Systematic literature review of machine learning for manufacturing supply chain

Abstract

PurposeIn today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.Design/methodology/approachThis research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.FindingsThe papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.Practical implicationsThe research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.Originality/valueThis study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.Highlights A comprehensive understanding of Machine Learning techniques is presented.The state of art of adoption of Machine Learning techniques are investigated.The methodology of (SLR) is proposed.An innovative study of Machine Learning techniques in manufacturing supply chain.