Digital twins in infrastructure: definitions, current practices, challenges and strategies

Biopharma Is Going Digital … Bit by Bit

The digital wave, represented by new technologies such as big data, IoT, and artificial intelligence, is sweeping the globe, driving all industries toward digitalization and intelligent transformation. Digital twins are becoming an indispensable opportunity for new infrastructure initiatives. As geotechnical engineering constitutes a critical component of new infrastructure, its corresponding digital transformation is essential to align with these initiatives. However, due to the difficulty of modeling, the demand for computing resources, interdisciplinary integration, and other issues, current digital twin applications in geotechnical engineering remain in their nascent stage. This paper delineates the developmental status of geotechnical digital twin technology in China, and it focuses on the advantages and disadvantages of digital twins in five application fields, identifying key challenges, including intelligent sensing and interconnectivity of multi-source heterogeneous physical entities, integrated sharing of 3D geological models and structural models, unified platforms for lifecycle information management, standardization of digital twin data protocols, and theoretical frameworks for digital twin modeling. Furthermore, this study systematically expounds future research priorities across four dimensions: intelligent sensing and interoperability technologies for geotechnical engineering; knowledge graph development and model-based systems engineering; integrated digital twin entity technologies combining 3D geological bodies with engineering structures; and precision enhancement, temporal extension, and spatial expansion of geotechnical digital twins. This paper systematically reviews the application status of digital twin technology in geotechnical engineering for the first time, reveals the common technical challenges in cross-domain implementation, and proposes a theoretical framework for digital twin accuracy improvement and spatiotemporal expansion for geotechnical engineering characteristics, which fills the knowledge gap in the adaptability of existing research in professional fields. These insights aim to provide references for advancing digitalization, intelligent transformation, and sustainable development of geotechnical engineering.

Industrial businesses are going through a period of digital disruption and firms are under severe pressure to undertake Digital Transformation and leverage the Industrial Internet of Things (IIoT). Yet, there is next to no scholarly guidance for such an endeavour. Most industrial firms are developing their Digital Transformation strategies, however, they are not sure what kind of capabilities they should develop for such transformation. Though there is limited academic literature about Digital Transformation and how firms are developing digital transformative capabilities, a systematic literature review was performed to disentangle capability transformation processes and how firms are developing dynamic capabilities to remain competitive in a high-velocity environment. The current study extended dynamic capability theory and proposed digital transformative capabilities (DTCs) for Digital Transformation. To understand the IIoT landscape and how it influences Digital Transformation, an industry review was performed. The research was conducted in two phases. Based on the literature review and industry review, in the first phase, two qualitative exploratory studies were performed. The preliminary exploratory study was conducted to get an understanding of the IIoT landscape and how firms were developing capabilities for transformation. Based on the insights from preliminary exploratory study, a detailed exploratory study was performed which revealed critical themes for Digital Transformation and, based on these themes, a conceptual framework for Digital Transformation was derived. The conceptual framework was divided into two models. The front-end model viii identified three DTCs (Business Model Transformation, Operating Model Transformation and Cultural Transformation), three inputs (Digital Twin, Digital Thread and Digital Mindset) and the factors influencing the DTCs. The back-end model examined the influence of DTCs on dynamic capabilities, which may be indicative of digital transformation in a company. In the second phase, these two models were tested through a quantitative analysis, utilizing data generated from 107 respondents from 87 industrial companies via a self-reported online questionnaire and the application of multiple linear regression analysis. The Digital Twin is widely touted as an important input for DTC but the result did not support that. Digital Thread as an input for DTC was supported and Digital Mindset as an input for DTC was partially supported. Using moderator analysis, important insights were identified. The moderators, Technology Turbulence, Market Turbulence, Competitor Turbulence and Path Dependency had some positive moderation effects. The positive influence of ‘DTC – Business Model Transformation’ on dynamic capabilities which may be indicative of digital transformation in a company was not supported. However, the positive influence of ‘DTC – Operating Model Transformation’ was supported and ‘DTC – Cultural Transformation’ was partially supported. The moderation effects of ecosystem partnership and resource scarcity and constraints were partially supported, and the moderation effects of customer and market demands and digital commitment were not supported or refuted.

Digital transformation and the adoption of technologies in the AEC-sector can lead to efficiency gains in facility management (FM). Digital twins, that is a living representative of the physical asset building, can facilitate real-time data gathering, data monitoring, data-based decision making and support predictive management. The purpose of this study is to analyse the gap between theory and practice regarding the application of digital twins for FM and to understand the benefits and challenges connected with its implementation during the use phase of a building. Despite the growing interest in this topic in academia, the concept of digital twins in connection with FM is rarely employed in practice in the AEC-industry. The findings obtained through a literature review and a set of semi-structured interviews with experts in the field, show that the main challenges for digital twins in FM relate to the organisational culture and that a robust framework for information management is a benefit for digital twins’ implementation. The adoption of digital twins in the built environment needs to be considered in the context of digital transformation. The study supports practitioners with the adoption of digital technologies for the built asset and suggests that future research should examine in further depth the challenges of implementing digital twins.

