Artificial intelligence applications in supply chain management

This paper presents a systematic review of studies related to artificial intelligence (AI) applications in supply chain management (SCM). Our systematic search of the related literature identifies 150 journal articles published between 1998 and 2020. A thorough bibliometric analysis is completed to develop the past and present state of this literature. A co-citation analysis on this pool of articles provides an understanding of the clusters of knowledge that constitute this research area. To further direct our discussions, we develop and validate an AI taxonomy which we use as a scale to conduct our bibliometric and co-citation analyses. The proposed taxonomy consists of three research categories of (a) sensing and interacting, (b) learning, and (c) decision making. These categories collectively establish the basis for present and future research on the application of AI methods in SCM literature and practice. Our analysis of the primary research clusters finds that learning methods are slowly getting momentum and sensing and interacting methods offer an emerging area of research. Finally, we provide a roadmap into future studies on AI applications in SCM. Our analysis underpins the importance of behavioral considerations in future studies.

Automated summary

This paper provides a systematic review of AI applications in supply chain management, identifying past and present research states and offering guidance for future studies. The analysis reveals that learning methods are gaining momentum while sensing and interacting methods present an emerging research area.