An Investigation on the Impact of Digital Revolution and Machine Learning in Supply Chain Management. (2022)

• Record Type: Journal Article
• Authors: Wisetsri, Worakamol; Donthu, Somasekhar; Mehbodniya, Abolfazl; Vyas, Sonali; Quiñonez-Choquecota, Jose; Neware, Rahul
• Is Part Of: Materials today. Volume 56:Part 6(2022)
• Page Start: 3207
• Page End: 3210
• Publication Date: 2022
• Subjects: Digital revolution -- Digital transformation -- Supply chain management -- Supply and demand -- Industry 4.0 -- Industrial development; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.09.367
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The machine learning has made it possible to discover various disciplines and patterns of the supply chain management (SCM). The entire industrial world is trying to gain contextual intelligence of machine learning discovering various new disciplines of the supply chain network. Here, the study has highlighted the iterative queries of digital revolution in supply chain. Physical inspection and potential applications have enlisted in the discussion here. The aim of the study is to acquire more knowledge and insight about the digital revolution and machine learning in activating the supply chain. The literary view in this article has illustrated significant role of machine learning addressing digital revolution in reducing the freight costs, enhancing delivery performance and minimizing the challenges in suppliers as the best advantages of supply network. It has processed discussing how machine learning targets the linear collaboration synergies in present day industrial world. Discussion on these factors has given a better understanding about machine learning. The article has been processed with secondary qualitative analysis highlighting the issues with formulation of appropriate themes. The core construction of machine learning is ideally suited offering knowledge into SCM performance which is not available in the prior technologies. Machine learning is offering today to be a new technology with highest efficiency in strengthening the Supply chain. The qualitative approach of this article expresses the power and emphasize its rate of use pointing the measurement in a comprehensive manner. The major purpose is to define the reach of endpoints of machine learning to ensure the highest productivity in SCM. In addition to this, global industrial pace is broadening day by day where the power of machine learning has become more applicable observing the recent trend in global enterprises. This research article is hopeful enough in giving the meaningful view on the topic following all its variables.