Multimodal RAG Architectures: Combining Computer Vision and NLP for Enhanced Supply Chain Analytics

Supply chain analytics increasingly relies on heterogeneous data sources, including structured enterprise records, unstructured textual reports, and visual data such as invoices, shipment images, and warehouse footage. Traditional retrieval and analytics pipelines struggle to integrate these modalities effectively, limiting situational awareness and decision accuracy. Multimodal Retrieval-Augmented Generation (RAG) architectures offer a promising solution by combining computer vision and natural language processing within a unified retrieval and reasoning framework. This paper presents a comprehensive study of multimodal RAG architectures designed for supply chain analytics, focusing on practical system design, data integration strategies, and enterprise deployment considerations. The work examines how visual embedding and textual representations can be jointly indexed, retrieved, and fused to support downstream analytics tasks such as demand forecasting, risk detection, and operational intelligence. Emphasis is placed on scalable architectures, cross-modal alignment, and real-world implementation challenges in cloud-native environments. The study aims to provide a structured foundation for deploying multimodal RAG systems that enhance analytical depth, contextual reasoning, and decision support in modern supply chain ecosystems.