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

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...

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Veröffentlicht in:Systems (Basel) 2023-01, Vol.11 (1), p.46
Hauptverfasser: El Baz, Jamal, Cherrafi, Anass, Benabdellah, Abla Chaouni, Zekhnini, Kamar, Beka Be Nguema, Jean Noel, Derrouiche, Ridha
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Sprache:eng
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Zusammenfassung: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.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems11010046