Risk identification-based association rule mining for supply chain big data

Salamai, A; Saberi, M; Hussain, O; Chang, E

Risk identification-based association rule mining for supply chain big data

Salamai, A Saberi, M

Hussain, O Chang, E

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11342 LNCS pp. 219 - 228
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Salamai, A	en_US
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078	en_US
dc.contributor.author	Hussain, O	en_US
dc.contributor.author	Chang, E	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11342 LNCS pp. 219 - 228	en_US
dc.identifier.isbn	9783030053444	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133591
dc.description.abstract	© Springer Nature Switzerland AG 2018. Since most supply chain processes include operational risks, the effectiveness of a corporation’s success depends mainly on identifying, analyzing and managing them. Currently, supply chain risk management (SCRM) is an active research field for enhancing a corporation’s efficiency. Although several techniques have been proposed, they still face a big challenge as they analyze only internal risk events from big data collected from the logistics of supply chain systems. In this paper, we analyze features that can identify risk labels in a supply chain. We propose defining risk events based on the association rule mining (ARM) technique that can categorize those in a supply chain based on a company’s historical data. The empirical results we obtained using data collected from an Aluminum company showed that this technique can efficiently generate and predict the optimal features of each risk label with a higher than 96.5% accuracy.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-05345-1_18	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Risk identification-based association rule mining for supply chain big data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11342 LNCS	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	recently_added	*
pubs.publication-status	Published	en_US
pubs.volume	11342 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2018. Since most supply chain processes include operational risks, the effectiveness of a corporation’s success depends mainly on identifying, analyzing and managing them. Currently, supply chain risk management (SCRM) is an active research field for enhancing a corporation’s efficiency. Although several techniques have been proposed, they still face a big challenge as they analyze only internal risk events from big data collected from the logistics of supply chain systems. In this paper, we analyze features that can identify risk labels in a supply chain. We propose defining risk events based on the association rule mining (ARM) technique that can categorize those in a supply chain based on a company’s historical data. The empirical results we obtained using data collected from an Aluminum company showed that this technique can efficiently generate and predict the optimal features of each risk label with a higher than 96.5% accuracy.

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

http://hdl.handle.net/10453/133591