Er Kara, MerveOktay Fırat, Seniye ÜmitGhadge, Abhijeet2018-12-192018-12-192018-12-06Er 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 1055700360-8352https://doi.org/10.1016/j.cie.2018.12.017http://dspace.lib.cranfield.ac.uk/handle/1826/13763Increased 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Data miningData analyticsDecision support systemSupply chain risk managementArticle