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Harnessing parallelism to power the next leap in Industry 5.0 with digital twins
In the early stages of digital transformation in manufacturing, the focus was on automation—streamlining workflows, replacing manual tasks, and improving efficiency. Today, in the era of Industry 5.0, the emphasis has shifted to human-machine collaboration, resilience, and sustainability.
At the center of this shift is a powerful capability: the digital twin.
By creating real-time, virtual replicas of industrial assets, processes, and environments, digital twins allow organizations to simulate outcomes, predict disruptions, and optimize performance without affecting live operations. This ability to experiment virtually alongside real-world operations is helping enterprises make faster, more confident decisions in complex environments.
In the UST Thinking Ahead Report 2025, Fram Akiki, on the future of Industry 5.0, highlights the strategic value of digital twins in enabling this kind of parallelism at scale: “Digital twins create a parallelism where we can make decisions in the virtual world before we implement them in the real one.”
Enterprises are taking notice. 72% of organizations plan to expand digital twin applications to new areas, including robotics, where adoption is driving increased automation (72%) and optimized performance (62%). This reinforces their value as a strategic foundation for human-centric, resilient, and sustainable operations.
This blog explores how digital twins help organizations accelerate innovation and build smarter, more sustainable systems for Industry 5.0.
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What are digital twins, and why is parallelism powerful?
Digital twins are dynamic, data-driven manufacturing models that mirror physical assets, systems, or processes in real time. Unlike traditional simulation technology, which offers static snapshots, digital twins continuously update using live data from Industrial IoT (IIoT) sensors, machines, and other sources across the enterprise. This creates a continuous feedback loop between the physical and digital worlds, enabling teams to observe, diagnose, and refine performance. As closed-loop digital systems, they continuously improve processes based on real-time input.
What sets digital twins apart is their ability to support parallelism in industrial systems. Rather than testing a single outcome, teams can simulate multiple scenarios side by side—adjusting variables and predicting outcomes—before making changes on the ground. Whether modifying a production schedule, testing a new supply chain route, or modeling energy use, this approach delivers greater agility with less risk.
This continuous visibility is essential for navigating complexity while aligning with human and environmental goals.
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Digital twins in Industry 5.0: A strategic foundation
As organizations move beyond automation toward more adaptive and responsive systems, digital twins emerge as a core enabler of Industry 5.0. Their real-time visibility and predictive capabilities support the priorities shaping this new era: empowering people, strengthening resilience, and meeting sustainability goals.
- Human-centricity
Digital twins give workers greater visibility into complex operations, helping them make informed decisions and intervene more effectively. Simulating processes and presenting them in intuitive formats—often through AR or VR—they support training, upskilling, and safer working conditions.
- Resilience
From equipment failures to supply chain disruptions, digital twins allow organizations to simulate scenarios, identify vulnerabilities, and develop contingency plans. This predictive insight leads to faster, more agile responses and helps build more resilient industrial infrastructure. In fact, 68% of organizations use digital twins for maintenance and operations, improving asset reliability through predictive analytics.
- Sustainability
Companies can model emissions, optimize energy consumption, and test environmentally conscious decisions in a virtual space before making real-world changes. Digital twins help companies align operations with long-term environmental, social, and governance (ESG) goals.
75% of organizations that use digital twins do so to enhance efficiency through real-time simulations and predictive analysis—a key capability for driving smarter, more sustainable operations.
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Parallel experimentation in action: Use cases for digital twins in smart factories
The strategic potential of real-time digital twin applications becomes even clearer in practice. By creating a virtual testing ground, organizations can simulate multiple scenarios in parallel, fine-tune decisions before deployment, and respond to change with speed and precision. Across industries, this approach is helping companies reduce risk, increase uptime, and accelerate innovation:
- Predictive maintenance and asset optimization
Digital twins support smart asset monitoring by using real-time performance data to detect early signs of wear and predict failures. This enables condition-based maintenance rather than fixed schedules. 60% of organizations use digital twins for predictive maintenance, proactively addressing maintenance needs to reduce downtime.
- Smart manufacturing and operational agility
Manufacturers use digital twins for virtual prototyping and testing layout designs, production workflows, and process improvements, enabling rapid iteration without disrupting operations. Combined with AI, these capabilities drive manufacturing innovation by uncovering patterns, optimizing decisions, and adapting processes in real time. 59% of organizations use digital twins for sustainability and environmental management, leveraging data-driven insights to reduce environmental impact.
