Baryannis, G, Validi, S, Dani, S ORCID: https://orcid.org/0000-0001-8547-0762 and Antoniou, G (2019) Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57 (7). 2179 - 2202.

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Abstract

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

Item Type: Article
Additional Information: The final version of this article and all relevant information regarding this paper can be found at; https://www.tandfonline.com/doi/full/10.1080/00207543.2018.1530476
Uncontrolled Keywords: supply chain risk management, artificial intelligence, decision-making, SCRM strategy, supply chain disruption
Subjects: H Social Sciences > H Social Sciences (General)
T Technology > T Technology (General)
Divisions: Faculty of Humanities and Social Sciences > Keele Business School
Depositing User: Symplectic
Date Deposited: 19 Jun 2020 10:05
Last Modified: 19 Jun 2020 10:05
URI: https://eprints.keele.ac.uk/id/eprint/8142

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