article · Systems
Smart technologies have altered organizational communication and improved the perception of environmental risks. Because of the rising volume of data generated in modern operations, business intelligence and data mining methods are increasingly necessary for supply chain risk management. A novel environmental supply chain risk management framework designed for Industry 4.0 uses data mining to identify, assess, and mitigate environmental hazards. Built upon a systematic literature review, the approach provides clear taxonomies for environmental risks, their severity levels, potential consequences, and mitigation strategies. It acts as a comprehensive guide to systematically gather, monitor, and evaluate risk data from diverse origins. The data mining framework supports the identification of key risk indicators, the creation of dedicated risk data warehouses, and the design of dedicated evaluation modules that yield practical insights for practitioners and researchers.
Modern industrial operations generate massive volumes of data that can make tracking environmental hazards complex. By structuring this information through data mining and unified taxonomies, businesses can better anticipate, monitor, and address environmental risks across their supply networks. This helps organisations improve sustainability and safeguard their operations against unexpected environmental disruptions.
This research outlines a conceptual framework, positioning it at an early stage of development. The architecture could enable the development of risk assessment modules and specialized risk data warehouses for supply chain managers and enterprise software developers. Because it is derived from a literature review, real-world deployment would require practical software implementation and testing across live industrial supply chains.
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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.
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DOI: 10.3390/systems11010046
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