Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability

Ali, SM; Rahman, AU; Kabir, G; Paul, SK

Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability

Ali, SM Rahman, AU Kabir, G Paul, SK

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sustainability, 2024, 16, (6)
Issue Date:: 2024-03

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Field	Value	Language
dc.contributor.author	Ali, SM
dc.contributor.author	Rahman, AU
dc.contributor.author	Kabir, G
dc.contributor.author	Paul, SK
dc.date.accessioned	2025-04-02T18:44:54Z
dc.date.available	2025-04-02T18:44:54Z
dc.date.issued	2024-03
dc.identifier.citation	Sustainability, 2024, 16, (6)
dc.identifier.issn	2071-1050
dc.identifier.issn	2071-1050
dc.identifier.uri	http://hdl.handle.net/10453/186511
dc.description.abstract	first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability by Syed Mithun Ali 1,ORCID,Amanat Ur Rahman 2ORCID,Golam Kabir 3ORCID andSanjoy Kumar Paul 4ORCID 1 Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh 2 Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA 3 Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada 4 UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia Author to whom correspondence should be addressed. Sustainability 2024, 16(6), 2373; https://doi.org/10.3390/su16062373 Submission received: 27 November 2023 / Revised: 19 February 2024 / Accepted: 8 March 2024 / Published: 13 March 2024 (This article belongs to the Special Issue Artificial Intelligence in Supply Chain Management: Promoting Enterprise Sustainability and Optimization) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Sustainability
dc.relation.isbasedon	10.3390/su16062373
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	12 Built Environment and Design
dc.title	Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	12 Built Environment and Design
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Business School
pubs.organisational-group	University of Technology Sydney/Business School/Management Discipline
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Business Intelligence and Data Analytics (BIDA)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Business Intelligence and Data Analytics (BIDA)/Centre for Business Intelligence and Data Analytics (BIDA) Associate Members
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-02T18:44:52Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	6

Abstract:

first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability by Syed Mithun Ali 1,*ORCID,Amanat Ur Rahman 2ORCID,Golam Kabir 3ORCID andSanjoy Kumar Paul 4ORCID 1 Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh 2 Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA 3 Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada 4 UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia * Author to whom correspondence should be addressed. Sustainability 2024, 16(6), 2373; https://doi.org/10.3390/su16062373 Submission received: 27 November 2023 / Revised: 19 February 2024 / Accepted: 8 March 2024 / Published: 13 March 2024 (This article belongs to the Special Issue Artificial Intelligence in Supply Chain Management: Promoting Enterprise Sustainability and Optimization) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.

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