A Decision Support Model for Sustainable Collaboration Level on Supply Chain Management using Support Vector Machines

국문 초록 (Abstract)

성공적인 공급사슬관리에 있어 성과에 따른 지속적 협업 통제는 매우 중요하다. 본 연구에서는 기계학습 알고리즘인 SVM(Support Vector Machines)을 이용해 균형성과표에 기반한 공급사슬관리 성과에 따른 지속적 협업 통제 모델을 개발하였다. 우리는 지속적 협업 통제모델 개발에 있어 108명의 전문가를 상대로 실증조사를 수행하였다. 본 연구 수행에 있어 4가지 형태의 SVM 커늘 (1) linear, (2) polynomial, (3) Radial Basis Function(RBF), (4) sigmoid kernel을 이용해 공급사슬관리 지속적 협업 예측 정확도를 비교하였다. SVM 커늘 4가지 중 linear kernel의 예측성과가 가장 좋았다. 그리고 본 연구에서는 SVM linear kernel의 예측성과를 ANN(Artificial Neural Network)의 예측성과와 비교하였다. 분석결과 SVM linear kernel이 공급사슬관리에 있어 지속적 협업 예측에 우수한 예측성과를 보여주는 것을 발견하였다. 이러한 결과는 SVM linear kernel이 공급사슬관리의 지속적 협업 예측통제에 있어 우수한 대안을 제공해 줄 것이다. 그러므로 공급사슬관리를 추구하는 기업들은 본 모델을 통해 지속적 협업 통제에 유용한 정보를 얻을 수 있을 것이다.

다국어 초록 (Multilingual Abstract)

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (BSC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM<SUP>dark</SUP>. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial, (3) Radial Basis Function (RBF), and (4) sigmoid kernel (linear ＞ RBF ＞ Sigmoid ＞ Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

목차 (Table of Contents)

• Ⅰ. Introduction
• Ⅱ. Research Background
• Ⅲ. Methodology
• Ⅳ. Experimental Design and Results
• Ⅴ. Conclusions
• 참고문헌
• Abstract

참고문헌 (Reference)

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