Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25026
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dc.contributor.authorTsolakis, N-
dc.contributor.authorSchumacher, R-
dc.contributor.authorDora, M-
dc.contributor.authorKumar, M-
dc.date.accessioned2022-08-03T12:31:00Z-
dc.date.available2022-06-21-
dc.date.available2022-08-03T12:31:00Z-
dc.date.issued2022-06-21-
dc.identifier.citationTsolakis, N.,et al. (2022) Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?. https://doi.org/10.1007/s10479-022-04785-2en_US
dc.identifier.issn0254-5330-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/25026-
dc.description.abstractDigitalisation is expected to transform end-to-end supply chain operations by leveraging the technical capabilities of advanced technology applications. Notwithstanding the operations-wise merits associated with the implementation of digital technologies, individually, their combined effect has been overlooked owing to limited real-world evidence. In this regard, this research explores the joint implementation of Artificial Intelligence (AI) and Blockchain Technology (BCT) in supply chains for extending operations performance boundaries and fostering sustainable development and data monetisation. Specifically, this study empirically studied the tuna fish supply chain in Thailand to identify respective end-to-end operations, observe material and data-handling processes, and envision the implementation of AI and BCT. Therefore, we first mapped the business processes and the system-level interactions to understand the governing material, data, and information flows that could be facilitated through the combined implementation of AI and BCT in the respective supply chain. The mapping results illustrate the central role of AI and BCT in digital supply chains’ management, while the associated sustainability and data monetisation impact depends on the parameters and objectives set by the involved system stakeholders. Afterwards, we proposed a unified framework that captures the key data elements that need to be digitally handled in AI and BCT enabled food supply chains for driving value delivery. Overall, the empirically-driven modelling approach is anticipated to support academics and practitioners’ decision-making in studying and introducing digital interventions toward sustainability and data monetisation.en_US
dc.description.sponsorshipThis research is supported by the Industrial Resilience Research Group at the Department of Engineering, University of Cambridge. This research has received funding from: Engineering and Physical Sciences Research Council (EPSRC) under Grant Reference No. EP/S036091/1, Project Full Title: “UK Manufacturing Symbiosis NetworkPlus” (UKMSN+); EPSRC under Grant Reference No. EP/K02888X/1, Project Full Title: “Engineering Driven Sustainable Supply Networks - A UK/India Collaborative Study”; UK-India Education and Research Initiative (UKIERI) under Grant Reference No. UKUTP201100194, Project Full Title: “Understanding Indian and UK Food Industries”; and EPSRC under Grant Reference No. EP/R513180/1, Project Full Title: “Industrial Resilience: Risks and Mitigation Strategies in the Automotive Industry”.en_US
dc.format.extent? - ? (54)-
dc.languageEnglish-
dc.publisherSpringeren_US
dc.rights© 2022 Springer Nature Switzerland AG. Part of Springer Nature. © Copyright Owner Authors-
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/-
dc.subjectSupply chain digitalisationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBlockchain technologyen_US
dc.subjectSustainabilityen_US
dc.subjectData monetisationen_US
dc.subjectFish supply networksen_US
dc.titleArtificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?en_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s10479-022-04785-2-
dc.relation.isPartOfAnnals of Operations Research-
pubs.issuein press-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn1572-9338-
dc.rights.licenseOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.-
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