Artificial intelligence in supply chain management: A systematic literature review.

Citation metadata

Publisher: Elsevier B.V.
Document Type: Report; Brief article
Length: 268 words

Document controls

Main content

Abstract :

Keywords Artificial intelligence; Supply chain management; Systematic literature review Abstract 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. Author Affiliation: (a) School of Innovation, Design and Engineering, Mälardalen University, Box 325, 631 05 Eskilstuna, Sweden (b) Department of Operations Management, Copenhagen Business School, Copenhagen, Denmark (c) SAVEGGY AB, Ideon Innovation, Ideon Science Park, Lund, Sweden (d) School of Business, Maynooth University, Maynooth, Co. Kildare, Ireland (e) School of Social Sciences, Sodertorn University, Alfred Nobels allé 7, Stockholm, Sweden (f) Siemens Gas and Power GmbH & Co. KG, Siemens Energy, Berlin, Germany * Corresponding author. Article History: Received 5 May 2020; Revised 4 September 2020; Accepted 6 September 2020 Byline: Reza Toorajipour (a), Vahid Sohrabpour [] (b,c), Ali Nazarpour [] (d), Pejvak Oghazi [] (e,*), Maria Fischl [] (f)

Source Citation

Source Citation   

Gale Document Number: GALE|A648491670