https://iaeme.com/Home/journal/IJARET 979 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 12, Issue 3, March 2021, pp. 979-991, Article ID: IJARET_12_03_091 Available online at https://iaeme.com/Home/issue/IJARET?Volume=12&Issue=3 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 © IAEME Publication Scopus Indexed OPTIMIZING SUPPLY CHAIN MANAGEMENT WITH MACHINE LEARNING ALGORITHMS Harish Narne Sr. Software Engineer, Gainwell Technologies, USA. ABSTRACT Machine learning's (ML) incorporation into SCM has been a game-changer, leading to more automation, efficiency, and strategic attention in the sector. Supply chain managers may optimise inventories with the help of ML, find the best suppliers, and make use of the massive amounts of data produced by logistics, transportation, and warehousing systems. This article delves into the revolutionary effects of ML on supply chain management, showcasing its uses in areas such as automated quality inspections, predictive analytics, forecasting production, reducing costs, managing warehouses, and last-mile tracking. The report also discusses the problems that supply chain sectors encounter and how ML may solve them, including issues with inventory management, quality and safety, limited resources, and ineffective interactions with suppliers. Amazon, Microsoft, Alphabet, Procter & Gamble, and Rolls Royce are just a few of the top organisations that have used ML to boost supply chain efficiency. Businesses that want to succeed in today's global market need to use ML technologies to improve their resource management, efficiency, and bottom line, according to the paper's findings. Key words: Machine learning's (ML), supply chain management, deep learning Cite this Article: Harish Narne. Optimizing Supply Chain Management with Machine Learning Algorithms. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(3), 2021, pp. 979-991. https://iaeme.com/Home/issue/IJARET?Volume=12&Issue=3 1. INTRODUCTION In today's fast-paced and unpredictable corporate world, supply chain management is key to maximising operational efficiency and satisfying customers. The advent of digital technology, especially deep learning algorithms, has caused a sea change in the supply chain optimisation environment [1]. Examining how deep learning algorithms might improve supply chain operations is the main goal of this research. It also provides a managerial viewpoint on how to harness these breakthroughs for a sustainable competitive advantage using these algorithms.For a considerable amount of time, supply chain optimisation has been a primary focus for businesses that are looking to streamline their operations, lower their expenses, and improve their agility. When it comes to responding to the