Purpose

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

Design/methodology/approach

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

Findings

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

Originality/value

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

中文翻译：

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可持续制造的机器学习应用：基于文献计量学的未来研究综述

目的

数据分析在制造业中的作用非常重要，因为它是解决可持续发展挑战和处理不同类型制造业务产生的大量数据的关键。因此，本研究旨在对机器学习 (ML) 技术在可持续制造 (SM) 中的应用进行系统和基于文献计量的审查。

设计/方法/方法

在本研究中，作者使用了文献计量审查方法，该方法专注于对已发表的科学文件进行统计分析，客观地评估 ML 在可持续制造中应用的现状和未来研究潜力。

发现

本研究强调了制造业在应用于解决 SM 问题时如何从 ML 技术中受益。基于这些发现，提出了一个 ML-SM 框架。该框架将有助于研究人员、政策制定者和从业者为成功管理 SM 实践提供指导。

原创性/价值

在现有文献中，对带有框架的 SM 中的 ML 技术机会的全面和文献计量审查仍然有限。本研究解决了 SM 中 ML 应用的文献计量分析，这进一步增加了独创性。