Earticle

초록

영어

This study introduces the DAINOS Framework as an integrated management approach for hyper-scale AI projects in manufacturing, characterized by technical complexity and multi-organizational collaboration. As open innovation becomes more common, large-scale AI initiatives increasingly involve diverse stakeholders such as data providers, AI developers, infrastructure suppliers, and users. Traditional Work Breakdown Structures (WBS) often fail to clarify roles, facilitate functional communication, or coordinate collaboration effectively. To address these limitations, DAINOS is proposed as a role-based meta-framework that complements WBS. It consists of six domains—Data, AI, Infrastructure, Network, Organizing, and Service —and provides functional context to tasks through auxiliary labeling. This enables clear role alignment and structured collaboration. Grounded in systems engineering, complexity theory, and collaborative network theory, DAINOS was applied to a large-scale AI service development project involving 15 institutions. Results showed it functioned as a common language among stakeholders, reducing confusion and enhancing project governance. In qualitative assessments, stakeholders reported clearer role recognition, reduced inter-organizational friction, and more effective communication; for example, 15 participating institutions achieved streamlined decision-making processes and observed measurable improvements in collaboration efficiency. The study contributes both theoretical insights and practical tools for managing complex socio-technical systems. DAINOS also shows promise as a generalizable framework applicable to sectors such as aerospace, defense, and healthcare. Future work will focus on quantitative evaluations, cross-industry comparisons, and developing a maturity model for framework evolution. In summary, these research directions aim to further develop DAINOS into a robust and generalizable governance framework with practical applicability and scalability, thereby enhancing project governance and collaboration in hyper-scale AI projects and similar multi-organizational R&D initiatives, and ultimately contributing to diverse industries.

목차

Abstract
1. 서론
1.1. 연구 배경 및 필요성
1.2. 연구 목적, 범위 및 독창성
2. 이론적 배경 및 선행 연구
2.1. 대형 AI 프로젝트의 특징 및 이론적 분석
2.2. 기존 프로젝트 관리체계의 한계
2.3. 도메인 기반 역할 분담의 필요성
3. DAINOS 프래임워크
3.1. DAINOS 프레임워크의 개념과 구성 체계
3.2. DAINOS 도메인 간 상호작용 구조 및 정보 흐름 모델링
4. DAINOS 프레임워크 적용 사례 분석
4.1. 연구 방법론 및 사례 개요
4.2. 초거대 제조 AI 프로젝트 적용 방식
4.3. 프레임워크 적용의 효과 분석 및 평가
4.4. 향후 정량적 성과 측정을 위한 제언
5. 결론 및 향후 연구 방향
5.1. 연구 결과 요약 및 학술적 기여
5.2. 실무적 시사점 및 정책적 함의
5.3. 연구의 한계 및 향후 연구 방향
References

키워드

저자

• 정환균 [ Jeong Hwan-Gyun | (재)경남테크노파크 경남인공지능혁신본부, 경상국립대학교 대학원 경영정보학과 석사과정 ] 제1저자
• 정대율 [ Jeong Dae-Yul | 경상국립대학교 경영대학 경영정보학과 교수 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  프로젝트경영연구 [Project Management Review]

• 간기

  반년간

• eISSN

  2799-3434

• 수록기간

  2021~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 325 DDC 658