A Recommender System and Risk Mitigation Strategy for Supply Chain Management Using the Counterfactual Explanation Algorithm

Ordibazar, AH; Hussain, O; Saberi, M

A Recommender System and Risk Mitigation Strategy for Supply Chain Management Using the Counterfactual Explanation Algorithm

Ordibazar, AH Hussain, O Saberi, M

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Service-Oriented Computing – ICSOC 2021 Workshops, 2022, 13236 LNCS, pp. 103-116
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Ordibazar, AH
dc.contributor.author	Hussain, O
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.editor	Hacid, H
dc.contributor.editor	Aldwairi, M
dc.contributor.editor	Bouadjenek, MR
dc.contributor.editor	Petrocchi, M
dc.contributor.editor	Faci, N
dc.contributor.editor	Outay, F
dc.contributor.editor	Beheshti, A
dc.contributor.editor	Thamsen, L
dc.contributor.editor	Dong, H
dc.date	2022-11-22
dc.date.accessioned	2023-02-09T23:12:01Z
dc.date.available	2023-02-09T23:12:01Z
dc.date.issued	2022-01-01
dc.identifier.citation	Service-Oriented Computing – ICSOC 2021 Workshops, 2022, 13236 LNCS, pp. 103-116
dc.identifier.isbn	9783031141348
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/166024
dc.description.abstract	Supply chain management (SCM) and its disruptions and risks have been the focus of many researchers in recent times. It is important how to identify these disruptions and risks to avoid them, therefore many risk mitigation strategies have been developed. Artificial intelligence (AI) is a powerful tool to identify and predict the occurrence of risks, it is also important that the solutions to avoiding risks must be explainable for risk managers. Recently, making transparent and explainable AI models has been the focus of a large number of research studies and many post-hoc algorithms such as counter-factual explanation (CE) algorithms have been developed. In this paper, first we propose an optimization problem to design a transportation schedule for the supply chain network (SCN), then to increase the resiliency and transparency of the designed schedule, the CE model is integrated into the model as a set of constraints. To design the CE, a logistic regression model is developed. The CE helps to plan the transportation schedule to avoid any transportation delay risk. The integrated CE and SCM model is used as a recommender system for risk managers to mitigate risk to the system. Finally, to validate the recommender system, a real case study is analyzed and the solutions of the model with and without the CE are compared and it is shown that the CE-added constraints increase the resiliency of the system significantly while the increase in financial cost is less than 1%. Therefore, the model is validated for use for different risks and disruptions.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Service-Oriented Computing – ICSOC 2021 Workshops
dc.relation.ispartof	International Conference on Service-Oriented Computing
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-031-14135-5_8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A Recommender System and Risk Mitigation Strategy for Supply Chain Management Using the Counterfactual Explanation Algorithm
dc.type	Conference Proceeding
utslib.citation.volume	13236 LNCS
utslib.location.activity	Dubai, United Arab Emirates
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 Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-09T23:11:59Z
pubs.finish-date	2022-11-25
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2022-11-22
pubs.volume	13236 LNCS
dc.location	Switzerland

Abstract:

Supply chain management (SCM) and its disruptions and risks have been the focus of many researchers in recent times. It is important how to identify these disruptions and risks to avoid them, therefore many risk mitigation strategies have been developed. Artificial intelligence (AI) is a powerful tool to identify and predict the occurrence of risks, it is also important that the solutions to avoiding risks must be explainable for risk managers. Recently, making transparent and explainable AI models has been the focus of a large number of research studies and many post-hoc algorithms such as counter-factual explanation (CE) algorithms have been developed. In this paper, first we propose an optimization problem to design a transportation schedule for the supply chain network (SCN), then to increase the resiliency and transparency of the designed schedule, the CE model is integrated into the model as a set of constraints. To design the CE, a logistic regression model is developed. The CE helps to plan the transportation schedule to avoid any transportation delay risk. The integrated CE and SCM model is used as a recommender system for risk managers to mitigate risk to the system. Finally, to validate the recommender system, a real case study is analyzed and the solutions of the model with and without the CE are compared and it is shown that the CE-added constraints increase the resiliency of the system significantly while the increase in financial cost is less than 1%. Therefore, the model is validated for use for different risks and disruptions.

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http://hdl.handle.net/10453/166024