科研管理

科研管理 ›› 2025, Vol. 46 ›› Issue (6): 1-9.DOI: 10.19571/j.cnki.1000-2995.2025.06.001

• 论文 •    下一篇

人工智能驱动企业智能制造转型的过程研究

陈燕萍1,邵云飞1,陈劲2,3   

  1. 1.电子科技大学经济与管理学院，四川 成都611731；
    2.清华大学技术创新研究中心，北京100084；
    3.清华大学经济管理学院，北京100084

  • 收稿日期:2024-08-28 修回日期:2025-02-18 出版日期:2025-06-20 发布日期:2025-06-06
  • 通讯作者: 邵云飞
  • 基金资助:
    国家自然科学基金项目：“组织竞合影响传统企业数字化转型：解构、演化和重塑” （72172024，2022—2025）;“创新生态系统视角下‘卡脖子’技术破解机制研究：资源共享、价值共创、协同共生”（72372017，2024—2027）”。

A study on the process of AI-driven smart manufacturing transformation in enterprises

Chen Yanping1, Shao Yunfei1, Chen Jin2,3   

  1. 1. School of Economics and Management, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China;
    2. Research Center for Technological Innovation, Tsinghua University, Beijing 100084, China; 
    3. School of Economics Management, Tsinghua University, Beijing 100084, China

  • Received:2024-08-28 Revised:2025-02-18 Online:2025-06-20 Published:2025-06-06

摘要：    人工智能的快速发展，为我国智能制造转型注入新的活力。如何将人工智能与智能制造融合以实现智能制造转型是我国制造企业亟须解决的难题。本研究基于“认知-行为”融合视角，通过对京东方的纵向案例研究，探究了人工智能驱动制造企业智能制造转型的过程。研究发现：（1）人工智能驱动制造企业智能制造转型经历了自动化阶段、融合化阶段与自主化阶段，并形成了从基础智能化向生态智能化的跃升；（2）制造企业分别通过“技术需求驱动型-战略觉醒驱动型-文化重塑驱动型”三种意义建构方式，分别作用于“利用式→探索式→动态式”三种资源编排方式，使得人工智能与智能制造转型过程融合；（3）意义建构对资源编排的作用机制呈现出“引导→重塑→强化”的多阶段动态演化过程。研究结果不仅为制造企业提供了实现智能制造转型的理论框架，也丰富了资源编排理论和管理者认知理论的应用场景，为制造企业在智能制造转型中的战略决策提供有价值的参考。

关键词: 智能制造转型, 人工智能, 资源编排理论, 意义建构

Abstract:    The rapid development of artificial intelligence (AI) has injected new vitality into China′s smart manufacturing transformation. How to integrate AI with smart manufacturing to achieve transformation is a critical challenge for Chinese manufacturing enterprises. From the perspective of "cognitive-behavior" integration, this paper explored the process of AI-driven smart manufacturing transformation in manufacturing enterprises through a longitudinal case study of BOE Technology Group. The findings indicated that: (1) The AI-driven transformation process of manufacturing enterprises experiences three stages: automation, integration and autonomy, culminating in a leap from basic intelligence to ecological intelligence; (2) Manufacturing enterprises employ three meaning construction approaches— "technology demand-driven", "strategic awakening-driven", and "cultural reshaping-driven"—which influence the three resource orchestration patterns of "utilization-oriented → exploration-oriented → dynamic-oriented", thus facilitating the integration of AI and smart manufacturing transformation; (3) The role of meaning construction in resource orchestration evolves dynamically in multiple stages, and presents a process of "guidance → reshaping → reinforcement". The results have not only provided a theoretical framework for manufacturing enterprises to achieve smart manufacturing transformation but also enriched the application of resource orchestration theory and managerial cognition theory. The study will offer valuable insights for strategic decision-making in smart manufacturing transformation within manufacturing enterprises.

Key words: smart manufacturing transformation, artificial intelligence, resource orchestration theory, meaning construction