A data mining-based framework for supply chain risk management. (January 2020)

• Record Type: Journal Article
• Authors: Er Kara, Merve; Oktay Fırat, Seniye Ümit; Ghadge, Abhijeet
• Is Part Of: Computers & industrial engineering. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Data mining -- Data analytics -- Decision support system -- Supply chain risk management; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.12.017
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Novel framework for data mining-based approach for supply chain risk management. Provides guideline for implementing data mining driven risk management methodology. Conversion of risk management problem into data mining problem. Successful implementation and testing of the framework in a case company. Abstract: Increased 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.