Simulation-based Analysis of the Interaction of a Physical and a Digital Twin in a Cyber-Physical Production System

Alla, Abderrahim Ait; Kreutz, Markus; Rippel, Daniel; Lütjen, Michael; Freitag, Michael

doi:10.1016/j.ifacol.2019.11.383

Cited by 23 publications

(11 citation statements)

References 11 publications

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“…Some researchers point out a trend towards digital twins, i.e. computerised models that represent a physical object in real time (Negri, Fumagalli, and Macchi 2017;Alla et al 2019;Frank, Dalenogare, and Ayala 2019;Singh et al 2019). One of the substantive areas of data analytics and digital twin applications is SC disruption risks (Choi, Chan, and Yue 2017;Ivanov, Dolgui, and Sokolov 2019).…”

Section: Introductionmentioning

confidence: 99%

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Ivanov

Dolgui

2020

Production Planning & Control

825

436

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We theorize a notion of a digital supply chain (SC) twina computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies.

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Section: Introductionmentioning

confidence: 99%

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Ivanov

Dolgui

2020

Production Planning & Control

825

436

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“…It is therefore important in the digital twin city framework to adopt the appropriate data fusion techniques to deal with these data [58] . Some researchers assert that data fusion technologies can be classified into three groups: data-level fusion, feature-level fusion, and decision-level fusion [59] .…”

Section: Data Fusionmentioning

confidence: 99%

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

Yang¹,

Karimi²,

Kaynak³

et al. 2021

CES

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Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.

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“…A szimulációval ellentétben a digitális iker nem a "Mi lenne, ha…?" típusú kérdésekre ad választ, hanem előrejelzésre, diagnosztikára és vezérlésre használják [10]. A kiber-fizikai rendszerek magukba foglalják a digitális ikerpárt és annak fizikai megvalósulását.…”

Section: Irodalomkutatásunclassified

Gyártósori anyagellátás hatékonyságnövelése modelltervezéssel és szimuláció alkalmazásával

Veres

Tamás

Norbert

2020

MDT

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A szimulációs technikák már évtizedek óta rendelkezésünkre állnak és folyamatosan fejlődnek. Az Ipar 4.0 és Logisztika 4.0 elvein belül is nagy szerepet kapnak az újabb szimulációs technikák, amelyek nem csak a valóságot képezik le minél hűebben, hanem már közvetlen valós idejű kapcsolattal rendelkeznek egy rendszer döntésképességének javítása érdekében. Ahhoz, hogy ezeket az elveket felhasználjuk a valóságban szükségünk van egy általánosított modellre, amely alapján egy gyártórendszer leképezhető. Jelen műben egy ilyen modell alapján mutatjuk be egy létező gyár belső logisztikai rendszerének a szimulációs vizsgálatát, amely elsősorban dolgozók feladatonkénti szétosztását és járműpark felülvizsgálatára koncentrál.

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Simulation-based Analysis of the Interaction of a Physical and a Digital Twin in a Cyber-Physical Production System

Cited by 23 publications

References 11 publications

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

Gyártósori anyagellátás hatékonyságnövelése modelltervezéssel és szimuláció alkalmazásával

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