Conversational AI Interface Transforming Complex Supply Chain Pricing Data into Actionable Insights for Warehouse Management

Abstract

This article explores the transformative potential of large language models (LLMs) in enhancing supply chain pricing transparency through conversational interfaces. The article examines how AI-powered tools can convert complex pricing data into intuitive, natural language insights for sellers, particularly benefiting those without specialized analytics training. Through a comprehensive article incorporating domain-specific model training, robust data integration frameworks, and rigorous validation mechanisms, the article demonstrates significant improvements in decision-making efficiency and accuracy compared to traditional dashboard analytics. Implementation findings across diverse warehouse and distribution environments reveal enhanced seller trust, accelerated pricing decisions, and improved operational metrics. While identifying data integration challenges and model maintenance requirements, the article outlines promising expansions to additional supply chain functions and proposes a research roadmap for future development focusing on multimodal integration, temporal reasoning capabilities, and increased system autonomy. The article contributes valuable insights into how conversational AI can democratize pricing intelligence within supply chain management while offering a path toward more transparent, efficient, and adaptive pricing ecosystems.