AI's evolution has led to a widespread application known as digital twins across industries. These twins efficiently oversee and enhance operations of machinery, equipment, staff and sites.
By bridging the gap between the physical and digital realms, they collect data about a system's structure, operations and environment. This data serves as a foundation for building intelligence using analytics, physics and machine learning.
Consequently, querying a system's digital twin offers insights into its historical and current performance, enabling early alerts, predictions and heightened productivity.
In fact, a study shows that 82% of businesses in the UK, US, France and Germany faced unplanned downtime in recent years. Implementing digital twin capabilities addresses such productivity issues, transforming reactive systems into intelligent, predictive ones.
The Promises and Progress of Digital Twins
In simple terms, a digital twin acts as a virtual counterpart of a physical object, person or process. This virtual replica helps simulate real-world behavior, providing a deeper understanding of its function.
Digital twins stay linked to real-time data sources from the actual environment, allowing them to continuously update and mirror the original version. They also come with layers of behavioral insights and visualizations derived from data.
When integrated within a single system, digital twins create an enterprise metaverse: an immersive digital space replicating and interconnecting every facet of an organization. This setup optimizes simulations, scenario planning and decision-making.
Now, let's look at the four most common types of digital twins:
Product digital twins: These digital twins represent physical products and assets. They play a role across the entire product lifecycle, from design and development to manufacturing, operation and maintenance.
Process digital twins: These digital twins simulate complex workflows like manufacturing processes. They identify bottlenecks, improve production efficiency and enhance overall workflow. These twins are also valuable for managing supply chain operations.
System digital twins: These digital twins replicate entire systems or environments, such as factories and industrial complexes. They allow comprehensive monitoring, analysis and optimization of interconnected components and processes.
Asset digital twins: Asset digital twins focus on individual physical assets like machinery, equipment or infrastructure. They enable remote monitoring, predictive maintenance and performance optimization.
Furthermore, the level of understanding about an asset and its surroundings can help classify digital twin maturity models:
Partial: A partial digital twin is useful for monitoring low-power or resource-constrained objects. It includes a limited number of data sources like temperature, pressure and device condition.
Cloned: The cloned model incorporates all relevant and verified data sources of an object. This data volume is crucial during IoT development's prototype and data characterization phases, especially when there are no power or data constraints.
Augmented: The enhanced digital twin supplements data from the networked asset with derivative data, correlated data from various sources and intelligent insights from analytics and algorithms.
A Glimpse on the Power of Digital Twin in Telecom
According to deploy services expert Mirjana Scheele, digital twin technology holds numerous opportunities yet to be fully tapped, particularly in the realm of RAN deployment.
Before delving into specifics, let's examine some key benefits that network operators can leverage with digital twins:
Predictive Maintenance: Advanced digital twins simulate real-time behavior of physical network assets. By analyzing data from these simulations, network operators can predict equipment failures and proactively address issues, reducing downtime and maintenance costs.
Network Optimization: Digital twins optimize network performance by identifying improvement areas and addressing bottlenecks. By analyzing data from various sources, operators enhance network traffic and overall efficiency.
Rapid Troubleshooting: High-fidelity digital twins aid swift identification and resolution of network problems. Real-time simulation and analysis enable operators to pinpoint issues promptly and apply appropriate solutions.
Virtual Testing: Digital twins facilitate testing of new network configurations, upgrades and deployments. This minimizes real-world risks by allowing operators to experiment in a virtual environment
For CSPs, digital twins hold significant potential across four main areas: operations, processes, customer experience and partner ecosystems. The concept of "connected twins" will allow CSPs to engage with vertical market prospects, aligning 5G services' digital twins with industry-specific models to demonstrate benefits.
As 2023 progresses, the rollout of 5G offers opportunities to explore digital twin technology in managing high-stakes projects and aiding transformation programs. The intricate nature of 5G networks makes digital twin technology increasingly valuable for supporting deployments.
Within the telecom landscape, digital twins typically encompass a service provider's network along with its environment, including terrain, buildings, subscribers and devices. Digital twins replicate live network environments for simulating interactions and scenarios, offering insights into 5G features and capabilities.
Digital twins prove invaluable for mobile network providers' operations. To explain, they facilitate optimal site design and precise material calculations. This digital site twin enables automatic bill of material (BOM) creation, reducing errors and material management efforts. Moreover, it curbs unnecessary site visits by 25%, benefiting both efficiency and the environment.
All essential site information is digitized, accessible at any time. Digital twins aid in hazard assessment, ensuring alignment with field crews before site work. Algorithms within digital twins calculate antenna height and size, influencing natural planning optimization.
In essence, digital twins emerge as a game-changing asset for mobile network operations, streamlining processes, enhancing planning and fostering environmental responsibility.
What’s More to Expect
The landscape of enhanced digital twin capabilities has been transformed by a convergence of groundbreaking innovations. Cloud-based data capture, data processing, advanced data analytics, simulation techniques and AI-driven decision-making, coupled with the evolution of intricate network virtualization and intricate machine-readable network element configurations, have collectively propelled digital twins into a new era of possibilities.
In response to these advancements, digital twins are rapidly gaining traction across a diverse range of industries. The projection by the Appledore Research Group is indicative of this surge, as they foresee the digital twin market surging to a remarkable US$10 billion by 2025.
The horizon of potential use cases for digital twins has expanded significantly. The only limiting factors now are the inventive capacity of solution developers, the availability of relevant raw data and the integration of suitable analytics required to construct intricate models that replicate real-world systems.
