Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3991
Publisher URL: https://www.epubli.de/shop/buch/Adapting-to-the-Future-Christian-M-Ringle-Thorsten-Blecker-Wolfgang-Kersten-9783754927700/121489
Title: Decision-making in multimodal supply chains using machine learning
Language: English
Authors: Weinke, Manuel 
Poschmann, Peter 
Straube, Frank 
Editor: Kersten, Wolfgang  
Ringle, Christian M.  
Blecker, Thorsten 
Keywords: Artificial Intelligence; Blockchain
Issue Date: 1-Dec-2021
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 31: 301-325 (2021)
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2021 
URI: http://hdl.handle.net/11420/11206
DOI: 10.15480/882.3991
ISBN: 978-3-754927-70-0
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
Peer Reviewed: Yes
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 31
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