This report explores the significant impact of machine learning on supply chain management. It begins with an introduction to supply chain management and machine learning, comparing traditional programming with machine learning approaches. The report then delves into various types of machine learning, including supervised, unsupervised, decision tree learning, and deep learning, highlighting the reasons for its growing adoption. The core of the report focuses on how machine learning influences key areas of supply chain management, such as inventory control and planning, transport networks, procurement, and customer relationship management (CRM). Specific examples are provided, including automated vehicles for transport, programmatic advertising, and automated negotiation in procurement. The report also examines the application of machine learning in order picking and risk reduction. Finally, it concludes by summarizing the key impacts of machine learning on supply chain management, emphasizing the need for businesses to adapt and integrate these technologies for improved efficiency and effectiveness. The report is well-researched and includes references to peer-reviewed journal articles and conference papers.
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