While the field of hydrography is crucial for maritime navigation and other maritime applications, oceanography is the field that provides the relevant data and knowledge for predicting climate change, monitoring marine resources, and exploring marine life. Digital ocean twins combine these two exciting fields and combine ocean observations and ocean models to establish virtual representations of a real world system, in this case the ocean or an ocean area, as well as assets in the ocean and processes within ocean industries or the natural environment. They have the potential to play a critical role in optimising and supporting sustainable ocean development. Digital Twins are synchronised with their real-world counterparts at a specific frequency and fidelity. They can provide valuable insights into the ocean's state and its evolution over time, which can be used to support decision-making in ocean governance and various ocean-related industries. Digital ocean twins can transform human ocean interactions by accelerating holistic understanding, optimal decision-making, and effective interventions. Digital twins of the ocean use ocean observations, historical and forecast data to represent the past and present and simulate possible future scenarios. They are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT systems. In this article, we explore the benefits of digital twins for the ocean, the challenges in developing them, and the current state of the art in ocean digital twin technology. One of the main benefits of digital ocean twins is their ability to provide accurate predictions of ocean conditions under expected interventions. Their information can be used to support decision- making in various applications including ocean-related industries, such as fishing, shipping, and offshore energy production. Additionally, digital twins can help to improve our understanding of the ocean's complex processes and their interactions with human activities, such as climate change, pollution, resource extraction and overfishing. Researchers and IT companies are combining various technologies and data sources, such as the Internet of Things for ocean observations, state of the art data science, artificial intelligence and machine learning, data spaces and vocabularies into digital ocean twins to contextualise data, improve the accuracy of ocean models and make ocean knowledge more accessible to a wide range of users.

Background: The growing availability of high-resolution imaging, biosensors, molecular profiling, and artificial intelligence has enabled the development of digital patient twins—computational models that reproduce individual physiological and pathological processes in silico. While digital twins have been widely proposed as tools for personalised medicine, their clinical and translational value across major disease domains has not yet been systematically synthesised. Methods: A narrative review was conducted of full-text publications from 2020–2025 addressing digital patient twins in cardiology, oncology, chronic disease management, and rehabilitation. The analysed literature included translational and clinical studies, mechanistic modelling papers, and healthcare system implementations. Evidence was prioritised from studies reporting patient-specific simulations, comparisons with real clinical or imaging data, and therapy-support scenarios. Results: In cardiology, electrophysiological and haemodynamic digital twins demonstrated high concordance with invasive mapping and imaging data and were associated with improved ablation planning, device optimisation, and reduced arrhythmia recurrence. In oncology, tumour digital twins integrating imaging and molecular data predicted tumour growth and treatment response with clinically meaningful accuracy, supporting personalised and adaptive cancer therapy. In chronic diseases, sensor-driven digital twins enabled early detection of physiological deterioration and supported proactive intervention, reducing exacerbations and hospitalisations. In rehabilitation, biomechanical and neurophysiological digital twins improved functional recovery by guiding personalised and robot-assisted therapy. Conclusions: Digital patient twins are transitioning from experimental computational tools to clinically relevant systems capable of influencing diagnosis, therapy selection, monitoring, and patient outcomes. By enabling in silico testing of therapeutic strategies on a virtual representation of the patient, digital twins reduce uncertainty in clinical decision-making and support truly personalised care. Continued progress in data integration, model validation, and regulatory governance will be essential for their safe and widespread adoption in clinical practice.

SUMMARY & CONCLUSIONSModel-Based Systems Engineering (MBSE) is a core technology in facilitating the digital transformation initiatives led by industry and defence governing bodies worldwide, where a prerequisite is the development of a "Digital Twin" (DT) of a given system asset. The definition of a DT and the corresponding mathematical and qualitative abstractions have been broadly defined within recent literature, however, in the context of the RAMS domain, a Digital Risk Twin (DRT) that can capture functional dependencies and their relationship to physical failures is necessary to digitally transform the RAMS domain. In this paper we formally introduce the concept of a Digital Availability Twin (DAT) - that will support a feedback loop for operational data to contextualize the dependencies identified by the DRT and enable the tailoring of baseline risk mitigation strategies such as maintenance policies and related sustainment activities, ensuring the required functions for a given set of missions is available for a corresponding asset fleet in an optimal manner and turn, enabling more agile risk mitigation when preparing for a specific mission condition.

