Artículo

Inventory Models in a Sustainable Supply Chain: A Bibliometric Analysis

Resumen

This paper presents a bibliometric analysis of inventory models in a sustainable supply chain. The methodology contains reviewing previous research with a performance evaluation, network analysis, and science mapping to identify the applications, trends, and future research topics. Scientific mapping examines the periods and volumes of publications, authors, journals, countries, regions, organizations, subject areas, and citation analyses. The dataset was obtained with the Scopus database and analyzed using MS Excel and VOSviewer. The search equation identified 335 research papers, which resulted in 131 significant manuscripts on the subject after being screened and filtered. The most notable countries in developing research were Iran, India, China, the United States, Canada, Taiwan, France, the United Arab Emirates, Turkey, and Denmark. Saha, S., Ajay, S.Y., and Baboli, A. were the most cited authors. The journals that publish the most research were Sustainability, the Journal of Cleaner Production, and the International Journal of Production Economics. Some research focuses on reducing carbon emissions and polluting agents applied in different industries in China, Brazil, India, and others. The main findings were the number of industry sectors researching this topic, increasing the number of publications, and promoting the proper use of resources within a sustainable supply chain. There are many investigations of theoretical models that have applications in real-life cases. There is also evidence of the high importance of promoting sustainable development. The emissions regulations in a green supply chain applied to agricultural products have allowed for more actions to achieve responsible production and consumption, as seen in applied research in the pulp and paper industry.
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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