인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study proposes a local LLM-RAG-based framework for estimating finished-goods safety stock in high-mix low-volume manufacturing environments. The proposed framework maintains a statistical safety stock calculation as a quantitative anchor and complements it with context-aware reasoning using a local Large Language Model (LLM) and Retrieval-Augmented Generation (RAG). The framework first preprocesses ERP/MES data through SQL-based filtering and calculates dynamic indicators such as risk lead time, seasonality, urgent-order volatility, demand trends, and open orders. These indicators are then injected into the local LLM as structured RAG context to generate explainable safety stock adjustment rationales. The framework was implemented using empirical data from an automotive parts supplier and applied to 180 finished-goods items. The developed program generated safety stock output logs over 22 ASOF points from January to May 2026. Because safety stock is a policy-level reference value linked to production planning and MRP, the quantitative evaluation was conducted using five monthly closing points. The results showed positive penalty improvement in all monthly closing periods, while the delivery response proxy increased from 80.7% to 83.0%. These findings indicate that the proposed framework can support context-aware safety stock adjustment by jointly considering ABC grade, seasonality, demand trends, urgent-order volatility, and open orders, while avoiding excessive weekly fluctuations in policy-level safety stock values.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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