Please use this identifier to cite or link to this item: https://doi.org/10.1109/INDIN.2007.4384811
DC FieldValue
dc.titleA framework for managing enterprise knowledge for collaborative decision support
dc.contributor.authorZhang, N.
dc.contributor.authorLu, W.F.
dc.date.accessioned2014-06-19T05:30:15Z
dc.date.available2014-06-19T05:30:15Z
dc.date.issued2007
dc.identifier.citationZhang, N., Lu, W.F. (2007). A framework for managing enterprise knowledge for collaborative decision support. IEEE International Conference on Industrial Informatics (INDIN) 1 : 517-522. ScholarBank@NUS Repository. https://doi.org/10.1109/INDIN.2007.4384811
dc.identifier.isbn1424408644
dc.identifier.issn19354576
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/73021
dc.description.abstractThis paper presents a new knowledge management paradigm in order to assist knowledge workers to make decision effectively and efficiently in the new economy age. The paradigm is called Knowledge Infused Decision Support (KIDS) framework for managing knowledge required for complex decisions in manufacturing processes. KIDS framework tackles two kinds of knowledge: quantitative knowledge and qualitative knowledge. Quantitative knowledge is discovered from enterprise's business databases through data mining, and qualitative knowledge like experience and judgment captured from human in the collaborative decision making process. The infusion of quantitative and qualitative knowledge provides better understanding of business processes and the decision contexts for decision makers to make decisions quickly under the pressures of time- and knowledge-based competition. The knowledge is then codified into enterprise's knowledge repository. A knowledge mapping mechanism is also provided in the framework for not only delivering the relevant knowledge to the knowledge workers, but also enabling them to access knowledge from knowledge repository efficiently and effectively at anytime and anywhere. It finally discusses several applications in manufacturing industry in order to speed up design decision making using quantitative and qualitative knowledge. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/INDIN.2007.4384811
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/INDIN.2007.4384811
dc.description.sourcetitleIEEE International Conference on Industrial Informatics (INDIN)
dc.description.volume1
dc.description.page517-522
dc.identifier.isiut000252372900088
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