Use of Digital Twins to Compete and Scale Manufacturing

By Samudrapom DamReviewed by Lauren HardakerJul 2 2025

Digital twin (DT) is one of the most transformative technologies driving industrial digitalization. A DT is a real-time, data-driven virtual model of a physical object, system, or process continuously updated with information from its physical counterpart. Unlike traditional simulations/computer-aided design models, DTs enable automatic bidirectional real-time data exchange between the digital and physical twins, allowing seamless monitoring and control.

The rise of the Internet of Things (IoT), artificial intelligence (AI), and sensor technologies has fueled the growth of DTs, making them central to Industry 4.0 initiatives. Their application leads to more intelligent decision-making, reduced operational costs, and improved efficiency.^1,2

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Importance of Digital Twins

The concept of a DT has evolved. Initially defined with three core components, physical entity, virtual model, and their connection, it was expanded to include data, services, and validation processes. Experts highlight the integration of cyber and physical spaces as central to DTs. As enabling technologies like machine learning and big data advance, the requirements for DTs continue to evolve.³

DTs have diverse applications, such as simulation, real-time monitoring, testing, analytics, and prototyping, which function as sub-systems within a DT. They offer a cost-effective and practical solution in cases where physical prototyping is expensive or lab replication is unfeasible. Multiple DTs can be created for a single entity to test various conditions. A generalized DT architecture uses six key dimensions: industrial sector, purpose, physical reference object, completeness, creation time, and the nature of the digital-physical connection.³

Despite these benefits, DT implementations face challenges related to data governance, model validation, and interoperability across systems, which researchers highlight as open issues in current deployments.

Key Technologies Driving DTs

Several technologies are driving these virtual models.^2,3

Intelligent Perception and Smart Sensing

These systems convert environmental data into actionable knowledge for decision-making. Unlike traditional energy-based devices, these systems use multi-source inputs, information fusion, and adaptive learning to enhance accuracy and context awareness. Key approaches include machine vision, multivariate analysis, and heterogeneous data fusion, offering improved monitoring and understanding of complex environments.²

Machine Vision

Machine vision approaches are increasingly used for object detection, tracking, virtual measurement, and online inspection in manufacturing, robotics, and autonomous navigation. Deep neural networks like GoogLeNet, ResNet, and restricted Boltzmann machines offer strong performance. Additionally, gesture and eye-tracking systems enable more intuitive human-in-the-loop designs, replacing traditional control interfaces with flexible, vision-based alternatives.²

Data-driven Modeling

Fueling simulation engines like ANSYS becomes challenging in complex scenarios where physics-based models are hard to construct. However, vast amounts of operational data, logs, sensor readings, images, and videos offer an alternative. Data-driven and hybrid approaches are crucial for building effective DTs. Shifting from hypothesis-driven methods using limited data to machine learning with big data enables accurate modeling, even in nonlinear and coupled systems. This transition enhances the applicability and performance of DTs across various domains, leveraging the strengths of machine learning in complex environments.²

Machine Learning

Unlike conventional knowledge-based methods that rely on static machine learning models, DTs are dynamic, continuously updated systems that leverage real-time machine learning. This capability enables DTs to simulate and predict asset behavior in unforeseen conditions using live data, allowing for proactive testing and anomaly detection. Machine learning enhances DT functionality by enabling self-improvement based on predictive outcomes and helping identify system flaws. Various algorithms like random forest, adaptive boosting, light gradient boosting machine, and neural networks have been applied to improve DTs in industries like petrochemicals. Additionally, reinforcement learning has been explored to make DTs resilient by allowing them to autonomously detect and resolve data or model errors.³

Extended Reality (XR)

These technologies, like augmented reality (AR), virtual reality (VR), and mixed reality, enhance human-DT interaction by providing intuitive, immersive experiences through three-dimensional (3D) visualization and multi-sensory fusion. These interfaces capture human intentions through cameras and sensors, supporting human-in-the-loop systems and enabling remote expert access. Implementing XR in DTs requires a robust information infrastructure and real-time communication bandwidth. In industry, devices like smartphones, smart glasses, and helmets are commonly used. Platform-independent web-based solutions like WebAR are gaining popularity due to their compatibility with standard web browsers across various terminals.²

