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.

1. 서 론
2. 이론적 배경
    2.1 NDF 정의 및 현상
    2.2 NDF 발생원인 및 패턴
    2.3 NDF의 영향
    2.4 NDF관련 기존 연구 사례 요약
3. 연구방법 및 절차
    3.1 NDF개선을 위한 데이터마이닝 적용 방법
    3.2 데이터마이닝 실행 절차
4. 사례 연구
    4.1 데이터셋 및 변수 선정
    4.2 데이터 전처리
    4.3 데이타마이닝 알고리즘 선정
    4.4 데이타마이닝 실행
    4.5 분석 결과
    4.6 대응방안 도출
5. 결 론
References

• Jewang Lee(한양대학교 일반대학원 경영컨설팅학과) | 이제왕
• Chang Hee Han(한양대학교 경상대학 경영학부) | 한창희 Corresponding Author