Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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A Study on the Big Data Analysis and Predictive Models for Quality Issues in Defense C5ISR

국방 C5ISR 분야 품질문제의 빅데이터 분석 및 예측 모델에 대한 연구

  • Hyoung Jo Huh (College of Business Consulting Administration, Hanyang University) ;
  • Sujin Ko (ICT Administration, Hanwha Systems) ;
  • Seung Hyun Baek (School of Business Administration, Hanyang University ERICA)
  • 허형조 (한양대학교 경영컨설팅학과) ;
  • 고수진 (한화시스템 ICT부문) ;
  • 백승현 (한양대학교 ERICA 경영학부)
  • Received : 2023.10.15
  • Accepted : 2023.11.06
  • Published : 2023.12.31
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Abstract

Purpose: The purpose of this study is to propose useful suggestions by analyzing the causal effect relationship between the failure rate of quality and the process variables in the C5ISR domain of the defense industry. Methods: The collected data through the in house Systems were analyzed using Big data analysis. Data analysis between quality data and A/S history data was conducted using the CRISP-DM(Cross-Industry Standard Process for Data Mining) analysis process. Results: The results of this study are as follows: After evaluating the performance of candidate models for the influence of inspection data and A/S history data, logistic regression was selected as the final model because it performed relatively well compared to the decision tree with an accuracy of 82%/67% and an AUC of 0.66/0.57. Based on this model, we estimated the coefficients using 'R', a data analysis tool, and found that a specific variable(continuous maximum discharge current time) had a statistically significant effect on the A/S quality failure rate and it was analysed that 82% of the failure rate could be predicted. Conclusion: As the first case of applying big data analysis to quality issues in the defense industry, this study confirms that it is possible to improve the market failure rates of defense products by focusing on the measured values of the main causes of failures derived through the big data analysis process, and identifies improvements, such as the number of data samples and data collection limitations, to be addressed in subsequent studies for a more reliable analysis model.

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