INSIGHTS
The notion of digital twins has roots in NASA’s Apollo 13 mission, where a twin model allowed engineers to test solutions from the ground while the spacecraft’s crew fought for survival 200,000 miles above Earth. This digital model of a complex physical system set a foundation for digital twins to become the strategic technology and business imperative they are today.
Having evolved from a tool used by NASA to one applied by advanced manufacturers, digital twins are now applicable in every industry. With billions of connected devices promising a future of connected ecosystems, the question is how quickly digital twins can be integrated into an organization’s strategic roadmap to deliver greater innovation at a faster pace. One answer is to create a cognitive digital twin that incorporates AI with a digital twin, as we discussed in our webinar “Digital Twins: Solving Pain Points for Connectivity and Accelerating Profitability” (click here to watch the on-demand recording).
Our work with enterprises and public agencies around the world has shown a clear ROI from optimizing digital twins with AI. Use cases range from smart cities that use digital twins to manage infrastructure, to smart buildings that accommodate workforce fluctuations. They include healthcare operators that trust AI-imbued digital twins to optimize operations and improve the patient experience, and numerous companies in retail, logistics and telecommunications that leverage the power of this combined technology.
By integrating AI with digital twins, network operators can reduce costs, increase their competitive advantage, and accelerate their profitability.
Yet simply incorporating AI into a digital twin won’t generate positive outcomes on its own. In fact, there are five critical success factors to boost the outcome of deploying digital twins with AI:
Using the telecom sector as an example, network operators must make heavy CapEx investments to get their network going, and they need to navigate extensive customer-experience issues and regulatory requirements. By using cognitive digital twins and embracing the five success factors above, they can achieve better decision-making in each area while realizing benefits across the value chain.
Two of the most immediate impacts of combining AI and digital twins are better demand forecasting and creating accurate models of a network based on actual operations. These functions are essential in facilitating where to rollout fiber, for example, yet they’re just a starting point. Network operators today need more from their digital twins. Fortunately, a well-deployed and integrated digital twin can deliver benefits in several other ways.
One such benefit is enabling detailed long-term scenario planning, such as when doing integrated network modeling. Another is integrating operational decision-making. A third is improving the end-to-end customer experience in part by creating informed strategies about targeted 5G deployments.
Operators are continually seeking to monetize and maximize their network investments. By integrating AI with digital twins, they – like leaders in any industry – can reduce costs, increase their competitive advantage, and accelerate their profitability.
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