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Paper w78-2000-742:
Toward a technology for corporate memories in construction

Facilitated by the SciX project

Ribeiro F L

Toward a technology for corporate memories in construction

Abstract:The recognition that knowledge is one of an enterprise’s most important assets, decisively influencing its competitivenss, has attracted interest in comprehensive approaches to the basis activities of knowledge management and corporate memories. During the design, construction and operation of any facility knowledge is gained and lessons are learned. Over time, organisations involved in the construction process have the opportunity to accumulate a plethora of knowledge, some of which is learned at great human or financial cost. However, this knowledge is not systematically incorporated into the procurement phase of subsequent projects. Few construction organisations that have systems for systematically capturing, acquiring, converting and connecting that knowledge or have any interest in doing so (Newcombe 1999, Philips, 1996). Besides, the traditional methods of gathering and converting lessons learned during the project life cycle have enjoyed limited success in the construction industry (Kartam, 1996). KM systems help organisations create value by converting information and individually available knowledge into group or organisationally available knowledge. Artificial Intelligence techniques – such intelligent agents, knowledge bases expert systems case-based reasoning, rationale systems and ontologies - play an important role in building corporate memory systems. This paper looks at the use knowledge management in the construction organisations. It addresses the use of artificial intelligence techniques with knowledge management techniques for building a corporate memory management system for construction enterprises. Finally it presents a framework to build a corporate memory management system in construction

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Full text:content.pdf (388,373 bytes) (available to registered users only)

Series:w78:2000 (browse)
Cluster:papers of the same cluster (result of machine made clusters)
Class:class.deployment (0.119675) class.processing (0.033375) class.education (0.026485)
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Permission to reproduce these documents have been graciously provided by Icelandic Building Research Institute. The assistance of the editor, Mr. Gudni Gudnason, is gratefully appreciated

 

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