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Digital twin technology is advancing quickly, especially in the manufacturing sector. What began as static digital replicas is evolving into intelligent, AI-enhanced systems that simulate, analyze and optimize production in real time.
Many manufacturers already use digital twins for tasks like predictive maintenance and product design, but a new wave of applications is emerging. Below, members of Forbes Technology Council share the next generation of use cases that could reshape how manufacturers design, build and manage operations.
1. Optimizing Production Workflows
Beyond predictive maintenance, digital twins can be set up to dynamically optimize production workflows based on real-time asset and environmental data. Instead of fixed schedules, systems adapt on the fly—rerouting tasks, adjusting robotics and improving energy use—creating more resilient, efficient and autonomous manufacturing. – Balaji Renukumar, Sensfix, Inc.
2. Simulating The Full Supply Ecosystem
Next-gen digital twins simulate the full supply ecosystem, not just factory lines. A smart factory that models supplier delays and reroutes logistics while simultaneously learning from and replicating supplier behavior can predict disruptions before they happen. The result? Better operational resilience for both the factory and its customers. – Savinay Berry, OpenText
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3. Enabling DTaaS Business Models
Digital twins enable a new business model: digital twin as a service (DTaaS), which turns costly one-off projects into a subscription-based cloud service. A DTaaS platform delivers ready-made 3D and behavioral models plus compute power; the manufacturer simply plugs in its data. Companies of any size can then model processes, predict risks and optimize production with zero capital outlay. – Illia Smoliienko, Waites
4. Tracking Real-Time Carbon Emissions
An emerging and disruptive use case for digital twins is real-time simulation of carbon emissions across production lines. By enabling instant visibility into environmental impact, manufacturers can dynamically optimize operations to meet ESG goals, elevating sustainability from compliance to strategic advantage. – Rishi Kumar, MatchingFit
5. Predicting Component-Level Failures
One emerging use case for digital twins that could disrupt traditional manufacturing is predictive maintenance at the component level. By creating real-time digital replicas of machinery parts, manufacturers can spot wear and failure risks before they happen, reducing unplanned downtime and extending asset life. This shifts maintenance from reactive to strategic, saving time and cost at scale. – Gopinath Kathiresan, Apple Inc.
6. Enabling Reverse Manufacturing Intelligence
With reverse manufacturing intelligence, digital twins that simulate end-user behavior patterns help predict product failure modes before products are even designed. Instead of building products and then testing durability, manufacturers can create virtual stress models based on how people actually use (and misuse) products in real life. This flips the entire design-build-test cycle, leading to enduring products from day one. – Prashanth Cecil, Amazon Inc.
7. Letting Consumers Co-Design Products
Digital twins enable consumers to co-design products in real-time virtual factories. Using augmented reality interfaces, customers tweak designs, test features and see instant production impacts. AI optimizes feasibility, significantly cutting costs. This shifts manufacturing to mass personalization, boosting loyalty (customers overwhelmingly prefer custom products) and slashing market research time and consumer-driven innovation hubs. – Durga Krishnamoorthy, Cognizant Technology Solutions
8. Building Self-Optimizing Pharmaceutical Systems
Agentic AI-powered digital twins redefine pharmaceutical manufacturing into self-optimizing, continuous systems. Swarm agents predict critical quality attributes, simulate what-ifs and auto-tune feeds in real time. This prevents deviations, significantly boosts yield, enables real-time release and slashes tech-transfer and compliance efforts. It also cuts costs, frees up operators and delivers a safer drug supply for patients. – Mike Walker, Microsoft
9. Powering Customization For Small Manufacturers
Digital twins can help by running simulations on complex products, opening up new opportunities for customization and personalization, especially as businesses across industries focus on these trends. This is useful for smaller manufacturing concerns, too, where creating unique products or tailoring the manufacturing process to the needs of a particular client is a value differentiator. – Kristjan Vilosius, Katana Cloud Inventory
10. Simulating Fabric Durability
An emerging use case for digital twins in knitwear manufacturing is real-time simulation of fabric durability and machine performance. By digitally replicating machines and processes, mills can detect defects early, optimize settings and reduce trial and error. This leads to lower material waste, higher quality output and significant ROI through cost savings and faster times to market. – Arslan Ihsan, ADDO AI
11. Creating Real-Time Operations Dashboards
To effectively monitor manufacturing lines and quickly respond to issues, personnel need to fuse data from numerous databases and other sources to create a complete picture of all components and their interactions. Beyond traditional use cases in product design, digital twins can enable continuous, real-time monitoring and intelligent alerting that immediately pinpoints problems needing attention. – William Bain, ScaleOut Software, Inc.
