Intelligence Journeys
AI Use Cases for Utilities
Start New Project →
Private Broadband for Utilities
Start New Project →

Digital Twin

A digital twin is a live virtual model of a physical asset, network, or environment, continuously updated with real data so it can be monitored, simulated, and optimized. In telecom, operators use network digital twins to plan deployments, test changes, and run “what-if” scenarios before touching live infrastructure; in enterprise settings, twins model factories, ports, and facilities that private networks connect. Combined with AI, digital twins support predictive maintenance, automated optimization, and safer experimentation across complex systems. For operators and enterprises, the appeal is reducing risk and cost by testing in simulation rather than production. This channel tracks digital twin technology across networks and connected industries — planning, simulation, and operations — with analysis of where twins are delivering practical value and how they connect to private networks, edge, and AI-driven automation.

Nokia FTTH digital twin and AI tools boost reliability

Nokia FTTH digital twin and AI tools boost reliability
Nokia has introduced a fiber-to-the-home (FTTH) digital twin and AI-powered applications inside its Altiplano platform to give operators a unified view of active and passive assets and to improve reliability with faster, first-time fixes. The core launch centers on creating a digital twin of the FTTH network that stitches together live data from active elements (OLT/ONT, IP edge, customer premises equipment) with outside-plant passive infrastructure (ducts, cables, splitters) maintained in inventory and geospatial systems. Together, these tools target the highest-impact operational pain points: early anomaly detection, automated topology audits, faster root cause analysis, and improved first-time fix rates.

Fujitsu and NVIDIA expand full-stack AI infrastructure

Fujitsu and NVIDIA expand full-stack AI infrastructure
Fujitsu is expanding its strategic collaboration with NVIDIA to deliver a full-stack AI infrastructure that pairs domain-specific AI agents with high-performance compute for enterprise and industrial use. The companies will co-develop an AI agent platform and a next-generation computing stack that tightly couples Fujitsu’s FUJITSU-MONAKA CPU series with NVIDIA GPUs using NVIDIA NVLink-Fusion. On the software side, Fujitsu plans to integrate its Kozuchi platform and AI workload orchestrator (built with Fujitsu AI computing broker technology) with the NVIDIA Dynamo platform.

Hitachi Launches NVIDIA AI Factory for Physical AI

Hitachi Launches NVIDIA AI Factory for Physical AI
Hitachi has launched a global AI Factory built on NVIDIA’s reference architecture to speed the development and deployment of “physical AI” spanning mobility, energy, industrial, and technology domains. Hitachi is standardizing a centralized yet globally distributed AI infrastructure on NVIDIA’s full-stack platform, pairing Hitachi iQ systems with NVIDIA HGX B200 platforms powered by Blackwell GPUs, Hitachi iQ M Series with NVIDIA RTX 6000 Server Edition GPUs, and the NVIDIA Spectrum-X Ethernet AI networking platform. The environment is designed to run production AI with NVIDIA AI Enterprise and support simulation and physically accurate digital twins using NVIDIA Omniverse libraries.

Alibaba Cloud integrates Nvidia Physical AI tools

Alibaba Cloud integrates Nvidia Physical AI tools
Alibaba Cloud is integrating Nvidia’s Physical AI toolchain into its Cloud Platform for AI, bringing robotics-grade simulation, training, and deployment capabilities to customers. Alibaba and Nvidia unveiled a partnership that embeds Nvidia’s embodied AI development tools directly into Alibaba’s machine learning platform. The integration targets robotics, autonomous driving, and “connected spaces” such as warehouses and factories. Physical AI refers to software that models the real world in 3D, generates synthetic data, and trains control policies with reinforcement learning before deploying to physical systems. Developers on Alibaba Cloud gain access to toolchains for data processing, simulation-based training, and real-world reinforcement learning.

How Connectivity Will Create a More Seamless and Efficient Experience in Aviation: Smarter Journeys, Lower Costs

How Connectivity Will Create a More Seamless and Efficient Experience in Aviation: Smarter Journeys, Lower Costs
Connectivity is transforming aviation from the ground up. Airports are deploying private 5G, Wi-Fi 6, edge computing, and IoT to deliver two major outcomes: smoother passenger experiences and lower operating costs. Travelers enjoy real-time updates, biometric check-in, and AR wayfinding — while operators benefit from predictive maintenance, smarter gate usage, and energy optimization. This dual-value framework positions connectivity as more than infrastructure, it’s a strategic differentiator that enhances revenue, reduces OPEX, and elevates the brand.

Stakeholder Collaboration in Aviation: Why Connectivity Must Extend Across the Ecosystem

Stakeholder Collaboration in Aviation: Why Connectivity Must Extend Across the Ecosystem
Aviation is no longer a siloed industry - it’s a globally connected ecosystem where airports, airlines, regulators, telecom operators, and tech vendors must work in sync. As digital transformation accelerates, connectivity becomes a critical layer for collaboration, enabling real-time decision-making, safety, operational alignment, and a seamless passenger experience. From private 5G and edge computing to biometric boarding and IoT, the aviation industry must co-invest, co-develop, and co-govern digital infrastructure. Case studies from Heathrow, Changi, and DFW show that stakeholder alignment leads to measurable gains in efficiency, innovation, and trust. Connectivity is the enabler, but collaboration is what makes it scalable and sustainable.

