Digital Twins
May 6, 2026
Digital Twins: From Factory Floors to the Surface of Mars
# Aerospace
# AI
# Automation & Digitalization
# Digital Manufacturing
# Digital Transformation
# Digital Twin
# Digital Twin & Simulation
# Ecosystem & Collaboration
# Factory of the Future
# Industrial AI
# Innovation
# Predictive Maintenance
# Smart Manufacturing
# Use Case
Operational Realities, Business Value, and the Expanding Frontier of Application
Last June, we took a close look at how digital twins were emerging as the connective layer in intelligent manufacturing drawing on reports from the World Economic Forum, TCS, and Deloitte to understand what some were calling the "Industry 4.5" moment. Over the last ten months, we have continued to see digital twins as operational backbones, with their scope broadening as time goes on.
Digital twins are active infrastructure in aerospace, healthcare, urban planning, and deep-space exploration, and the pace at which they're absorbing AI capabilities is reshaping what they can do across every one of those domains. In this edition, we examine:
- Scientific Reports' editorial review of how digital twins evolved from an aerospace concept into a cross-sector framework, and what technical challenges still stand between the current state and the full vision;
- Forbes Technology Council members on what leaders get wrong at the start of a digital twin investment, and what to do instead;
- IoT Tech News on how digital twins are changing day-to-day machine operations on factory floors, and where the practical value actually shows up; and
- Business Insider on how NASA is using AI and digital twins together to operate in environments so extreme they have no physical alternative.
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Scientific Reports on the State of Digital Twin Technology
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This Scientific Reports piece traces the arc of digital twin technology from its origins in 1960s aerospace to its current deployment across manufacturing, healthcare, urban planning, and planetary climate science, acting as a useful grounding document on the topic.
Key ideas:
- The concept dates to NASA's early spacecraft simulations, but the term itself was coined in 2002 by Michael Grieves in the context of product lifecycle management. By around 2010, NASA had formalized the definition: a physical entity, a virtual model, and the live data connections that let each one inform the other. What elevates a digital twin beyond a 3D model is that bidirectional link. Sensor data flows into the model, and simulation outputs flow back to guide decisions in the physical world.
- In manufacturing and aerospace, digital twins now serve as working models of machines, production lines, and full factories, letting engineers run "what if" simulations on the virtual version and stress-test changes before committing to them in the real environment. In automotive, real-time production data fed into virtual twins helps surface bottlenecks and quality issues without touching the actual line.
- Some technical challenges remain. Integrating heterogeneous data in real time â from physics-based sensors to human-generated inputs â remains difficult, and fragmented data standards make building a coherent twin hard across systems. Model validation is unsolved at scale: in high-stakes domains like healthcare and aerospace, users need to trust the twin's predictions, which requires rigorous uncertainty quantification and continuous calibration against real-world measurements. Governance, data privacy, and cybersecurity questions also remain open as twins expand into critical infrastructure.
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Forbes Technology Council on Making Digital Twins Deliver Business Value
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Forbes Technology Council convened perspectives from technology leaders across industries on a practical question: what do organizations need to get right from the start to turn digital twin investments into genuine business value?Â
Key ideas:
- The most consistent piece of advice across contributors is to start narrow. Pick a single use case with a fast payback (for example, predictive maintenance on one production line), nail the metrics, then scale. Leaders who pursue a broad vision before proving value in a specific context tend to produce pilot projects that never make the transition to operational infrastructure.
- Several contributors draw a sharp distinction between digital twins as observation tools and digital twins as decision engines. A twin built to display performance data is, as one contributor put it, an expensive 3D monitor. The return comes when the twin is designed from the outset to shape actions: triggering or recommending interventions through defined workflows, tied to a named KPI owner, with a clear baseline for measuring improvement.
- Leaders named data fidelity as the foundational requirement that everything else depends on. Sophisticated models fed by incomplete, delayed, or siloed data reflect a distorted version of real conditions, and decisions made on that basis can be worse than no model at all. Multiple contributors stress establishing a shared, continuously updated source of operational data before investing in model complexity.
- Pilot design matters as much as pilot selection. A pilot shaped only for unit-level success often fails to scale because the conditions that made it work don't generalize. The better approach is to design the pilot with scalability in mind from the start, including business and technical team alignment on goals, ownership, and what happens after initial success.
