Unleashing the power of digital twin and big data as a new frontier for smart mobility: An ecosystem perspective

Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a ‘digital twin’ is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the ‘cradle-to-gate’ lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.

A product performance rapid simulation approach driven by digital twin data: Part 1. For variable product structures

A product performance rapid simulation approach driven by digital twin data: Part 2. For variable operating conditions

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the operators are facing much more challenges in oilfield management especially how to improve their decision efficiency and situation awareness. Thanks to the different sensors we deployed on oilfield from drilling to completion and production, tremendous data contributed to the digital asset we are having now. The digital twin makes oilfield management much easier than ever before, hundreds of wells’ performance could be displayed in front of the decision maker or key management level of oil companies, and big data technique helps them get easy understanding of real time behavior on well construction progress, cost management, pain spot of each project. Combining these two methods, it is possible to have an up-to-date awareness of oilfield development status and perceptual intuition to very detail situations. There is a major operator manages over 200 wells per year and some of these wells are challenging exploration well with measured depth over 20000ft which requires experienced team to get the well to total depth, also a lot of shale gas wells with lateral intervals over 8000ft which demands intensive control of cost. All above operations or targets need be done under a safe and efficient way, then the management team taking digital twins to monitor the real time well status which help them get up to date information about whole oilfield status like drilling, completion, production and more. Big data analysis is also used to help enhance the decision- making efficiency and overcome puzzles that traditional method could not solved, like recommending the best practice way on well construction engineering parameters, or ROI (return on investment) assess. The oil company could achieve a better management level with less human resources and much more workload. By the advantages of digital twins and big data analysis, the oil company now managing more than 200 drilling rigs and 300 completion wells in the high efficiency way, and now involving the production wells into next phase digital construction target. Furthermore, considering develop an integrative digital twin of geology and engineering map which get whole formation and well construction more intuitive. Besides, it is proven that digital method like digital twins and big data technique could improve the skill of oilfield management significantly, which optimized the resource and expenditures investigated in modern oil and gas industry.

This paper discusses how Big Data became one of the huge factors of how Digital Twin has developed especially in healthcare. Digital Twin itself has become the bridge in connecting the physical world and the cyber world. Many implementations of Digital Twin helped in creating a safe place to optimize its physical object without having the risk of implementing the changes right away. With the development of Digital Twin, other technologies are now being taken into consideration to improve and optimize it. One of these technologies is Big Data, which is implemented by taking valuable information from the data generated and using it to create a more optimal analyzation of the Digital Twin. Healthcare is one of the fields where Digital Twin with the support of Big Data can be implemented to improve on its process ranging from its facility to a more personalized Human Digital Twin.

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.

Intrinsic value of food chain data

Chapter 9 - Digital Twin and Big Data

Digital twin (DT) emphasizes the visual of biological systems based on in silico computational methods that include data both from the individual as well as the community. By augmenting medical care with digital surveillance and advanced simulation of the human body, the usage of DT in medical field is transforming clinical operations and healthcare administrators. Investigators can use these technologies to learn more about diseases, new medications, and medical gadgets. In the future, this could potentially be used to assist clinicians in maximizing the effectiveness of physician therapeutic approaches. Nevertheless, in the medium run, DTs will aid the healthcare system in bringing life-saving breakthroughs to the marketplace more quickly, at cheaper prices, also with enhanced patient safety. In the medical field, DT can be used to maintain medical devices and enhance their effectiveness. By translating a significant volume of patient records into valuable information, DT and Artificial Intelligence (AI) technologies are furthermore utilized to elevate the life-cycle of healthcare. The supreme goal of digital twinning in medicine is to assist organizations with patient management and coordination. Increasing services, patient desire, deteriorated technology, a lack of beds, enhanced waiting period, and lines plagued Mater remote clinics in Dublin (intended for radiology and cardiology). These issues showed that the existing framework needed to be improved in order to meet rising demand.

