POSSUM: PrOactive diSruption riSk identification for sUpply chain Management

Aboutorab, H; Saberi, M; Hussain, OK; Hussain, FK

POSSUM: PrOactive diSruption riSk identification for sUpply chain Management

Aboutorab, H Saberi, M

Hussain, OK Hussain, FK

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023, 00, pp. 319-321
Issue Date:: 2023-06-21

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Field	Value	Language
dc.contributor.author	Aboutorab, H
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Hussain, OK
dc.contributor.author	Hussain, FK
dc.date	2023-03-13
dc.date.accessioned	2024-03-19T14:31:40Z
dc.date.available	2024-03-19T14:31:40Z
dc.date.issued	2023-06-21
dc.identifier.citation	2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2023, 00, pp. 319-321
dc.identifier.isbn	9781665453813
dc.identifier.uri	http://hdl.handle.net/10453/176927
dc.description.abstract	Proactive and agile supply chain risk management SCRM ensures that the activities of a supply chain achieve their desired aims The first task in SCRM is risk identification In this work we focus on external risks by capturing relevant information about external occurrences such as political or environmental events etc which may negatively impact the supply chain operations The timely identification of external risk events assists risk managers in deploying mitigation strategies to address such risks One of the great resources for such identification is news However the large volume of news makes manual identification of the relevant news frustrating time consuming and almost impossible We have built a proactive risk identification system that automatically identifies and retrieves the most relevant news relying on advanced natural language processing NLP reinforcement learning RL and risk managers feedback to address the abundance of news problem We will demo the functionality of this system using informative scenarios
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
dc.relation.ispartof	2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events
dc.relation.isbasedon	10.1109/percomworkshops56833.2023.10150317
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	POSSUM: PrOactive diSruption riSk identification for sUpply chain Management
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Atlanta, GA, USA
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-19T14:31:38Z
pubs.finish-date	2023-03-17
pubs.publication-status	Published
pubs.start-date	2023-03-13
pubs.volume	00

Abstract:

Proactive and agile supply chain risk management SCRM ensures that the activities of a supply chain achieve their desired aims The first task in SCRM is risk identification In this work we focus on external risks by capturing relevant information about external occurrences such as political or environmental events etc which may negatively impact the supply chain operations The timely identification of external risk events assists risk managers in deploying mitigation strategies to address such risks One of the great resources for such identification is news However the large volume of news makes manual identification of the relevant news frustrating time consuming and almost impossible We have built a proactive risk identification system that automatically identifies and retrieves the most relevant news relying on advanced natural language processing NLP reinforcement learning RL and risk managers feedback to address the abundance of news problem We will demo the functionality of this system using informative scenarios

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176927