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Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions
- Source :
- Expert Systems with Applications. 173:114702
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Today’s supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes.
- Subjects :
- 0209 industrial biotechnology
Knowledge management
Bibliometric analysis
Process (engineering)
business.industry
Computer science
Supply chain
General Engineering
Competitive economy
02 engineering and technology
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
Analytics
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Applications of artificial intelligence
business
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 173
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........fc9641d44f62ab685f470e6620623662