산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제44권1호
  • /
  • Pages.60-70
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  • 2021
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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데이터 마이닝 기법을 활용한 Mobile Device NDF(No Defect Found) 개선

The Improvement of NDF(No Defect Found) on Mobile Device Using Datamining

  • 이제왕 (한양대학교 일반대학원 경영컨설팅학과) ;
  • 한창희 (한양대학교 경상대학 경영학부)
  • Lee, Jewang (School of Management Consulting, Hanyang University) ;
  • Han, Chang Hee (School of Business Administration, Hanyang University)
  • 투고 : 2021.01.28
  • 심사 : 2021.03.17
  • 발행 : 2021.03.31
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초록

Recently, with the development of technologies for the fourth industrial revolution, convergence and complex technology are being applied to aircraft, electronic home appliances and mobile devices, and the number of parts used is increasing. Increasing the number of parts and the application of convergence technologies such as HW (hardware) and SW (software) are increasing the No Defect Found (NDF) phenomenon in which the defect is not reproduced or the cause of the defect cannot be identified in the subsequent investigation systems after the discovery of the defect in the product. The NDF phenomenon is a major problem when dealing with complex technical systems, and its consequences may be manifested in decreased safety and dependability and increased life cycle costs. Until now, NDF-related prior studies have been mainly focused on the NDF cost estimation, the cause and impact analysis of NDF in qualitative terms. And there have been no specific methodologies or examples of a working-level perspective to reduce NDF. The purpose of this study is to present a practical methodology for reducing NDF phenomena through data mining methods using quantitative data accumulated in the enterprise. In this study, we performed a cluster analysis using market defects and design-related variables of mobile devices. And then, by analyzing the characteristics of groups with high NDF ratios, we presented improvement directions in terms of design and after service policies. This is significant in solving NDF problems from a practical perspective in the company.

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참고문헌

  1. Artficial Inteligence Times, http://www.aitimes.kr/news/articleView.html?idxno=10993.
  2. Bea, Y.J., A Comparison and Case Study of Cluster Algorithms for Mixed Data with Quantitative and Qualitative Variable, Journal of the Korean Data Analysis Society, 2015, Vol. 17, No. 6(B), pp. 2991-3000.
  3. Beniaminy, I. and Joseph, D., Reducing the 'No Fault Found' Problem : Contributions from Expert-System Methods, Proceedings, IEEE Aerospace Conference, 2002.
  4. Cho, G.H. and Park, H.C., Comparison of Clustering Algorithms in Data Mining, Journal of The Korean Data Analysis Society, 2006, Vol. 8, No. 2, pp. 585-596.
  5. Erkoyuncu, J.A., Khan, S., Hussain, S.M.F., and Roy, R., A Framework to Estimate the Cost of No-Fault Found Events, International Journal of Production Economics, 2016, Vol. 173, pp. 207-222. https://doi.org/10.1016/j.ijpe.2015.12.013
  6. Hockleya, C. and Lacey, L., A Research Studky of No Fault Found(NFF) in the Royal Air Force, Procedia CIRP, 2017, Vol. 59, pp. 263-267. https://doi.org/10.1016/j.procir.2016.09.034
  7. IBM, "IBM SPSS Modeler 14.2 Algorithms Guide", 2011.
  8. James, I., Lumbard, D., Willis, I., and Goble, J., Investigating No Fault Found in the Aerospace Industry, In : Proceedings of Annual Reliability and Maintainability Symposium, 2003.
  9. Jang, H., Kim, K.G., and Kim, C., Comparison of Clustering Methods for Categorical Data, Journal of the Korean Data Analysis Society, 2014, Vol. 16, No. 5, pp. 2439-2445.
  10. Ji, W.C., Data Mining in the Era of Big Data, minyeongsa, 2017.
  11. Khan, S., Farnsworth, M., and Erkoyuncu, J., A Novel Approach for No Fault Found Decision-Making, CIRP Journal of Manufacturing Science and Technology, 2017, Vol. 17, pp. 18-31. https://doi.org/10.1016/j.cirpj.2016.05.011
  12. Khan, S., Phillips, P., Jennions, I., and Hockley, C., No Fault Found Events in Maintenance Engineering Part 1 : Current Trends, Implications and Organizational Practices, Reliability Engineering and System Safety, 2014, Vol. 123, pp. 183-195. https://doi.org/10.1016/j.ress.2013.11.003
  13. Khan, S., Phillips, P., Jennions, I., and Hockley, C., No Fault Found Events in Maintenance Engineering Part 2 : Root Causes, Technical Developments and Future Research, Reliability Engineering and System Safety, 2014, Vol. 123, pp. 196-208. https://doi.org/10.1016/j.ress.2013.10.013
  14. Linoff, G.S. and Berry, M.J.A., Data Mining Techniques For Marketing, Sales, and Customer Relationship Management (3nd ed.), Wiley, 2011.
  15. Schwab, K., The Fourth Industrial Revolution, Crown Pub, 2016.
  16. Soderholm, P., A System View of the No Fault Found (NFF) Phenomenon, Reliability Engineering and System Safety, 2007, Vol. 92, pp. 1-14. https://doi.org/10.1016/j.ress.2005.11.004
  17. TechSee, https://techsee.me/blog/nff-survey.
  18. Thomas, D.A., Ayers, K., and Pecht, M., The "Trouble not Identified" Phenomenon in Automotive Electronics, Microelectronics Reliability, 2002, Vol. 42, No. 4-5, pp. 641-651. https://doi.org/10.1016/S0026-2714(02)00040-9
  19. Williams et al., An Investigation of 'Cannot Duplicate' Failures, Quality and Reliability Engineering International, 1998, Vol. 14, No. 5, pp. 331-337. https://doi.org/10.1002/(SICI)1099-1638(199809/10)14:5<331::AID-QRE183>3.0.CO;2-L