This study aims to develop a comprehensive predictive model for Digital Quality Management (DQM) and to analyze the impact of various quality activities on different levels of DQM. By employing the Classification And Regression Tree (CART) methodology, we are able to present predictive scenarios that elucidate how varying quantitative levels of quality activities influence the five major categories of DQM. The findings reveal that the operation level of quality circles and the promotion level of suggestion systems are pivotal in enhancing DQM levels. Furthermore, the study emphasizes that an effective reward system is crucial to maximizing the effectiveness of these quality activities. Through a quantitative approach, this study demonstrates that for ventures and small-medium enterprises, expanding suggestion systems and implementing robust reward mechanisms can significantly improve DQM levels, particularly when the operation of quality circles is challenging. The research provides valuable insights, indicating that even in the absence of fully operational quality circles, other mechanisms can still drive substantial improvements in DQM. These results are particularly relevant in the context of digital transformation, offering practical guidelines for enterprises to establish and refine their quality management strategies. By focusing on suggestion systems and rewards, businesses can effectively navigate the complexities of digital transformation and achieve higher levels of quality management.

1. 서 론
2. 문헌연구
    2.1 디지털 품질경영 모형
    2.2 디지털 품질경영의 5대 범주
3. 연구 방법
    3.1 머신러닝 방법론
    3.2 분석 대상 및 자료
    3.3 분석 방법 및 절차
4. 분석 및 모형 개발 결과
5. 결 론
    5.1 연구 결과 요약
    5.2 시사점
References

• Byung-Hoon Park(Department of Industrial Engineering, Sungkyunkwan University) | 박병훈 (성균관대학교 산업공학과)
• Ho-Jun Song(Department of Industrial Engineering, Sungkyunkwan University) | 송호준 (성균관대학교 산업공학과)
• Wan-Seon Shin(Department of Industrial Engineering, Sungkyunkwan University) | 신완선 (성균관대학교 산업공학과) Corresponding author