OPTIMIZING SUPPLY CHAIN MANAGEMENT WITH MACHINE LEARNING ALGORITHMS
Keywords:
Machine Learnings (ML), Supply Chain Management, Deep LearningAbstract
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.
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Copyright (c) 2021 Harish Narne (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
