Journal of Advances in Developmental Research

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Fine-Tuning Large Language Models for Domain-Specialized Supply Chain Agents A Comprehensive Approach Using Supervised Learning on Enterprise Knowledge Bases

Author(s) Sandeep Nutakki
Country United States
Abstract The increasing complexity of global supply chains demands intelligent systems capable of providing specialized guidance on logistics, procurement, inventory management, and operational optimization. This paper presents a comprehensive methodology for fine-tuning large language models (LLMs) to create domain-specialized supply chain agents. We developed a novel data pipeline that extracts, processes, and transforms 131 authoritative supply chain textbooks and professional resources into 161,741 high-quality question-answer training pairs using an automated bootstrapping approach with GPT-4o-mini. Using supervised fine-tuning (SFT) on GPT-4.1-mini via Microsoft Azure AI Foundry, we achieved strong training convergence (73% token accuracy, final loss 0.94) and 87% expert-rated correctness on held-out evaluation samples. Our results demonstrate that domain-specific fine-tuning significantly enhances LLM performance on supply chain reasoning tasks, producing models capable of explaining causal relationships, evaluating trade-offs, and providing actionable insights grounded in established supply chain principles. The methodology presented offers a reproducible framework for creating domain-specialized AI agents in enterprise domains.
Keywords Large Language Models, Fine-Tuning, Supply Chain Management, Domain Adaptation, Supervised Learning, Domain-Specialized Systems, Azure OpenAI, Transfer Learning.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-01-28
Cite This Fine-Tuning Large Language Models for Domain-Specialized Supply Chain Agents A Comprehensive Approach Using Supervised Learning on Enterprise Knowledge Bases - Sandeep Nutakki - IJAIDR Volume 17, Issue 1, January-June 2026. DOI 10.71097/IJAIDR.v17.i1.1681
DOI https://doi.org/10.71097/IJAIDR.v17.i1.1681
Short DOI https://doi.org/hbm79r
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