The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Rathore, M. Mazhar; Shah, Syed Attique; Shukla, Dhirendra; Bentafat, Elmahdi; Bakiras, Spiridon

doi:10.1109/access.2021.3060863

Cited by 256 publications

(126 citation statements)

References 119 publications

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“…Another SLR with a more tapered orientation by Rathore et al [25] captures the role of big data and ML in digital twinning. In a review of 61 sources in various databases, patents, and technical reports, the authors identified different applications of big data and ML in the context of DTs in various industries.…”

Section: Related Workmentioning

confidence: 99%

“…Examples of potential ML algorithms and data models include applications in production, healthcare, transportation, education, and business. Furthermore, Rathore et al [25] proposed a model for the integration of IoT, big data, ML, and DTs, in which (1) IoT and other data sources create big data, (2) data are employed in data models and ML algorithms, (3) simulations and automation procedures are executed in the virtual environment, and (4) such simulations and automated processes are used for deployments in the physical part of a DT. The SLR offers a detailed analysis of this model with a description of the applied ML approaches in different industries.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture

Rossmann¹,

Hertweck²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The concept of digital twins (DTs) is receiving increasing attention in research and management practice. However, various facets around the concept are blurry, including conceptualization, application areas, and reference architectures for DTs. A review of preliminary results regarding the emerging research output on DTs is required to promote further research and implementation in organizations. To do so, this paper asks four research questions: (1) How is the concept of DTs defined? (2) Which application areas are relevant for the implementation of DTs? (3) How is a reference architecture for DTs conceptualized? and (4) Which directions are relevant for further research on DTs? With regard to research methods, we conduct a meta-review of 14 systematic literature reviews on DTs. The results yield important insights for the current state of conceptualization, application areas, reference architecture, and future research directions on DTs.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture

Rossmann¹,

Hertweck²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…While real-time data may be a factor for the DT to show the current state of the physical entity with the help of a visualized DT, historical or accumulated data during, e.g., the operation process of the physical entity, allow more complex simulations for optimization, failure studies or predictive maintenance, thus building a bridge to batch analytics (see Section 3.3.2). To realize these complex applications, authors research how concepts from AI, machine learning or other data science aspects such as big data analytics, may be applied in an efficient manner, or for which use cases of the Smart Factory they are applicable (e.g., [50]).…”

Section: Simulation and Decision Making In Smart Factories Using Digital Twinsmentioning

confidence: 99%

Knowledge Integration in Smart Factories

et al. 2021

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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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“…Within the conceptual framework that is herein proposed, the DT is firstly characterized by virtually replicating the MPS, an operation also known as digital twinning [86].…”

Section: Integrating the Dt Into The Sc Contextmentioning

confidence: 99%

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz

Mula

2021

Computers

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Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

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The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Cited by 256 publications

References 119 publications

Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture

Digital Twins: A Meta-Review on Their Conceptualization, Application, and Reference Architecture

Knowledge Integration in Smart Factories

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

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