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IoT Tech News on Digital Twins in Industrial Machine Operations
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IoT Tech News examines how digital twins are being deployed in industrial machine operations today in the day-to-day work of managing equipment fleets. The piece focuses on how firms combine IoT sensor data with software platforms to build and maintain virtual machine models.
Key ideas:
- Most industrial firms began their connected-operations journey with asset tracking: sensors added to machines to monitor location and basic health metrics. Digital twins build on that same sensor infrastructure but use the data differently. Rather than showing only current status, they create a live model of a machine capable of projecting what might happen next.
- Predictive maintenance is where the value is clearest in practice. Operators monitor the digital model for early signs of wear and schedule repairs before production is disrupted. The same principle extends to energy efficiency: simulating different operating conditions on the twin can identify settings that reduce power consumption, a meaningful lever given that industry accounts for around 37% of global energy use, according to the International Energy Agency.
- Operational planning is another practical application. Factories can model what happens if a machine goes offline for repair or if production speeds change, allowing planners to make better decisions without testing those scenarios on live systems.
- Integration complexity remains the persistent challenge. Many factory floors run older equipment not designed for connectivity, and data formats vary enough across systems that making them communicate reliably requires substantial integration work per deployment. Edge computing helps by processing some data closer to the machine, reducing latency, but the underlying interoperability problem has to be solved site by site. Data accuracy is the dependency everything else rests on: a twin fed by incomplete or delayed data gives a false picture, and decisions made on that picture reflect the gap.
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Business Insider on NASA's Use of AI and Digital Twins in Space
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Business Insider spoke with Kevin Murphy, NASA's acting chief AI officer, and Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, about how NASA has come to depend on AI-paired digital twins to operate in environments where physical access is impossible and the cost of being wrong is total.
Key ideas:
- NASA pioneered the digital twin concept during the Apollo missions in the 1960s. What has changed is AI's role layered on top: Murphy describes the combination as transforming twins from virtual clones into systems that can make predictions, diagnose issues, and recommend actions in real time. AI can sort through incoming sensor data faster than humans, catching anomalies or risks that manual review might miss.
- The Perseverance Mars rover's first AI-planned drives, executed in December 2025, are the most visible demonstration of this pairing. Engineers fed terrain imagery and mission data to an AI model, which generated a route avoiding hazardous terrain. Before any command was transmitted to Mars, the team ran it through a digital twin of the rover and verified more than 500,000 variables. Both drives succeeded, covering a combined distance of nearly 1,500 feet. The communication lag between Earth and Mars means there is no margin for sending instructions that haven't been thoroughly pre-tested.
- The James Webb Space Telescope presented a different kind of verification problem. Too large to fit inside a thermal vacuum chamber for ground testing, the telescope was validated using two digital twins before launch: one a 3D video-based model used to track the 344-step unfurling of the sunshield, the other a thermal model of the telescope's core used to monitor temperature and prevent the kind of overheating that would have rendered it blind.
- Willcox offers a useful frame for thinking about where and why the aerospace industry moves more carefully with AI than other sectors: in aerospace and defense, being wrong can be life and death, which changes the calculus on speed. She sees the right model as AI and digital twins complementing human judgment â providing a continuous two-way flow of information between physical and virtual environments â rather than replacing the human in the loop. Murphy's summary of the underlying mission applies equally well outside of space: explore options, make decisions, spot problems, without putting people or hardware at risk.
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Looking Ahead
Digital twins are a technology at a genuinely interesting point in its development. The scientific foundations are solid and the application domains are multiplying. The value in industrial settings is demonstrable, even if integration complexity and data quality remain the unglamorous work that determines whether a deployment succeeds.Â
NASA's use of digital twins is arguably the most demanding test case the technology has faced. It shows a context where physical access is impossible, communication lags are measured in minutes, and the failure cost is a $2.7 billion rover stranded on another planet. It is meaningful that the twin-plus-AI pipeline worked on the first attempt.Â
The Forbes Technology Council perspectives add the note worth carrying into any investment conversation: the technology can clearly deliver, but the organizations that get the most out of it are the ones that started with a specific decision they wanted to improve, not a general ambition to digitize. That distinction between observation and intervention is where digital twins either earn their place in operations or become very expensive dashboards.
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That's a wrap for this edition of Industry Signals. Have a report, use case, or event you'd like to see featured in an upcoming issue? Send a note via PM. We're always looking to spotlight what's shaping the future of industry, and recommendations from the Xcelerator Community are especially valuable. Your insights and experiences continually shape Industry Signals.
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