무역학회지 (Korea Trade Review)

한국무역학회 (Korea Trade Research Association)
권호 표지
DOI QR코드

DOI QR Code

물류공동화 활성화를 위한 빅데이터 마이닝 적용 연구 : AHP 기법을 중심으로

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

  • 박영현 (경남대학교 무역물류학과) ;
  • 이재호 (성균관대학교 무역연구소) ;
  • 김경우 ((사)양산발전연구원)
  • 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)
  • 투고 : 2021.09.30
  • 심사 : 2021.10.28
  • 발행 : 2021.10.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. Bre, Facundo, Juan M. Gimenez and Victor D. Fachinotti (2018) "Prediction of wind pressure coefficients on building surfaces using artificial neural networks.", Energy and Buildings, 158, pp. 1429-1441. https://doi.org/10.1016/j.enbuild.2017.11.045
  2. Hassanzadeh, Hamed and Mohammad Reza Keyvanpour (2012), "Semantic web requirements through web mining techniques." International Journal of Computer Theory and Engineering, 4(4), pp. 616-620.
  3. Kim, Seung-Wook (2017), "Value Model for Applications of Big Data Analytics in Logistics" Journal of Digital Convergence, 15(9), pp. 167-178.
  4. Kingsford, Carl and Steven L. Salzberg (2008), "What are decision trees?" Nature biotechnology, 26(9), pp. 1011-1013. https://doi.org/10.1038/nbt0908-1011
  5. Lee, Eun-Jeong (2021), "A Study on Application and Utilization method of Smart Logistics", The Korean Academy for Trade Credit Insurance, 22(1), pp. 69-84. DOI : 10.22875/jiti.2021.22.1.005
  6. Lee, Sang-Won, Kim Sung-Hyun (2016), "Finding Industries for Big Data Usage on the Basis of AHP", Journal of Digital Convergence, 14(7), pp. 21-27. https://doi.org/10.14400/JDC.2016.14.7.21
  7. Noh, Kyoo-Sung et. al. (2016), Big data : Analyse data, KASOM, Dept. of Bigdata, Gyeonggi-do, Korea.
  8. Park Kwang-O, Jung Dae-Hyun, Kwon Sang-Min. (2020), "Necessity of the Physical Distribution Cooperation to Enhance Competitive Capabilities of Healthcare SCM -Bigdata Business Model's Viewpoint-", Management & Information Systems Review, 39(3), pp. 17-35.
  9. Petz, Gerald, et al. (2012), "On text preprocessing for opinion mining outside of laboratory environments." International Conference on Active Media Technology. Springer, Berlin, Heidelberg, pp. 618-629.
  10. Saaty, Thomas L. (1988), "What is the analytic hierarchy process?", Mathematical models for decision support. Springer, Berlin, Heidelberg, pp. 109-121.
  11. Shin, Na-la and Kim, Yong-Jin (2018), "Activating Strategies of Logistics Big Data through Case Studies" Korea Logistics Review, 28(2), pp. 91-101.
  12. Tai, Yueh-Ming and Hung-Wen Chiu (2009), "Comorbidity study of ADHD: applying association rule mining (ARM) to National Health Insurance Database of Taiwan." International journal of medical informatics, 78(12), pp. 75-83.
  13. UC Business Analytics R Programming Guide, K-means Cluster Analysis. Available from https://uc-r.github.io/kmeans_clustering (accessed September 30, 2021)
  14. Yoon, Cheol-Ho, Choi, Kwang-Don (2019), "A Study on the Analytic Hierarchy Process of Group Decision Making Using R" Journal of Information Technology and Architecture, 16(4), pp. 405-418.  https://doi.org/10.22865/JITA.2019.16.4.405