Title: Artificial intelligence applied to supply chain operations management: a systematic literature review

Authors: Guilherme Dayrell Mendonça; Orlando Fontes Lima Junior

Addresses: Transportation Department, Faculty of Civil Engineering, Architecture and Urbanism (FEC), Campinas State University (UNICAMP), P.O. Box 13083-852, Rua Albert Einstein, 951 – Cidade Universitária Zeferino Vaz – Campinas, São Paulo, Brazil ' Transportation Department, Faculty of Civil Engineering, Architecture and Urbanism (FEC), Campinas State University (UNICAMP), P.O. Box 13083-852, Rua Albert Einstein, 951 – Cidade Universitária Zeferino Vaz – Campinas, São Paulo, Brazil

Abstract: Artificial intelligence (AI) has been a key driver to reduce operational uncertainty and improve performance in supply chain management. Due to the advent of new data gathering technologies (IoT) and greater storage capacity, big data analytics (BDA) is rapidly growing as one of the main fields within AI research. We examined a representative sample of AI works applied to SCM from 2000 to 2020 and analysed them considering the main areas of the SCOR model framework of operations. The systematic literature review was based on a meta-synthesis methodology. The main research questions addressed were: 1) What are the main research methodologies used in AI SCM literature? 2) In what areas of SCM operations is AI (including BDA) mostly applied? 3) What are the most used AI models? The discussion addressing these three questions reveals a number of research gaps, which leads to future research directions.

Keywords: artificial intelligence; supply chain management; logistics; data mining; big data analytics; BDA; machine learning; supply chain operations reference; SCOR model.

DOI: 10.1504/IJLSM.2023.130970

International Journal of Logistics Systems and Management, 2023 Vol.45 No.1, pp.1 - 30

Received: 22 Jun 2020
Accepted: 14 Feb 2021
Published online: 17 May 2023 *

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