A Data Mining-Based Framework for Supply Chain Risk Management

Computers & Industrial Engineering, Online, 2019

30 Pages Posted: 20 May 2019

See all articles by Merve Er-Kara

Merve Er-Kara

Marmara University - Faculty of Business Administration

S. Oktay Fırat

affiliation not provided to SSRN

Abhijeet Ghadge

Cranfield University - School of Management

Date Written: April 20, 2019

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.

Keywords: data mining, data analytics, decision support system, supply chain risk management

Suggested Citation

Er-Kara, Merve and Oktay Fırat, S. and Ghadge, Abhijeet, A Data Mining-Based Framework for Supply Chain Risk Management (April 20, 2019). Computers & Industrial Engineering, Online, 2019, Available at SSRN: https://ssrn.com/abstract=3375328 or http://dx.doi.org/10.2139/ssrn.3375328

Merve Er-Kara

Marmara University - Faculty of Business Administration ( email )

Bahcelievler Campus, Ressam Namık İsmail Sokak
No: 1, Bahçelievler
Istanbul, 34840
Turkey

S. Oktay Fırat

affiliation not provided to SSRN

Abhijeet Ghadge (Contact Author)

Cranfield University - School of Management ( email )

Bedfordshire, MK43 0AL
United Kingdom

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