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

• Published in: International journal of advanced computer science & applications Vol. 15; no. 7
• Main Authors: Es-satty, Asmaa, Naimi, Mohamed, Lemghari, Radouane, Okar, Chafik
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2024
• Subjects: Bibliometrics; Big Data; Business process management; Cluster analysis; Content analysis; Critical field (superconductivity); Data analysis; Impact analysis; Knowledge management; Literature reviews; Machine learning; State-of-the-art reviews; Supply chains
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150719

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.