12. Embed AI Agents For Predictive Support
AI agents embedded in digital twins power predictive, real-time support by continuously analyzing live equipment data against expected performance. When anomalies or wear patterns emerge, agents trigger diagnostics, recommend fixes and even schedule preventive maintenance. This shifts support from reactive to proactive while allowing human agents to focus on strategic, high-value interactions. – Katherine Kostereva, Creatio
13. Optimizing And Monitoring Field Equipment
Emerging use cases, especially in the oil and gas industry, include product optimization in onshore operations and unconventionals and predictive failure detection in electrical submersible pumps. These use cases are enabled by oil and gas fields’ digital twins, which use AI/ML/hybrid intelligence models and real-time Internet of Things data streams from the field, coupled with petro-technical models for hydrocarbon production and petro-technical workflow automation. – Vinay Makkaji, Capgemini America Inc.
14. Uncovering Bottlenecks In Real Time
By creating digital replicas of entire manufacturing systems, companies can simulate, monitor and predict performance to identify bottlenecks or inefficiencies in real time. This enables proactive adjustments, reducing downtime and increasing productivity without physically altering the production process. – Paul Kovalenko, Langate Software
15. Streamlining Regulatory Compliance
Emerging digital twins are being used for regulatory compliance simulation, especially in sectors like pharmaceuticals and aerospace. By modeling processes digitally, manufacturers can prevalidate regulatory requirements in silico, dramatically cutting time to certification. Compliance shifts from a bottleneck to a blueprint. – Haider Ali, WebFoundr
16. Simulating Component Performance In Extreme Environments
Digital twins are transforming spacecraft component testing by simulating performance in extreme environments, reducing reliance on physical prototypes. This model reshapes terrestrial manufacturing, enabling real-time diagnostics, predictive maintenance and rapid iteration. By eliminating trial-and-error inefficiencies, digital twins cut downtime and accelerate innovation on Earth and in orbit. – Shelli Brunswick, SB Global LLC
17. Preparing For Rogue Or Unexpected Behaviors
Beyond the usual cases for digital twins, one that doesn’t get talked about much is the use of digital twins to simulate rogue or unexpected behaviors in a system. Rather than the twin doing the right thing all the time, you can make it behave erratically, respond incorrectly or slowly, or perform cyberattacks on other parts of your system. – Arthur Hicken, Parasoft
18. Reducing The Impact Of Human Error
Cognitive closed-loop manufacturing (CCLM) is an emerging use case for digital twins that’s set to disrupt traditional manufacturing. By collecting real-time sensor data, utilizing AI/ML feedback loops, simulating operational scenarios and implementing closed-loop control, organizations can bridge IT-OT silos and reduce the impact of human error and indecisiveness. – Robert Martin, Oil City Iron Works, Inc.
19. Detecting And Reducing Packaging Variations And Waste
A perfect example of the effective use of digital twins for manufacturing would be the detection of variations in the operation of a packaging machine within a food production plant. Thanks to the observations enabled by digital twins, the correct adjustment of sealing pressure can significantly reduce packaging waste and improve the final product for the customer. – David Barberá Costarrosa, Beeping Fulfilment
20. Ensuring Ethical Operations
Digital twins could disrupt manufacturing by adding a new layer: ethical compliance. Imagine simulating not just production efficiency, but also labor conditions, emissions and material traceability before a single unit is made. It shifts decisions from faster-cheaper to faster-fairer-smarter. In the age of conscious consumers, that changes everything. – Akhilesh Sharma, A3Logics Inc.
