Navigating digital twin adoption in agri-food supply chains: a resource orchestration perspective on challenges and pathways

Digital twin (DT) technology is revolutionizing the architecture, engineering, construction, and operation (AECO) industry, driven by the advancements of Construction 4.0 (C4.0). Despite this, adopting DT in the AECO sector remains limited due to various barriers. This study, driven by four research questions (RQ), aims to advance DT adoption in the AECO industry under C4.0 using an integrated approach. The degrees of influence and importance ranking of barriers are assessed through the decision-making trial and evaluation laboratory (DEMATEL). The hierarchy and causal relationships among barriers are revealed through interpretative structural modeling (ISM). Then, barriers are divided into four clusters employing the cross-impact matrix multiplication applied to classification (MICMAC) method. The paper reveals that "lack of government financial and policy support" is the most critical barrier, "lack of trust and long-term perspective in DT" is the most significant direct-influence barrier, and "immature 3D engine technology" is the most fundamental barrier. By exploring interrelationships and prioritizing barriers, the study provides insights to enhance adopting DT in the AECO industry in the context of C4.0.

The digital twin (DT) presents an opportunity for the integration of the physical world into the digital world. DT technology has the potential to transform the construction industry and respond to some of its challenges. In conventional construction projects, progress is largely monitored by direct observation and measurement which suffers from numerous challenges, including low productivity, blunders, and poor technology advancements. Concerns are now being raised about integrating technology for autonomously monitoring building activity. In other sectors, DT technology has been responsible for saving product development time and costs by up to 50%. However, DT is still lagging the adoption of new technologies in the construction industry. The overarching aim of this study was to explore the adaptability of DT in construction site progress monitoring. This study comprehensively reviews and analyses DT concepts, technologies, and applications in the construction industry, parameters of applications of DT in construction site progress monitoring, how DT could be used for site progress monitoring in construction, common challenges in the implementation of DT in site progress monitoring, and strategies such as barriers related to DT in site progress monitoring, using literature findings while incorporating qualitative analysis of semi-structured interviews. This research shows that DT has a high potential to solve the numerous challenges in construction site progress monitoring, rather than other current technologies in use. Thus, this study raises awareness and the need for the application of DT in construction site progress monitoring

Simple SummaryA digital twin can be described as a digital replica of a real-world entity. It simulates the physical state and maybe the biological state and behavior of the real-world entity based on input data. It helps in predicting, optimizing, and improving decision making. It has revolutionized the industrial world, particularly the manufacturing industry, construction and healthcare sector, smart cities, and energy industry. In this perspectives paper, we explore the development and implementation of the digital twin in modern animal farming. In addition to showcasing potential applications, this review provides in-depth insights about the potential implementation and characterization of digital twins in modern animal farming.Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

Background: Digital twins have the potential to significantly improve the efficiency and sustainability of the agri-food supply chain by providing visibility, reducing bottlenecks, planning for contingencies, and improving existing processes and resources. Additionally, they can add value to businesses by lowering costs and boosting customer satisfaction. This study is aimed at responding to common scientific questions on the application of digital twins in the agri-food supply chain, focusing on the benefits, types, integration levels, key elements, implementation steps, and challenges. Methods: This article conducts a systematic literature review of recent works on agri-food supply chain digital twins, using a list of peer-reviewed studies to analyze concepts using precise and well-defined criteria. Thus, 50 papers were selected based on inclusion and exclusion criteria, and descriptive and content-wise analysis was conducted to answer the research questions. Conclusions: The implementation of digital twins has shown promising advancements in addressing global challenges in the agri-food supply chain. Despite encouraging signs of progress in the sector, the real-world application of this solution is still in its early stages. This article intends to provide firms, experts, and researchers with insights into future research directions, implications, and challenges on the topic.

