Generative AI-driven knowledge management in manufacturing firms: a five-stage framework for dynamic knowledge optimization and digital innovation
Qiong He, Zhenwei Yang
Abstract
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.