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Study on the Application of Big Data Mining to Activate Physical Distribution Cooperation : Focusing AHP Technique

물류공동화 활성화를 위한 빅데이터 마이닝 적용 연구 : AHP 기법을 중심으로

  • Young-Hyun Pak (Department of International Trade and logistics, Kyungnam University) ;
  • Jae-Ho Lee (Institute of Foreign Trade, Sungkyunkwan University) ;
  • Kyeong-Woo Kim (Institute of Yangsan Development Research)
  • 박영현 (경남대학교 무역물류학과) ;
  • 이재호 (성균관대학교 무역연구소) ;
  • 김경우 ((사)양산발전연구원)
  • Received : 2021.09.30
  • Accepted : 2021.10.28
  • Published : 2021.10.30

Abstract

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

Keywords

References

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