- 영문명
- Current Status and Case Analysis of AI Applications in Procurement
- 발행기관
- 한국구매조달학회
- 저자명
- 문형남(Hyung Nam Moon)
- 간행물 정보
- 『한국구매조달학회지』제24권 제1호, 128~142쪽, 전체 15쪽
- 주제분류
- 경제경영 > 경제학
- 파일형태
- 발행일자
- 2025.06.30
4,600원
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이 학술논문 정보는 (주)교보문고와 각 발행기관 사이에 저작물 이용 계약이 체결된 것으로, 교보문고를 통해 제공되고 있습니다.
국문 초록
본 연구는 인공지능(AI) 기술이 조달 분야에 적용되는 현황과 주요 사례를 분석하여 AI 조달의 효과성과 향후 발전 방향을 제시하고자 한다. 전통적인 조달 프로세스는 복잡한 업무 절차, 대량의 데이터 처리, 그리고 의사결정의 복잡성으로 인해 효율성 제고의 필요성이 지속적으로 제기되어 왔다. AI 기술의 도입은 이러한 문제점들을 해결하고 조달 프로세스의 디지털 혁신을 가능하게 하고 있다.
본 연구에서는 국내외 공공기관과 민간기업의 AI 조달 적용 사례를 분석하여 성공 요인과 한계점을 도출하였다. 연구 결과, AI 조달은 업무 효율성 향상, 비용 절감, 투명성 제고 등의 긍정적 효과를 보이고 있으나, 기술적 한계, 법제도적 미비, 조직 문화의 저항 등의 과제가 여전히 존재함을 확인하였다.
영문 초록
This study analyzes the current status and major implementation cases of artificial intelligence (AI) technology applications in the procurement field to assess the effectiveness of AI-enabled procurement and propose future development directions. Traditional procurement processes have continuously faced challenges in efficiency improvement due to complex administrative procedures, massive data processing requirements, and decision-making complexity. The introduction of AI technology is addressing these issues and enabling digital transformation in procurement processes.
Through literature review and case study analysis, this research examined AI procurement implementation cases from domestic and international public institutions and private enterprises to identify success factors and limitations. The primary analysis targets included Korea's Public Procurement Service Intelligent Procurement Platform, Samsung Electronics' SSCI (Samsung Supply Chain Intelligence) system, and Amazon's AI-based procurement optimization system.
The research findings demonstrate that AI procurement delivers positive outcomes including improved operational efficiency (average 30% reduction in processing time), cost reduction (average 8.3%), and enhanced transparency. However, challenges persist including technological limitations, inadequate legal frameworks, and organizational cultural resistance. The study reveals that successful AI procurement implementation requires strong executive support, high-quality integrated data, phased adoption strategies, and organizational capability enhancement.
Key technological limitations identified include data quality issues, algorithmic bias, lack of explainability, and system integration complexity. Organizational challenges encompass change resistance, shortage of specialized personnel, and rigid organizational structures. Legal and institutional barriers include insufficient regulatory frameworks, regulatory uncertainty, and lack of standardization. Ethical concerns involve privacy and security issues, potential erosion of human-centricity, and social responsibility considerations.
For the successful expansion of AI procurement, this study proposes a comprehensive approach across four dimensions: technological, institutional, human resource, and ecosystem development. Technological advancement requires development of explainable AI (XAI), adoption of federated learning, implementation of edge computing, and integration with blockchain technology. Institutional improvements necessitate legal framework development, standardization initiatives, and governance system establishment. Human resource development involves training hybrid specialists, re-educating existing personnel, and building organizational learning culture. Ecosystem development requires strengthening public-private partnerships, expanding support for small and medium enterprises, and enhancing international cooperation.
The study's quantitative analysis reveals average improvements of 25-40% in processing time reduction, 5-12% in cost savings, 15-25 percentage points in prediction accuracy enhancement, and 40-60% in error reduction across analyzed cases. Qualitative benefits include enhanced transparency and fairness, improved decision-making quality, increased job satisfaction, and strengthened organizational capabilities.
This research contributes to the academic understanding of AI procurement by providing a systematic analytical framework and empirically derived success factors and limitations. For practitioners, it offers practical guidelines for AI procurement implementation strategies and solutions for anticipated challenges. From a policy perspective, it provides insights for legal framework improvement, standardization promotion, and support program development.
목차
Ⅰ. 서론
Ⅱ. 이론적 배경
Ⅲ. 인공지능(AI)의 조달 적용 현황
Ⅳ. 주요 적용 사례 분석
Ⅴ. 결론
참고문헌
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