Human Digital Shadow: Data-based Modeling of Users and Usage in the Internet of Production

Mertens, Alexander; Pütz, Sebastian; Brauner, Philipp; Brillowski, Florian; Buczak, Nadine; Dammers, Hannah; Dyck, Marc Van; Kong, Iris; Königs, Peter; Kordtomeikel, Frauke; Rodemann, Niklas; Schaar, Anne Kathrin; Steuer-Dankert, Linda; Wlecke, Shari; Gries, Thomas; Leicht-Scholten, Carmen; Nagel, Saskia K.; Piller, Frank T.; Schuh, Günther; Ziefle, Martina; Nitsch, Verena

doi:10.1109/hsi52170.2021.9538729

Cited by 23 publications

(6 citation statements)

References 33 publications

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“…A special case is the DT of humans, so‐called human digital twins . Human IDTs in manufacturing comprise all connected data that can be assigned to human actors (e.g., patterns of behavior, knowledge, skills, and experiences) to support decision‐making and enhance activities within a production system (Mertens et al, 2021). As human IDTs entail personal data and algorithmic decisions, the protection of the affected personal rights of employees and other stakeholders becomes crucial to create acceptance when employing such technology.…”

Section: Resultsmentioning

confidence: 99%

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Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Dyck

Lüttgens

Piller

et al. 2023

J of Product Innov Manag

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Digital twins (DTs) are virtual representations of real-world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real-time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data-driven and AI-enabled applications for prediction and regulation to drive better decisionmaking and continuous innovation.

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Section: Resultsmentioning

confidence: 99%

“…An extreme design choice from our Delphi findings is the comprehensive virtualization of human resources and decisions in manufacturing by Human DTs (Mertens et al, 2021). INVISTA (Case no.…”

Section: Idts and The Selection Of Innovationmentioning

confidence: 99%

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Dyck

Lüttgens

Piller

et al. 2023

J of Product Innov Manag

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“…For each layer, we highlight research that has already been started to address these issues. We will point the reader to other publications by the authors in which more concrete research results are reported, such as the usage of Digital Shadows in process mining [22] or adaptation of the production system to the capabilities of the worker [102].…”

Section: Setting the Stage For The Internet Of Productionmentioning

confidence: 99%

“…Also, by continuously capturing the interactions of experienced workers with the production systems, their knowledge can be integrated to improve future recommendations for novices. Furthermore, digital images of the workers' capabilities and requirements can be generated and used to orchestrate the collaboration between production systems and workers, for example, by adopting the speed of production process and human-robot collaboration to the workers' needs [102].…”

Section: Humane Interfaces For Interacting With Digital Shadowsmentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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“…While recent advances concerning CPS and the IIoT contribute to promising developments with an application in production [4], [10], [11], [14]- [16], [19], [21]- [23], [25], [31], [41], [45], [46], [52], [62], [65], [68], [70]- [72], [76], [77], [81], [83], [97]- [99], [108], [109], the need for security to enable novel forms of industrial collaboration must be especially considered for real-world deployments, where CPS are likely connected to the Internet. Apart from operational reasons (e.g., to ensure safety or privacy), secure approaches also convince traditionally conservative stakeholders, such as companies that fear for their competitiveness, to contribute to cross-domain and inter-company collaborations.…”

Section: A Towards An Internet Of Productionmentioning

confidence: 99%

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

Pennekamp¹,

Buchholz²,

Dahlmanns³

et al. 2020

Proceedings 2020 Learning From Authoritative Security Experiment Results Workshop

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Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research. We look forward to promising research initiatives, projects, and directions that emerge based on our methodological work.

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Human Digital Shadow: Data-based Modeling of Users and Usage in the Internet of Production

Cited by 23 publications

References 33 publications

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

A Computer Science Perspective on Digital Transformation in Production

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

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