Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. (1st July 2021)

• Record Type: Journal Article
• Authors: Riahi, Youssra; Saikouk, Tarik; Gunasekaran, Angappa; Badraoui, Ismail
• Is Part Of: Expert systems with applications. Volume 173(2021)
• Publication Date: 2021-07-01
• Subjects: Artificial intelligence -- Supply chain -- Bibliometric analysis -- Systematic literature review -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114702
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Systematic literature review based on 136 papers on artificial intelligence applications in supply chain. The distribution of the different AI techniques used across the SCOR areas is presented. A classification of the research articles is proposed based on four critical structural dimensions. Future research perspectives and areas needing further investigations are suggested. 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.