Digital Twins and AI: A Powerful Combination for Smarter Asset Management

Did you know that nearly 75% of companies that have adopted digital twin technology combined with AI report lower maintenance costs and improved operational efficiency? This game-changing duo is revolutionizing EAM by creating virtual replicas of physical assets, allowing businesses to simulate performance, predict failures, and optimize maintenance strategies like never before. From enhancing CMMS to transforming platforms such as Infor and Oracle EAM, digital twins and AI are paving the way for smarter, more efficient asset management—and the results speak for themselves.

What are Digital Twins and How Do They Work?

A digital twin is a virtual model of a physical object, process, or system. Paired with AI, it continuously learns and updates itself using real-time data. This dynamic simulation helps organizations monitor equipment performance, predict failures, and optimize maintenance activities. By integrating digital twins with AI-driven EAM solutions, companies can enhance functional location tracking, streamline preventive maintenance, and make data-driven decisions. This not only improves operational efficiency but also reduces maintenance costs.

Impact in the Real World

NASA uses digital twins powered by AI to monitor spacecraft health, ensuring mission safety and reliability. By simulating real-time space conditions, NASA predicts potential failures and makes data-driven decisions for preventive maintenance. In particular, NASA’s digital twin that was incorporated in a recent James Webb Space Telescope mission was able to track 800 million data points daily. This proactive approach increased system reliability and reduced maintenance costs. Digital twins also improved mission planning accuracy, which ultimately reduced the risk of unexpected technical issues.

In the fast-moving consumer goods sector, Unilever adopted digital twin technology to optimize its production lines. This approach enabled Unilever to simulate manufacturing processes, predict bottlenecks, and enhance operational efficiency. As a result, Unilever increased overall equipment effectiveness and reduced energy consumption. The company also reported a reduction in maintenance costs by 50% at their plant in Brazil due to more accurate maintenance planning and their switch from traditional methods.

Microsoft leverages its own platform, Azure Digital Twins, to help businesses create comprehensive digital models of physical environments. By integrating AI and IoT data, Azure Digital Twins enables companies to simulate real-time performance, predict equipment failures, and optimize maintenance operations. For instance, Bentley Systems uses Azure Digital Twins to monitor infrastructure assets such as bridges and buildings, which allows them to predict maintenance needs and prevent costly failures. This approach has helped clients reduce maintenance costs and improve their overall operational efficiency. By using AI-driven insights, Azure Digital Twins empowers organizations to make data-driven decisions, which enhances asset reliability and performance as a result.

The Impact of Digital Twins and AI on Asset Management

According to Gartner, organizations using Digital Twin technology see a 10% improvement in critical asset performance and overall effectiveness. The integration of AI enhances predictive analytics, enabling companies to:

• Minimize unplanned downtime by using advanced machine learning algorithms that accurately predict equipment failures before they occur.
• Reduce unnecessary preventive maintenance tasks, minimizing resource wastage and maximizing productivity.
• Make proactive asset management decisions, leading to improved operational efficiency and reliability.

Digitalization and the migration to AI-driven digital twins are rapidly gaining traction. By migrating from traditional maintenance to advanced digital twin solutions, organizations are maximizing their return on investment.

Future Outlook: Digital Twins and AI in EAM

The future of EAM lies in Digital Twins and AI. Platforms such as IBM Maximo, IFS, and Oracle EAM are already adopting these technologies to deliver smarter asset management solutions. As organizations continue to migrate towards digitalization, the synergy between digital twins and AI will play a crucial role in driving operational efficiency and sustainability.

Conclusion

Digital twins combined with AI are reshaping EAM by providing predictive analytics, real-time monitoring, and intelligent decision-making. Organizations adopting this powerful combination are experiencing reduced maintenance costs, increased asset reliability, and improved operational efficiency. As digitalization progresses, migrating to AI-powered digital twins will be essential for staying ahead in a competitive landscape.