Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions.
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| Title: | Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions. |
|---|---|
| Authors: | Kumar, Devinder1 (AUTHOR), Singh, Rajesh Kr1 (AUTHOR) rajesh.singh@mdi.ac.in, Mishra, Ruchi2 (AUTHOR), Vlachos, Ilias3 (AUTHOR) |
| Source: | International Journal of Production Research. Feb2024, Vol. 62 Issue 4, p1489-1509. 21p. |
| Subjects: | Carbon dioxide mitigation, Supply chains, Big data, Supply chain management, Thematic analysis, Systems theory |
| Abstract: | Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 174974239 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Devinder%22">Kumar, Devinder</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Rajesh+Kr%22">Singh, Rajesh Kr</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rajesh.singh@mdi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Mishra%2C+Ruchi%22">Mishra, Ruchi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vlachos%2C+Ilias%22">Vlachos, Ilias</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Feb2024, Vol. 62 Issue 4, p1489-1509. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Carbon+dioxide+mitigation%22">Carbon dioxide mitigation</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chains%22">Supply chains</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+theory%22">Systems theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2023.2179346 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1489 Subjects: – SubjectFull: Carbon dioxide mitigation Type: general – SubjectFull: Supply chains Type: general – SubjectFull: Big data Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Thematic analysis Type: general – SubjectFull: Systems theory Type: general Titles: – TitleFull: Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Devinder – PersonEntity: Name: NameFull: Singh, Rajesh Kr – PersonEntity: Name: NameFull: Mishra, Ruchi – PersonEntity: Name: NameFull: Vlachos, Ilias IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 62 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
