국방 C5ISR 분야 품질문제의 빅데이터 분석 및 예측 모델에 대한 연구 (A Study on the Big Data Analysis and Predictive Models for Quality Issues in Defense C5ISR)

서지정보

· 발행기관 : 한국품질경영학회
· 수록지 정보 : 품질경영학회지 / 51권 / 4호 / 551 ~ 571페이지
· 저자명 : 허형조, 고수진, 백승현

초록

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.

영어초록

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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