This systematic literature review examines the evolving landscape of digital transformation in the construction industry within the context of Industry 4.0. Drawing from a comprehensive analysis of 81 peer-reviewed publications from the Scopus database spanning 2014-2024, this study provides an in-depth exploration of research trends, technological innovations, and implementation challenges. The bibliometric analysis reveals a significant acceleration in research output since 2021, with particular emphasis on Building Information Modelling (BIM), Digital Twins, Cyber-Physical Systems, and emerging technologies such as Artificial Intelligence and Internet of Things. This review identifies critical research gaps and proposes future research directions to advance the digital transformation agenda in construction. The findings suggest that while technological adoption is increasing, significant research gaps persist terms of implementation at large-scale, economic justification, sustainability, systems integration, and human factors.

Relevance. “Digital transformation”, “digital twin” and similar terms are media phenomena. However, these and related terms hide meanings, goals, objectives, technologies, prospects and dangers inevitable at the present stage of an industrial society development and transformation into post-industrial and beyond. In literature, digital transformation is usually applied to business management and is not concerned with material production. Research objective is to consider "digitalization" in relation to technical and technological objects of industrial production by the example of a coal mine. Methods of research. In the context of industrial civilization and industrial production, using the methods of system analysis and mathematical modeling, the article considers the development and connection between the processes of automation, digitalization, informatization, and intellectualization. It has been shown that it is possible to preserve and develop industrial production based on its digital transformation, which is based on digital twins that transform the concept of automation. The digital twin, which operates in real time and is synchronized with the simulated production process, is considered as a simulation model of matter and energy transformation processes. The descriptions of a coal mine and its digital twin are considered in a general form. The objectives have been formulated for the optimal production control and its digital twin synchronization. Results. The necessary and critical technologies and possibilities for implementing a digital twin have been identified. The relationship has been shown between the digital transformation of the industry, the objectives of large-scale planning, and the emergence of social problems.

Over a past decade, a development of Artificial Intelligent (AI) in medical discipline has been utilized to predict and prescribe drugs on day basis. It has large influences on predicting and diagnosing ailments totally based on the information that have been accrued through an embedded sensor. The sensor collects excessive dimensional facts which have a descriptive clarification on every symptom and diagnostics. It has been stated that more than 75% of clinical executives count on to make investments in digital twin (DT) technological know-how over previous few years. As DT is the replication of any physical/virtual objects like people, manner, and equipment which help to put together a digital transformation of scientific files that are gathered. Due to developments in big data analytics and AI in analyzing records that have been accumulated over a length of time for prediction, DT science is being developed and commercialized aviation and manufacturing processes. Rather than making an attempt to acquire a best duplicate of the human mind, AI structures take advantage of strategies emulating human reasoning as information to grant each helping equipment and higher services. Super intelligence of AI not only performs all physical and manpower-related task but also handles prediction and discovering novel applications and remedies to medical problems as business perspectives. DT is defined as virtual object or computer entity or model that simulates or twinning any real-world things like human or objects. Every DT is linked with a unique digital key that normalizes a bijective relationship with its original. The DT has been used to surveillance the functionalities of physical entities uninterruptedly and make a decision over a problem. This paper portraits digital recreation of healthcare systems like lab, hospital ecosystems, and psychology of a human being and how far it is useful in medical science which in flips hikes revenue and enterprise market of healthcare industry along with a study that focuses on DT applications in medical industries and healthcare applications. Finally, the research has serious diversion into various challenges in medical digitalized system and DT technology.

Intrinsic value of food chain data

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

Digitalización y transformación digital en la industria turística: una revisión bibliométrica y una agenda de investigaciónPropósitoEn las últimas décadas, se ha realizado un número significativo de contribuciones de investigación a la intersección de las tecnologías digitales y la industria del turismo. Sin embargo, no se ha prestado suficiente atención a un examen exhaustivo de la digitalización y la transformación digital en la industria del turismo. Este estudio tiene como objetivo proporcionar una revisión bibliométrica de la investigación sobre digitalización y transformación digital en la industria del turismo y diseñar futuras agendas de investigación para avanzar en el campo de la investigación.Diseño/Metodología/EnfoqueEste estudio utiliza el protocolo de Procedimientos y fundamentos científicos para revisiones sistemáticas de la literatura (SPAR-4-SLR) y un análisis bibliométrico para examinar el progreso de la investigación y mapear científicamente el dominio de investigación de la digitalización y la transformación digital en la industria del turismo. de 2002 a 2023 utilizando datos bibliográficos recuperados de Scopus y Web of Science (WOS).HallazgosEste estudio presenta las tendencias en publicaciones y citas dentro del dominio de investigación sobre digitalización y transformación digital en turismo. Los hallazgos también brindan información sobre los cuatro grupos principales del campo de investigación: innovación digital, ecosistema de turismo inteligente, turismo electrónico y experiencia de destino inteligente. Para aumentar aún más la aplicación de la transformación digital, este estudio ofrece varias recomendaciones para futuras investigaciones sobre la digitalización y la transformación digital de la industria turística.OriginalidadEste estudio avanza en el campo de investigación de la digitalización y la transformación digital en la industria del turismo al examinar en profundidad los principales grupos de investigación en el corpus de investigación de las últimas dos décadas. Además, orienta la investigación futura, sentando así las bases para mayores avances en este ámbito.ImplicaciónEste estudio proporciona implicaciones valiosas para los investigadores, administradores y formuladores de políticas que buscan comprender el estado actual y las direcciones futuras de la investigación en el campo de la digitalización y la transformación digital del turismo.