Secure sharing of big digital twin data for smart manufacturing based on blockchain

Digital twins can facilitate high-fidelity representations of container terminals by applying various technologies and methods to better measure, understand, and improve operations. In this paper, a decision support system (DSS) based on digital twin and big data technologies is designed to demonstrate how real-time monitoring and an integrated decision support can be established. The DSS provides optimal operation plans and the benchmark for vessel delay early warnings through different resource allocation simulations at the planning level. It further enables real-time operational decision making through real-time monitoring and efficiency analyses using big data engines at the operational level. A case study is conducted for the ultralarge Yangshan Deepwater Automated Container Terminal Phase IV (ACT4) in Shanghai (China) and experimental results have revealed that the proposed digital twin-based DSS can help ACT4 operators to evaluate vessel service using optimized resource allocation plans and operations.

The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises.

This study addresses the critical problem that many infrastructure organizations still lack empirical, data-driven evidence on how digital twin frameworks concretely improve climate-resilient infrastructure design and performance. The purpose is to quantify how digital twin integration, data and analytics capability, and organizational resilience capacity jointly influence climate risk assessment effectiveness and climate-resilient infrastructure outcomes in real cloud and enterprise infrastructure cases. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 professionals working on digital twin enabled infrastructure projects across transport, energy, water, and urban systems. Key variables included digital twin integration (DTI), data and analytics capability (DAC), organizational resilience capacity (RESCAP), climate risk assessment effectiveness (CRAE), and climate-resilient infrastructure performance (CRIP), all measured on five-point Likert scales. The analysis plan combined descriptive statistics, reliability testing, correlation analysis, and multiple regression with sector, organization type, and prior climate event experience as controls. Results show moderately high mean scores for DTI (M = 3.74) and CRIP (M = 3.79), with strong internal consistency (α up to 0.90) and robust positive correlations between DTI, DAC, RESCAP, CRAE, and CRIP (r up to .68, p < .001). The regression model explains 53 percent of the variance in CRIP (adjusted R² = .52), with DTI (β = .34, p < .001), DAC (β = .29, p < .001), and RESCAP (β = .22, p < .001) all significant predictors, indicating that well integrated, cloud-based digital twin frameworks and strong analytics and resilience capabilities materially enhance climate-resilient infrastructure performance in enterprise environments. These findings imply that infrastructure agencies and firms should treat digital twins, data governance, and organizational resilience as an integrated capability stack for climate risk management and design.

The Iliad Digital Twins of the Ocean project [1] is a large (55 partners) European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EU DTO) initative including interoperability with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The approach of Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline.The four digital twin pipeline steps are: Digital Twin Data Acquisition/Collection, Digital Twin Data Representation, Digital Twin Hybrid and Cognitive/AI Analytics Models and Digital Twin Visualisation and Control. The Iliad project has idenified these four steps as main architectural pipeline areas from an interoperability perspective, as described in the following. The architecture is system of systems based and the figure also shows the existence of potential multiple digital twins interactions.The first Digital Twin step focuses on Data acquisition and collection from various sources including collection of realtime see\nsor data, for input to the Digital Twin. This is supported by various Data Spaces and also through a direct Stream Handler. This includes both streaming data and data extraction from relevant external data sources and sensors. It includes support for handling all relevant data types and also relevant data protection handling for this step. In the Digital Twin sensor context this includes the full Observation Pyramid from remote sensing through airborne sensors to surface and subsea sensors and in-situ and IoT sensors.The second Digital Twin step focuses on Digital Twin Data Representation. The data availability for the digital twins is supported by various Digital Twin Data Lakes – connected to Data Spaces and also potentially directly to streaming observations from the previous step.The third Digital Twin step focuses on Digital Twin Hybrid and Cognitive/AI Analytics Models. The processing execution for the models is supported by various Digital Twin Engines. The fourth Digital Twin step focuses on Digital Twin Visualisation and Control. This is being supported by various types of 2D/3D/4D visualisations, and immersive visualisations and further evolutions towards the GeoVerse perspective on MetaVerse.The Iliad project is providing a framework with tools and services for these four digital twin pipeline steps aiming at technical and semantic interoperability with, and portability to, the EU DTO ecosystem of digital twins of the ocean.[1] Iliad – Digital Twins of the Ocean project, https://ocean-twin.eu/