A knowledge accumulation approach based on bilayer social wiki network for computer-aided process innovation

Under the fierce competition, manufacturing companies pay more attention to innovation and the knowledge that enables innovation. Manufacturing process innovation is a knowledge-intensive activity, and efficient knowledge accumulation is the prerequisite and basis for computer-aided process innovati...

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Bibliographic Details
Published inInternational journal of production research Vol. 53; no. 8; pp. 2365 - 2382
Main Authors Wang, Gangfeng, Tian, Xitian, Geng, Junhao, Guo, Biao
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 18.04.2015
Taylor & Francis LLC
Subjects
CAM
Collaboration
Competition
Computer aided manufacturing
Computer-aided process innovation
Construction
Evolution
Information management
Information resources
Innovation
Innovations
Knowledge
knowledge accumulation
Knowledge management
Networks
Neural networks
process innovation knowledge
Similarity
Social aspects
Social interaction
Social networks
social wiki network
Studies
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Summary:Under the fierce competition, manufacturing companies pay more attention to innovation and the knowledge that enables innovation. Manufacturing process innovation is a knowledge-intensive activity, and efficient knowledge accumulation is the prerequisite and basis for computer-aided process innovation (CAPI). Hence, this research aims to build an open knowledge accumulation approach to obtain organised and refined process innovation knowledge (PIK). By considering the similarity of PIK network with biological neural network and combining the technical characteristics of social network with wiki, a novel PIK accumulation schema based on bilayer social wiki network is proposed. In social wiki network environment, PIK is accumulated in public knowledge space through participants' social interactions and knowledge activities. The process of knowledge fusion is investigated to form the preliminary knowledge containing collective intelligence, and the mechanisms of collaborative editing and collaborative evolution are studied to refine the knowledge. The outcomes of this study lay the foundation for knowledge application of CAPI. Finally, a case study is presented to demonstrate the applicability of the proposed approach.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2014.958591