Generative AI-driven knowledge management in manufacturing firms: a five-stage framework for dynamic knowledge optimization and digital innovation

知识管理 计算机科学 知识整合 杠杆(统计) 隐性知识 制造业 显性知识 建筑 知识获取 知识价值链 知识工程 知识建模 知识抽取 实证研究 数据科学 生成语法 知识经济 基于知识的系统 智能代理 领域知识 信息技术 过程管理 个人知识管理 知识共享 人工智能 系统工程 计算机集成制造 智能决策支持系统 组织学习 工业4.0 信息系统
作者
Qiong He,Zhenwei Yang
出处
期刊:Journal of Knowledge Management [Emerald Publishing Limited]
卷期号:30 (4): 1447-1467 被引量:3
标识
DOI:10.1108/jkm-03-2025-0418
摘要

Purpose As artificial intelligence (AI) technologies continue evolving, the transformation of knowledge and information paradigms offers new perspectives on comprehensive innovation in knowledge management (KM) within manufacturing firms. This study aims to explore the innovative application of generative artificial intelligence (GenAI) in the KM of manufacturing firms, to address challenges such as the acquisition of tacit knowledge, cross-departmental silos and dynamic knowledge optimization and to promote the effective use of knowledge resources and intelligent innovation. Design/methodology/approach This study combines literature analysis with Chinese manufacturing case studies to develop a five-phase GenAI-enhanced KM framework (acquisition, sharing, integration, application and optimization). Through empirical validation, this study establishes an intelligent KM innovation model integrating explicit-tacit knowledge dynamics and GenAI’s technical features. Findings This study constructs scenarios demonstrating how GenAI can facilitate intelligent KM in manufacturing firms. These scenarios broaden the channels for knowledge acquisition, sharing, integration and application, thereby contributing to the development of a logical model and a proposed operational architecture for intelligent KM within such firms. Research limitations/implications This study has limitations including GenAI implementation costs, data privacy concerns and industry-specific applicability. Future research should address cost-effective implementation, enhanced data privacy measures and cross-sector adaptation. Originality/value By proposing specific scenarios in which GenAI can be leveraged to enhance intelligent KM, this study refines a logical model and operational architecture that have the potential to significantly improve the efficiency of knowledge utilization. The findings of this study provide practical guidance and theoretical support for manufacturing firms aiming to leverage GenAI to enhance KM and foster innovation.
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