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Machine learning in supply chain management: A scoping review
Citation Link: https://doi.org/10.15480/882.3961
Publikationstyp
Conference Paper
Date Issued
2021-12-01
Sprache
English
Author(s)
Herausgeber*innen
TORE-DOI
First published in
Number in series
31
Start Page
377
End Page
406
Citation
Hamburg International Conference of Logistics (HICL 2021)
Contribution to Conference
Scopus ID
Publisher
epubli
Peer Reviewed
true
Purpose: Especially in supply chain management (SCM), data has become essential to the success of companies. Traditional analytical methods are being augmented by machine learning (ML), which is considered the foremost relevant branch of artificial intelligence. This article maps various ML use-cases and assigns them to the appropriate SCM tasks.
Methodology: We applied a scoping review and checked scientific databases for relevant literature. Subsequently, the articles were assigned to different categories to map the research area. In the categorization, we considered, amongst others, the ML tasks and algorithms, data source and type, and the field of application.
Findings: The results show that there are numerous ML use cases in SCM. These range from predictive demand forecasting and intelligent partner selection to the use of assistance systems for resource management. Various data sources, such as internal company data and publicly available data, are used for these applications.
Originality: By mapping ML use cases in SCM, this complex and multifaceted field of research is presented in a transparent and structured way. Science and practice can deploy the results to improve existing ML use cases in SCM on the one hand and to identify promising areas of application on the other.
Methodology: We applied a scoping review and checked scientific databases for relevant literature. Subsequently, the articles were assigned to different categories to map the research area. In the categorization, we considered, amongst others, the ML tasks and algorithms, data source and type, and the field of application.
Findings: The results show that there are numerous ML use cases in SCM. These range from predictive demand forecasting and intelligent partner selection to the use of assistance systems for resource management. Various data sources, such as internal company data and publicly available data, are used for these applications.
Originality: By mapping ML use cases in SCM, this complex and multifaceted field of research is presented in a transparent and structured way. Science and practice can deploy the results to improve existing ML use cases in SCM on the one hand and to identify promising areas of application on the other.
Subjects
Artificial Intelligence
Blockchain
DDC Class
330: Wirtschaft
Publication version
publishedVersion
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Brylowski et al. (2021) - Machine Learning in Supply Chain Management A Scoping Review.pdf
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853.95 KB
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Adobe PDF