Digital Twins and ESG (eBook)

Surajit Mondal, Adesh Kumar, Mudassir Khan (Herausgeber)

eBook Download: EPUB

2025
600 Seiten
Wiley-Scrivener (Verlag)
978-1-394-30322-9 (ISBN)

Infos

Digital Twins and ESG provides essential insight on how integrating cutting-edge Digital Twin technology with ESG practices can transform the understanding of sustainability and propel businesses towards a more transparent, accountable, and responsible future.

Digital Twins and ESG introduces the dynamic world of ESG practices, emphasizing the pivotal role technology plays in shaping and advancing sustainability goals. It introduces readers to the multifaceted world of Digital Twin technology, offering a comprehensive understanding of its historical development and diverse applications across industries. This volume will intricately examine the integration of Digital Twins in ESG metrics and reporting frameworks. Artificial intelligence, machine learning, and blockchain technologies are also discussed as key enablers for achieving ESG goals, providing readers with a glimpse into the potential advancements and breakthroughs that lie ahead. Through detailed analyses and case studies, readers will gain insights into how Digital Twins enhance data collection, monitoring, and reporting, elevating transparency and accountability. Digital Twins and ESG serves as a rallying call, urging businesses to embrace Digital Twins as an integral component of their ESG strategies, ultimately paving the way for a more sustainable and responsible future.

Textauszug

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Digital Twins: Driving Innovation Through Virtual Optimization – A Systematic Review

Supriya*, Vansh Joshi, Yash Singhal, Mudit Kumar and Parth Sharma

School of Computing, COER University, Roorkee, India

Abstract

The digital twin unlocks their full potential by significantly leveraging digitalization, intelligence, and services. Digital twins optimize physical entities’ performance by surmounting limitations in time, space, cost, and security by generating virtual representations of physical objects. This occurrence has been extensively researched in academia as well as business. This incredible technology is being deployed on a greater scale, and it is improving the businesses operation. Digital twins have been deployed in a number of noteworthy commercial applications in the last few years, and it is expected that the technology will spread to new industries, use cases, and applications. The objective of this review article is to investigate the role of digital twins in simplifying intelligent automation in various sectors. This paper explains the concept of the technology, demonstrates the growth and development of digital twins, explores its trends and challenges, and investigates its uses in various sectors and discuss directions for future development of this technology.

Keywords: Digital twins, cyber-physical systems, virtual modeling, Industry 4.0, Internet of Things

1.1 Introduction

The innovative “digital twins” technology augments the digital and physical realms, allowing for the simultaneous exhibiting, monitoring, and optimization of systems, processes, or physical assets. The process of generating a virtual “twin” of a real entity, digital twins enable enterprises to obtain profound insights into the behavior and performance of their assets in a risk-free virtual environment. This technology has several uses in a variety of sectors, including smart cities, training and manufacturing, education, and aerospace. It boosts productivity, forecasts maintenance requirements, minimizes downtime, and stimulates creativity. Digital twins are emerging as a crucial Industry 4.0 enabler, providing previously unheard-of chances for innovation and expansion as companies look more and more to utilize data-driven decision-making. Figure 1.1 highlights the concepts of digital twins discussed in this research work.

This modern technology has completely changed the industry by replicating nearly every aspect of a process, product, or product. As shown in Figure 1.2, it is capable of reproducing every element in the physical globe in the digital sphere and providing engineers input from the digital realm. Thus, the technology makes it possible for businesses to identify and address physical issues fast, create better designs and construction products, and experience value and benefits more quickly than in the past. Moreover, companies can utilize the Digital Twin technology to enhance corporate performance and procedures [1].

Digital twin technology provides a unique advantage by allowing organizations to simulate scenarios, predict outcomes, and make data-driven decisions without risking physical assets.

Figure 1.1 Graphical abstract.

Figure 1.2 Digital twins technology.

Digital twins are playing an increasingly important role in improving operational efficiency, cutting costs, and spurring innovation as sectors continue to embrace digital transformation.

1.1.1 Evaluation of Digital Twins Technology

This concept, was introduced by NASA during the late 1900s, as depicted in Figure 1.3, has rapidly evolved into a critical technology across various industries. It represents a digital replica of a physical object, process, or system, enabling real-time monitoring, analysis, and optimization of its physical counterpart. The integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics has significantly expanded the capabilities of this Technology, making them indispensable in industries like manufacturing, healthcare, and smart cities [2].

Figure 1.3 Evaluation of digital twins technology.

