Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Abstract

In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

“…In the realm of interactive analysis, particularly critical for managing extensive medical data, the introduction of the Apache Drill framework signifies a noteworthy advancement. This platform, celebrated for its adaptability, surpasses similar systems such as Google's Dremel, especially in its ability to support diverse query languages, data formats, and sources [48]. Designed for scalability, Apache Drill excels in its smooth functionality across a vast network of servers, skillfully orchestrating data down to the byte and efficiently overseeing countless user records with scarce latency.…”

Fashion Intelligence in the Metaverse: Promise and Future Prospects

“…The use of AI has brought considerable convenience to the fashion industry [7,8]. Increasing numbers of researchers have drawn attention to the mining and analysis of fashion data to achieve an in-depth understanding of fashion elements.…”

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

“…As a new way of farm management, precision agriculture backed up by technological developments, has started to address some of the agricultural challenges in recent years (Gray et al 2018 ). Internet of things (IoTs) and artificial intelligence (AI) applications have contributed significantly to the rise of precision agriculture and its application in the agriculture sector (Ayaz et al 2019 ; Hosseinnia Shavaki and Ebrahimi Ghahnavieh 2022 ; Kwaghtyo and Eke 2022 ; Wang et al 2021 ;). Notably, utilizing IoTs, physical objects are easily identified and tracked by gathering relevant data, which will be turned into fruitful information and disseminated over a network (Gaspar et al 2021 ).…”