Model construction of planning and scheduling system based on digital twin

Biopharma Is Going Digital … Bit by Bit

The digital twin, adapted to the needs of urban planning and monitoring systems, is the subject of research and development in the academic, commercial and public sectors. The article provides international context in conceptual and practical implementation through their maturity level, scope, purpose, structure, input, processing features and output. The City of Zagreb is presented as an example of the implementation of GIS and 3D city models in urban planning, with activities towards a digital twin city. Based on research and local specifics, the concept of a digital city twin was proposed for the needs of urban planning, and future challenges were identified.

The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to 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 the 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 independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.

The keyword “human digital twin” has received considerable attention in recent years, and information technology has been developed in healthcare and sports training systems to guide human behavior to a better state. In contrast, from optimizing the production and maintenance processes of industrial products, which is the origin of the term “digital twin,” intelligent robot systems can be interpreted as a mainstream of digital twin. In other words, assistive robots that support humans in their daily lives and improve their life behavior require the integration of human digital twin and conventional object digital twin. However, integrating these two digital twins is not easy from the viewpoint of system integration. In addition, it is necessary to encourage humans to change their behavior to provide users with subjective and immersive experiences rather than simply displaying numerical information. This study reviews the current status and limitations of these digital twin technologies and proposes the concept of a virtual reality (VR) digital twin that integrates digital twins and VR toward assistive robotic systems. This will expand the experience of both humans and robots and open the way to the realization of robots that can better support our daily lives.

Based on the current applications in the oil and gas industry, this paper approaches the main challenges in the development of a Digital Twin for subsea production system assets. Bibliographical research was carried out to select relevant digital twin solutions developed in the different sectors of the oil and gas industry, in order to identify the main challenges, risks and prospects for implementing this type of technology. Furthermore, this manuscript also provides solutions developed by the authors based on the experiences in research, development and innovation of national projects for digital transformation. These solutions contemplate data analysis, connections with databases, definitions of recurrent degradation models in the oil and gas industry. A digital twin can be defined as the digital representation of a system or asset, which calculates states and provides relevant information through integrated models and data, to support decisions throughout the life cycle. In the upstream, whose main activities involve the exploration and production of oil and gas, digital twin can be used as a tool for asset integrity management in the production system and associated with integrity management methodologies such as Risk Based Inspection (RBI). The implementation of a digital twin is a complex task and has several challenges, since it is composed of many models and data that need to be interconnected. However, it is common for data from different sources to be stored on different systems. Another relevant challenge concerns the models associated with digital twins, which are sources of uncertainties. The lack of consistent models and coordination between current physical and virtual models, complex data validation, collection and processing challenges are obstacles in implementing a digital twin. If well implemented, the digital twin can help in the decision-making process related to asset integrity management by following the asset throughout its life cycle. In an RBI approach, it can reduce the OPEX cost of subsea systems. This paper will approach the stages of the development of a digital twin for subsea production systems assets and the main challenges based on recent studies carried out in the oil and gas industry such as the requirement for transparency in data exchange while maintaining security and confidentiality, data-model assimilation, uncertainty and validation of models. In addition, it will be presented the experience acquired by the authors in the development of solutions for integrity management.

The application of digital twin technology in healthcare has emerged as a significant advancement, offering new possibilities for personalized medicine, surgical planning, and healthcare system optimization. By creating real-time, data-driven virtual models of patients, organs, and hospital environments, digital twins enable precise simulations, predictive analytics, and personalized treatment strategies. This review and meta-analysis evaluate the role of digital twins in improving clinical decision-making, patient outcomes, and operational efficiency while also addressing the technical, ethical, and regulatory challenges that hinder their widespread adoption. The analysis synthesizes data from studies examining digital twins in disease modeling, surgical rehearsals, and hospital resource management, highlighting their ability to enhance patient-specific interventions and optimize healthcare workflows. The results suggest that digital twins significantly improve diagnostic accuracy, treatment efficacy, and hospital efficiency, demonstrating their potential as transformative tools in modern medicine. However, challenges such as data integration complexities, patient privacy concerns, computational demands, and regulatory uncertainties must be addressed to ensure reliable implementation. This study underscores the importance of further research, cross-disciplinary collaboration, and the establishment of standardized regulatory frameworks to facilitate the responsible integration of digital twin technology in healthcare. With continued advancements and appropriate safeguards, digital twins can contribute to more precise, efficient, and patient-centered healthcare delivery, paving the way for a new era of medical innovation.