In recent years, the implementation of digital twin (DT) technology has gained significant attention in various industries. However, the fire safety management (FSM) sector has been relatively slow in adopting this technology compared to other major industries. Therefore, this study aims to explore the limitations, opportunities, and challenges associated with adopting DT technology in the FSM sector and further develop a DT-based FSM framework towards smart facility management (FM). To achieve this objective, this research started by reviewing several promising DTs for FSM, including building information modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and augmented reality (AR). On this basis, a conceptual framework was synthesized in consideration of the benefits of each technology. A questionnaire was conducted for FM professionals to evaluate the proposed framework and identify the challenges of adopting DT in the FSM sector. The survey results reveal that the proposed framework can assist decision makers in obtaining comprehensive information about facilities’ communication among stakeholders. The survey results validate the potential of the adoption of DTs toward smart FM practices in FSM. The survey results provide insights into the perception of DT technology among FM practitioners and identify the current state of DT technology in the FSM sector, its expected benefits, and its potential challenges. The main barriers to adopting DTs in FSM are a lack of knowledge about DTs, their initial costs, user acceptance, difficulties in systems integration, education training costs, a lack of competence, development complexity, difficulties in data management, and a lack of trust in data security.

Abstract: Digital twin technology, which creates virtual replicas of physical assets, processes, and systems, is transforming industries by enabling real-time monitoring, simulation, and optimization. This book chapter explores the fundamental principles, key components, and diverse applications of digital twins across various sectors, including manufacturing, healthcare, energy, automotive, and smart cities. Through detailed case studies, the chapter illustrates the successful implementation and significant benefits of digital twins, such as enhanced operational efficiency, cost reduction, improved risk management, and accelerated innovation. It also addresses the technical challenges, security and privacy concerns, and regulatory issues associated with digital twin technology. Looking forward, the chapter highlights future trends and developments, predicting advancements in AI, edge computing, 5G, and quantum computing that will further enhance digital twin capabilities. The chapter concludes with a forward-looking perspective on the transformative potential of digital twins in shaping a smarter, more connected, and sustainable world. Keywords: Digital Twin Technology,Real-Time Monitoring,Simulation and Optimization,Manufacturing Efficiency,Predictive Maintenance,Healthcare Innovation,Energy Management,Smart Cities,IoT Integration,AI and Machine Learning,Edge Computing,5G Connectivity,Quantum Computing,Operational Efficiency,Risk Management,Sustainability,Industry 4.0,Virtual Prototyping and Future Trends in Technology. Condori, P. P. C. (2022). Digital Twin in Development of Products. Digital Twin Technology, 205–218. Portico. https://doi.org/10.1002/9781119842316.ch13 Fryer, T. (2019). Digital Twin - Introduction. This is the age of The Digital Twin. Engineering & Technology, 14(1), 28–29. https://doi.org/10.1049/et.2019.0125 Gnanamalar, R. H. (2024). Human Digital Twin Processes and their Future. Transforming Industry Using Digital Twin Technology, 187–217. https://doi.org/10.1007/978-3-031-58523-4_10 Korhan, O. (2023). Introductory Chapter: Digital Twin Technology. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.113345 Mythily, M., David, B., & Vijay, J. A. (2024). Digital Twin Application in Various Sectors. Transforming Industry Using Digital Twin Technology, 219–237. https://doi.org/10.1007/978-3-031-58523-4_11 Seolin Galindo, E., & Chagas, U. (2023). Perspective Chapter: Digital Twin Applied in the Brazilian Energy Sector. Digital Twin Technology - Fundamentals and Applications. https://doi.org/10.5772/intechopen.112598

