Artificial Intelligence and Optimization of Eeverse Logistics: An Analysis in the Aquatic Industry of The Mekong Delta

Authors

  • Chuyen Tran Trung Nam Can Tho University
  • Phan Tran Xuan Trinh Nam Can Tho University
  • Tran Thanh Huy Nam Can Tho University
  • Nguyen Tri Khiem Nam Can Tho University
  • Nguyen Van Tac Nam Can Tho University

DOI:

https://doi.org/10.37385/jaets.v7i1.7471

Keywords:

Aquatic industry, artificial intelligence, reverse logistics, seafood supply chain

Abstract

In recent years, artificial intelligence (AI) has become an important technology that enhances the competitive advantages for businesses. This study investigates the application of artificial intelligence and how it can optimize reverse logistics for the aquatic industry in the Mekong Delta. It also explores the current situation in applying AI, its benefits, and challenges when using AI in reverse logistics for aquatic enterprises. The research uses qualitative and quantitative methods to collect data from interviewing managers, logistics staff, and technicians to deliver a survey to 41 seafood businesses. Results show AI applications in forecasting, storage, and recycling can cut operational costs by over 10% for 46.3% of firms and improve recovery time by over 10% for 56.1%. Benefits also include higher operational efficiency and better environmental performance. However, challenges persist in system integration, data access, and workforce readiness. The study provides practical recommendations, including enhancing AI workforce training, system integration, and collaboration with technology providers, to help seafood companies overcome barriers and maximize the benefits of AI in reverse logistics.

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Published

2025-12-29

How to Cite

Tran Trung, C., Trinh, P. T. X., Huy, T. T., Khiem, N. T., & Tac, N. V. (2025). Artificial Intelligence and Optimization of Eeverse Logistics: An Analysis in the Aquatic Industry of The Mekong Delta. Journal of Applied Engineering and Technological Science (JAETS), 7(1), 393–413. https://doi.org/10.37385/jaets.v7i1.7471