Purpose This study explores the integration of monitored data from Tunnel Boring Machine (TBM) operations into a digital twin framework to generate real-time status information for mechanized tunnelling. The study investigates the potential benefits that can be derived in terms of lean operations and reduced waste of rework. Design/methodology/approach Using a design science research approach within a case study of a tunnelling project, the researchers developed, tested and refined the Digital Twin Construction (DTC) system and observed use cases of its application. The empirical data collected was analyzed to draw conclusions concerning the feasibility of the integration of TBM excavation data into a digital twin and the impacts of its use in improving flow and avoiding rework by reducing uncertainty. Findings The study identifies key technical challenges, such as imperfect data interoperability across different systems involved in tunnelling operations and the need for standardized approaches to modelling. It also highlights the value of the digital twin in supporting lean design and construction workflows by providing the project team with information drawn from a dynamic representation of the tunnel’s physical state in short cycle times. Originality/value This study presents an innovative computational design algorithm that automates data integration, enabling digital twin generation as a functional product. Furthermore, it includes use cases illustrating how the resulting digital twin supports the improvement of specific operations, such as bracketry design and construction. By addressing both technical and operational dimensions, this case study serves as a valuable resource for researchers and industry practitioners aiming to advance digital twin technology and lean construction in tunnelling and broader construction applications.

Aim. Development of the author’s concept of increasing the efficiency of financial risk management on the basis of application of the technology of creating and using the digital twin of the enterprise.Objectives. To formalize the problems of existing approaches to solving the problems of financial risk management of enterprises of the real sector of economy; to identify trends in the development of digital twins, to formulate the advantages of their practical application and to give the author’s interpretation of the concept of “digital twin of the enterprise”; to build and substantiate a multilayer system of indicators of the digital twin of the enterprise in the context of improving the efficiency of financial risk management; to develop the principles of building a digital twin of the enterprise, focused on solving the problems of financial risk management.Methods. The research was carried out on the basis of system approach, in the process of realization of which the methods of synthesis, logical comparative, factor and graphical analysis, basic provisions of financial risk management and the results obtained in domestic and foreign scientific literature on the problems of realization of approaches and methods of financial risk management were used.Results. The author’s concept of increasing the efficiency of financial risk management based on the application of new technologies for creating digital twins is proposed. In particular, by means of formalizing the problems of existing approaches to solving the problems of financial management, identifying trends in the development of methodology and technologies for creating digital twins, the idea of increasing the efficiency of financial risk management on the basis of creating a digital twin of the enterprise is outlined. The multilayer system of indicators defining the structure of this digital twin is proposed and substantiated, the tasks are defined and the principles of its construction oriented to solving the tasks of financial risk management are developed. The financial kernel of the digital twin of the enterprise is constructed, containing the contours of formation of financial indicators common for different enterprises, determining the achievement of the objectives of financial risk management and its efficiency. The qualitative substantiation of increasing the efficiency of financial risk management with the application of the methodological basis for the creation and use of digital twins proposed in the article is given.Conclusions. Conceptual provisions of creation and use of digital twins for the purposes of financial risk-management, given in the article, create a theoretical and methodological basis for increasing the efficiency of financial risk-management taking into account the use of new digital technologies and can serve as a basis for practical construction of effective systems of financial risk-management at enterprises of the real sector of economy.

Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities

The modern world is undergoing rapid changes, driven by both technological progress and dynamic political and economic processes. In this constantly changing environment, adaptability and flexibility have become critical criteria for business survival, especially in secondary sectors such as transport and logistics. These industries are increasingly faced with the urgent need to optimize financial flows in conditions of high uncertainty and market volatility. In parallel, the pressure for transparency, cost-effectiveness and rapid response in the decision-making process is growing.One of the most promising solutions to these problems is the integration of digital twins into the planning and execution of logistics processes. Digital twins are dynamic virtual models that reflect real objects, systems and processes in real time, allowing companies to simulate operations, track changes and predict results with a high degree of accuracy. While the use of digital twins has been widely studied in engineering, manufacturing, and supply chain optimization, their application to financial management in logistics companies remains relatively underdeveloped and understudied.This paper addresses this critical gap by proposing a conceptual model for integrating digital twin technology into financial decision-making processes in logistics. It explores how digital twins can be used to dynamically monitor costs, simulate cash flows, and increase transparency and traceability of financial transactions. With these capabilities, logistics firms can achieve better cost control, reduce inefficiencies, and improve overall financial performance. The novelty and significance of this study is due to the lack of standardized approaches to implementing digital twin technologies into financial workflows. To address this issue, the paper also provides a review of practical examples and current implementations of digital twins by logistics companies around the world, focusing on the measurable impact of such integrations on financial performance and strategic agility.Key words:logistics process, financial flows, digital twin, cost planning, business process.

