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Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain
Business Strategy and the Environment
Swansea University Author:
Mohammad Abedin
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DOI (Published version): 10.1002/bse.3971
Abstract
This paper addresses the challenges of low-carbon sourcing in intertwined supply chains by proposing a data-driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low-carbon objectives and reduce environmental impact. Existing static models fail to ca...
Published in: | Business Strategy and the Environment |
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ISSN: | 0964-4733 1099-0836 |
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Wiley
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa67579 |
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2024-12-02T14:53:45.7495255 v2 67579 2024-09-04 Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2024-09-04 CBAE This paper addresses the challenges of low-carbon sourcing in intertwined supply chains by proposing a data-driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low-carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real-time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low-carbon measures. We assess two data-driven algorithms, the nonlinear auto-regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision-making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant “ripple effect” where decisions at one node profoundly affect others within the chain. Journal Article Business Strategy and the Environment 0 Wiley 0964-4733 1099-0836 data-driven control, dynamic modeling, low-carbon sourcing intertwined supply chains,optimization algorithms, prey–predator model 22 9 2024 2024-09-22 10.1002/bse.3971 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-12-02T14:53:45.7495255 2024-09-04T15:25:41.6699749 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Imad El Harraki 1 Mohammad Abedin 0000-0002-4688-0619 2 Amine Belhadi 3 Sachin Kamble 4 Karim Zkik 5 Mustapha Oudani 0000-0003-1185-395x 6 67579__32724__87b0c14edaf1416e8ddd36b5fe8998e5.pdf 67579.VoR.pdf 2024-10-25T13:28:15.2675750 Output 3039427 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/ |
title |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
spellingShingle |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain Mohammad Abedin |
title_short |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
title_full |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
title_fullStr |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
title_full_unstemmed |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
title_sort |
Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain |
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4ed8c020eae0c9bec4f5d9495d86d415 |
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4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin |
author |
Mohammad Abedin |
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Imad El Harraki Mohammad Abedin Amine Belhadi Sachin Kamble Karim Zkik Mustapha Oudani |
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Business Strategy and the Environment |
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Wiley |
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This paper addresses the challenges of low-carbon sourcing in intertwined supply chains by proposing a data-driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low-carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real-time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low-carbon measures. We assess two data-driven algorithms, the nonlinear auto-regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision-making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant “ripple effect” where decisions at one node profoundly affect others within the chain. |
published_date |
2024-09-22T08:21:05Z |
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1824020250880901120 |
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11.051218 |