Early twin technology development concentrated on building replicas of real-world objects to aid in assessment, diagnosis, and forecasting. Nevertheless, this method was limited in terms of cost, real-time interaction, and distinctiveness. Researchers started looking into the usage of digital virtual entities to enhance physical entities’ performance through feedback in order to address these problems. A semi-digital twin system was used for training and troubleshooting in NASA’s Apollo program in 1970, which is regarded as an early example of this methodology.

As depicted in Figure 1.3, the concept formulation phase uses the idea of digital twins, which was put forward by Professor Grieves. In order to track and improve a physical product’s performance, he envisioned a digital version of it. The idea developed over time, leading to the official designation of “digital twin” in 2011. However, due to technological limitations, early adoption was restricted. Over the past few years, digital twin technology has seen a considerable increase in industrial utilization. The application phase shows how digital twins can be useful tools for numerous tasks, including managing huge installations, producing better products, and optimizing aviation systems. A number of industries have benefited from the technology’s adoption, as seen by the success of companies like General Electric, Siemens AG, and Dassault in doing so [3].

During the industry penetration phase, digital twin technology starts to rapidly gain prominence in a variety of sectors. Governments all throughout the world are recognizing its potential and putting laws in place to encourage its use. Innovative digital twin solutions are being developed by businesses to boost decision-making, simplify operations, and improve product design. The future of manufacturing, cities, and many other industries is anticipated to be significantly shaped by technology as it develops. Digital twin technology provides a unique advantage by allowing organizations to simulate scenarios, predict outcomes, and make data-driven decisions without risking physical assets. The importance of digital twins in improving operational efficiency, cutting costs, and spurring innovation is becoming more and more important as companies embrace digital transformation [4].

Digital twins, an emerging technology that has seen a recent increase in case studies that emphasize lifecycle management and predictive analysis for a broad range of sectors and disciplines. In a way that is useful for users and stakeholders, technology provides a thorough understanding of any system’s internal workings, the interactions between its various components, and the future behavior of its physical counterpart. In some fields, including smart cities, urban areas, freight logistics, medical, engineering, and the automobile industry, among others, digital twins research and application have grown in popularity. The objective of this work is to provide a domain-specific review of applications along with a thorough view of the technological issues, constraints, and trends [5].

Concerning this emerging technology, there has been a significant increase in case studies with an emphasis on lifecycle management and predictive analysis for a variety of industries and disciplines. Technology offers a deep grasp of any system’s internal workings, the relationships between its numerous components, and the future behavior of its physical counterpart in a way that is beneficial to users and stakeholders. Digital twins research and application have become more and more common in a number of industries, including medical, engineering, smart cities, urban regions, freight logistics, and the automotive industry. This work aims to provide an all-encompassing view of the technological concepts, challenges, and limitations along with a domain-specific application. Along with its technical elements and the advantages, future research agenda of this technology is also analyzed [6].

1.1.2 Evolution and Technological Components

The evolution of Digital Twin technology, as shown in Figure 1.4, has been marked by several key developments [7, 8]:

Figure 1.4 Technological components.

Phase 1: Model-Based Systems Engineering (MBSE): In its early stages, Digital Twin technology was largely confined to the aerospace sector, where MBSE was used to create digital models of spacecraft and other complex systems. These models were primarily static, representing only the structural and functional aspects of the physical object.

Phase 2: IoT Integration: The advent of IoT marked a significant turning point for Digital Twins. By integrating IoT sensors, Digital Twins began to incorporate data from real entity, empowering dynamic simulations and more accurate predictions. This phase saw the expansion of Digital Twins into industries such as manufacturing, where they were used to monitor and optimize production lines.

Phase 3: AI and Machine Learning: By combining machine learning and AI algorithms, digital twins’ skills have been significantly improved. By enabling anomaly...

Produkt-Details

• Erscheint lt. Verlag: 8.8.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: Case Studies on Sustainable Practices • Corporate Governance in Sustainability • Digital Twin Technology • Environmental Impact Simulation • ESG 2.0 • Ethical Technology Adoption • Future Trends in Sustainability • Human-centric Design in Sustainability • Integrating Technology in ESG Strategies • Real-world ESG Applications • Risk Management with Digital Twins • Social Responsibility Technology • Sustainable Innovation • Sustainable Resource Optimization • Technology for ESG Compliance
• ISBN-10: 1-394-30322-X / 139430322X
• ISBN-13: 978-1-394-30322-9 / 9781394303229