The study discusses the importance of keeping supply chain analysis relevant by continuously updating data through the integration of innovative AI techniques. It emphasizes the need for timely and informed insights as supply chain efficiency is increasingly affected by sudden disruptions. The research also highlights how the effectiveness of large language models can be enhanced when paired with advanced retrieval techniques that minimize data latency.
By employing a method that includes adaptive iterative retrieval, the research finds that adjusting the retrieval depth based on context significantly improves the performance of supply chain queries. Although fine-tuning the large language models showed marginal benefits, the study reveals that the quality of data retrieval plays a much more critical role. This indicates that organizations should prioritize effective retrieval methods to gain better analytical results from their AI systems in complex supply chain environments.
👉 Pročitaj original: arXiv AI Papers
