Abstract
Context: As industries are heading for digital transformation through Industry 4.0, the concept of Digital Twin (DT) - a software for digital transformation, has become popular. Many industries use DT for its advantages, such as predictive maintenance and real-time remote monitoring. Within DT domain, an emerging topic is the concept of an ecosystem—a digital platform that would create value for different stakeholders in an ecosystem of DT-driven products and services. The identification of potential stakeholders and their requirements provides valuable contributions to the development of healthy Digital Twin Ecosystems (DTE). However, current empirical knowledge of potential stakeholders and their requirements are limited. Objective/Methodology: Thus, the objective of this research was to explore potential stakeholders and their requirements. The research employed an empirical research methodology in which semi-structured interviews were conducted with DT professionals for data collection. Results: Data analysis of the study revealed 13 potential stakeholders who were categorized as primary (manufacturers, suppliers, subcontractors, and intelligent robots), secondary (maintenance service providers, platform integration service providers, tech companies, etc.), and tertiary (research organizations, third-party value-added service providers, cyber security firms, etc.). This study also presents the different requirements of these stakeholders in detail. Contribution: The study contributes to both research and industry by identifying possible stakeholders and their requirements. It contributes to the literature by adding new knowledge on DTEs and fills a research gap while contributing industry by providing ample knowledge to the industry’s practitioners that is useful in the development and maintenance of a healthy DTE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rocha‐jácome, C., Carvajal, R.G., Chavero, F.M., Guevara‐cabezas, E., Fort, E.H.: Industry 4.0: a proposal of paradigm organization schemes from a systematic literature review. Sensors 22 (2022). https://doi.org/10.3390/s22010066
Xu, Y., Päivärinta, T., Kuvaja, P.: Digital twins as software and service development ecosystems in Industry 4.0: towards a research agenda. In: Tian, Y., Ma, T., Khan, M.K. (eds.) ICBDS 2019. CCIS, vol. 1210, pp. 51–64. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-7530-3_5
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4
Rosen, R., Von Wichert, G., Lo, G., Bettenhausen, K.D.: About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine. 28, 567–572 (2015). https://doi.org/10.1016/j.ifacol.2015.06.141
Durão, L.F.C.S., Haag, S., Anderl, R., Schützer, K., Zancul, E.: Digital twin requirements in the context of Industry 4.0. In: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (eds.) PLM 2018. IAICT, vol. 540, pp. 204–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01614-2_19
Augustine, P.: The industry use cases for the Digital Twin idea. Elsevier Inc. (2020). https://doi.org/10.1016/bs.adcom.2019.10.008
Wang, T., Liu, Z., Liao, M., Mrad, N.: Life prediction for aircraft structure based on Bayesian inference: towards a digital twin ecosystem. In: Proceedings of the Annual Conference on Prognosis Health Management Soceity PHM 12, 1–8 (2020). https://doi.org/10.36001/phmconf.2020.v12i1.1261
Silva, H.D., Azevedo, M., Soares, A.L.: A vision for a platform-based digital-twin ecosystem. IFAC-PapersOnLine 54, 761–766 (2021). https://doi.org/10.1016/j.ifacol.2021.08.088
Yan, X.: Construction of digital twin ecosystem for coal-fired generating units. J. Phys. Conf. Ser. 1748 (2021). https://doi.org/10.1088/1742-6596/1748/5/052037
Rosen, R., Fischer, J., Boschert, S.: Next generation digital twin: an ecosystem for mechatronic systems? IFAC-PapersOnLine. 52, 265–270 (2019). https://doi.org/10.1016/j.ifacol.2019.11.685
