Exploring the Nexus of Translation Studies and Artificial Intelligence in Logistics: A Review of Current Trends and Future Directions

In recent years, new artificial intelligence (AI) technology has been introduced into logistics operations around the world, creating a new logistics paradigm: smart logistics. This paradigm can be seen as the core of the fourth industrial revolution (so-called Industry 4.0), which introduces staggering data flows relevant to real-world operations. These systems are designed to make fast and accurate decisions in uncertain environments and process large amounts of data while mimicking human (or similar intelligence) cognitive functions. The translation process is key to logistics operations. The concept of logistics aims to balance the flow of materials and information (shipping the right items at the right time) to ensure efficient and effective operations. In this context, artificial intelligence processes and systems in particular have revolutionized the field of translation studies. In particular, machines are increasingly used for translation (machine translation) to manage heterogeneous data streams, while the subdiscipline of natural language processing (NLP) has begun to contribute its insights to the construction of new systems we call neural machine translation. (NMT), translation and translation studies, now known as “translation studies”, play a crucial role in the future of logistics, since logistics is based on multilingual data flows (“logistics is logos”). Literally, this means that translation studies “bring meaning.” We believe that the future direction of translation research needs to be better integrated with artificial intelligence. Practice and theory can benefit from each other, leading to new insights and increased efficiency. This will enable smarter, more seamless supply chain solutions. This article is particularly useful for researchers who want to study artificial intelligence-driven logistics translation systems. NMT and NLP are technologies that can help in this direction and complement the effectiveness of existing artificial intelligence algorithms.