Challenges in Knowledge Management for Structuring Systems (original) (raw)

Abstract

Structuring Systems can be defined as systems, designed, installed, used and maintained within their operational environment for an extremely long duration, answering to the fundamental needs of the society. Examples of such systems are water systems, transport or electricity network. Due to their nature, these systems are characterized to be highly complex, according to the general system theory. In this context, the classical Knowledge Management (KM) approaches are not suited, due to the nature and origins of some data, information and knowledge, the complexity of partner networks that are interacting around the systems, the evolution history of such systems, etc. In this article, we present the main challenges for KM in Structuring Systems, especially in terms of knowledge traceability and heritage from one hand, and knowledge preservation at another one.

Figures (5)

Fig. 1. The problem of knowledge capitalization in company (Grundstein, 2012) Knowledge Management (KM) is defined as the management of activities anc processes aiming at increasing the creation and usage of knowledge inside an organi- sation. The valorisation of knowledge satisfies an economic issue at stake for bot society (UNESCO, 2005) as well as companies. As a consequence, a large literature exists on different domains. Nevertheless, most of KM works have been developec for companies specialised in design, production and control of industrial products o1 complex systems. Structuring Systems (Zolghadri et al., 2013), like water systems o1 transport networks, are systems that currently require a new vision of knowledge management suited to their characteristics. The article ic canctricted ac fallau, After a Anief ctate_afuart in Sectian 9 an mair

Fig. 1. The problem of knowledge capitalization in company (Grundstein, 2012) Knowledge Management (KM) is defined as the management of activities anc processes aiming at increasing the creation and usage of knowledge inside an organi- sation. The valorisation of knowledge satisfies an economic issue at stake for bot society (UNESCO, 2005) as well as companies. As a consequence, a large literature exists on different domains. Nevertheless, most of KM works have been developec for companies specialised in design, production and control of industrial products o1 complex systems. Structuring Systems (Zolghadri et al., 2013), like water systems o1 transport networks, are systems that currently require a new vision of knowledge management suited to their characteristics. The article ic canctricted ac fallau, After a Anief ctate_afuart in Sectian 9 an mair

Fig. 3. Iterative process for Structuring Systems: incipience-germination-coalescence

Fig. 3. Iterative process for Structuring Systems: incipience-germination-coalescence

Fig. 4. Influences among stakeholders and a Structuring System

Fig. 4. Influences among stakeholders and a Structuring System

KM approaches have been partially deployed in several Structuring Systems, spe- cifically at each system. The major contributions are related to knowledge-based en- gineering, with the development of knowledge-based systems to support decision processes associated to these structuring systems, potentially by simulating human expertise during the problem solving process, either for the water domain (Wukovits et al., 2003), (Chau, 2007), (Garrido-Baserba et al., 2012), or the urban planning one (Rubenstein-Montano, 2000). In particular, (Davis, 2000) details a specification that identifies the types of data, knowledge, and internal and external factors likely to be of relevance in a full decision-support tool (Fig. 5), proving the diversity of partners implied in the decision process and the complexity of knowledge management re- quired to support such process.

KM approaches have been partially deployed in several Structuring Systems, spe- cifically at each system. The major contributions are related to knowledge-based en- gineering, with the development of knowledge-based systems to support decision processes associated to these structuring systems, potentially by simulating human expertise during the problem solving process, either for the water domain (Wukovits et al., 2003), (Chau, 2007), (Garrido-Baserba et al., 2012), or the urban planning one (Rubenstein-Montano, 2000). In particular, (Davis, 2000) details a specification that identifies the types of data, knowledge, and internal and external factors likely to be of relevance in a full decision-support tool (Fig. 5), proving the diversity of partners implied in the decision process and the complexity of knowledge management re- quired to support such process.

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