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Use case of self-organizing adaptive supply chain

In: Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23

Author

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  • Wagner, Julia
  • Kontny, Henning

Abstract

The main drivers for the development and implementation of self-organizing adaptive operation processes are the increase of flexibility while lowering costs in times of growing consumer demands, shaping the supply chain into 'the source of enterprises core competence'. This paper is based on the 'Guidelines for conducting and reporting case study research in software engineering' and it demonstrates the design of the self-organizing adaptive supply chain through an integration of processes and systems. As a result, the automated near real-time data flow enables shorter order-lead-time with a high accuracy of information. Although the study includes the architecture for the self-organizing adaptive supply chain, it cannot be completely standardized due to individual processes and IT-systems within different companies. Academics and practitioners may find it useful to identify appropriate scenarios while looking for the ways to digitize their supply chains.

Suggested Citation

  • Wagner, Julia & Kontny, Henning, 2017. "Use case of self-organizing adaptive supply chain," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 255-273, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  • Handle: RePEc:zbw:hiclch:209312
    DOI: 10.15480/882.1471
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    References listed on IDEAS

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    1. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    2. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    3. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
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    Cited by:

    1. Feldt, Julia & Kontny, Henning & Niemietz, Frank, 2020. "How disruptive start-ups change the world of warehouse logistics," 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 3-24, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Feldt, Julia & Kontny, Henning & Wagenitz, Axel, 2019. "Breaking through the bottlenecks using artificial intelligence," 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 30-56, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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