Edge Computing

Machine vision enhances DTs with advanced features but requires significant computing power for real-time image processing. At the same time, cloud computing offers high computational capacity, but excessive workloads strain network bandwidth and cause delays. Edge computing addresses this by enabling local data processing and reducing latency. AI chips benefit as trained neural networks are deployed locally, e.g., to cameras connected to nearby edge servers, ensuring efficient, timely operations in critical applications.²

Application in Robotics

DTs in industrial robotics enable real-time simulation and decision-making, optimizing actions for improved efficiency, accuracy, and production quality. They help understand machine health and the impact of changes on processes, enhancing scheduling and productivity. In the automotive industry, DTs provide performance insights, support predictive maintenance, and allow vehicle testing in complex simulated environments before real-world deployment. Organizations like the National Aeronautics and Space Administration use DTs to simulate difficult conditions cost-effectively. Additionally, DTs aid in mapping challenging terrains and improving advanced driving assistance systems.^4,5

In 2017, a versatile DT framework using the model-view-view-model paradigm was developed for robot work-cell simulation, validating complex robot trajectories accurately. Recent advancements integrate factory telemetry and machine vision for responsive simulations in VR and AR. Applications include multi-robot commissioning and virtual robot work-cells, enhancing flexibility and precision across different environments.⁵

DTs are effective for various industrial plant maintenance types, especially predictive and preventive maintenance, including in robot-integrated systems. A study showed that DT-optimized predictive analysis using genetic algorithms reduced production time by 5.2%, though latency was about one second, slower than modern millisecond requirements. Later, a physics-based robotic DT model used virtual sensors for predictive analysis, yielding a significant reduction in torque signal prediction error after iterative refinement, enhancing reliability in robot maintenance.⁵

Machine vision and reinforcement learning have advanced the development of DTs for robot work-cells, enabling visualization, control, and skill assessment. A key challenge in data-driven DTs is the lack of historical data, which can be addressed by generating synthetic data enhanced by AI techniques. Advanced AI, including deep transfer learning, improves fault diagnosis accuracy in robot-assisted manufacturing. Due to practical constraints in physical robots, developing AI algorithms through extensive DT simulations offers an effective solution for efficient and robust robot performance.⁵

Application in Process Optimization

In Industry 4.0 and smart manufacturing, factories adopt digital solutions powered by IoT, AI, big data, and cloud computing to enhance process optimization, customization, quality, and efficiency. DTs play a key role in decision-making and operational optimization.^6,7

A recent paper in Systems proposed a deep reinforcement learning-based DT for manufacturing process optimization, using plastic injection molding as an example. The full-duplex, specific-purpose DT combined supervised and deep reinforcement learning for autonomous control, enabling real-time updates and decision-making. The approach improved product quality and reduced costs with minimal human input. The study introduced an AI-driven DT methodology that shifted from traditional physics-based models to data-driven representations, emphasizing process stability and accuracy. It also addressed adoption challenges by targeting specific operational goals and enabling continuous learning for real-time optimization of manufacturing parameters.⁶

A study in the Journal of Technology in Entrepreneurship and Strategic Management investigated the role of DTs in manufacturing process optimization for small businesses by examining how DTs improved efficiency, quality, and competitiveness in small manufacturing firms. Based on interviews with 28 stakeholders, the analysis identified key themes: adoption drivers, efficiency impacts, challenges, and future prospects.

Motivation included cost reduction and competitive advantage, while benefits involved enhanced process optimization, quality control, and cost management. Challenges like technical and financial barriers, organizational resistance, and regulatory issues were noted. Despite this, the outlook was positive, with advancements in AI and IoT expected to strengthen DT capabilities and support broader adoption in small manufacturers.⁷ The study also suggested that cloud-based, modular DT platforms could ease entry barriers for resource-constrained firms.

