Decision support system for risk assessment using fuzzy inference in supply chain big data

Salamai, A; Hussain, O; Saberi, M

Decision support system for risk assessment using fuzzy inference in supply chain big data

Salamai, A Hussain, O Saberi, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019, 2019, pp. 248-253
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Salamai, A
dc.contributor.author	Hussain, O
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.date	2019-05-09
dc.date.accessioned	2020-04-30T21:11:17Z
dc.date.available	2020-04-30T21:11:17Z
dc.date.issued	2019-05-01
dc.identifier.citation	2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019, 2019, pp. 248-253
dc.identifier.isbn	9781728104669
dc.identifier.uri	http://hdl.handle.net/10453/140369
dc.description.abstract	© 2019 IEEE. Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2019
dc.relation.ispartof	International Conference on High Performance Big Data and Intelligent Systems
dc.relation.isbasedon	10.1109/HPBDIS.2019.8735465
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Decision support system for risk assessment using fuzzy inference in supply chain big data
dc.type	Conference Proceeding
utslib.location.activity	Shenzhen, China
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-04-30T21:11:11Z
pubs.finish-date	2019-05-11
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-05-09
utslib.start-page	248
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.

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http://hdl.handle.net/10453/140369