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Enabling explainable artificial intelligence capabilities in supply chain decision support making

Femi Olan Orcid Logo, Konstantina Spanaki Orcid Logo, Wasim Ahmed Orcid Logo, Guoqing Zhao Orcid Logo

Production Planning and Control, Pages: 1 - 12

Swansea University Author: Guoqing Zhao Orcid Logo

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DOI (Published version): 10.1080/09537287.2024.2313514

Abstract

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to condu...

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Published in: Production Planning and Control
ISSN: 0953-7287 1366-5871
Published: Informa UK Limited 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65709
first_indexed 2024-02-27T12:05:48Z
last_indexed 2024-11-28T13:42:36Z
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spelling 2024-11-28T11:27:45.1684742 v2 65709 2024-02-27 Enabling explainable artificial intelligence capabilities in supply chain decision support making 2ff29aa347835abe2af6d98fa89064b4 0009-0003-9537-9016 Guoqing Zhao Guoqing Zhao true false 2024-02-27 CBAE Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty. Journal Article Production Planning and Control 0 1 12 Informa UK Limited 0953-7287 1366-5871 Explainable artificial intelligence; supply chains; decision support systems; supply chains management; SHAP; innovation 27 2 2024 2024-02-27 10.1080/09537287.2024.2313514 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Another institution paid the OA fee 2024-11-28T11:27:45.1684742 2024-02-27T12:01:53.8613922 Faculty of Humanities and Social Sciences School of Management - Business Management Femi Olan 0000-0002-7377-9882 1 Konstantina Spanaki 0000-0001-6332-1731 2 Wasim Ahmed 0000-0001-8923-1865 3 Guoqing Zhao 0009-0003-9537-9016 4 65709__29589__bcd5286c52aa45a6b3ded0317752220f.pdf Enabling explainable artificial intelligence capabilities in supply chain decision support making.pdf 2024-02-27T12:05:19.3677450 Output 1492127 application/pdf Version of Record true © 2024 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Enabling explainable artificial intelligence capabilities in supply chain decision support making
spellingShingle Enabling explainable artificial intelligence capabilities in supply chain decision support making
Guoqing Zhao
title_short Enabling explainable artificial intelligence capabilities in supply chain decision support making
title_full Enabling explainable artificial intelligence capabilities in supply chain decision support making
title_fullStr Enabling explainable artificial intelligence capabilities in supply chain decision support making
title_full_unstemmed Enabling explainable artificial intelligence capabilities in supply chain decision support making
title_sort Enabling explainable artificial intelligence capabilities in supply chain decision support making
author_id_str_mv 2ff29aa347835abe2af6d98fa89064b4
author_id_fullname_str_mv 2ff29aa347835abe2af6d98fa89064b4_***_Guoqing Zhao
author Guoqing Zhao
author2 Femi Olan
Konstantina Spanaki
Wasim Ahmed
Guoqing Zhao
format Journal article
container_title Production Planning and Control
container_volume 0
container_start_page 1
publishDate 2024
institution Swansea University
issn 0953-7287
1366-5871
doi_str_mv 10.1080/09537287.2024.2313514
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 Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty.
published_date 2024-02-27T02:48:21Z
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score 11.048042

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