Abstract
Digital Twins are increasingly perceived as critical enablers for improving operational performance and sustainability of Smart Production Logistics. Addressing the lack of empirical research on this topic, this study explores the economic, environmental, and social sustainability impact of Digital Twin-based services for Smart Production Logistics. The study presents findings from a Smart Production Logistics demonstrator in an academic environment and underscores the contributions and limitations of current understanding about Digital Twin-based services in relation to their impact on economic, environmental, and social sustainability. The study presents valuable implications for managers responsible for material handling.
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
Abubakr, M., Abbas, A.T., Tomaz, I., Soliman, M.S., Luqman, M., Hegab, H.: Sustainable and smart manufacturing: an integrated approach. Sustainability. 12(6), 2280 (2020)
Beier, G., Ullrich, A., Niehoff, S., Reißig, M., Habich, M.: Industry 4.0: how it is defined from a sociotechnical perspective and how much sustainability it includes–A literature review. Journal of cleaner production, p. 120856 (2020)
Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019)
Cao, J., Wang, J., Lu, J.: A referenced cyber physical system for compressor manufacturing. MATEC Web of Conferences 306, 02005 (2020)
Wang, W., Zhang, Y., Zhong, R.Y.: A proactive material handling method for CPS enabled shop-floor. Robot. Comput. Integr. Manuf. 61, 101849 (2020)
Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Paper 1, 1–7 (2014)
Longo, F., Nicoletti, L., Padovano, A.: Ubiquitous knowledge empowers the smart factory: the impacts of a service-oriented digital twin on enterprises’ performance. Annu. Rev. Control. 47, 221–236 (2019)
Qi, Q., Tao, F., Zuo, Y., Zhao, D.: Digital twin service towards smart manufacturing. Procedia Cirp 72, 237–242 (2018)
Zheng, P., et al.: Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2), 137–150 (2018)
Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., Xu, X.: Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robot. Comput.-Integrat. Manuf. 61, 101837 (2020)
Uhlemann, T.H.J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia Cirp 61, 335–340 (2017)
Flores-García, E., Kim, G.Y., Yang, J., Wiktorsson, M., Noh, S.D.: Analyzing the characteristics of digital twin and discrete event simulation in cyber physical systems. In: IFIP International Conference on Advances in Production Management Systems, pp.238–244. Springer, Cham (2020). Dio: https://doi.org/10.1007/978-3-030-57997-5_28
Baalsrud Hauge, J., Zafarzadeh, M., Jeong, Y., Li, Y., Khilji, W.A., Wiktorsson, M.: Employing digital twins within production logistics. In: IEEE International Conference on Engineering, pp.1–8 (2020)
Kusiak, A.: Smart manufacturing. Int. J. Prod. Res. 56(1–2), 508–517 (2018)
Strandhagen, J.W., Alfnes, E., Strandhagen, J.O., Vallandingham, L.R.: The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study. Adv. Manuf. 5(4), 344–358 (2017). https://doi.org/10.1007/s40436-017-0200-y
He, B., Cao, X., Hua, Y.: Data fusion-based sustainable digital twin system of intelligent detection robotics. J. Clean. Prod. 280, 24181 (2021)
Park, K.T., Im, S.J., Kang, Y.S., Noh, S.D., Kang, Y.T., Yang, S.G.: Service-oriented platform for smart operation of dyeing and finishing industry. Int. J. Comput. Integr. Manuf. 32(3), 307–326 (2019)
He, B., Bai, K.J.: Digital twin-based sustainable intelligent manufacturing: a review. Adv. Manuf. 9(1), 1–21 (2021)
Tao, F., Zhang, M., Liu, Y., Nee, A.Y.C.: Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67(1), 169–172 (2018)
Tao, F., Zhang, H., Liu, A., Nee, A.Y.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15(4), 2405–2415 (2018)
Acknowledgement
This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) in South Korean, and Eureka SMART and Vinnova in Sweden through the International Cooperative R&D program [P0009839, the Cyber Physical Assembly and Logistics Systems in Global Supply Chains (C-PALS)]. In addition, it was supported by the Ministry of SMEs and Startups through the WC300 Project [S2482274, Development of Multi-vehicle Flexible Manufacturing Platform Technology for Future Smart Automotive Body Production].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kim, GY., Flores-García, E., Wiktorsson, M., Do Noh, S. (2021). Exploring Economic, Environmental, and Social Sustainability Impact of Digital Twin-Based Services for Smart Production Logistics. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-030-85914-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-85914-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85913-8
Online ISBN: 978-3-030-85914-5
eBook Packages: Computer ScienceComputer Science (R0)