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Digital twin for real-time data processing in logistics

In: Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27

Author

Listed:
  • Haße, Hendrik
  • Li, Bin
  • Weißenberg, Norbert
  • Cirullies, Jan
  • Otto, Boris

Abstract

Purpose: Key performance indicators (KPIs) are an essential management tool. Realtime KPIs for production and logistics form the basis for flexible and adaptive production systems. These indicators unfold their full potential if they are seamlessly integrated into the 'Digital Twin' of a company for data analytics. Methodology: We apply the Design Science Research Methodology for Information Systems Research for deriving a digital twin architecture. Findings: Research in the field of digital twins is at an early state, where the main objective is to find new applications for this technology. The majority of digital twin applications relate to the fields of manufacturing. Finally, it became apparent that existing architectures are too generic for usage in logistics. Originality: The approach presented is an affordable solution for stakeholders to start with a digital transformation, based on standards and therefore highly technology-independent. The combined use of a lambda architecture with a semantic layer for flexible KPI definition is a special case.

Suggested Citation

  • Haße, Hendrik & Li, Bin & Weißenberg, Norbert & Cirullies, Jan & Otto, Boris, 2019. "Digital twin for real-time data processing in logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 4-28, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:209367
    DOI: 10.15480/882.2462
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
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    Cited by:

    1. Amelio, Andrea & Giardino-Karlinger, Liliane & Valletti, Tommaso, 2020. "Exclusionary pricing in two-sided markets," International Journal of Industrial Organization, Elsevier, vol. 73(C).
    2. Haße, Hendrik & van der Valk, Hendrik & Weißenberg, Norbert & Otto, Boris, 2020. "Shared Digital Twins: Data sovereignty in logistics networks," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 763-795, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Monahan, Lisa & Espinosa, Jennifer A. & Langenderfer, Jeff & Ortinau, David J., 2023. "Did you hear our brand is hated? The unexpected upside of hate-acknowledging advertising for polarizing brands," Journal of Business Research, Elsevier, vol. 154(C).
    4. Çevik, Hasan Hüseyin & Çunkaş, Mehmet & Polat, Kemal, 2019. "A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Konstantinos Siassiakos & Stamatia Ilioudi & Tsaktsira Effrosyni & Vasiliki Mitsiou & Dimitris Nanouris, 2020. "Utilization of Blockchain Technology in Greek Public Administration," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(4), pages 1-12.
    6. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    7. Hendrik Haße & Hendrik Valk & Frederik Möller & Boris Otto, 2022. "Design Principles for Shared Digital Twins in Distributed Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 751-772, December.

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    Keywords

    Digital Twin; Real-time; KPI; IoT;
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