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Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
International Journal of Production Research, Pages: 1 - 21
Swansea University Author:
Yogesh Dwivedi
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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...
Published in: | International Journal of Production Research |
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ISSN: | 0020-7543 1366-588X |
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2022
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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 |
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d154596e71b99ad1285563c8fdd373d7 |
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d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi |
author |
Yogesh Dwivedi |
author2 |
Lai-Wan Wong Garry Wei-Han Tan Keng-Boon Ooi Binshan Lin Yogesh Dwivedi |
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International Journal of Production Research |
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Informa UK Limited |
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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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1801115378244386816 |
score |
11.021648 |