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A Case Study on the Target Sampling Inspection for Improving Outgoing Quality

타겟 샘플링 검사를 통한 출하품질 향상에 관한 사례 연구

  • Kim, Junse (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Lee, Changki (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Kim, Kyungnam (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Kim, Changwoo (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Song, Hyemi (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Ahn, Seoungsu (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Oh, Jaewon (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Jo, Hyunsang (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS) ;
  • Han, Sangseop (Quality Innovation Team, Global CS Center, SAMSUNG ELECTRONICS)
  • 김준세 (삼성전자 Global CS센터 품질혁신팀) ;
  • 이창기 (삼성전자 Global CS센터 품질혁신팀) ;
  • 김경남 (삼성전자 Global CS센터 품질혁신팀) ;
  • 김창우 (삼성전자 Global CS센터 품질혁신팀) ;
  • 송혜미 (삼성전자 Global CS센터 품질혁신팀) ;
  • 안성수 (삼성전자 Global CS센터 품질혁신팀) ;
  • 오재원 (삼성전자 Global CS센터 품질혁신팀) ;
  • 조현상 (삼성전자 Global CS센터 품질혁신팀) ;
  • 한상섭 (삼성전자 Global CS센터 품질혁신팀)
  • Received : 2021.08.05
  • Accepted : 2021.08.20
  • Published : 2021.09.30

Abstract

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

Keywords

References

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