Knowledge Engineering Technique for Cluster Development

After the concept of industry cluster was tangibly applied in many countries, SMEs trended to link to each other to maintain their competitiveness in the market. The major key success factors of the cluster are knowledge sharing and collaboration between partners. This knowledge is collected in form of tacit and explicit knowledge from experts and institutions within the cluster. The objective of this study is about enhancing the industry cluster with knowledge management by using knowledge engineering which is one of the most important method for managing knowledge. This work analyzed three well known knowledge engineering methods, i.e. MOKA, SPEDE and CommonKADS, and compares the capability to be implemented in the cluster context. Then, we selected one method and proposed the adapted methodology. At the end of this paper, we validated and demonstrated the proposed methodology with some primary result by using case study of handicraft cluster in Thailand

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

Knowledge Engineering Technique for Cluster Development

After the concept of industry cluster was tangibly applied in many countries, SMEs trended to link to each other to maintain their competitiveness in the market. The major key success factors of the cluster are knowledge sharing and collaboration between partners. This knowledge is collected in form of tacit and explicit knowledge from experts and institutions within the cluster. The objective of this study is about enhancing the industry cluster with knowledge management by using knowledge engineering which is one of the most important method for managing knowledge. This work analyzed three well known knowledge engineering methods, i.e. MOKA, SPEDE and CommonKADS, and compares the capability to be implemented in the cluster context. Then, we selected one method and proposed the adapted methodology. At the end of this paper, we validated and demonstrated the proposed methodology with some primary result by using case study of handicraft cluster in Thailand.Knowledge Engineering; Industry Cluster; CommonKADS; Knowledge Management System

The integration of new technologies : the stakes of knowledge

In order to remain competitive in an increasingly competitive international context, French companies are forced to follow one or more of various possible routes: relocating some of the activities, optimizing the design and / or production process, or innovate technologically. When they choose to develop new technologies, it is advisable to seek outside expertise in different areas. Thus they must exchange and create knowledge in partnership with other companies. But in order to control and integrate this future technology, we support that the acquisition and the capitalization of the technical training, during the process of innovation, are of primary importance. This article demonstrates that the construction of this knowledge base can be achieved only by formalizing close and rigorous collaboration. To do this, we propose a model of the collaborative process, meant for the leaders of innovative projects to support design.Cifr

Framework for Product Lifecycle Management integration in Small and Medium Enterprises networks

In order to improve the performance of extended enterprises, Small and Medium Enterprises (SMEs) must be integrated into the extended networks. This integration must be carried out on several levels which are mastered by the Product Lifecycle Management (PLM). But, PLM is underdeveloped in SMEs mainly because of the difficulties in implementing information systems. This paper aims to propose a modeling framework to facilitate the implementation of PLM systems in SMEs. Our approach proposes a generic model for the creation of processes and data models. These models are explained, based on the scope and framework of the modeling, in order to highlight the improvements provided

GAMETH A Process Modeling Approach to Identify and Locate Crucial Knowledge.

In a knowledge management initiative, one of the main issues is to identify and locate which knowledge to capitalize on. To deal with this issue, a General Analysis Methodology so called GAMETH® has been developed. In this article, we describe the postulates, the guiding principles, and the main phases, which constitute the basis of GAMETH® Framework. Notably, we emphasize the process modeling approach that is inherent to the second phase of the methodology. This process modeling approach supports the effective capability to locate and identify “crucial knowledge”. Furthermore, we present lessons learned from two case studies.Process modeling; Knowledge Management (KM); GAMETH; Identifying and Locating Company’s Crucial Knowledge; Crucial knowledge;