텍스트 마이닝을 활용한 공급사슬 리스크관리의 논제 분석
Analysis on the Research Topics of Supply Chain Risk Management using Text Mining
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초록
연구목적: 본 연구의 목적은 공급사슬에서 리스크관리의 핵심 논제를 분석하는 것이다. 이를 위해 학술 논문을 대상으로 텍스트 마이닝 기법을 적용하여 시사점을 도출하였다. 논문구성/논리: 텍스트 마이닝 기법을 적용하기 위해 먼저 학술 논문으로부터 텍스트 데이터를 추출하는 작업이 필요하다. 그리고 텍스트를 추출한 후 머신러닝 모델을 이용하여 단어 정보를 분석하게 된다. 본 연구에서는 텍스트 데이터를 추출하고자 Science Direct, Emerald, Springer사의 온라인 학술 DB로부터 2015-2021까지의 논문을 활용하였다. 결과: 본 연구의 시사점은 네 가지로 요약된다. 첫째, 단어의 빈도 분석을 통해 공급사슬 붕괴로 인한 피해를 최소화하는 영역의 다양한 연구가 진행되었다는 것을 확인하였다. 둘째, 단어의 유사도 분석을 통해 리스크관리의 개념이 위험요인을 식별하고 평가한 후 완화 전략을 수립하는 것임을 반영하고 있었다. 셋째, LDA 분석을 통해 여덟 가지의 연구 논제, 즉 운영적 위험, 붕괴의 유형, 위험의 평가, 위험의 완화, 위험의 분산, 물결효과, 회복력, 기업의 성과로 구분하였다. 넷째, IDM 분석을 통해 운영적 위험 관련 연구는 위험의 평가 및 위험의 완화와 연관성이 높지만, 붕괴의 유형, 위험의 분산, 물결효과, 회복력, 기업의 성과에 관한 연구는 상호 연관성이 낮은 것으로 확인되었다. 독창성/가치: 공급사슬 리스크관리 관련 학술 논문을 대상으로 한 기존의 머신러닝 기법의 연구에서는 초록이나 키워드를 중심으로 연구가 진행되었다. 하지만 본 연구는 논문의 본문을 추가로 포함하여 머신러닝 기법을 적용하여 분석하였다. 이를 통해 더 많은 정보를 이용하여 새로운 통찰력을 제공하고 기존 문헌을 보완하는 측면에서 학술적 기여가 있다고 판단된다.
Abstract
Purpose: This study was conducted to analyze key research topics of risk management in supply chain areas. Text mining methods were employed to generate useful insights from existing literature. Composition/Logic: For applying a text mining method, the first step was to extract text from journal articles on supply chain risk management published in the Science Direct, Emerald, and Springer databases from 2015 to 2021. Then, the extracted word information was analyzed using machine learning models to identify the important implications. Findings: The findings are four-fold: 1) Word frequency analysis showed that several research topics have been dealt with minimizing the damage caused by supply chain disruption; 2) Word similarity analysis showed that the concept of risk management contained risk identification and assessment and then formulation of mitigation strategies; 3) Through LDA(Latent Dirichlet Allocation) analysis, eight topics were derived, namely operational risk, types of disruption, risk assessment, risk mitigation, risk sharing, ripple effect, resilience, and company performance; 4) Through IDM(Intertopic Distance Map) analysis, the topic of operational risk is highly related to risk assessment and risk mitigation, but the topics on types of disruption, risk sharing, ripple effects, resilience, and corporate performance were found to have low correlations. Originality/Value: In previous research of machine learning models for supply chain risk management, the research was conducted mainly on abstracts or keywords. However, this study was analyzed by applying the machine learning models, including the main body of the paper in addition to abstracts and keywords. Thus, there is an academic contribution in terms of providing new insights by using more information and supplementing the existing literature.
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