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빅데이터 도입을 위한 중소제조공정 4M 데이터 분석

Data analysis of 4M data in small and medium enterprises

  • 김재성 (충북대학교 경영정보학과) ;
  • 조완섭 (충북대학교 경영정보학과)
  • Kim, Jae Sung (Department of Management Information Systems, Chungbuk National University) ;
  • Cho, Wan Sup (Department of Management Information Systems, Chungbuk National University)
  • 투고 : 2015.08.08
  • 심사 : 2015.09.22
  • 발행 : 2015.09.30

초록

오늘날 ICT기술의 눈부신 발전으로 많은 부분에 정보화와 자동화가 이루어져 있으며, 제조업에서도 경쟁우위를 확보하기 위해 설계, 생산 공정의 자동화와 정보시스템을 도입하고 있다. 그러나 정보화 투자 여력이 없는 영세 중소제조 기업의 경우 생산현장에서 정보화의 힘이 미치지 못하고 있으며, 작업자의 경험과 수기데이터에 의존하여 생산 공정을 관리하고 있는 실정이다. 수기데이터로 관리되고 있는 제조공정에서는 불량 발생 시 불량원인을 명확히 밝혀내는데 한계가 있다. 본 연구에서는 수기데이터로 관리되고 있는 중소제조 자동차 부품 가공공정에 대하여, 수기데이터를 수집, 향후 센서데이터를 활용할 수 있도록 중소 제조 맞춤형 분석시스템을 구축하고, 중요도가 큰 일부 공정에 대하여 품질에 영향을 미치는 핵심요인을 4M관점에서 분석하였다. 분석결과, 호기별 불량수량에는 유의한 차이가 없었으며, 원자재, 생산수량, 작업자간 유의한 차이가 있는 것으로 분석되었다.

In order to secure an important competitive advantage in manufacturing business, an automation and information system from manufacturing process has been introduced; however, small and medium enterprises have not met the power of information in the manufacturing fields. They have been managing the manufacturing process that is depending on the operator's experience and data written by hand, which has limits to reveal cause of defective goods clearly, in the case of happening of low-grade goods. In this study, we analyze critical factors which affect the quality of some manufacturing process in terms of 4M. We also studied the automobile parts processing of the small and medium manufacturing enterprises controlled with data written by hand so as to collect the data written by hand and to utilize sensor data in the future. Analysis results show that there is no deference in defective quantity in machines, while raw materials, production quality and task tracking have significant deference.

키워드

참고문헌

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피인용 문헌

  1. 정형 비정형 빅데이터의 융합분석을 위한 소비 트랜드 플랫폼 개발 vol.15, pp.6, 2015, https://doi.org/10.14400/jdc.2017.15.6.133
  2. 물류에서 빅데이터 분석의 활용을 위한 가치 모델 vol.15, pp.9, 2015, https://doi.org/10.14400/jdc.2017.15.9.167
  3. Effects of Smart Factory Quality Characteristics and Dynamic Capabilities on Business Performance: Mediating Effect of Recognition Response vol.11, pp.12, 2015, https://doi.org/10.13106/jidb.2020.vol11.no12.17
  4. Application of FOM Methodology for 4M Optimization Based on the Data of Manufacturing Process of Mechanical Parts vol.30, pp.6, 2015, https://doi.org/10.7735/ksmte.2021.30.6.456
  5. Application of FOM Methodology for 4M Optimization Based on the Data of Manufacturing Process of Mechanical Parts vol.30, pp.6, 2015, https://doi.org/10.7735/ksmte.2021.30.6.456