Systematic literature review of machine learning for manufacturing supply chain

IF 3.8 Q2 MANAGEMENT TQM Journal Pub Date : 2023-08-08 DOI:10.1108/tqm-12-2022-0365
Smita A. Ganjare, Sunil M. Satao, V. Narwane
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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 The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study.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.
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制造供应链中机器学习的系统文献综述
目的在当今快速发展的时代,数据量与日俱增。传统的方法在有效管理海量数据方面显得滞后。机器学习技术的采用有助于有效管理数据,并从数据中提取相关模式。本研究论文的主要目的是提供关于在制造供应链的不同部门中采用机器学习技术的简要信息。设计/方法论/方法本研究论文对2015年至2023年机器学习技术在制造供应链中的应用进行了严格的系统文献综述。在511篇论文中,有74篇论文入围详细分析。论文分为8个部分,有助于仔细审查制造业供应链中所做的工作。本文有助于了解机器学习技术在制造领域的应用(主要是在汽车领域)的贡献。实际含义该研究仅限于2015年至2023年发表的论文。目前研究的局限性是,不考虑书籍章节、未发表的作品、白皮书和会议论文进行研究。只有英文文章和评论论文被简要研究。本研究有助于在制造供应链中采用机器学习技术。原创性/价值本研究是为数不多的通过系统的文献调查研究制造业和供应链中的机器学习技术的研究之一。亮点介绍了对机器学习技术的全面理解。研究了采用机器学习技术的现状。提出了(SLR)的方法论。制造供应链中机器学习技术的创新研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
TQM Journal
TQM Journal Business, Management and Accounting-Business, Management and Accounting (all)
CiteScore
9.10
自引率
0.00%
发文量
114
期刊介绍: Commitment to quality is essential if companies are to succeed in a commercial environment which will be virtually unrecognizable in less than a decade. Changing attitudes, changing perspectives and changing priorities will revolutionise the structure and philosophy of future business practice - and TQM will be at the heart of that metamorphosis. All aspects of preparing for, developing, introducing, managing and evaluating TQM initiatives.
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