Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Integrating Machine Learning for Supply Chain Optimization in Manufacturing and Logistics: Enhancing Retail Management and Efficiency

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  • Dr. Luis García
Dr. Luis García
Associate Professor of Industrial Engineering, Monterrey Institute of Technology and Higher Education (ITESM), Mexico
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Published 19-09-2024

Keywords

  • Supply Chain Optimization,
  • Manufacturing,
  • Logistics,
  • Retail Management
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Luis García, “Integrating Machine Learning for Supply Chain Optimization in Manufacturing and Logistics: Enhancing Retail Management and Efficiency”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 123–136, Sep. 2024, Accessed: Nov. 14, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/153

Abstract

Machine learning (ML) has emerged as a powerful tool for enhancing efficiency in manufacturing and logistics by optimizing supply chain processes. Forecasting plays a crucial role in retail supply chain management, and the application of AI/ML models, such as Cognitive Demand Forecasting and Demand Integrated Product Flow, has become increasingly prevalent in addressing this challenge [1]. The use of reinforcement learning (RL) algorithms in supply chain forecasting has gained traction, with companies like UPS and Amazon leveraging RL to improve forecast accuracy and meet rising consumer delivery expectations. The OpenAI Gym toolkit has become a preferred choice for building RL algorithms for supply chain use cases, enabling the development of suitable RL models for supply chain optimization challenges.

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