https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artificial intelligence in supply chain management: A systematic literature review
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. (Product realization)
Copenhagen Business School, Copenhagen, Denmark;SAVEGGY AB, Ideon Innovation, Ideon Science Park, Lund, Sweden.
School of Business, Maynooth University, Maynooth, Co. Kildare, Ireland.
School of Social Sciences, Sodertorn University, Stockholm, Sweden.
Show others and affiliations
2021 (English)In: Journal of Business Research, ISSN 0148-2963, E-ISSN 1873-7978, Vol. 122, p. 502-517Article in journal (Refereed) Published
Abstract [en]

This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis. 

Place, publisher, year, edition, pages
Elsevier Inc. , 2021. Vol. 122, p. 502-517
Keywords [en]
Artificial intelligence, Supply chain management, Systematic literature review
National Category
Other Mechanical Engineering Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:mdh:diva-51324DOI: 10.1016/j.jbusres.2020.09.009ISI: 000590682800003Scopus ID: 2-s2.0-85091631950OAI: oai:DiVA.org:mdh-51324DiVA, id: diva2:1474373
Available from: 2020-10-08 Created: 2020-10-08 Last updated: 2021-01-07Bibliographically approved

Open Access in DiVA

AI in SCM-Reza Toorajipour(1638 kB)4055 downloads
File information
File name FULLTEXT02.pdfFile size 1638 kBChecksum SHA-512
92a42f90c80d420db8e0c3c431074cde560275e529198cafb51eab8017a874825aaa0f54052e0f6191b45f41c1ae8f8f86c4e85276744e88d8e99ab811012ec2
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Toorajipour, Reza
By organisation
Innovation and Product Realisation
In the same journal
Journal of Business Research
Other Mechanical EngineeringTransport Systems and Logistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 4070 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1361 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf