Digital twin

Why are digital twins important?

The use of digital twins is growing quickly, with the market expected to reach $73.5 billion by 2027. Around 70% of C-suite executives are investing in this technology because it delivers clear benefits to their operations.

Digital twins speed up how quickly organizations can prepare data and implement AI systems, while also reducing their operating costs. The integration of artificial intelligence in business operations has made digital twins increasingly powerful, enabling them to process complex data streams and make sophisticated predictions. When problems arise, the real-time nature of digital twins helps teams spot and fix issues before they become expensive.

Organizations use digital twins to test new ideas without disrupting their actual operations. A manufacturer can try new production methods virtually, or a hospital can practice new patient care approaches safely. It is therefore easier to innovate and improve while avoiding risks.

By combining safe testing and real-time insights, digital twins become valuable for any organization that wants to work smarter and adapt to change.

What are the goals of digital twins?

Digital twins support smarter decisions, make work more efficient, and lower risks. 

By creating a virtual version of systems or processes, companies can test and improve ideas before making changes in the real world.

Let’s break down the goals of digital twins in more detail.

Making smarter decisions

• Shares up-to-date information to help businesses make better choices.
• Spots problems before they happen so issues can be fixed early.
• Shows what might happen in the future by testing different plans.
• Uses real data through data mining techniques that extract valuable insights.

Boosting efficiency

• Finds waste or slow parts of a process so they can be improved.
• Lets businesses test changes virtually, saving time and resources.
• Speeds up work by fixing delays and making tasks easier.
• Ensures time, materials, and workers are used in the best way possible.

Reducing risks

• Predicts when repairs are needed to stop equipment from breaking.
• Tests changes in a safe, virtual space to avoid real-life problems.
• Helps prevent costly mistakes by simulating risks beforehand.
• Prepares for challenges by trying out different ideas in advance.

Who is involved in creating and managing digital twins?

Creating and managing digital twins involves different stakeholders both inside and outside the company. These people ensure digital twins are built, connected, and used to meet the business goals.

Let’s focus more on these stakeholders.

Internal stakeholders

• Data scientists/engineers: Design and analyze digital twins.
• IT department: Support the technology needed for digital twins.
• CIO/CTO: Drive the digital strategy and make sure digital twins meet business goals.
• Operations teams: Use digital twins to improve operations and performance.
• Product development teams: Enhance products with insights from digital twins.
• Security teams: Protect the data used in digital twins.

External stakeholders

• Digital twin providers: Supply the tools and platforms for digital twins.
• Consultants: Help build, implement, and improve digital twin systems.
• Third-party vendors: Offer extra software or hardware for digital twin data collection.
• Regulatory bodies: Ensure digital twins meet industry standards and rules.

What is required for digital twin success?

To successfully implement digital twins, it is essential to focus on three main areas that ensure the technology is used effectively across the organization:

Integrate data effectively

Ensure all relevant data from physical assets is gathered and connected to the digital twin. This requires robust digital infrastructure to support reliable data collection and processing. Use reliable systems that keep data up to date and synchronized, so the digital twin always reflects accurate, real-time information. This helps the organization make informed decisions based on the latest data.

Set clear goals

Clearly define what the digital twin is meant to achieve within the organization. Whether the goal is to improve efficiency, reduce costs, or speed up product development, make sure the objectives are aligned with the company’s overall strategy. Setting these goals helps keep the project focused and valuable to the business.

Promote collaboration across teams

Encourage close collaboration between different departments, such as IT, engineering, and operations. A digital twin needs input from various teams to ensure that it works well and adds value. By working together, these departments can maximize the potential of the digital twin and make sure it benefits the whole organization.

Why do digital twin projects fail?

Digital twin projects can face several challenges that lead to failure. Identifying these obstacles can help organizations address issues before they become major setbacks.

Poor data quality and integration

One of the main reasons digital twin projects fail is poor data quality. If the data collected from physical assets is inaccurate or inconsistent, it can lead to incorrect digital representations. Without reliable data integration, the digital twin cannot reflect real-time conditions, making it ineffective.

Lack of clear objectives and scope

Digital twin projects often fail when there is no clear goal or well-defined scope. If the organization does not know what it wants to achieve with the digital twin, the project can lack focus, leading to misaligned efforts and wasted resources.

Insufficient collaboration across teams

A lack of collaboration between departments such as IT, engineering, and operations can hinder the success of a digital twin project. When teams don’t work together or share necessary information, it becomes difficult to fully utilize the digital twin and ensure its effectiveness across the organization.

Digital twin use cases

Digital twins are becoming more valuable across various industries, and they can take on many different forms.

While the core idea remains the same—creating a virtual model of a real-world object, system, or process—the way they are applied can vary widely.

Below are three examples of how digital twins are used in business.

Manufacturing

Scenario: A car manufacturer wants to improve production efficiency.

Method: The company builds a digital twin of its assembly line, using real-time data to track equipment performance and spot issues.

Outcome: Using the digital twin helps identify delays, predict equipment breakdowns, and optimize production, cutting costs and speeding up delivery times.

Healthcare

Scenario: A hospital aims to improve patient care and reduce errors.

Method: The hospital creates digital twins of patient data, such as vital signs and medical histories, to test treatment options.

Outcome: Doctors can provide more personalized care, avoid complications, and improve patient outcomes.

Energy

Scenario: A power company wants to better maintain wind turbines.

Method: The company uses digital twins to track the performance of turbines, including wind speed and wear on parts. This enables predictive maintenance strategies that can significantly reduce downtime and operating costs.

Outcome: Predictive maintenance is made possible, reducing downtime and increasing the lifespan of turbines, leading to lower costs and higher energy production.