Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

George Baryannis; Samir Dani; Sahar Validi; Grigoris Antoniou

doi:10.1007%2F978-3-030-03813-7_4

Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

George Baryannis, Samir Dani, Sahar Validi, Grigoris Antoniou

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

54 Citations (Scopus)

Abstract

This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

Original language	English
Title of host publication	Revisiting Supply Chain Risk
Editors	George A. Zsidisin, Michael Henke
Publisher	Springer, Cham
Pages	53-71
Number of pages	19
Volume	SSSCM, volume 7
Edition	1st
ISBN (Electronic)	9783030038137
ISBN (Print)	9783030038120
DOIs	https://doi.org/10.1007%2F978-3-030-03813-7_4
Publication status	Published - 1 Jan 2019

Publication series

Name	Springer Series in Supply Chain Management
Publisher	Springer, Cham
Volume	7
ISSN (Print)	2365-6395
ISSN (Electronic)	2365-6409

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1007%2F978-3-030-03813-7_4Licence: Unspecified

Decision Support Systems and AI in Supply Chain Risk ManagementAccepted author manuscript, 340 KBLicence: Unspecified

Cite this

Baryannis, G., Dani, S., Validi, S., & Antoniou, G. (2019). Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management. In G. A. Zsidisin, & M. Henke (Eds.), Revisiting Supply Chain Risk (1st ed., Vol. SSSCM, volume 7, pp. 53-71). (Springer Series in Supply Chain Management; Vol. 7). Springer, Cham. https://doi.org/10.1007%2F978-3-030-03813-7_4

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keywords = "Artificial Intelligence, Supply chain risk management",

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Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management. / Baryannis, George; Dani, Samir; Validi, Sahar et al.
Revisiting Supply Chain Risk. ed. / George A. Zsidisin; Michael Henke. Vol. SSSCM, volume 7 1st. ed. Springer, Cham, 2019. p. 53-71 (Springer Series in Supply Chain Management; Vol. 7).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

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AU - Validi, Sahar

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PY - 2019/1/1

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N2 - This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

AB - This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

KW - Artificial Intelligence

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M3 - Chapter

SN - 9783030038120

VL - SSSCM, volume 7

T3 - Springer Series in Supply Chain Management

SP - 53

EP - 71

BT - Revisiting Supply Chain Risk

A2 - Zsidisin, George A.

A2 - Henke, Michael

PB - Springer, Cham

ER -

Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

Abstract

Publication series

UN SDGs

Access to Document

Other files and links

Fingerprint

A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches

Artificial Intelligence Opportunities for Resilient Supply Chains

Digital Supply Chain Hub - Academic Network (External organisation)

ChatGPT is influencing the apparel supply chain. Here’s how

Cite this

Decision Support Systems and Artificial Intelligence in Supply Chain Risk Management

Abstract

Publication series

UN SDGs

Access to Document

Other files and links

Fingerprint

Research output

A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches

Artificial Intelligence Opportunities for Resilient Supply Chains

Activities

Digital Supply Chain Hub - Academic Network (External organisation)

Press/Media

ChatGPT is influencing the apparel supply chain. Here’s how

Cite this