Tsujimoto, M., Kajikawa, Y., Tomita, J., Matsumoto, Y.: A review of the ecosystem concept — Towards coherent ecosystem design. Technol. Forecast. Soc. Change 136, 49–58 (2018). https://doi.org/10.1016/j.techfore.2017.06.032
Zheng, Y., Yang, S., Cheng, H.: An application framework of digital twin and its case study. J. Ambient. Intell. Humaniz. Comput. 10(3), 1141–1153 (2018). https://doi.org/10.1007/s12652-018-0911-3
Armengaud, E., Sams, C., von Falck, G., List, G., Kreiner, C., Riel, A.: Industry 4.0 as digitalization over the entire product lifecycle: opportunities in the automotive domain. In: Stolfa, J., Stolfa, S., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2017. CCIS, vol. 748, pp. 334–351. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64218-5_28
Lee, J., Cameron, I., Hassall, M.: Improving process safety: what roles for digitalization and Industry 4.0? Process Saf. Environ. Prot. 132, 325–339 (2019). https://doi.org/10.1016/j.psep.2019.10.021
Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y.: Digital Twin as a Service (DTaaS) in Industry 4.0: an architecture reference model. Adv. Eng. Inform. 47 (2021). https://doi.org/10.1016/j.aei.2020.101225
Bhatti, G., Mohan, H., Raja Singh, R.: Towards the future of ssmart electric vehicles: digital twin technology. Renew. Sustain. Energy Rev. 141, 110801 (2021). https://doi.org/10.1016/j.rser.2021.110801
Wang, F., Lai, X., Shi, N.: A multi-objective optimization for green supply chain network design. Decis. Support Syst. 51, 262–269 (2011). https://doi.org/10.1016/j.dss.2010.11.020
Hove, S.E., Anda, B.: Experiences from conducting semi-structured interviews in empirical software engineering research. Proc. - Int. Softw. Met. Symp. 2005, 10–23 (2005). https://doi.org/10.1109/METRICS.2005.24
Thomas, D.R.: A general inductive approach for qualitative data analysis (2003)
Yin, R.K.: Case study research. Des. Methods 1, 93–95 (2009)
Niu, X., Qin, S.: Integrating crowd-/service-sourcing into digital twin for advanced manufacturing service innovation. Adv. Eng. Inform. 50, 101422 (2021). https://doi.org/10.1016/j.aei.2021.101422
Gackstetter, D., Moshrefzadeh, M., Machl, T., Kolbe, T.H.: Smart rural areas data infrastructure (SRADI) – an information logistics framework for digital agriculture based on open standards. In: Lecture Notes Informatics (LNI), Proceedings - Series Gesellschaft fur Informatics P-309, pp. 109–114 (2021)
Kanak, A., Ugur, N., Ergun, S.: A visionary model on blockchain-based accountability for secure and collaborative digital twin environments. In: Conference Proceedings - IEEE Conference on Systems, Man and Cybernetics, pp. 3512–3517 (2019). https://doi.org/10.1109/SMC.2019.8914304
Javaid, M., Haleem, A., Singh, R.P., Suman, R.: Substantial capabilities of robotics in enhancing Industry 4.0 implementation. Cogn. Robot. 1, 58–75 (2021). https://doi.org/10.1016/j.cogr.2021.06.001
Dhanabalan, T., Sathish, A.: Transforming Indian industries through artificial intelligence and robotics in Industry 4.0. Int. J. Mech. Eng. Technol. 9, 835–845 (2018)
Ebert, N., Weber, K., Koruna, S.: Integration platform as a service. Bus. Inf. Syst. Eng. 59(5), 375–379 (2017). https://doi.org/10.1007/s12599-017-0486-0
Acknowledgement
This study was part of Oxilate ITEA3 project (https://itea4.org/project/oxilate.html) funded by Business Finland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liyanage, R., Tripathi, N., Päivärinta, T., Xu, Y. (2022). Digital Twin Ecosystems: Potential Stakeholders and Their Requirements. In: Carroll, N., Nguyen-Duc, A., Wang, X., Stray, V. (eds) Software Business. ICSOB 2022. Lecture Notes in Business Information Processing, vol 463. Springer, Cham. https://doi.org/10.1007/978-3-031-20706-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-20706-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20705-1
Online ISBN: 978-3-031-20706-8
eBook Packages: Computer ScienceComputer Science (R0)