랜덤포레스트를 이용한 모기업의 하향 거래처 기업의 분류: 자동차 부품산업의 가치사슬을 중심으로

The value chain has been utilized as a strategic tool to improve competitive advantage,mainly at the enterprise level and at the industrial level. However, in order to conduct valuechain analysis at the enterprise level, the client companies of the parent company shouldbe classified according to whether they belong to it’s value chain. The establishment ofa value chain for a single company can be performed smoothly by experts, but it takesa lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain basedon actual transaction data. A total of 19 transaction attribute variables were extracted fromthe transaction data and processed into the form of input data for machine learning method.
The proposed model was constructed using the Random Forest algorithm. The experimentwas conducted on a automobile parts company. The experimental results demonstrate thatthe proposed model can classify the client companies of the parent company automaticallywith 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirmthat a few transaction attributes such as transaction concentration, transaction amount andtotal sales per customer are the main characteristics representing the companies that forma value chain.