Artículo

Paradigms, Methods, and Tools for Multicriteria Decision Models in Sustainable Industry 4.0 Oriented Manufacturing Systems

Resumen

The rise of Industry 4.0 tools transforms the way production plants are planned, controlled, and monitored, allowing organizations to achieve greater flexibility, efficiency, and cost reduction. This way, the use of emerging technologies provides a new look at the industrial sector, particularly concerning sustainability issues in a society that suffers worsening effects from climate change. On the other hand, the degree of success when implementing Industry 4.0 practices in sustainable-oriented manufacturing systems is closely related to hard decision-making, which involves, in practice, multiple and even conflicting criteria to model multidimensional problems. For that reason, it is worth recognizing the importance of the multi-criteria decision-making/aid (MCDM/A) approach to support decision modeling and application so that this research field can be better explored to enhance Industry 4.0 implementation and innovative advances in operations management. Given this backdrop, this paper undertakes a systematic literature review (SLR) of 118 papers, thereby combining a set of predefined keywords with several exclusion criteria to detect the literature that is related to sustainable decisions in manufacturing systems that introduced Industry 4.0 practices in the Web of Science (Clarivate Analytics) database. Cross-matching important research metrics from these papers encourages this work to provide readers with two axes of discussion with the aid of five research questions: a bibliometric analysis and a content analysis. Among many findings, some guidelines for decision-making are put forward to share insights and provide decision-makers, scholars, experts, stakeholders, and other professionals with an overall managerial background when applying sustainable-oriented multicriteria models in manufacturing systems.
Hidayat, Erwin Yudi (57205439013); Hastuti, Khafiizh (56485990500); Muda, Azah Kamilah (23390362900)
Artificial intelligence in digital image processing: A bibliometric analysis
2025
10.1016/j.iswa.2024.200466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212984349&doi=10.1016%2fj.iswa.2024.200466&partnerID=40&md5=14c2a82f6cb46e9efa80c050fc79c24f
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Dinus Research Group for AI in Medical Science (DREAMS), Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
All Open Access; Gold Open Access
Scopus
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