Enhancing viability for dairy products manufacturing through explicit and tacit knowledge management: the interacting role of artificial and human intelligence

Purpose This study aims to address the challenge of managing new knowledge created by interactions between artificial intelligence (AI) and human intelligence (HI), and investigate how explicit knowledge processed by AI and tacit knowledge processed by HI are integrated to comprehensively enhance viability for dairy products manufacturing (DPM). Design/methodology/approach A novel hybrid approach integrating Gaussian mixture model, multi-attribute group decision-making and graphical evaluation and review technique has been adopted to investigate the role of AI–HI interaction in DPM viability knowledge management. Findings This study obtains the following research findings: the Gaussian mixture model can examine the internal and external real-time data and effectively process the explicit knowledge regarding disruption risk. The multi-attribute group decision-making can gather the collaborative intelligence of human expertise and effectively process the tacit knowledge regarding anti-risk ability. The graphical evaluation and review technique can enable the whole DPM process to take the interacting role of AI and HI for enhancing its viability. Practical implications The findings guide governments and enterprises’ managers to foster AI investment, expert spatio-temporal collaboration and knowledge management resources sharing and seek technological supports from our hybrid models to manage new knowledge created by interactions between AI and HI. Originality/value To the best of the authors’ knowledge, this study is one of the very first to investigate the role of AI–HI interaction in the field of DPM and viability knowledge management, and has significant importance in contributing to the theoretical development and methodological innovation of these two fields.