Abstract

The decision-making areas ideally suited to support AI (Artificial Intelligence) are decisions regarding the choice of Incoterms in cross-border trade. AI makes it possible to analyze huge datasets of historical transactions, considering all relevant decision factors in Incoterm's choice. Based on this data, AI might recommend Incoterms for maximum control and clear landed cost estimation, and real-time landed cost estimation. Consequently, using AI to model Incoterms decisions can streamline buying and selling processes. The article aims to assess the current state of knowledge and identify directions for future research on optimizing decision-making processes related to the choice of Incoterms in cross-border trade based on AI solutions. The study used the Scoping Review method and the VOSviewer IT tool. The keywords co-occurrence analysis showed that there is a lack of in-depth research relating AI issues to choosing Incoterms and modeling and optimizing these decision processes in supply chains.

Recommended Citation

Pettersen-Sobczyk, M. & Mańkowska, M. (2024). Artificial Intelligence in Optimizing the Selection of Incoterms Rules in Cross-Border Trade. State of Knowledge and Needs for Further Research. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.16

Paper Type

Short Paper

DOI

10.62036/ISD.2024.16

Share

COinS
 

Artificial Intelligence in Optimizing the Selection of Incoterms Rules in Cross-Border Trade. State of Knowledge and Needs for Further Research

The decision-making areas ideally suited to support AI (Artificial Intelligence) are decisions regarding the choice of Incoterms in cross-border trade. AI makes it possible to analyze huge datasets of historical transactions, considering all relevant decision factors in Incoterm's choice. Based on this data, AI might recommend Incoterms for maximum control and clear landed cost estimation, and real-time landed cost estimation. Consequently, using AI to model Incoterms decisions can streamline buying and selling processes. The article aims to assess the current state of knowledge and identify directions for future research on optimizing decision-making processes related to the choice of Incoterms in cross-border trade based on AI solutions. The study used the Scoping Review method and the VOSviewer IT tool. The keywords co-occurrence analysis showed that there is a lack of in-depth research relating AI issues to choosing Incoterms and modeling and optimizing these decision processes in supply chains.