供应链4.0中的大数据分析和机器学习:文献综述

IF 0.9 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Stats Pub Date : 2023-05-05 DOI:10.3390/stats6020038
Elena Barzizza, Nicolò Biasetton, R. Ceccato, L. Salmaso
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引用次数: 1

摘要

由于工业4.0技术的发展,近年来出现了供应链管理的新概念,即供应链4.0 (SC 4.0)。对信息技术的巨大投资使制造商能够追踪无形的信息流,但需要工具来利用可用的数据源:大数据分析(BDA)和机器学习(ML)是完成这项任务的重要工具。先进技术的使用可以改善供应链绩效并支持实现战略目标,但它们的实施在供应链管理中具有挑战性。本研究的目的是了解SC 4.0中BDA和ML的主要优点、挑战和应用领域,以及了解该领域最常用的BDA和ML技术,特别关注非参数技术。为此,我们进行了文献综述。从我们的分析中,我们确定了三个主要差距,即需要适当的分析工具来管理具有挑战性的数据配置;需要与实践建立更可靠的联系;需要仪器来选择最合适的BDA或ML技术。作为解决方案,我们提出并评论了两个可行的解决方案:非参数统计,情感分析和聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review
Owing to the development of the technologies of Industry 4.0, recent years have witnessed the emergence of a new concept of supply chain management, namely Supply Chain 4.0 (SC 4.0). Huge investments in information technology have enabled manufacturers to trace the intangible flow of information, but instruments are required to take advantage of the available data sources: big data analytics (BDA) and machine learning (ML) represent important tools for this task. Use of advanced technologies can improve supply chain performances and support reaching strategic goals, but their implementation is challenging in supply chain management. The aim of this study was to understand the main benefits, challenges, and areas of application of BDA and ML in SC 4.0 as well as to understand the BDA and ML techniques most commonly used in the field, with a particular focus on nonparametric techniques. To this end, we carried out a literature review. From our analysis, we identified three main gaps, namely, the need for appropriate analytical tools to manage challenging data configurations; the need for a more reliable link with practice; the need for instruments to select the most suitable BDA or ML techniques. As a solution, we suggest and comment on two viable solutions: nonparametric statistics, and sentiment analysis and clustering.
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来源期刊
CiteScore
0.60
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