With the advancement of metaverse technology and the need for real-time financial data, theaccounting sector is undergoing rapid digital transformation. An innovative solution to improve theaccuracy, transparency, and efficiency of corporate financial reporting is the digital twin, a virtualreplica of a physical system. developed an accounting digital twin system to model a company'sbalance sheet in real-time in the business metaverse world and assess its effectiveness and efficiency.This study employed a research methodology and a systems development approach. Requirementsanalysis, architectural design, implementation, testing, and validation were the necessary steps todevelop the system. Data were collected through direct observation, stakeholder interviews, literatureresearch, and system testing on 15,000 transactions during a simulation period. Validation wasconducted by fifty business stakeholders and twenty accounting experts. With an accuracy rate of 99.2% and an average response time of 1.8 seconds, the accounting digital twin method successfullycreated a virtual replica of the company's balance sheet. The system had an uptime of 99.7 % and wascapable of processing up to 850 transactions per minute. 3D visualization in the metaverse achieved auser engagement rate of 91%. Simulation results showed a 78% reduction in reporting time, a 38%reduction in operational costs, and a 42% increase in data accuracy. Furthermore, the systemsuccessfully provided early warnings 72 hours before a liquidity crisis. Validation demonstrated a 96.7% technical integration success rate and a 93% user satisfaction rate. In a real-time company balancesheet simulation in the business metaverse, the digital accounting twin was proven to improve thequality of financial information, accelerate the decision-making process, and enhance corporateaccountability and transparency. This system helps the contemporary accounting sector transform intoa digital one. This research contributes to the development of accounting information systems,business digital transformation, and financial technology innovation. The results can be used as areference for companies wishing to implement digital twin technology in their accounting practices.

The article explores the role of digital technologies in shaping a new direction for organizational crisis management in the post-war period. The relevance of this topic is driven by profound economic transformations occurring in Ukraine due to military events, as well as the need to create efficient development strategies that can ensure business resilience under conditions of uncertainty. The introduction emphasizes that digitalization is becoming not merely a tool for modernization but a strategic condition for enterprise survival. It allows for more efficient resource management, transparent communication, rapid risk response, and integration into global economic processes. The aim of the study is to systematically substantiate the importance of digital technologies in forming development strategies for post-war organizations and to develop a digital roadmap that facilitates the transition from crisis response to sustainable innovative growth. Within the scope of the stated goal, tasks have been identified that include the analysis of post-war recovery challenges, the identification of directions for the digital transformation of management processes, the creation of a conceptual model for implementing digital technologies, and the determination of mechanisms to enhance organizational adaptability. The methodological foundation is based on a combination of theoretical and empirical methods: analysis, synthesis, induction, deduction, generalization, comparison, and observation. Their application provided a systematic understanding of digital transformation processes, identified key factors of organizational resilience, and facilitated the development of a structured digital roadmap for development. The research findings are summarized in the stages of the digital roadmap: crisis diagnosis and response, resource optimization, digital integration of communications, the development of digital leadership and cultural transformation, as well as the establishment of innovative development and proactive resilience. Specific digital tools have been defined for each stage – big data analytics systems, ERP and CRM solutions, cloud services, digital twins, artificial intelligence technologies, and blockchain platforms. The proposed model demonstrates how digitalization transforms traditional crisis management into a flexible system of strategic recovery aimed at risk forecasting and ensuring business process continuity. The conclusions emphasize that the implementation of digital technologies creates a new management paradigm based on data analytics, automation, open communication, and innovative leadership. The developed digital roadmap is a scientifically substantiated tool of strategic management that combines short-term crisis actions with long-term sustainable development goals. The implementation of this model promotes more efficient use of resources, the development of staff digital competence, the strengthening of corporate culture, and the creation of competitive advantages for organizations in the post-war period. Thus, digitalization acts not only as a technological but also as a strategic factor ensuring economic resilience, innovation, and global integration of Ukrainian organizations. Further research should focus on evaluating the practical efficiency of digital tools in crisis management systems, improving mechanisms of digital leadership, and developing sector-specific models of digital resilience.