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Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis

Lai-Wan Wong Orcid Logo, Garry Wei-Han Tan Orcid Logo, Keng-Boon Ooi Orcid Logo, Binshan Lin Orcid Logo, Yogesh Dwivedi Orcid Logo

International Journal of Production Research, Pages: 1 - 21

Swansea University Author: Yogesh Dwivedi Orcid Logo

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DOI (Published version): 10.1080/00207543.2022.2063089

Abstract

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on S...

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Published in: International Journal of Production Research
ISSN: 0020-7543 1366-588X
Published: Informa UK Limited 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59746
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spelling v2 59746 2022-03-31 Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2022-03-31 CBAE This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model. Journal Article International Journal of Production Research 0 1 21 Informa UK Limited 0020-7543 1366-588X Supply chain agility, re-engineering capabilities, risk management, artificial intelligence, ANN, PLS-SEM 23 5 2022 2022-05-23 10.1080/00207543.2022.2063089 The data that support the findings of this study are available from the corresponding author Y. K. D. upon reasonable request. COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-06-06T13:37:55.9244992 2022-03-31T07:42:56.7286545 Faculty of Humanities and Social Sciences School of Management - Business Management Lai-Wan Wong 0000-0003-1961-8452 1 Garry Wei-Han Tan 0000-0003-2974-2270 2 Keng-Boon Ooi 0000-0002-3384-1207 3 Binshan Lin 0000-0002-8481-302x 4 Yogesh Dwivedi 0000-0002-5547-9990 5 59746__24599__59c44a78b4874729a897608e93dd1d29.pdf 59746.VOR.pdf 2022-07-14T11:32:40.5055710 Output 3048792 application/pdf Version of Record true © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/
title Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
spellingShingle Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
Yogesh Dwivedi
title_short Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
title_full Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
title_fullStr Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
title_full_unstemmed Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
title_sort Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Lai-Wan Wong
Garry Wei-Han Tan
Keng-Boon Ooi
Binshan Lin
Yogesh Dwivedi
format Journal article
container_title International Journal of Production Research
container_volume 0
container_start_page 1
publishDate 2022
institution Swansea University
issn 0020-7543
1366-588X
doi_str_mv 10.1080/00207543.2022.2063089
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model.
published_date 2022-05-23T13:37:57Z
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score 11.013082

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