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Environmental Supply Chain Risk Management for Industry 4.0: A Data Mining Framework and Research Agenda

Author

Listed:
  • Jamal El Baz
  • Anass Cherrafi
  • Abla Chaouni Benabdellah
  • Kamar Zekhnini

    (ENSAM - Ecole Nationale Supérieure d'Architecture de Montpellier)

  • Jean Noel Beka Be Nguema
  • Ridha Derrouiche

Abstract

Smart technologies have dramatically improved environmental risk perception and altered the way organizations share knowledge and communicate. As a result of the increasing amount of data, there is a need for using business intelligence and data mining (DM) approaches to supply chain risk management. This paper proposes a novel environmental supply chain risk management (ESCRM) framework for Industry 4.0, supported by data mining (DM), to identify, assess, and mitigate environmental risks. Through a systematic literature review, this paper conceptualizes Industry 4.0 ESCRM using a DM framework by providing taxonomies for environmental risks, levels, consequences, and strategies to address them. This study proposes a comprehensive guide to systematically identify, gather, monitor, and assess environmental risk data from various sources. The DM framework helps identify environmental risk indicators, develop risk data warehouses, and elaborate a specific module for assessing environmental risks, all of which can generate useful insights for academics and practitioners.

Suggested Citation

  • Jamal El Baz & Anass Cherrafi & Abla Chaouni Benabdellah & Kamar Zekhnini & Jean Noel Beka Be Nguema & Ridha Derrouiche, 2023. "Environmental Supply Chain Risk Management for Industry 4.0: A Data Mining Framework and Research Agenda," Post-Print hal-04335003, HAL.
    • Handle: RePEc:hal:journl:hal-04335003
    DOI: 10.3390/systems11010046
    as

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