Abstract
Supplier quality evaluation in industrial sectors is well-recognized due to its direct impact on quality assurance and improvement. This task is challenging due to the need to process extensive qualitative and quantitative data, multi-dimensional attributes, and numerous suppliers. Traditional methods are increasingly inadequate to manage this large amount of data and facilitate effective decision-making. This research paper presents a systematic literature review on adopting machine learning techniques for sustainable supplier selection from 2010 to 2024. From an initial pool of 99 papers in the Scopus database, 20 papers from Web of Science, and 52 papers in Google Scholar, 25, 12 and 13 papers, respectively, were selected for in-depth analysis. The study elucidates the role of machine learning in enhancing supplier selection across various sectors, focusing on literature published in English. The findings indicate that machine learning significantly improves organizational performance by refining supplier selection processes, addressing inconsistencies in traditional methods, and leveraging vast data repositories. Integrating Artificial Intelligence (AI) into supply chain operations enables more rapid and reliable decision-making, especially when conventional approaches falter due to large data volumes. The developed framework identifies the significance of traditional and machine learning methods in different stages of supplier selection, including criteria definition, weighting, evaluation, and ranking.
•Examine machine learning (ML) application integration in sustainable supplier selection.•Show how ML techniques can enhance decision-making by incorporating environmental, social, and economic factors.•Analyse various ML techniques, including supervised, unsupervised, and reinforcement learning, highlighting their strengths and limitations.•Offer practical insights and recommendations for practitioners implementing ML models in sustainable supplier selection.•Identify the gaps in the current literature and suggest future research directions.