Mapping restaurant and supplier product nomenclatures using LLM – Case Study for a restaurant holding

S. Jin, P. B. Panfilov, A. S. Suleikin

National Research University Higher School of Economics

Abstract: In the modern restaurant business, accurate mapping of product nomenclatures between restaurants and suppliers is a critical task. Effective inventory management and procurement optimization directly impact business profitability. With the increase in suppliers and product variety, traditional mapping methods become less efficient. This study proposes using large language models (LLM) to automate and improve the accuracy of product matching. Through a pilot project for a restaurant holding, we tested five product groups (shrimp, eel, parmesan cheese, cottage cheese, butter), achieving an average test accuracy of 83.8%. The solution architecture leverages prompt engineering, low-code platforms like Flowise, and Telegram integration for user-friendly processing. Key challenges, including semantic ambiguity and model hallucinations, were addressed via domain-specific dictionaries and validation. This approach reduces manual effort by approximately 90

Keywords: Large Language Models, Supply Chain Management, Product Mapping, Automation, Inventory Optimization

DOI: 10.15514/ISPRAS-2025-37(6)-43