Digital Availability Twin – Targeted Risk Mitigation from Design to Operation

SUMMARYWhile the definition of a Digital Twin (DT) is provided within recent literature, each DT should be designed to meet the specific requirements of the user. As RAMS is focused on the identification, understanding and mitigation of technical risk in a system, a RAMS engineer requires a DT that can autonomously establish the potential dependencies and impacts of functional and physical failures on a system, and auto-generate various analyses to identify the appropriate mitigation approach.The concept of a Digital Risk Twin (DRT) described in this paper should uses an integrated and inter-related set of information about the system, autonomously reflecting changes across analyses that utilize causal simulation to understand potential risks and map their dependencies. This definition has been examined thoroughly from the original literature for Digital Twins, through an examination of core system safety/risk assessment practices demonstrated in RAMS and finally arrives at the main point of the definition and key aspects of a Digital Risk Twin (DRT).As the DRT digitizes the RAMS process across each stage of the Product Lifecycle, it is important that it offers common DT features such as integration, visualization, and simulation. The DRT will also implicitly digitize the engineering domain knowledge utilized in the design process (‘Digital Domain Knowledge’), providing a persistent context for analysis and design decisions, and so requires a framework of automated data management, maintaining traceability between activities and decisions made in relation to identified risks.

Sustained continuous monitoring and replication of organizational development in digital organizational twins is of particular importance for labour-intensive enterprises and also those in which reciprocal relations between social, corporate, normative and performative aspects assume the leading role. The main purpose of the research is the developing of a digital representation of organizational processes, which focuses on the performance, working activities, organizational issues, behaviour and interactions between of the organizational members. Consequently, the objectives of research include the monitoring of current research state, concept and design of a digital twin. The implementation of digital organizational twin should improve considering timely optimization of proactive and reactive organizational development measures in the company in relation to the core variables of the 7S model. The created digital twin should map the dynamics of organizational development, as well as concomitant and deviating processes. Systematization literary sources and approaches for the digital replication of organizational development issues indicates the lack of publications on research and diffuse distribution of scientific interest. The initial design of organizational development in the digital twin is based on four main objects and limited to a certain number of investigated parameters. This paper compare the conventional and digitalized organizational development process, explain the data flow in digital organizational twin, the design of organizational development in the digital organizational twin, provide an overview of the individual facets of organizational development, list the parameterization models and exemplarily illustrate the visualization of selected parameters. The results of the research can be useful for the expansion of the tension bridge between organisational development and technologies and the development of new potentials for the study of socio-technical effects in companies. This can be extended to include the other facets of business management and supplemented by the connection of other technological resources.

Biopharma Is Going Digital … Bit by Bit

The Digital Twin (DT) concept — defined as a continuously updated virtual replica of a physical entity — is transforming pharmacotherapy and the pharmaceutical industry. Evolving from basic simulations to multi - scale intelligent systems powered by AI, IoT, and Big Data, DTs provide unprecedented predictive capabilities across the drug development pipeline. In drug discovery, DTs using New Approach Methodologies (NAMs) and in silico models help identify targets, predict toxicity, and reduce reliance on animal te sting. In pharmaceutical manufacturing (Pharma 4.0), DTs of production systems, such as bioreactors, support real - time quality control, predictive maintenance, and process optimization. The Digital Patient Twin, integrating genomics, clinical records, and wearable - sensor data, enables genuine personalized therapy by simulating patient-specific treatment responses. DTs also enhance clinical trials through in silico cohorts and improved subgroup identification. Despite challenges related to data standards, mo del complexity, and information security, DTs are set to drive a future of highly precise and personalized healthcare.

Production processes must allow high flexibility and adaptivity to ensure food supply. This includes reacting to disruptions in the supply of ingredients, as well as the varying quality of ingredients, e.g., seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of these raw materials, such a digital twin has to take into account not only the processing steps, but also the chemical, physical, or microbiological properties that change the food independent of the processing. We propose a model-based learning and reasoning loop, which is known from self-aware computing (SeAC) systems in the so-called learn–reason–action loop (LRA-M loop), for modeling the input for the LRA-M loop of food production, not as a pure knowledge database, but data that are generated by simulations of the bio-chemical and physical properties of food. This work presents a conceptual framework on how to include data provided by a digital food twin in a self-aware food processing system to respond to fluctuating raw material quality and to secure food supply and discusses the applicability of the concept.

