A data mining-based framework for supply chain risk management

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dc.contributor.authorEr Kara, Merve
dc.contributor.authorOktay Fırat, Seniye Ümit
dc.contributor.authorGhadge, Abhijeet
dc.date.accessioned2018-12-19T15:50:42Z
dc.date.available2018-12-19T15:50:42Z
dc.date.issued2018-12-06
dc.description.abstractIncreased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM. The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study. The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions.en_UK
dc.identifier.citationEr Kara M, Oktay Fırat S, Ghadge A. (2018) A data mining-based framework for supply chain risk management. Computers and Industrial Engineering, Volume 139, January 2020, Article number 105570en_UK
dc.identifier.issn0360-8352
dc.identifier.urihttps://doi.org/10.1016/j.cie.2018.12.017
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13763
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectData miningen_UK
dc.subjectData analyticsen_UK
dc.subjectDecision support systemen_UK
dc.subjectSupply chain risk managementen_UK
dc.titleA data mining-based framework for supply chain risk managementen_UK
dc.typeArticleen_UK

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