Artificial intelligence in supply chain management: A systematic literature review

被引:258
|
作者
Toorajipour, Reza [1 ]
Sohrabpour, Vahid [2 ,3 ]
Nazarpour, Ali [4 ]
Oghazi, Pejvak [5 ]
Fischl, Maria [6 ]
机构
[1] Malardalen Univ, Sch Innovat Design & Engn, Box 325, S-63105 Eskilstuna, Sweden
[2] Copenhagen Business Sch, Dept Operat Management, Copenhagen, Denmark
[3] SAVEGGY AB, Ideon Innovat, Ideon Sci Pk, Lund, Sweden
[4] Maynooth Univ, Sch Business, Maynooth, Kildare, Ireland
[5] Sodertorn Univ, Sch Social Sci, Alfred Nobels Alle 7, Stockholm, Sweden
[6] Siemens Gas & Power GmbH & Co KG, Siemens Energy, Berlin, Germany
关键词
Artificial intelligence; Supply chain management; Systematic literature review; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM APPROACH; DECISION-SUPPORT-SYSTEM; NEURAL-NETWORKS; MULTIAGENT SYSTEMS; BUSINESS AIRCRAFT; CONSUMER-BEHAVIOR; SALES MANAGEMENT; VECTOR MACHINES; TABU SEARCH;
D O I
10.1016/j.jbusres.2020.09.009
中图分类号
F [经济];
学科分类号
02 ;
摘要
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 po-tential 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.
引用
收藏
页码:502 / 517
页数:16
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