Research themes in machine learning applications in supply chain management using bibliometric analysis tools

Abstract

PurposeThis paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.Design/methodology/approachUnlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.FindingsUsing contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.Originality/valueModern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

“…Green supply chains incorporate eco-friendly practices in production and logistics, aiming to reduce carbon emissions and minimize resource wastage. Conversely, circular economies prioritize maximizing resource recycling, extending product lifecycles, and enhancing the reuse of waste materials [ 6 , 7 ]. A “green supply chain” strategy focuses on minimizing resource usage and reducing environmental impacts throughout production, transportation, storage, and sales [ 8 ].…”

Mapping the Role and Impact of Artificial Intelligence and Machine Learning Applications in Supply Chain Digital Transformation: A Bibliometric Analysis

“…Similar to this, Riahi et al ( 2021 ) examined 136 AI-related research publications from the Scopus database that were written between 1996 and 2020. More recently, Raza et al ( 2022 ) used modern methods and software to review 155 ML-related research publications that were published between 2008 and 2018. However, this study offers a comprehensive perspective in examining 338 relevant research papers over ten years (2002 – August 2022) and analyzing them into distinct research themes, providing a complete perspective of AI as well as ML implications in the supply chain through descriptive data analysis, bibliometric, and network analysis.…”