AI-Driven Optimization in Supply Chain Management: Enhancing Efficiency and Reducing Costs

EasyChair Preprint 14551

Abstract

Supply chain management (SCM) plays a pivotal role in the success of businesses by ensuring that goods and services are efficiently and effectively delivered from suppliers to consumers. The advent of artificial intelligence (AI) has introduced new possibilities for optimizing supply chain processes, leading to significant improvements in efficiency and cost reduction. This paper presents a comprehensive AI-driven framework for optimizing various aspects of SCM, including demand forecasting, inventory management, transportation logistics, and supplier selection. By leveraging machine learning models, big data analytics, and cloud computing, the proposed framework aims to enhance decision-making processes and streamline supply chain operations. The results of the study, based on a synthetic dataset, demonstrate the effectiveness of AI in improving key performance indicators (KPIs) within supply chain management. A comparative analysis with existing literature highlights the superior performance of the proposed AI-driven approach.

Keyphrases: Artificial Intelligence, Big Data, Cloud Computing, Optimization, Supply Chain Management, machine learning

@booklet{EasyChair:14551,
  author    = {Yun Chen},
  title     = {AI-Driven Optimization in Supply Chain Management: Enhancing Efficiency and Reducing Costs},
  howpublished = {EasyChair Preprint 14551},
  year      = {EasyChair, 2024}}