Digital Twins
Digital twins are one of the most recent technologies that help design, develop, and operate
complex systems cost-effectively. The overall performance of modelling smart cities,
warehouses, and healthcare units has improved exponentially with the onset of the digital twin
concept. The digital twins can replicate the current behaviour history to analyse the situation[1].
The digital twins aim to help make quick decisions in the most cost-efficient way.
The principal characteristics of the digital twins include predictive analysis, where the
historical and current data can be compared to form a decision. Using the simulation capacity
of the digital twins, various scenes can be recreated, and the potential danger or the reason for
the anomaly can be easily analysed. In extension, this technology can integrate real-time value
with the existing dataset for real-time analysis[1].
I.
Types of Digital Twins
Digital twins come in various forms, including product twins that mirror the entire product
lifecycle, from concept to operation. Data twins leverage real-time data to create a virtual
replica, enabling analysis, prediction, and optimisation. The best example of the data twin
would be Google Maps, which represents the entire area or region. This can be used for realtime calculations of commuting time and connectivity between places. The systems twins
model helps in the interactions between physical and digital processes, especially in the field
of logistics and warehouse management. They provide a comprehensive view of how these
components work together to achieve specific goals[2]. This article briefly reviews the role of
digital twins in product development as product twins and in data analysis are data twins.
A. Digital Twin in Product Development
Developing innovative products that truly resonate with consumers is becoming increasingly
challenging. The products must consistently evolve and offer superior performance and unique
features, often necessitating the integration of advanced technologies.
Sustainability is becoming a key consumer expectation for next-generation products. This shift
introduces constraints related to material selection, product durability, and end-of-life
considerations. Meanwhile, the relentless pursuit of cost-effective research and development
continues[3]. More than 70% of the companies in the industry have started using Product Twin
to increase their productivity and reach their goal faster. The complexity of modern-day
products is exponentially increasing in terms of weight, visual appeal and utility[1], [4].
Dimensions of Product Twins
The digital twins can be divided into three dimensions based on their use and where[3].
1. Value Stream: The chain starts with product manufacturing, analysing components,
and finally ends with the service of the product.
2. Product Scope: The interaction between the intercomponent and intracomponent
interaction extends the product's scope. Each combination of the components
communicates with each other so each specific task can be analysed.
3. Design Refinement: The product's workings in a continuously changing or dynamic
environment can be tested and analysed using this technology. Artificial intelligence
can be used here for more analysis.
B. Digital Twins in Supply Chain
A study by MIT Sloan Management Review [5] highlights the unique capabilities of digital
twins, which include mimicking human decision-making and even automating specific tasks.
Many large companies use digital twins in their supply chains to optimise operations, from
shipment consolidation to predictive maintenance. Their application can be divided broadly
into three functional areas [5].
i. Planning: The digital twin can use historical data and current market trends to
predict or forecast the market. Supply delays or transportation delays can be
mitigated.
ii. Transportation: Supply chain transportation, along with the quantity of goods, the
best route, and the most efficient methods, can be optimised using digital twins.
iii. Stock/ Warehouse Management: Digital twins help optimise inventory
management by providing a real-time view of inventory levels and flows. They
support strategies like just-in-time delivery and predictive maintenance, ensuring
smooth supply chain operations.
II.
Challenges in Including Digital Twins in the Industry
Digital twins, while promising, face challenges similar to AI and IoT: data
standardisation, management, and security[6]. Implementation barriers and legacy
system integration are also obstacles. Other challenges include IT infrastructure
updates, connectivity, data privacy and security, and a lack of standardised
modelling. High deployment costs, increased power and storage needs, integration
issues, and complex architecture hinder market growth. Maintaining digital twins
requires significant investment in technology, infrastructure, and operations, which
can slow down deployment.
III.
Summary and Conclusion
The growing demand for automation in various industries is anticipated to trigger
the high demand for the Digital Twin platform over the forecast period. Digital
Twin solutions are poised to play an increasingly important role in different
industries as we recover from the pandemic. The benefits of creating a Digital Twin
solution are too vast and still not fully explored. While there are challenges to
addressing data quality and security, increased demand for power and storage, and
integration with existing infrastructures, Digital Twin solutions are thriving to
provide a highly advanced digital revolution to make the world a better place for
humankind. In the future, Digital Twins will expand to more use cases and
industries. The solutions will combine with more technologies, such as augmented
reality (AR), for an immersive experience and AI capabilities for better connections,
insights, and analytics. In addition, more technologies enable us to use Digital Twin
solutions, removing the need to check the 'real' thing. These exponentially higher
insights and analytics, in turn, lead to even more possibilities for applications of
Digital Twin solutions in complex operations.
References
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[6]
"Digital Twins for Predictive Maintenance - MATLAB & Simulink." Accessed: Aug. 27,
2024. [Online]. Available: https://in.mathworks.com/campaigns/offers/next/digitaltwins-for-predictive-maintenance.html
"Digital twins in manufacturing & product development | McKinsey." Accessed: Aug.
28, 2024. [Online]. Available: https://www.mckinsey.com/industries/industrials-andelectronics/our-insights/digital-twins-the-key-to-smart-product-development
W. Li, A. Y. C. Nee, and S. K. Ong, "A state-of-the-art review of augmented reality in
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"Digital Twins for improved product development - Plain Concepts." Accessed: Aug.
28, 2024. [Online]. Available: https://www.plainconcepts.com/digital-twins-productdevelopment/
Ö. Tozanli and M. Jesús Saénz, "Unlocking the Potential of Digital Twins in Supply
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M. Attaran and B. G. Celik, "Digital Twin: Benefits, use cases, challenges, and
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