The large volume of data generated across different stages of supply chain has necessitated the adoption of new technologies to decipher patterns and yield meaningful results useful to managers and practitioners. Various machine learning (ML) tools have revolutionized data analysis across all industrial sectors. ML tools hold immense potential in supply chain management (SCM) by providing a comprehensive understanding and analysis of the data generated in the supply chain ecosystem. Limitations of existing data analysis tools such as statistical techniques have led researchers to dive deep into the ML paradigm to lend a better understanding of the large volume of data generated in supply chain processes. The main objective of this article is to understand the concept of ML in decision making and classification problems and to assess the utility of ML techniques in various supply chain areas including Demand Forecasting, Revenue Management, Transportation Planning, Inventory Management, and Circular Economy. 

Keywords
Machine Learning, Supply Chain Management, Data Analytics, Artificial Intelligence.