Environmental Supply Chain Risk Management for Industry 4.0: A Data Mining Framework and Research Agenda - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Systems Année : 2023

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

Jamal El Baz
Anass Cherrafi
  • Fonction : Auteur
Abla Chaouni Benabdellah
  • Fonction : Auteur
Jean Noel Beka Be Nguema
Ridha Derrouiche

Résumé

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.

Dates et versions

hal-04335003 , version 1 (11-12-2023)

Identifiants

Citer

Jamal El Baz, Anass Cherrafi, Abla Chaouni Benabdellah, Kamar Zekhnini, Jean Noel Beka Be Nguema, et al.. Environmental Supply Chain Risk Management for Industry 4.0: A Data Mining Framework and Research Agenda. Systems, 2023, 11 (1), pp.46. ⟨10.3390/systems11010046⟩. ⟨hal-04335003⟩
7 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More