Integrating Retrieval-Augmented Generation with Large Language Models for Supply Chain Strategy Optimization

Publication Date

1-1-2025

Document Type

Conference Proceeding

Publication Title

Communications in Computer and Information Science

Volume

2251 CCIS

DOI

10.1007/978-3-031-85628-0_34

First Page

475

Last Page

486

Abstract

This paper presents the integration of Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to support supply chain management and financial decision-making. RAG combines effective retrieval process with Generative neural-based language models to improve data privacy and expand knowledge. The proposed dual-module system consists of a retrieval module and a generation module that retrieves generation as the process of generating contextual response. Contextual analysis and contextual generation achieved via iterative chunk retrieval and optimization. Contextualized visual analytics, predictive analytics supported by chunk retrieval and optimization help achieve data-driven forecasting and inventory planning supply chain as well as data-driven insights for financial reporting. An analysis set including balance sheet analysis evaluates the effectiveness of the RAG system through recursive retrieval and multi-hop questioning and generative and retrieval module integration. The results show that RAG can contribute to disrupting the supply chain and finance process for increased agility, resilience, and added data privacy. The implication includes data-driven forecasting, inventory, supplier risk assessment, and financials. However, RAG should consider counter exploitation and ethical evaluation. The next steps should include scalable and advanced metrics evaluation and interdisciplinary research between machine learning, retrieval systems, supply chain, and finance.

Keywords

Deep Learning, Finance Unstructured Big Data, LLM, RAG, Supply Chain Operations

Department

Computer Engineering

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