Digital Transformation in Airport Ground Operations: IoT, Automation & Connectivity

Digital Transformation in Airport Ground Operations: IoT, Automation & Connectivity
Airport ground operations — from baggage handling and fueling to aircraft turnaround - are undergoing rapid digital transformation. Powered by IoT, automation, private 5G, and edge computing, airside workflows are becoming more predictive, efficient, and sustainable. Sensors track assets, optimize vehicle dispatch, and enhance worker safety. Autonomous tugs, computer vision, and AI-driven maintenance cut delays and reduce manual errors. Private networks and edge computing provide the real-time connectivity needed for mission-critical applications. Leading airports like Schiphol, Changi, and DFW are already adopting these technologies, proving that digital transformation on the ground isn't just possible, it's essential for next-gen airport performance.

The Connected Terminal: Rethinking Safety and Scalability with Smarter Infrastructure

The Connected Terminal: Rethinking Safety and Scalability with Smarter Infrastructure
Airport terminals are evolving into connected, intelligent environments powered by biometrics, IoT, and scalable infrastructure. These technologies are helping airports manage increasing passenger volumes, improve security, and deliver seamless experiences. From facial recognition at check-in to IoT-based baggage tracking and AR navigation, the connected terminal offers faster processing, predictive safety, and energy-efficient operations. Scalable, cloud-native systems future-proof infrastructure for demand surges and enable rapid integration of emerging tech like AI, digital twins, and virtual queuing. As global air travel rebounds, the connected terminal represents a blueprint for smarter, safer, and more sustainable airport growth.

The Digital Transformation of Airport Operations and Passenger Experiences

The Digital Transformation of Airport Operations and Passenger Experiences
Airports are no longer just transit points - they’re evolving into intelligent, connected environments powered by AI, private 5G, and digital twins. These technologies enable predictive maintenance, real-time baggage tracking, and biometric check-ins, while optimizing operational efficiency and sustainability. Private 5G ensures low-latency, high-reliability communication across airport systems, from autonomous luggage handling to AR-powered passenger navigation. Digital twins create real-time simulations of airport environments, helping operators plan, respond, and allocate resources more effectively. This digital transformation is redefining how passengers experience travel — with less stress, fewer delays, and more personalization, while equipping operators with tools to boost resilience, performance, and environmental responsibility.

Huawei F5G-A FTTO: 50G-PON for AI-Ready Campuses

Huawei F5G-A FTTO: 50G-PON for AI-Ready Campuses
Campus AI is moving from pilots to production, and the bottlenecks are increasingly in the wired and wireless underlay that must feed models, sensors, and edge compute reliably and efficiently. Huawei’s F5G-A FTTO (Fiber-to-the-Office) push aligns with this shift: fiber as the default access medium, symmetrical bandwidth for uplink-heavy AI flows, and deterministic performance for time-sensitive applications in healthcare, education, hospitality, and manufacturing. With 50 Gbps to rooms and 10 Gbps to Wi‑Fi APs, the design targets uplink-intensive workloads—think whole-slide imaging uploads, multi-stream 4K conferencing, and XR labs—while lowering latency and jitter compared with legacy copper tiers.

Siemens and TRUMPF unify IT/OT for AI-ready manufacturing

Siemens and TRUMPF unify IT/OT for AI-ready manufacturing
Siemens and TRUMPF are aligning digital platforms and machine-tool expertise to tackle the long-standing integration gap between enterprise IT and shop-floor OT—laying groundwork for AI-enabled, software-defined manufacturing. The partnership centers on open, interoperable interfaces that connect CNCs, robots, sensors, and enterprise systems without brittle, bespoke integrations. Digital twins of machines and lines—paired with standardized interfaces—let teams test control logic, validate process changes, and train AI models before they hit the floor. The companies are positioning their combined ecosystem as a credible path to “AI readiness” for motion-centric operations where latency, determinism, and safety are non-negotiable. An edge-first data fabric can normalize time-series, vision, and event data for low-latency decisions, while cloud services handle training and fleet-scale analytics.

Telefónica Level 4 Network Automation Achievements

Telefónica Level 4 Network Automation Achievements
Telefónica is translating years of network automation into tangible Level 4 autonomous operations in targeted domains—an inflection point for service quality, cost, and speed at 5G scale. Under its Autonomous Network Journey (ANJ), Telefónica is aligning to the TM Forum Autonomous Networks framework and pushing selected processes to Level 4—closed-loop autonomy with minimal human oversight. The company reports a 70% reduction in flapping-related service impact and removal of manual work in these incidents, advancing this use case to Level 4 maturity. The operator cites 80% faster analyses for planning, operations, and optimization; a 40% drop in capacity issues; more than 90% reduction in sites experiencing high load with widespread customer impact; and a 5% latency improvement via virtual optimization prior to rollout.

