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Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 53, No. 4, pp.997-1025
ISSN: 1226-1874 (Print)
Print publication date 31 Aug 2024
Received 27 Dec 2023 Revised 25 Mar 2024 Accepted 24 Apr 2024
DOI: https://doi.org/10.17287/kmr.2024.53.4.997

기계학습 방법을 활용한 가격 예측 모형 개발: 공군 항공유 구매 사례를 기반으로

임세환 ; 민순홍 ; 최경환
(주저자) 연세대학교 limseh@yonsei.ac.kr
(교신저자) 연세대학교 sminscm@yonsei.ac.kr
(공저자) 방위사업청 borita@hanmail.net
Developing a Machine Learning-Based Model for Price Forecasting: A Case Study on ROKAF Jet Fuel Procurement
Sehwan Lim ; Soonhong Min ; Kyunghwan Choi
(First Author) Yonsei University School of Business limseh@yonsei.ac.kr
(Corresponding Author) Yonsei University School of Business sminscm@yonsei.ac.kr
(Co-Author) Defense Acquisition Program Administration borita@hanmail.net


Copyright 2024 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

초록

공군은 매년 수천억 원 규모의 항공유를 구매 중이며, 급격한 항공유 가격 변동은 작전 준비태세에 리스크 요인으로 작용하고 있다. 정확한 가격 예측은 유류 재고관리를 최적화하는 데 중요하지만, 기존 연구는 구매기관에서 바로 적용이 제한되는 거시적 수준의 경제지표에 초점을 맞추는 경우가 많았다. 본 연구에서는 구매기관 관점에서 항공유 가격 예측 정확도를 향상하기 위한 기계학습 방법의 효과를 평가한다. 이때, 예측 정확도 향상을 위해 공급망, Google 검색 트렌드(Google Trends), 지정학적 요인과 전통적인 경제 요인을 통합한 기계학습 예측모형을 제안한다. 본 연구의 결과는 XGBoost 모형이 RMSE(Root Mean Squared Error)를 67%까지 줄여 가장 정확한 성과를 나타내었다. 따라서 공군을 비롯한 구매기관이 기계학습 가격 예측 모형을 채택하면 공급 가격관리 능력이 크게 향상될 수 있다. 또한, 본 연구 결과는 구매가 반복되고 가격 변동성이 큰 다른 제품 관리에도 효과적으로 적용할 수 있다.

Abstract

The Republic of Korea Air Force (ROKAF) spends hundreds of billions of Korean won annually on jet fuel, with price fluctuations posing a significant logistical and operational readiness challenge. Accurate price forecasting is crucial for optimizing fuel inventory management, yet existing research often focuses on macro-level economic indicators with limited practical application. This study investigates the potential of machine learning (ML) for enhancing jet fuel price forecasting accuracy. We propose ML models incorporating supply chain data, Google Trends data, and geopolitical factors alongside traditional economic variables for more realistic predictions. Our results demonstrate that a XGBoost model achieves the best performance, reducing Root Mean Squared Error (RMSE) by 67%. Adoption of this model by the ROKAF could significantly improve supply price management capabilities. Furthermore, the study's findings have broader applicability, potentially benefiting the inventory management of other commodities with significant price volatility and recurring purchases.

Keywords:

commodities, jet fuel, machine learning, price forecasting, purchasing

키워드:

석유제품, 항공유, 기계학습 방법, 가격 예측, 구매관리

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∙ 저자 임세환은 현재 연세대학교 경영대학 Operations Management 전공 석사과정에 재학 중이다. 공군사관학교 국방경영학과를 졸업하였으며, 공군 군수분야에 복무 중이다. 주요 연구 관심분야는 물류서비스품질, 공급사슬 리스크 관리, 운영관리에서의 예측이다.

∙ 저자 민순홍(Ph.D., Haslam College of Business, the University of Tennessee)은 연세대학교 경영대학 오페레이션/공급사슬 담당 교수이자 ESG/기업윤리연구센터 부센터장으로 일하고 있다. 주요 연구/강의 분야는 지속가능경영, 공급사슬관리 (SCM), B2B 관계 전략 등이다. International Journal of Logistics Management에 발표한 논문으로 ‘Emerald 최우수논문상’, 생산관리학회지에 발표한 논문으로 ‘현우 곽수일 생산관리 학술상’, 국제적으로 인용지수 높은 학술지 논문으로 ‘한국로지스틱스대상 학술상’ 및 연세대학교 우수업적교수상 연구부문 최우수상을 수상하였다.

∙ 저자 최경환은 방위사업청에서 근무중이다. 국방대학교를 졸업하였으며, 국방관리 석사와 군사 운영분석 박사를 취득하였다. 그후에는 방위사업청에서 F-35A, F-15K 등과 같은 전투기 사업을 담당해 왔고, 2020년부터 2년간 국방대학교에서 교수를 역임하기도 했다. 주요연구분야는 공급체인관리, 군수, 방위산업 등이다.