Application in Predictive Planning

DTs for predictive planning enable real-time simulation and forecasting of physical systems by leveraging live data and advanced analytics, supporting proactive decision-making and risk mitigation. In industries like automotive, digital supply chain twins enhance predictive planning, disruption management, and sustainable logistics. Integrating Graph Neural Networks (GNNs) with DTs further improves precision and adaptability in supply chain management by continuously adjusting to real-time data. Specifically, pairing dynamic graph neural networks (DGNNs) with DTs allows companies to evaluate policy changes, identify alternative suppliers, and improve demand forecasting in complex supply chains.

This combination creates a closed-loop system where predictions, optimizations, and simulations are constantly updated, enabling better strategic planning and a deeper understanding of supply chain dynamics. Ultimately, this integration boosts supply chain resilience, efficiency, and agility through real-time insights and proactive adjustments.^8,9 As this research area is emerging, its operational maturity is still evolving, with ongoing work validating real-world performance.

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Conclusion

In conclusion, DTs represent a revolutionary technology that bridges physical and virtual worlds for more intelligent industrial decision-making. By leveraging real-time data, AI, and advanced sensing, DTs optimize processes, enhance predictive maintenance, and improve operational efficiency. Challenges such as cybersecurity, interoperability, and validation of data-driven models remain critical areas for research. Their continued evolution promises greater adaptability and resilience across diverse industrial sectors.

References and Further Reading

1. Singh, M. et al. (2022). Applications of Digital Twin across Industries: A Review. Applied Sciences, 12(11), 5727. DOI: 10.3390/app12115727, https://www.mdpi.com/2076-3417/12/11/5727
2. Jiang, Y., Yin, S., Li, K., Luo, H., & Kaynak, O. (2021). Industrial applications of digital twins. Philosophical Transactions of the Royal Society A, 379(2207), 20200360. DOI: 10.1098/rsta.2020.0360, https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0360
3. Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital Twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383. DOI: 10.1016/j.jii.2022.100383, https://www.sciencedirect.com/science/article/pii/S2452414X22000516
4. Baidya, S., Das, S. K., Uddin, M. H., Kosek, C., & Summers, C. (2022). Digital twin in safety-critical robotics applications: Opportunities and challenges. 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC), 101-107. DOI: 10.1109/IPCCC55026.2022.9894313, https://ieeexplore.ieee.org/abstract/document/9894313
5. Mazumder, A. et al. (2023). Towards next generation digital twin in robotics: Trends, scopes, challenges, and future. Heliyon, 9(2). DOI: 10.1016/j.heliyon.2023.e13359, https://www.cell.com/heliyon/fulltext/S2405-8440(23)00566-2
6. Khdoudi, A., Masrour, T., El Hassani, I., & El Mazgualdi, C. (2024). A Deep-Reinforcement-Learning-Based Digital Twin for Manufacturing Process Optimization. Systems, 12(2), 38. DOI: 10.3390/systems12020038, https://www.mdpi.com/2079-8954/12/2/38
7. de Almeida, L., & Rivarola, M. (2022). The Role of Digital Twins in Optimizing Manufacturing Processes for Small Businesses. Journal of Technology in Entrepreneurship and Strategic Management, 1(2), 16-27. DOI: 10.61838/kman.jtesm.1.2.3 , https://www.researchgate.net/publication/383006142_The_Role_of_Digital_Twins_in_Optimizing_Manufacturing_Processes_for_Small_Businesses
8. Kim, D., Kim, G., & Noh, S. D. (2025). Digital Twin-Based Prediction and Optimization for Dynamic Supply Chain Management. Machines, 13(2), 109. DOI: 10.3390/machines13020109, https://www.mdpi.com/2075-1702/13/2/109
9. Wasi, A. T., Anik, M. A., Rahman, A., Hoque, M. I., Islam, M. D., & Ahsan, M. M. (2025). A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization. ArXiv. DOI: 10.48550/arXiv.2504.03692, https://arxiv.org/abs/2504.03692

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