In today's world, digital transformation is becoming increasingly crucial in industry. The Digital Twins and simulations become much more important with growing complexity of products, the need to accelerate product development processes while reducing costs. The increasing value of Digital Twins is prompting organizations in the aviation industry to actively utilize them in research and development efforts. However, due the idea of difficulties of using Digital Twins in the industries, product developers don’t have a clear idea where to begin. This article provides readers with a general overview of the current situation by offering an examination of the aviation sector. Initially, the concept of Digital Twin, its evolution, and relevant studies are explored, followed by an examination of the impact of creating Digital Twins for companies operating in aviation on the product development process. In this study, unlike other published works, the concept of Digital Twin is explained under three subheadings. These include the Design Digital Twin for design steps, the Production Digital Twin for manufacturing processes, and the Performance Digital Twin for post-delivery work related to the product. The final section of the study evaluates the contribution of Product Lifecycle Management (PLM) systems to the process of creating Digital Twins and the advantages they provide to companies in product development processes. The present research work does not contain any studies performed on animals/humans subjects by any of the authors.

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.

Computer modeling and simulation is found in many industrial applications, for example there exist CAD models, finite element simulations of mechanical parts, simulations of chemical plants, models of remaining life prediction and many more. Those models and simulations are used both in the design of systems, and during their operations. Furthermore, during a system's operation, data is collected and stored at a large scale, this is a result of what is commonly called the fourth industrial revolution, or industry 4.0. This revolution is that of computer automation and large scale data exchange, seen in the manufacturing industry of the 21st century. These computer models, and the widespread collection of data gave rise to the concept of a digital twin. A digital twin is a virtual representation or a mirroring of a real-world system. Data is continually collected from that real-world system, and fed back to the digital twin such that it contains the most up-to-date model of the system it mirrors. In exchange, a digital twin offers its users a variety of helpful services based on its models and large collection of data, examples of such services are historical data replay, virtual testing, virtual commissioning and optimization. Despite stemming from the manufacturing field, digital twins find applications in many more domains such as healthcare, geophysical processes and construction engineering. A key aspect in all digital twins is that of evolution, and this in two different ways. The first way deals with the mirroring of the real-world system. When that real-world system evolves, for example, because components are replaced or because it ages and shows signs of wear and tear, the models in the digital twin should reflect that evolution. The second way deals with the evolution of the services in the twin itself. Like any other software system, the purpose and requirements of the digital twin can evolve over time. In this thesis the focus is on a subset of issues encountered in these two types of digital twin evolution. It provides reusable techniques that aid digital twin developers with handling the evolution of their digital twin. The first part of this thesis tackles the digital twin evolution driven by requirements/purpose changes of the digital twin itself. Here, the challenge is interweaving new software components of the digital twin with the existing components, and solving their conflicting interactions. To this end, we developed a notation and a set of common transformation templates that occur during a twin's evolution. This notation allows twin developers to visualize and reason about the impact the evolution has on the digital twin. The second part tackles the digital twin evolution driven by changes in the real-world system. The main issue we identify is how to handle divergences between the real-world system and the twin as the real-world system ages over its lifetime. It is important, since the accuracy of the twin's services depends on the agreement between the model and the real-world system's behavior. To this end, we drew inspiration from model verification and validation techniques to provide a method that detects deviations between a digital twin's model and a real-world system. We additionally applied state-of-the-art classification methods to further pinpoint the cause of the divergence as to ease the model updating process. Combining those two techniques we demonstrate a workflow that incorporates them for the continuous detection of system variations during a software release cycle to provide accurate testing results when the digital twin is used for virtual testing purposes. Supporting these two parts, we developed a domain specific language for defining model validation experiments and a gantry crane twin system. The domain specific language allows non-domain experts to define and execute model validation experiments without requiring knowledge of model simulation. The gantry crane twin system consists of a physical gantry crane and an accompanying digital twin, it is used throughout this thesis to demonstrate or evaluate the proposed techniques.

