Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T01:49:53.806Z Has data issue: false hasContentIssue false
  • English
  • Français

A Digital Twin Trust Framework for Industrial Application

Published online by Cambridge University Press:  26 May 2022

J. Trauer ,
S. Schweigert-Recksiek ,
T. Schenk ,
T. Baudisch ,
M. Mörtl  and
M. Zimmermann
J. Trauer*
Affiliation:
Technical University of Munich, Germany
S. Schweigert-Recksiek
Affiliation:
Technical University of Munich, Germany
T. Schenk
Affiliation:
Siemens AG, Germany
T. Baudisch
Affiliation:
Siemens AG, Germany
M. Mörtl
Affiliation:
Technical University of Munich, Germany
M. Zimmermann
Affiliation:
Technical University of Munich, Germany
*
[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A reason for the slow adoption of digital twins in industry is a lack of trust in the concept and between the stakeholders involved. This paper presents a Trust Framework for Digital Twins based on a literature review and an interview study, including seven recommendations: (1) explain your twin, (2) create a common incentive, (3) make only one step at a time, (4) ensure IP protection and IT security, (5) prove your quality, (6) ensure a uniform environment, and (7) document thoroughly. Together with 20 concrete measures it supports practitioners in improving trust in their Digital Twin.

Keywords

digital twintrustindustry 4.0socio-technical systemsorganisation of product development
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 293 - 302
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Aheleroff, S., Xu, X., Zhong, R.Y. and Lu, Y. (2021), “Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model”, Advanced Engineering Informatics, Vol. 47, p. 101225. 10.1016/j.aei.2020.101225.CrossRefGoogle Scholar
Ba, S. and Pavlou, P.A. (2002), “Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior”, MIS Quarterly, Vol. 26 No. 3, p. 243. 10.2307/4132332.CrossRefGoogle Scholar
Barricelli, B.R., Casiraghi, E. and Fogli, D. (2019), “A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications”, IEEE Access, Vol. 7, pp. 167653–167671. 10.1109/ACCESS.2019.2953499.Google Scholar
Durão, L.F.C.S., Haag, S., Anderl, R., Schützer, K. and Zancul, E. (2018), “Digital Twin Requirements in the Context of Industry 4.0”, in Chiabert, P., Bouras, A., Noël, F. and Ríos, J. (Eds.), Product Lifecycle Management to Support Industry 4.0, 2018, Cham, Springer International Publishing, Cham, pp. 204–214.CrossRefGoogle Scholar
Eckert, C., Isaksson, O., Hallstedt, S., Malmqvist, J., Öhrwall Rönnbäck, A. and Panarotto, M. (2019), “Industry Trends to 2040”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 2121–2128. 10.1017/dsi.2019.218.Google Scholar
Hallstedt, S., Isaksson, O. and Öhrwall Rönnbäck, A. (2020), “The Need for New Product Development Capabilities from Digitalization, Sustainability, and Servitization Trends”, Sustainability, Vol. 12 No. 23, p. 10222. 10.3390/su122310222.Google Scholar
Hoff, K.A. and Bashir, M. (2015), “Trust in automation: integrating empirical evidence on factors that influence trust”, Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 57 No. 3, pp. 407–434. 10.1177/0018720814547570.CrossRefGoogle ScholarPubMed
Jones, D., Snider, C., Nassehi, A., Yon, J. and Hicks, B. (2020), “Characterising the Digital Twin: A systematic literature review”, CIRP Journal of Manufacturing Science and Technology, Vol. 29, pp. 36–52. 10.1016/j.cirpj.2020.02.002.CrossRefGoogle Scholar
Lee, J.D. and See, K.A. (2004), “Trust in automation: designing for appropriate reliance”, Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 46 No. 1, pp. 50–80. 10.1518/hfes.46.1.50_30392.CrossRefGoogle ScholarPubMed
Liu, L. and Loper, M. (2018), “Trust as a Service: Building and Managing Trust in the Internet of Things”, in 2018 IEEE International Symposium on Technologies for Homeland Security (HST), 10/23/2018 - 10/24/2018, Woburn, MA, IEEE, pp. 1–6. 10.1109/THS.2018.8574169.Google Scholar
Mayer, R.C., Davis, J.H. and Schoorman, F.D. (1995), “An Integrative Model of Organizational Trust”, The Academy of Management Review, Vol. 20 No. 3, p. 709. 10.2307/258792.Google Scholar
