What question did this study set out to answer?

This study aims to analyze the relationship between failure rates and process variables in C5ISR quality.

March 14, 2026Open Access

A Study on the Big Data Analysis and Predictive Models for Quality Issues in Defense C5ISR

Key Points

This study aims to analyze the relationship between failure rates and process variables in C5ISR quality.
Data analyzed using Big Data Analysis methodology.
Utilized the CRISP-DM process for data mining analysis.
Performance of models evaluated; logistic regression selected as final model.
Assessment of inspection data and A/S history conducted.
Logistic regression achieved 82% accuracy compared to decision tree's 67%.
Model showed an AUC of 0.66 compared to 0.57 for decision tree.
Continuous maximum discharge current time significantly influenced A/S quality failure rate.
Predicted 82% of the quality failure rate was achievable.

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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