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Decision-making in multimodal supply chains using machine learning

In: Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31

Author

Listed:
  • Weinke, Manuel
  • Poschmann, Peter
  • Straube, Frank

Abstract

Purpose: To strengthen efficiency and resilience of supply chains at the same time, shippers and logistics companies needs proactive transparency about their orders. Machine Learning (ML) offers huge potential for precise predictions of complex logistics processes. This paper shows the results of a perennial research for implementing a ML-based system, which predicts multimodal supply chains, detects upcoming disruptions and provides suitable actor-specific measures. Methodology: For each process of the considered supply chain an individual prediction model is developed, using four years historical data, about 50 identified features and various ML methods. The developed cross-actor ETA was linked with preventive measures based on expert knowledge. Both was integrated into a web-based prototype of a self-learning decision support system. Findings: Thanks to the development of different ML approaches, most reliable model configurations were identified for each process. Moreover, important insights were gained regarding the availability of required data as well as the potentials and challenges of using ML-based solutions for decision-making processes in logistics. Originality: The potentials from the use of ML for predicting supply chains has only been carried out for particular processes. An integrated approach including different processes like rail transports and transshipment points as well as a linkage with prediction-based measures is still missing.

Suggested Citation

  • Weinke, Manuel & Poschmann, Peter & Straube, Frank, 2021. "Decision-making in multimodal supply chains using machine learning," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 301-325, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
  • Handle: RePEc:zbw:hiclch:249620
    DOI: 10.15480/882.3991
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    References listed on IDEAS

    as
    1. Fabian Walter, 2015. "Informationsaustausch in der maritimen Transportkette," Springer Books, Springer, edition 127, number 978-3-658-09661-8, June.
    2. Lechtenberg, Sandra & de Siqueira Braga, Diego & Hellingrath, Bernd, 2019. "Automatic identification system (AIS) data based ship-supply forecasting," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 3-24, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
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    Keywords

    Artificial Intelligence; Blockchain;

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