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

Jamal El Baz, Anass Cherrafi, Abla Chaouni Benabdellah, Kamar Zekhnini (), Jean Noel Beka Be Nguema and Ridha Derrouiche ()
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Kamar Zekhnini: ENSAM - École nationale supérieure d'architecture de Montpellier

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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.

Date: 2023-01-13
Note: View the original document on HAL open archive server: https://hal.science/hal-04335003v1
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Published in Systems, 2023, 11 (1), pp.46. ⟨10.3390/systems11010046⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04335003

DOI: 10.3390/systems11010046

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