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

The basis of the digital twin is models, big data, the connection between the real and virtual object, and services that are created as a result of the developed and implemented digital twin. The paper presents the main trends in the development of the digital twin, drivers and barriers, and highlights the key players in the aviation industry. Companies are creating and implementing strategies that link business goals to the improvement of work processes in order to accelerate them efficiently. The main incentive for businesses is the opportunity to reduce costs and increase the lifespan of aviation equipment. Based on the analysis of scientific publications and a number of expert interviews with industry manufacturers, the possibilities of using digital twins for aircraft maintenance, improving flight safety, and optimizing the work of aviation enterprises have been studied. At the same time, it is considered how digital twin technologies can be applied in Russia's small aviation and what conditions are necessary for their successful implementation. Based on the results of the discussions, the best options have been identified. Special attention has been paid to the current readiness of Russian organizations for digital changes, as well as to the difficulties that hinder the implementation of such technologies. In the small aviation segment, the digital twin technology can be used as a tool for reducing costs, improving safety, and developing a digital engineering school. The research has led to the conclusion that there is a need to transition from fragmented digital projects to strategic management of the implementation of digital twins through the gradual development of potential by aligning the interests of business and the government. The goal is to integrate the digital twin technology into the long-term development strategy of the industry.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Digital twin (DT) technology is revolutionizing the construction industry by enhancing project performance, predictive maintenance, and resource management via real-time data integration and virtual simulation. Despite its latent potential, successful DT implementation in construction projects requires an understanding of critical success factors (CSFs) that support effective deployment and integration. A bibliometric study was conducted after extracting 89 articles from the Scopus database to achieve such an understanding. Subsequently, a systematic approach was performed to synthesize the extant literature and identify the CSFs, knowledge gaps, and future research directions. Emergent findings indicate a growing number of published research articles on CSFs for DT implementation, underscoring exponential interest in the technology throughout the scientific community and industry. Moreover, significantly influential articles and the quintessential role of geographic collaboration were recognized. Finally, the following four knowledge gaps were emphasized: (1) DT integration with emerging technologies, (2) lifecycle management and sustainability integration with the DT, (3) human-centric factors and stakeholder collaboration for DT decision making, and (4) policy and regulatory framework for DT implementation. This research serves as the first study to employ a state-of-the-art review that would provide scholars and practitioners with an extensive understanding of successful DT implementation in construction projects. This research would enhance digital innovation in the construction industry by implementing CSFs for DT adoption and offer actionable recommendations for industry practitioners who seek to augment their digital transition.

Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.

Background and Aims: The effects of digital twin (DT) technology for remission of T2D on cardiovascular risk reduction is unknown. We evaluated the effectiveness of the DT to improve A1c and body weight and ASCVD risk, in patients enrolled to achieve remission of T2DM. DT platform uses AI and Internet of Things, to integrate multi-dimensional data to give precision nutrition and health recommendations via the mobile app and by coaches. Materials and Methods: Data from 208 participants who had been on DT for 1 year were analyzed. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Outcomes included the change in HbA1c, body weight and change in ASCVD risk scores. The DT uses a machine learning algorithm to integrate clinical and sensor data to predict personal glucose response. Patients were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using the mobile app. Results: The study participants had a mean diabetes duration of 3.7±2.7 years and a mean age of 44±8.5 years. Based on the ADA criteria, at 1 year, 72.6% (n=151/208) continued to be under remission. The mean 10-year ASCVD risk (%) at baseline was 7.9 (±7.8, 95% CI 6.8 to 8.9) which reduced to 3.6 (±4, 95% CI 3.1 to 4.2) at 360 days, p<0.0001. A1c (%) at baseline was 8.9 (±1.8, 95% CI 8.7 to 9.2, minimum 5.5, maximum 16.2) which reduced to 6 (±0.6, 95% CI 5.9 to 6.1, minimum 4.7, maximum 9.2), p<0.0001. Body weight (kg) at baseline was 78 (±14.3, 95% CI 76.1 to 79.9) which reduced to 70.4 (±12.9, 95% CI 68.7 to 72.1), p<0.0001. There was a significant correlation between the reduction in A1c and ASCVD (Pearson r 0.41, 95% CI 0.31 to 0.5, p<0.0001). Similarly, there was a significant correlation between the reduction in body weight and ASCVD (Pearson r 0.25, 95% CI 0.12 to 0.3, <0.0001). At baseline, there were 49% of participants who had low risk score (<5%), 16% as borderline risk (5-7.5%), 27% as intermediate risk (7.5-19.9), 8% as high risk (>20%) that at 1 year shifted to 77% as low risk, 11% as borderline, 11% as intermediate risk and 1% as high-risk scores. Conclusion: The implementation of digital twin (DT) technology, utilizing AI and Internet of Things, demonstrated significant potential in T2DM management in participants enrolled to achieve remission. In our study with 208 participants, DT effectively reduced A1c levels, body weight, and ASCVD risk scores over 360 days. The correlations between reductions in A1c, body weight, and ASCVD underscore the potential of DT as a transformative tool in diabetes and cardiovascular risk management.