Meyer, U. (2020), “Industrie 4.0 als sozio-technische Zukunftsvorstellung. Zur Bedeutung von organisationaler Sinnerzeugung und -stiftung”, in Maasen, S. and Passoth, J.-H. (Eds.), Soziologie des Digitalen - Digitale Soziologie?, Nomos Verlagsgesellschaft mbH & Co. KG, pp. 349–378. 10.5771/9783845295008-349.Google Scholar
Neto, A.A., Deschamps, F., da Silva, E.R. and Lima, E.P. de (2020), “Digital twins in manufacturing: an assessment of drivers, enablers and barriers to implementation”, Procedia CIRP, Vol. 93, pp. 210–215. 10.1016/j.procir.2020.04.131.Google Scholar
Rasheed, A., San, O. and Kvamsdal, T. (2020), “Digital Twin: Values, Challenges and Enablers From a Modeling Perspective”, IEEE Access, Vol. 8, pp. 21980–22012. 10.1109/ACCESS.2020.2970143.Google Scholar
Rosen, R., Fischer, J. and Boschert, S. (2019), “Next Generation Digital Twin: an Ecosystem for Mechatronic Systems?”, IFAC-PapersOnLine, Vol. 52 No. 15, pp. 265–270. 10.1016/j.ifacol.2019.11.685.Google Scholar
Sauer, C., Schleich, B. and Wartzack, S. (2021), “A data model for linking testbed and field test data”, in Krause, D. and Paetzold, Kristin, Wartzack, Sandro (Eds.), DS 111: Proceedings of the 32nd Symposium Design for X, September 27-28. 2021, The Design Society. 10.35199/dfx2021.01.Google Scholar
Schweigert-Recksiek, S., Trauer, J., Engel, C., Spreitzer, K. and Zimmermann, M. (2020), “CONCEPTION OF A DIGITAL TWIN IN MECHANICAL ENGINEERING – A CASE STUDY IN TECHNICAL PRODUCT DEVELOPMENT”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 383–392. 10.1017/dsd.2020.23.Google Scholar
Singh, S., Shehab, E., Higgins, N., Fowler, K., Reynolds, D., Erkoyuncu, J.A. and Gadd, P. (2020), “Data management for developing digital twin ontology model”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 095440542097811. 10.1177/0954405420978117.Google Scholar
Singh, S., Shehab, E., Higgins, N., Fowler, K., Tomiyama, T. and Fowler, C. (2018), “Challenges of Digital Twin in High Value Manufacturing”, in SAE Technical Paper Series, NOV. 06, 2018, SAE International400 Commonwealth Drive, Warrendale, PA, United States. 10.4271/2018-01-1928.Google Scholar
“Steam Refunds” (2021), available at: https://store.steampowered.com/steam_refunds/ (accessed 2 November 2021).Google Scholar
Stjepandić, J., Sommer, M. and Stobrawa, S. (2022), “Digital Twin: Conclusion and Future Perspectives”, in Stjepandić, J., Sommer, M. and Denkena, B. (Eds.), DigiTwin: An Approach for Production Process Optimization in a Built Environment, Springer eBook Collection, 1st ed. 2022, Springer International Publishing; Imprint Springer, Cham, pp. 235–259. 10.1007/978-3-030-77539-1_11.CrossRefGoogle Scholar
Thielsch, M.T., Meeßen, S.M. and Hertel, G. (2018), “Trust and distrust in information systems at the workplace”, PeerJ, Vol. 6, e5483. 10.7717/peerj.5483.CrossRefGoogle Scholar
Trauer, J., Mutschler, M., Mörtl, M. and Zimmermann, M. (2022), “CHALLENGES IN IMPLEMENTING DIGITAL TWINS - A SURVEY”, In Review, in Volume 2: 42nd Computers and Information in Engineering Conference (CIE), 14.-17.08.2022, American Society of Mechanical Engineers.Google Scholar
Trauer, J., Pfingstl, S., Finsterer, M. and Zimmermann, M. (2021), “Improving Production Efficiency with a Digital Twin Based on Anomaly Detection”, Sustainability, Vol. 13 No. 18, p. 10155. 10.3390/su131810155.CrossRefGoogle Scholar
Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K. and Zimmermann, M. (2020), “WHAT IS A DIGITAL TWIN? – DEFINITIONS AND INSIGHTS FROM AN INDUSTRIAL CASE STUDY IN TECHNICAL PRODUCT DEVELOPMENT”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 757–766. 10.1017/dsd.2020.15.Google Scholar
Utz, S., Kerkhof, P. and van den Bos, J. (2012), “Consumers rule: How consumer reviews influence perceived trustworthiness of online stores”, Electronic Commerce Research and Applications, Vol. 11 No. 1, pp. 49–58. 10.1016/j.elerap.2011.07.010.Google Scholar
Wang, B.T. and Burdon, M. (2021), “Automating Trustworthiness in Digital Twins”, in Wang, B.T. and Wang, C.M. (Eds.), Automating Cities, Advances in 21st Century Human Settlements, Springer Singapore, Singapore, pp. 345–365. 10.1007/978-981-15-8670-5_14.Google Scholar
Yadav, A., Chakraverty, , S. and Sibal, R. (2019), “A framework for classifying trust for online systems”, World Wide Web, Vol. 22 No. 2, pp. 1499–1521. 10.1007/s11280-018-0626-6.Google Scholar