a sustainable future

Supply chain disruptions have been the order of the day for the past few months, bringing a new understanding of the gaps and vulnerabilities companies face when meeting customer demand. Smart companies use the simulation capabilities and operational insights of virtual supply chain twins to more proactively detect and adapt to disruptions.

Companies with valuable, large assets – such as buildings, ships, aircraft and heavy equipment – ​​use digital twins to understand the health of those assets, often involving machine learning used to predict maintenance and to proactively order spare parts.

Manufacturing and service companies are using digital twins to connect their employees and provide greater situational awareness around alarms, performance degradation and security issues. They achieve this by reducing the amount of ‘tribal’ (only available to a limited circle) knowledge needed to make production lines work efficiently.

For example, companies in the aerospace industry run digital replicas of their jet engines while their colleagues fly around the world. Researchers at Cranfield University in the United Kingdom are extending this awareness to what they call an “aware aircraft,” an aircraft that can order self-maintenance, saving time and cost. Digital twins can also find ways to reduce resource consumption and waste and improve warehouse operations.

Not just made

Building an effective digital twin is no easy task. It requires clarity business casebuilding awareness, building the right teams and knowing where the company stands with the skills required to build twins with even a basic level of sophistication.

Carrier, a leading provider of healthy, safe, sustainable and intelligent solutions for buildings and cold chains, also embraces the ‘digital twin’ technology. Carrier clones its carrier.io platform, the foundation of its digital services, to monitor operational data for smart buildings. It delivers critical asset modeling to its platform, allowing applications to easily create and integrate digital twins of real-world systems. “These applications allow our customers to use their data beyond advanced machine learning and data analytics to reduce service costs, optimize maintenance schedules and increase the reliability, efficiency and profitability of their Carrier equipment,” explains Dan Levine, senior director of IoT, cloud and software engineering at Carrier.

In fast times, planning can be difficult. But whether it is a product, a company or a country, the emergence of the digital twin removes unknown risks and provides a safe space for experimentation, rapid adaptation and preparation, reducing the impact of resource-intensive industries on people and the planet. .

About the Author: Michael MacKenzie is the General Manager of AWS Enterprise and Industrial IoT at Amazon Web Services.

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