A review on unsupervised learning algorithms and applications in supply chain management.
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| Title: | A review on unsupervised learning algorithms and applications in supply chain management. |
|---|---|
| Authors: | Rolf, Benjamin1 (AUTHOR) benjamin.rolf@ovgu.de, Beier, Alexander2 (AUTHOR), Jackson, Ilya3 (AUTHOR), Müller, Marcel1 (AUTHOR), Reggelin, Tobias1 (AUTHOR), Stuckenschmidt, Heiner2 (AUTHOR), Lang, Sebastian1,4 (AUTHOR) |
| Source: | International Journal of Production Research. Mar2025, Vol. 63 Issue 5, p1933-1983. 51p. |
| Subjects: | Industry classification, Machine learning, Supply chain management, Generative adversarial networks, Association rule mining, Market volatility |
| Abstract: | Due to pressing challenges such as high market volatility, complex global logistics, geopolitical turmoil and environmental sustainability, compounded by radical events such as the COVID-19 pandemic, the complexity of supply chain management has reached unprecedented levels. Together with increasing data availability and computing power, machine learning algorithms can help to address these challenges. In particular, unsupervised learning can be invaluable in extracting new knowledge from unstructured, unlabelled data. This article systematically reviews the current state of research on unsupervised learning techniques in supply chain management. We propose a classification framework that categorises the literature sample based on supply chain drivers, sectors, data sources, and UL algorithms, and reveal the following insights. The most common applications are information processing and typical operations research optimisation problems such as location planning and vehicle routing. From an algorithmic perspective, clustering and other traditional unsupervised learning techniques dominate recent approaches, owing their popularity to algorithmic simplicity, robustness and accessibility. More advanced and generative techniques have been slow to gain acceptance. In contrast to other machine learning paradigms, unsupervised learning mainly plays a supporting role. The large number of publications using real-world data confirms the importance and maturity of unsupervised learning in supply chain management. Abbreviations: ARM: Association rule mining; AE: Autoencoder; BIRCH: Balanced iterative reducing and clustering hierarchies; DBSCAN: Density-based clustering for applications with noise; FA: Factor analysis; GAN: Generative adversarial network; ML: Machine learning; ISIC: International Standard Industrial Classification; OR: Operations research; PCA: Principal component analysis; SOM: Self-organising map; SICE: Sparse inverse covariance estimation; SCM: Supply chain management; UL: Unsupervised learning [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183415974 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A review on unsupervised learning algorithms and applications in supply chain management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rolf%2C+Benjamin%22">Rolf, Benjamin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> benjamin.rolf@ovgu.de</i><br /><searchLink fieldCode="AR" term="%22Beier%2C+Alexander%22">Beier, Alexander</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jackson%2C+Ilya%22">Jackson, Ilya</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Müller%2C+Marcel%22">Müller, Marcel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reggelin%2C+Tobias%22">Reggelin, Tobias</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stuckenschmidt%2C+Heiner%22">Stuckenschmidt, Heiner</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lang%2C+Sebastian%22">Lang, Sebastian</searchLink><relatesTo>1,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Mar2025, Vol. 63 Issue 5, p1933-1983. 51p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Industry+classification%22">Industry classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Market+volatility%22">Market volatility</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Due to pressing challenges such as high market volatility, complex global logistics, geopolitical turmoil and environmental sustainability, compounded by radical events such as the COVID-19 pandemic, the complexity of supply chain management has reached unprecedented levels. Together with increasing data availability and computing power, machine learning algorithms can help to address these challenges. In particular, unsupervised learning can be invaluable in extracting new knowledge from unstructured, unlabelled data. This article systematically reviews the current state of research on unsupervised learning techniques in supply chain management. We propose a classification framework that categorises the literature sample based on supply chain drivers, sectors, data sources, and UL algorithms, and reveal the following insights. The most common applications are information processing and typical operations research optimisation problems such as location planning and vehicle routing. From an algorithmic perspective, clustering and other traditional unsupervised learning techniques dominate recent approaches, owing their popularity to algorithmic simplicity, robustness and accessibility. More advanced and generative techniques have been slow to gain acceptance. In contrast to other machine learning paradigms, unsupervised learning mainly plays a supporting role. The large number of publications using real-world data confirms the importance and maturity of unsupervised learning in supply chain management. Abbreviations: ARM: Association rule mining; AE: Autoencoder; BIRCH: Balanced iterative reducing and clustering hierarchies; DBSCAN: Density-based clustering for applications with noise; FA: Factor analysis; GAN: Generative adversarial network; ML: Machine learning; ISIC: International Standard Industrial Classification; OR: Operations research; PCA: Principal component analysis; SOM: Self-organising map; SICE: Sparse inverse covariance estimation; SCM: Supply chain management; UL: Unsupervised learning [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2024.2390968 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 51 StartPage: 1933 Subjects: – SubjectFull: Industry classification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Association rule mining Type: general – SubjectFull: Market volatility Type: general Titles: – TitleFull: A review on unsupervised learning algorithms and applications in supply chain management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rolf, Benjamin – PersonEntity: Name: NameFull: Beier, Alexander – PersonEntity: Name: NameFull: Jackson, Ilya – PersonEntity: Name: NameFull: Müller, Marcel – PersonEntity: Name: NameFull: Reggelin, Tobias – PersonEntity: Name: NameFull: Stuckenschmidt, Heiner – PersonEntity: Name: NameFull: Lang, Sebastian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Production Research Type: main |
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