But what differentiates Digital Twin from traditional modeling? The answer lies in the degree of integration between the product and its virtual counterpart, which can be broken into three common setups. In the case of traditional digital models, data is exchanged between the physical and virtual representations manually, and decisions in one environment do not impact the other. Extending this setup to automate the flow of data in one direction, from the physical to digital environment, can be referred to as a Digital Shadow, and permits decisions in the physical environment to automatically impact the virtual one (but not vice-versa). Digital Twins synchronize with their physical representations via algorithms that process real-time data in a flow that is bi-directional. Once the modeled information is used as input for real-life decisions, the output is visualized in the virtual environment and ultimately becomes new information for future modeling. This live link between reality and the virtual environment lays the foundation for intelligent decision-making, in real-time.

Use cases

The potential for Digital Twin applications is ever-increasing. To emphasize its added-value, we’ll discuss its current use in two industries where it is particularly well-established: manufacturing, and energy.

Manufacturing

As we transition into the era of Industry 4.0, Digital Twin technology is increasingly becoming a catalyst for businesses’ strategic development toward new paradigms, such as smart manufacturing. A key factor in this shift is the availability of information. The connection to real-time data provided by Digital Twin can shift approaches to maintenance and quality control from reactive to proactive by enabling more intelligent decision-making and sustainable production. In this regard, Digital Twin is particularly well-positioned to support processes centered on product lifecycle management.

By fitting assets with a network of sensors, data on the production process and its equipment can be captured and later enriched with additional data (metadata, derived data, etc.) to compose a virtual representation (Digital Twin) of both that can interact in a virtual environment. Manufacturing companies have used this capability to model production parameters, output measurements and other data to visualize relationships among multiple processes. This has produced synergies such as enabling measures from continuous quality control of output to also act as a secondary status indicator for predictive maintenance need. Shared sources of enriched information minimize the amount of overall resources used to make decisions and detect key areas for improvement. Better decision-making at the cost of less resources can maximize productivity and translate directly to revenue growth.

Energy

As the manufacturing industry has harnessed the potential of Digital Twin for managing the product lifecycle, companies at the forefront of the proptech (property technology) industry have begun experimenting with extending these same capabilities to managing the lifecycle of buildings. Although models of building information have been in use by architects, engineers and facility managers since the 90s, this practice - called “Building Information Modeling” - has been primarily used in the design and construction phases. Unlike Digital Twin, traditional building information modeling does not benefit from a sensor network and thus has not been used for real-time decision-making. Although Digital Twin can easily integrate with existing building information modeling systems by extracting and building upon their 3D models, Digital Twin can also be a cost-effective alternative on its own for older buildings by simply retrofitting the facility with sensors in lieu of an otherwise costly building information modeling system.

A recent study established that even a limited Digital Twin of a building proved useful enough to detect real-time patterns in facade temperature, light distribution and movement within the building’s environment. Such information has been confirmed useful by previous studies for supporting smart systems such as hybrid air conditioning, that maximize energy savings by sourcing air from cooler parts of the building outdoors, rather than cooling and recirculating air indoors. Using small sensor networks to collect information on light, ambient temperature and relative humidity, Digital Twins have also been used to detect movement patterns in buildings to learn when assets such as elevators are typically used in order to optimize energy efficiency by continually tailoring asset operation to time periods only when it is useful. By offering a continual, aggregate view of components, Digital Twin can also ensure that building owners are immediately alerted to faults detected within the systems that normally would have come to light only at the next scheduled maintenance. This means you can take corrective actions that restore performance and stop the waste of energy --and money-- sooner.

What does it achieve?

Digital Twin has the potential to integrate into and add value at any phase of the product lifecycle. During the design phase, Digital Twin can aid product improvement by providing a cost-effective solution to evaluating design. Through reference models of products, users can iteratively visualize new concepts and test their interoperability before putting them into practice.

In the manufacturing stage, Digital Twin can manage both individual assets and the larger processes they comprise. Via their virtual environments, production assets can be remotely monitored and controlled, either for maintenance purposes, or directed with the help of optimization algorithms, in order to achieve significant time and cost reductions.

The distribution phase can benefit from real-time tracking to provide support for quick decision-making. Enriched data from multiple sources along the supply chain can be applied to analytics models for various purposes, such as performing outcome analysis and calculating return on investment, to provide support in optimizing anything from warehouse operations to logistics processes.

Digital Twin can also catalyze significant efficiency gains by simulating usage, including detecting faults and predicting failure. Leveraging sensor data, performance models can establish activity patterns to pinpoint off-peak times where energy consumption can be reduced. The modeled interrelationships can also provide a comprehensive overview of a system or process that facilitates diagnosing inefficiencies early, before they lead to failures.

At the end-of-life stage, Digital Twin’s process modeling capabilities can be extended to modeling reverse logistics processes. Virtual environments can be used to iteratively test smart recycling flows, as well as processes that increase the efficiency of material recovery.

Current landscape

The majority of real-life applications of Digital Twin currently derive from manufacturing, but the end goals that motivate its implementation are universal. At the strategic level, optimizing resource allocation and operational efficiency directly translate to cost savings and timely support for decision-making. However, it can also be argued that the decision itself to incorporate Digital Twin is strategically significant as an effort toward maintaining relevancy amid a growing number of industries --including those previously unconnected-- that have also decided to embrace the new era of smart technology.

When Digital Twin becomes a part of strategy, it also unleashes the potential to create value that can then be given back to customers, in the forms of better-designed products/ services at a cheaper cost of development, and more consistent service with a lower probability of failure. Creating value for stakeholders improves the customer-client relationship, which can ultimately help sustain investment capital for the company into the long-term.

How Helicon can help

Implementing digital twins can be daunting as they require a deep understanding of AI, advanced analytics, and the infrastructure to support it. At Helicon, we have experience navigating the complexity of digital twins, and the software development and data science services needed to implement them. Using our framework Teron to simplify the technical complexity, we can create a 3D representation of a real entity within two weeks.

References

• F. Tao, H. Zhang, A. Liu and A. Y. C. Nee, "Digital Twin in Industry: State-of-the-Art," in IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405-2415, April 2019, doi: 10.1109/TII.2018.2873186
• Haße, Hendrik; Li, Bin; Weißenberg, Norbert; Cirullies, Jan; Otto, Boris (2019) : Digital twin for real-time data processing in logistics, In: Kersten, Wolfgang Blecker, Thorsten Ringle, Christian M. (Ed.): Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27, ISBN 978-3-7502-4947-9, epubli GmbH, Berlin, pp. 4-28, http://dx.doi.org/10.15480/882.2462. (This Version is available at: http://hdl.handle.net/10419/209367)
• Jones, D., Snider, C., Nassehi, A., Yon, J. and Hicks, B., 2020. Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, pp.36-52.
• Khajavi, S., Motlagh, N., Jaribion, A., Werner, L. and Holmstrom, J., 2019. Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings. IEEE Access, 7, pp.147406-147419.
• Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W., 2018. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), pp.1016-1022.
• Lim, K., Zheng, P. and Chen, C., 2019. A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), pp.1313-1337, https://doi.org/10.1007/s10845-019-01512-w
• Lindström, J., Larsson, H., Jonsson, M. and Lejon, E., 2017. Towards Intelligent and Sustainable Production: Combining and Integrating Online Predictive Maintenance and Continuous Quality Control. Procedia CIRP, 63, pp.443-448.