Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 2179-2202 |
| Number of pages | 24 |
| Journal | International Journal of Production Research |
| Volume | 57 |
| Issue number | 7 |
| Early online date | 6 Oct 2018 |
| DOIs | |
| Publication status | Published - 3 Apr 2019 |
Fingerprint
Cite this
}
Supply chain risk management and artificial intelligence : state of the art and future research directions. / Baryannis, George; Validi, Sahar; Dani, Samir; Antoniou, Grigoris.
In: International Journal of Production Research, Vol. 57, No. 7, 03.04.2019, p. 2179-2202.Research output: Contribution to journal › Article
TY - JOUR
T1 - Supply chain risk management and artificial intelligence
T2 - state of the art and future research directions
AU - Baryannis, George
AU - Validi, Sahar
AU - Dani, Samir
AU - Antoniou, Grigoris
PY - 2019/4/3
Y1 - 2019/4/3
N2 - 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.
AB - 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.
KW - Supply chain risk management
KW - Artificial intelligence
KW - Decision making
KW - SCRM strategy
KW - Supply chain disruption
UR - http://www.scopus.com/inward/record.url?scp=85063877082&partnerID=8YFLogxK
U2 - 10.1080/00207543.2018.1530476
DO - 10.1080/00207543.2018.1530476
M3 - Article
VL - 57
SP - 2179
EP - 2202
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 7
ER -