Frequently Asked Questions

What is a digital twin, in practical terms?
In practical terms, a digital twin is a software model that mirrors a real physical object, system, or process closely enough, and updates frequently enough using real sensor data, that decisions can be tested against the model with meaningful confidence they’ll reflect what would actually happen in reality. Unlike a static 3D model or design blueprint, a digital twin is meant to be continuously synchronized with its physical counterpart, reflecting current conditions rather than a fixed, one-time snapshot. This makes it possible to ask ‘what happens if’ questions, like what happens if we reroute network traffic this way, and get a reasonably reliable answer without actually making that change to the real, physical system first.
How are digital twins used in telecom networks specifically?
Telecom operators build digital twins of their own networks, modeling everything from radio coverage and capacity to the behavior of virtualized core network functions, to simulate the impact of potential changes before deploying them on a live, customer-facing network. This might mean testing how a planned software update will affect performance, modeling how traffic would be rerouted during maintenance, or simulating how adding capacity in one area might affect neighboring cells, all without risking an actual outage if the simulation reveals an unexpected problem. As networks become more complex with virtualization and multi-vendor components, digital twins are increasingly valuable for catching configuration mistakes before they reach the live network.
What industries outside telecom use digital twins heavily?
Manufacturing uses digital twins extensively to simulate entire factory floors, testing how changes to production line layout would affect output before making physical changes, and to predict equipment failures by modeling wear patterns from real sensor data. Smart city initiatives use digital twins to model traffic flow, energy grids, and infrastructure systems, helping planners simulate the effects of new development before implementation. Logistics companies use digital twins to model warehouse operations and transportation networks, optimizing routes and inventory placement. Aerospace and automotive industries, where the concept originated, use digital twins extensively for design and ongoing maintenance, modeling how individual aircraft or vehicles age based on their specific real-world usage.
Why do digital twins need low-latency, high-bandwidth connectivity to work well?
A digital twin is only as useful as how current its underlying data is; if sensor data takes too long to reach the model, simulations and predictions end up being based on stale information that no longer reflects actual real-world conditions, undermining the entire premise of using the twin to make confident decisions. This is particularly critical for digital twins modeling fast-changing systems, like network traffic patterns or industrial equipment operating in real time, where conditions can shift meaningfully within seconds. Low-latency, high-bandwidth connectivity, often provided through 5G, private networks, or edge computing, ensures the constant stream of sensor data feeding a digital twin arrives quickly enough for the model to remain genuinely accurate.
What’s the difference between a digital twin and a simple simulation or 3D model?
A simple 3D model or simulation is typically a static or one-time representation, useful for visualization or testing a specific scenario at a fixed point in time, but it doesn’t automatically update as real-world conditions change. A digital twin, by contrast, is specifically designed to be continuously synchronized with its physical counterpart through an ongoing stream of real sensor data, meaning the model reflects current, evolving conditions rather than a fixed snapshot from whenever it was created. This continuous synchronization is what allows a digital twin to support ongoing operational decisions and predictive maintenance, rather than just one-time design validation, which is generally the more limited role a static simulation plays.
How accurate do digital twins actually need to be to be useful?
The required level of accuracy depends heavily on what decisions the digital twin is meant to support; a twin used for high-level capacity planning might tolerate more approximation than one used to predict the exact moment a piece of critical equipment is likely to fail. In practice, most digital twins involve a deliberate trade-off between modeling fidelity and the cost and complexity of building and maintaining that level of detail, since a perfectly accurate model of every possible variable would often be prohibitively expensive to build and keep updated. Organizations typically aim for the twin to be accurate enough that decisions made based on its predictions reliably hold up in the real world for the specific use case at hand.
What role does AI play in digital twin technology?
AI plays an increasingly central role in digital twin technology, particularly in interpreting the large volumes of sensor data feeding the twin and identifying patterns that wouldn’t be obvious through simple rule-based monitoring. Machine learning models are commonly used to predict equipment failures based on subtle patterns in sensor data that precede a breakdown, to optimize complex systems by testing many possible scenarios within the twin far faster than a human planner could manually evaluate, and increasingly, to generate realistic predictions about how a system would behave under conditions that haven’t actually been observed yet. This combination is what allows modern digital twins to move beyond simple visualization toward genuinely predictive, decision-supporting tools.
What are the biggest challenges in building and maintaining a digital twin?
Building and maintaining an accurate digital twin presents several recurring challenges. Gathering sufficiently detailed, reliable real-world sensor data across a complex system, especially older infrastructure not originally designed with extensive sensor instrumentation, can require significant upfront investment in new sensors and connectivity. Keeping the model genuinely synchronized with reality over time requires reliable, low-latency data pipelines that can become a meaningful technical undertaking in their own right, particularly at scale. There’s also an ongoing maintenance burden, since a digital twin that gradually drifts out of sync with its real-world counterpart can become actively misleading rather than simply less useful.
Private Network Leadership Awrads 2026

Partner Hubs

Download content, access intelligence tools, and hear from executives.

Partner Events

  • M360 ASEAN
  • FutureNet Asia 2026
  • Network X Vienna 2026
Scroll to Top