Artículo

The Interplay Between Machine Learning Techniques and Supply Chain Performance: A Structured Content Analysis

Resumen

Abstract: Over recent years, disruptive technologies have shown considerable potential to improve supply chain efficiency. In this regard, numerous papers have explored the link between machine learning techniques and supply chain performance. However, research works still need more systematization. To fill this gap, this paper aims to systematize published papers highlighting the impact of advanced technologies, such as machine learning, on supply chain performance. A structured content analysis was conducted on 91 selected journal articles from the Scopus and Web of Science databases. Bibliometric analysis has identified nine distinct groupings of research papers that explore the relationship between the machine learning and supply chain performance. These clusters cover topics such as big data and supply chain management, knowledge management, decision-making processes, business process management, and the applications of big data analytics within this domain. Each cluster’s content was clarified through a rigorous systematic literature review. The proposed study can be seen as a kind of comprehensive initiative to systematically map and consolidate this rapidly evolving body of literature. By identifying the key research themes and their interrelationships, this analysis seeks to elucidate the current state-of-the-art and to highlight potential directions for future research in this critical field.
Li, Huike (58483126400); Li, Bo (55555125500)
The state of metaverse research: a bibliometric visual analysis based on CiteSpace
2024
10.1186/s40537-024-00877-x
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181874649&doi=10.1186%2fs40537-024-00877-x&partnerID=40&md5=5385c9710572959722f9a4055717fd27
College of Home and Art Design, Northeast Forestry University, 26, Hexing Street, Nangang District, Harbin, 150040, China
All Open Access; Gold Open Access; Green Open Access
Scopus
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