Digital twins (DTs) are transforming healthcare systems (HSs) by enabling real-time, data-driven decision-making. Despite their potential, research on DTs’ role in long-term HS sustainability remains nascent. This study systematically reviews DT use cases in HSs through the lens of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">triple-bottom-line</i> framework of sustainability, identifying their economic, environmental, and social contributions. Additionally, it maps these use cases to the United Nations (UN) Sustainable Development Goals (SDGs) to assess their alignment with global sustainability policies. A systematic literature review following the PRISMA framework was conducted across four databases (Scopus, Web of Science, Engineering Village, and PubMed), identifying 81 peer-reviewed studies. DT use cases were categorized into sustainability dimensions and qualitatively mapped to UN SDGs. We identify 28 unique DT use cases supporting HS sustainability – 13 contributing to economic (e.g., precision medicine, early diagnosis), 8 to environmental (e.g., energy-efficient hospital operations, waste management), and 7 to social sustainability (e.g., provider burnout prevention, equitable access). Our mapping reveals that DTs could support 11 of 17 UN SDGs, including SDG 3 (good health and well-being), SDG 8 (economic growth), SDG 9 (innovation), and SDGs 12–15 (environmental impact mitigation), among others. This study documents the significant potential of DTs to enhance HS sustainability across economic, environmental, and social dimensions while supporting multiple SDGs. However, most existing DT studies overlook explicit sustainability linkages, with limited attention to assessing or prioritizing DTs’ impact on HS sustainability. Future research should develop standardized sustainability metrics, conduct empirical studies, and create frameworks linking DT outcomes to SDGs.

The global logistics landscape is experiencing unprecedented transformation, driven by rapid technological advancements and increasing complexity of supply chain networks. Digital twin technology emerges as a revolutionary approach to supply chain management, offering comprehensive solutions for optimization and disruption mitigation. This comprehensive review examines the transformative potential of digital twin technologies in revolutionizing global logistics through systematic analysis of existing literature, implementation frameworks, and case studies. Our investigation reveals that digital twin implementation can potentially reduce operational costs by 30-40%, decrease supply chain disruption times by up to 60%, and improve overall supply chain resilience through advanced predictive modeling. The research synthesizes evidence from multiple domains, demonstrating digital twins' capacity to address critical challenges in contemporary supply chain management. By exploring emerging trends, implementation mechanisms, and critical challenges, this review provides a balanced perspective on the opportunities and limitations of digital twin technologies. The findings suggest that while digital twins present promising solutions for supply chain optimization, successful implementation requires careful consideration of technical infrastructure, data integration strategies, and organizational capabilities.

Urban food production can contribute to sustainable development goals by reducing land use and shortening transportation distances. Despite its advantages, the implementation of digital twin (DT) technology for urban food systems has received less investigation compared to manufacturing. This article examines the influence of DT technology on adaptive decision-making in urban food production, focusing on the “Grow It York” case study. Utilising mixed integer linear programming (MILP) and Q-learning models, this study explores how DT data enhances production decisions regarding service level and resource utilisation under demand fluctuations. The findings highlight that the Q-learning model achieves up to demand fulfillment compared to for the MILP model, demonstrating a significant improvement in operational efficiency. Additionally, electricity usage per fulfilled demand is reduced by approximately , advocating for broader DT application to the synergy between economic resilience and environmental sustainability. Future research directions include scaling DT implementation to manage complex supply chains, including advancing real-time data integration and incorporating sustainability considerations at supply chain level.