As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.

1. 서 론
2. 선행 연구
    2.1 스마트팩토리 R&D 데이터 기반 자료포락분석
    2.2 스마트팩토리 R&D 데이터 기반 토픽 모델링분석
    2.3 주제별 효율성 비교분석
3. 연구 방법론
    3.1 연구방법론 설계
    3.2 토픽 모델링 분석
    3.3 자료포락분석
    3.4 비모수 통계 검정
4. 연구실험 비교분석
    4.1 데이터 구성
    4.2 스마트팩토리 R&D 과제의 토픽 모델링 분석
    4.3 스마트팩토리 R&D 과제의 주제별 효율성분석
    4.4 비모수 통계적 검정
5. 결 론
References

• Yue Luo(Dongguk Business School, Dongguk University) | 라월 (동국대학교 경영대학)
• Sungmook Lim(Dongguk Business School, Dongguk University) | 임성묵 (동국대학교 경영대학)
• Sanggook Kim(Center for R&D Investment and Strategy Research, Korea Institute of Science and Technology Information) | 김상국 (한국과학기술정보연구원 R&D투자전략연구센터) Corresponding author