供应链
系统回顾
供应链管理
计算机科学
科学文献
管理科学
人工智能应用
知识管理
过程管理
风险分析(工程)
数据科学
业务
人工智能
营销
工程类
政治学
梅德林
生物
古生物学
法学
作者
Reza Toorajipour,Vahid Sohrabpour,Ali Nazarpour,Pejvak Oghazi,Maria Fischl
标识
DOI:10.1016/j.jbusres.2020.09.009
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 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.
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