Is Machine Learning Revolutionizing Supply Chain?

Abstract

The current supply chain ecosystem benefits from a great dynamic: the digitalization of companies and exchanges. For all the players in the sector, this is a real revolution, and machine learning is at the heart of this revolution. It has radically transformed companies: the evolution of communication media, the automation of many processes, the growing importance of information systems, etc. However, this fundamental transformation of work environments and organizational modes is far from over. In the current economic context of globalization of trade and increased competition, the greatest attention is focused on the objective of continuously reducing cost prices. Optimization requires efforts from all links in the supply chain to ensure very fine management. In this context, machine learning and the data on which it is based, is a real opportunity. In more recent years, a series of practical supply chain applications of machine learning (ML) have been introduced. By interconnecting the ML methods applied to the SC, the document indicates current SC applications and visualizes potential research gaps. In this article, we examine the applicability of machine learning techniques to the supply chain. The main objective of this paper is therefore to study how Machine Learning can be integrated into the range of tools available to Supply Chain decision-makers to take advantage of the increase in the volume of available data, through these tools particularly adapted to this type of processing.