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dc.contributor.authorKosasih, Edward
dc.contributor.authorBrintrup, alexandra
dc.date.accessioned2022-05-11T23:30:24Z
dc.date.available2022-05-11T23:30:24Z
dc.identifier.issn2405-8963
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337047
dc.description.abstractIn this paper, we define and conceptualize the emerging practice of “Digital Supply Chain Surveillance (DSCS)” as the proactive monitoring of digital data that allows firms to track, manage, and analyze information related to a supply chain network without needing the explicit consent of firms involved in the supply chain. After reviewing approaches to surveillance challenges that have been raised, we find that several approaches have been proposed, in particular for risk management, which have made use of Artificial Intelligence (AI) as a key enabler. By interconnecting surveillance data sources and systems, appropriate AI techniques can make surveillance easier, larger scale and possibly more informative, whilst at the same time bringing about a number of technical, ethical and managerial challenges with it. We discuss these challenges, highlighting the need to integrate multiple surveillance data and insights, the potential for hidden bias and the consequent need for AI skills to prevent bias, and the need to design guidance for embedding DSCS insights into business processes ethically, and transparently.
dc.publisherElsevier
dc.rightsPublisher's own licence
dc.titleTowards Digital Supply Chain Risk Surveillance
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-05-09T15:08:55Z
prism.publicationNameIFAC-PapersOnLine
dc.identifier.doi10.17863/CAM.84470
rioxxterms.versionAM
dc.contributor.orcidKosasih, Edward [0000-0001-5293-2641]
rioxxterms.typeJournal Article/Review
cam.orpheus.counter5*
cam.depositDate2022-05-09
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2025-05-11


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