- Supply chain scenario planning
From shipping delays to demand fluctuations, supply chains face ongoing volatility. Digital twins help simulate disruptions and evaluate responses across suppliers, regions, and routes, enabling faster, more resilient decisions in the face of real-world disruptions.
- Energy and emissions modeling
Companies use digital twins to model energy consumption and emissions, enabling more intelligent decisions about resource use, infrastructure upgrades, and decarbonization strategies.
- Fleet management and operational visibility
From increasing vehicle complexity to evolving compliance standards, fleet operations demand greater transparency and agility. For example, one global automotive manufacturer used UST’s connected digital twin solution to monitor vehicle systems—including ECUs and TCUs—across multiple models in real time. The platform enabled predictive fleet management, streamlined firmware updates, and improved safety compliance without manual intervention. By surfacing actionable insights at scale, the solution enhanced both operational resilience and decision-making speed.
These applications illustrate how digital twins turn complexity into clarity, helping organizations adapt faster and operate more efficiently.
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Empowering people: The human-machine interface
While digital twins are often associated with intelligent automation and system optimization, their most transformative role may lie in how they support human decision-making. Digital twins support human-centric automation, where intelligent systems augment workers rather than replace them. They provide a more intuitive way to interact with complex data, processes, and environments—bridging the gap between operational systems and human insight.
Workers no longer need to sift through siloed dashboards or static reports. Digital twins provide real-time visualizations that make system behavior easier to understand and act on, helping frontline teams and planners move faster and more accurately.
Digital twins are also reshaping how employees train, learn, and engage with systems through:
- Augmented and virtual reality (AR/VR) simulations for guided operations and safe procedural training
- Immersive onboarding environments that accelerate learning without disrupting production
- Interactive visualizations that simplify data from sensors, machines, and processes
As Fram Akiki explains, “Digital twins become the intuitive interface between humans and machines.”
By enhancing understanding, increasing confidence, and simplifying interactions, digital twins are helping organizations bring out the best in their people, making advanced systems more accessible and actionable across every enterprise level.
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Overcoming barriers to scale
Despite growing adoption, scaling digital twins remains a challenge for many organizations. While pilots often succeed in isolated use cases, expanding these capabilities system-wide introduces new levels of complexity.
Common barriers include data fragmentation, legacy system integration, and the need for cross-functional collaboration. Only one-third of organizations report advanced or mature digital twin implementations. At the same time, 28% have yet to adopt the technology at all—underscoring the need for stronger data foundations and integration strategies.
- Real-time data integration across OT and IT systems
Scalable deployments increasingly rely on edge computing and digital twins working together to enable low-latency data processing close to the source, making it even more critical to connect operational equipment, sensors, and back-end platforms through unified data models, reliable pipelines, and standard interfaces—especially when legacy systems are involved.
- Interoperability between platforms and vendors
Digital twins often rely on a mix of simulation tools, analytics engines, and business applications. Without a common architecture, ensuring seamless coordination across technologies and partners can be difficult.
- Skills and governance gaps
Sustaining digital twin programs requires cross-functional expertise in systems engineering, data science, and domain operations. Without clear ownership and governance, initiatives can lose momentum or drift from strategic goals.
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Conclusion: Building the intelligent enterprise
Digital twins are more than a digital overlay. They are becoming the foundation for innovative, adaptive enterprises. By enabling parallel experimentation, they help organizations shift from static planning and reactive operations to a more proactive, precise, and resilient approach.
Whether optimizing manufacturing, streamlining supply chains, or advancing sustainability goals, digital twins provide real-time visibility and control across complex systems. Their ability to integrate data, simulate outcomes, and support informed decisions makes them a key enabler of Industry 5.0, where technology enhances human insight rather than replaces it.
AI-powered digital twins are already delivering measurable results. They support agility while advancing long-term goals in sustainability, safety, and workforce development.
To stay competitive, organizations should begin by identifying high-impact use cases, developing cross-functional capabilities, and building the data foundations required to scale digital twin initiatives with confidence.
Learn how UST helps enterprises design, scale, and operationalize digital twins for measurable impact. Connect with our digital twin experts to get started.
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