No Cover Image

Journal article 146 views 8 downloads

Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain

Imad El Harraki, Mohammad Abedin Orcid Logo, Amine Belhadi, Sachin Kamble, Karim Zkik, Mustapha Oudani Orcid Logo

Business Strategy and the Environment

Swansea University Author: Mohammad Abedin Orcid Logo

  • 67579.VoR.pdf

    PDF | Version of Record

    © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.

    Download (2.9MB)

Check full text

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...

Full description

Published in: Business Strategy and the Environment
ISSN: 0964-4733 1099-0836
Published: Wiley 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa67579
first_indexed 2024-09-04T14:32:04Z
last_indexed 2024-12-02T19:45:58Z
id cronfa67579
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2024-12-02T14:53:45.7495255</datestamp><bib-version>v2</bib-version><id>67579</id><entry>2024-09-04</entry><title>Data&#x2010;driven control and a prey&#x2013;predator model for sourcing decisions in the low&#x2010;carbon intertwined supply chain</title><swanseaauthors><author><sid>4ed8c020eae0c9bec4f5d9495d86d415</sid><ORCID>0000-0002-4688-0619</ORCID><firstname>Mohammad</firstname><surname>Abedin</surname><name>Mohammad Abedin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-09-04</date><deptcode>CBAE</deptcode><abstract>This paper addresses the challenges of low-carbon sourcing in intertwined supply chains by proposing a data-driven control framework and a prey&#x2013;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 &#x201C;ripple effect&#x201D; where decisions at one node profoundly affect others within the chain.</abstract><type>Journal Article</type><journal>Business Strategy and the Environment</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0964-4733</issnPrint><issnElectronic>1099-0836</issnElectronic><keywords>data-driven control, dynamic modeling, low-carbon sourcing intertwined supply chains,optimization algorithms, prey&#x2013;predator model</keywords><publishedDay>22</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-09-22</publishedDate><doi>10.1002/bse.3971</doi><url/><notes/><college>COLLEGE NANME</college><department>Management School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CBAE</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>Swansea University</funders><projectreference/><lastEdited>2024-12-02T14:53:45.7495255</lastEdited><Created>2024-09-04T15:25:41.6699749</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Imad El</firstname><surname>Harraki</surname><order>1</order></author><author><firstname>Mohammad</firstname><surname>Abedin</surname><orcid>0000-0002-4688-0619</orcid><order>2</order></author><author><firstname>Amine</firstname><surname>Belhadi</surname><order>3</order></author><author><firstname>Sachin</firstname><surname>Kamble</surname><order>4</order></author><author><firstname>Karim</firstname><surname>Zkik</surname><order>5</order></author><author><firstname>Mustapha</firstname><surname>Oudani</surname><orcid>0000-0003-1185-395x</orcid><order>6</order></author></authors><documents><document><filename>67579__32724__87b0c14edaf1416e8ddd36b5fe8998e5.pdf</filename><originalFilename>67579.VoR.pdf</originalFilename><uploaded>2024-10-25T13:28:15.2675750</uploaded><type>Output</type><contentLength>3039427</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 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
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Imad El Harraki
Mohammad Abedin
Amine Belhadi
Sachin Kamble
Karim Zkik
Mustapha Oudani
format Journal article
container_title Business Strategy and the Environment
container_volume 0
publishDate 2024
institution Swansea University
issn 0964-4733
1099-0836
doi_str_mv 10.1002/bse.3971
publisher Wiley
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 - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description 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
_version_ 1824020250880901120
score 11.051218