Journal Article

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

El Baz, Jamal, Benabdellah, Abla Chaouni, Derrouiche, Ridha, Zekhnini, Kamar, Beka Be Nguema, Jean Noel, Cherrafi, Anass

2023

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: 3-D printers / Additive manufacturing / Big Data / Business administration / Communication / Communications technology / Data mining / Data warehouses / Environmental management / environmental risk management / framework / Humanities and Social Sciences / Industry 4.0 / Information management / Intelligence (information) / Internet of Things / Literature reviews / mitigation strategies / Product development / Risk management / Risk perception / Supply chains / sustainability / Taxonomy