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

The Architecture, Engineering, Construction, Operation and Facility Management (AECO-FM) industry presents a well-known discrepancy in terms of productivity and digitalization compared with other sectors. The introduction of building lifecycle management (BLM) could lead to several benefits covering all the phases of the building lifecycle (BLC) and enabling the implementation of a model-based integrated management system. Nevertheless, its adoption is far from being spread across the construction field and the case studies in the existing literature present several criticalities related to the adopted technologies and policies which hinder the complete development of a BLM system. To overcome these obstacles, digital twins (DTs) could be exploited to enhance the collaboration among actors, preserving the integrity of data, and offering a multidisciplinary environment to carry out realistic simulations. The purpose of this research is to conduct a scientometric analysis to evaluate the relationships between the topics of BLM and DT in the AECO-FM sector. The most common key studies and patterns are identified by processing bibliometric metadata. The most investigated topics related to BLM and DT are system design, system management, and Building Information Modeling (BIM) applications. The analysis reveals a gap regarding the connection between BLM and DT, identifying a potential research area that is currently unexplored in the academic field. The integrated adoption of these topics could contribute to promoting a holistic approach in the built environment by preserving data consistency, reducing information loss between design and construction phases, and strengthening connectivity among the actors involved in the process.

The food supply chain is a critical part of modern societies. As with other facets of life, it is thoroughly digitalized, and uses network connections. Consequently, the cyber security of the supply chain becomes a major concern as new threats emerge. Cyber ranges can be used to prepare for such cyber security threats by creating realistic scenarios mimicking real-world systems and setups. Organizations can participate in cyber security training and exercises that present them with these scenarios. Cyber ranges can also be used efficiently for research and development activities, because cyber ranges are realistic environments and can be used for the generation of realistic data. The aim of this study is to describe a digital twin of the food supply chain built for cyber range-based cyber security exercises. The digital twin mirrors the real-world situation with sufficient detail, as required by the cyber exercise. This research uses the design science methodology, which describes the construction and evaluation of the proposed system. The study explains the general capabilities of the food supply chain digital twin and its use in the cyber range environment. Different parts of the supply chain are implemented as Node.js services that run on the Realistic Global Cyber Environment (RGCE) platform. The flow of ingredients and products is simulated using an apparatus model and message queues. The digital twin was demonstrated in a real live cyber exercise. The results indicate that the apparatus approach was a scalable and realistic enough way to implement the digital twin. The main limitations of the implemented system are the implementation on one specific platform, and the need for more feedback from multiple exercises. Creation of a digital twin enables the use of cyber ranges to train organizations related to the food supply chain.

Increased operational constraints on spacecraft and ground station operations have led to stricter ground software management and better performance throughput. For Mission Planning and Scheduling Systems, the ability to process numerous scheduling and planning input requests from multiple systems, applying high-level logistical rules, and providing export capabilities to all external entities, including the spacecraft itself, must be balanced against system performance. The constraint on the processing time of the schedule comes from a mission-defined need-by time, which is driven by execution or contact time. flexplan, an operational Mission Planning and Scheduling (MPS) system, has addressed the balance of performance and system capabilities. flexplan utilizes a combination of a relational database and soft-algorithm scheduling to maximize system performance. This paper demonstrates the method for the design of the internal database processing and maintenance. Performance methodologies and capabilities are presented to demonstrate software strength in two case studies: The Lunar Reconnaissance Orbiter (LRO) low lunar-orbit science mission currently operational and the Ground Network Schedule System (GNSS) multi-mission, multi-stations study of NASA’s infrastructure.

The dynamic nature of manufacturing and evolving customer demands require agile adaptation within Manufacturing Ecosystems—interconnected networks of enterprises and institutions collaborating to develop market—oriented solutions. To support this adaptation, it is crucial to evaluate large volumes of data and assess alternative scenarios electively. Digital Twins (DTs) enable the replication of physical systems into virtual models, facilitating the exploration of such scenarios. In most applications, Decision Support (DS) is essential and can be considered intrinsic to DTs. By integrating DS within DTs, the loop can be closed—transforming simulation information into actionable decisions. This study investigates recent advances and trends in the use of DTs for DS in production processes, with a focus on applications in Manufacturing Ecosystems. A systematic review is conducted to examine how DTs contribute to complex and holistic decision‐making, including tasks such as production planning, maintenance scheduling, and defect management. Special attention is given to how decisions are made within DT‐based applications and the extent of their autonomy and complexity. The review contributes to the identification of current research directions and gaps regarding the integration of DTs and DS, with the aim of supporting more effective and adaptive manufacturing strategies.