Many core technologies of Industry 4.0 have gained substantial advancement in recent years. Digital Twin (DT) has become the key technology and tool for manufacturing industries to realize intelligent cyber-physical integration and digital transformation by leveraging these technologies. Although there have been many DT-related works, there is no standard definition, unified framework, and implementation approach of DT until now. Widely developing DTs for the manufacturing industry is still challenging. Thus, this paper proposes a novel implementation framework of digital twins for intelligent manufacturing, denoted as IF-DTiM, which possesses several distinct merits to distinguish itself from previous works. First, IF-DTiM fully utilizes new-generation container technology so that DT-related applications and services can be packaged in a self-contained way, rapidly deployed, and robustly operated with the capabilities of failover, autoscaling, and load balancing. Second, it leverages existing intelligent cloud manufacturing services to realize the intelligence for DT externally in a scalable and plug-and-play manner instead of using traditional approaches to embed intelligence in DT. Third, IF-DTiM contains Product DT for products, Equipment DT (i.e., EQ DT) for equipment, and Process DT for production lines, which can generically fulfill the demands and scenarios to achieve intelligent manufacturing for various manufacturing industries. Testing results show that IF-DTiM can achieve remarkable performance in rapid deployment and real-time data exchanges of DT-related applications. Finally, we develop an example DTiM system for CNC machining based on IF-DTiM to demonstrate its efficacy and applicability in facilitating the manufacturing industry to build their DT systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Developing Digital Twin (DT) systems to realize intelligent manufacturing is challenging. The proposed IF-DTiM (Implementation Framework of Digital Twins for Intelligent Manufacturing) provides a novel container-technology and cloud-manufacturing-service-based systematic methodology for building DTiM. In this paper, we present the system architecture and several operational scenarios (e.g., how to create and use DTs) of IF-DTiM, together with the design of its core functional mechanisms (e.g., rapid deployment scheme for DT, real-time data exchange for DT, DT interface pattern, and general workflow architecture for DT). Also, an example DTiM system for CNC machining based on IF-DTiM is presented to facilitate the practitioners to adopt the designs and niches in IF-DTiM to build their desired DTiM systems.

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 development of process-driven systems and the advancements in digital twins have led to the birth of new ways of monitoring and analyzing systems, i.e., digital process twins. Specifically, a digital process twin can allow the monitoring of system behavior and the analysis of the execution status to improve the whole system. However, the concept of the digital process twin is still theoretical, and process-driven systems cannot really benefit from them. In this regard, this work discusses how to effectively exploit a digital process twin and proposes an implementation that combines the monitoring, refinement, and enactment of system behavior. We demonstrated the proposed solution in a multi-robot scenario.

Digital twins are a concept often reduced to simulation. While simulation has been used in logistics for a long time, digital twins extend the common notion of simulation through its capacity to support near real-time planning and decision-making. Digital twins enable a dynamic, detailed, and functional real-time representation of physical assets to monitor their performance, anticipate their future state, or control how their resources are used. Its approaches and methods to designing, developing, implementing, and updating digital models that allow modularity, reuse and evolution of their components have not yet been exhaustively developed. It is not the first time the modeling &amp; simulation community has attempted to create real-time simulation models and tools. However, it is the first time the technologies required to develop such advanced performance assessment and planning and control systems are available to enable the functions needed to support real-time adaptive planning and decision-making. Several conceptual frameworks have been proposed to build a coherent and common understanding of planning and control systems, but its interpretation and adaptation to specific systems still require the development of dedicated methodological and technological architectures. By exploring the body of knowledge before the popularization of the term digital twins, this paper proposes a critical analysis of the technological requirements and methodological steps to implement digital twins in logistics. Ultimately, the goal of this project is to propose a methodological framework dedicated to the design, development, and operation of digital supply chain twins.

Digital transformation utilizing the digital twin of a process can provide enormous benefit. It is possible to effectively monitor the process operation and control the process erroneous behavior by using the high-fidelity digital twin. In addition, the better operational strategy can be identified through process optimization from the digital twin. To perform such activities about the huge scale of process, we established a digital platform that allows the operational personnel to efficiently monitor the operation condition with the useful information such as economics and representative indexes that can be obtained from the digital twin. A chemical plant utility system that has a goal to supply the process utilities such as steam and power stably was used as the target process. As exclusive features of the project for the utility system the platform suggests the multi-level optimization results in terms of the process specifications as well as the trends of the key performance indicators of the process. It also provides a tool, what-if analysis, to simulate the hypothetical situation in preparation for the possible change of the external factors. Using the digital process twin technology, we were able to suggest the capability to save the cost and present the high-level information that cannot be utilized from the process data alone.

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

This study examines the connection between digital twin mechanisms and digital twin uses. We examined digital twins through three dimensions: navigation; interaction; and discovery. The effects were examined by considering two different uses: enabling use and controlling use. This study found a direct and positive relationship between the interaction mechanism and the enabling and controlling uses of digital twins. The results also indicated the nonexistence of any relationship between the navigation and discovery mechanisms, and digital twins’ enabling and controlling uses. The results indicate that digital twin realism exerts a negative moderating effect on the relationship between digital twins’ discovery mechanism and controlling use. Managers can leverage research findings as they adopt new digital tools to support their service businesses and management needs. For example, managers can leverage research findings by using digital technologies to inspire and motivate employees to fulfil organisational objectives.