EconPapers    
Economics at your fingertips  
 

The role of artificial intelligence in supply chain management: mapping the territory

Rohit Sharma, Anjali Shishodia, Angappa Gunasekaran, Hokey Min and Ziaul Haque Munim

International Journal of Production Research, 2022, vol. 60, issue 24, 7527-7550

Abstract: The study aims to identify the current trends, gaps, and research opportunities in research pertaining to the disruptive field of artificial intelligence (AI) applications in supply chain management (SCM). Since SCM represents managerial innovation due to its new way of integrated system thinking, SCM has emerged as one of the most fruitful business disciplines for AI applications. The study utilises bibliometric review in tracing the evolution of AI research in SCM and further synthesises decades of past AI research efforts to develop viable solutions for various supply chain problems and then proposes promising future research themes that would enrich supply chain decision-aid tools. The study identified five main research clusters through scholarly network and content analysis. The identified themes were: (a) supply chain network design (SCND), (b) supplier selection, (c) inventory planning, (d) demand planning, and (e) green supply chain management. As the role of AI in SCM continues to grow, there is a growing need for exploiting AI as a way to add value to supply chain process. The study proposes a research framework which will help academicians and practitioners in identifying current research patterns of AI in SCM.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2022.2029611 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:60:y:2022:i:24:p:7527-7550

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2022.2029611

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tprsxx:v:60:y:2022:i:24:p:7527-7550