Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3765
Publisher DOI: 10.3390/logistics5030062
Title: A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
Language: English
Authors: Schröder, Meike 
Lodemann, Sebastian 
Issue Date: 8-Sep-2021
Publisher: Multidisciplinary Digital Publishing Institute
Source: Logistics 5 (3): 62 (2021)
Journal: Logistics 
Abstract (english): 
The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we analyzed which risks were addressed in the use cases as well as how ML might shape SCRM. For this purpose, we conducted a systematic literature review. The results showed that the applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM (e.g., the integration of new data sources such as social media or weather data). From the systematic literature analysis results, we developed four propositions, which can be used as motivation for further research.
URI: http://hdl.handle.net/11420/10324
DOI: 10.15480/882.3765
ISSN: 2305-6290
Institute: Logistik und Unternehmensführung W-2 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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