Knowledge Management for a Cooperative Design System

Knowledge Management for a Cooperative Design System

Author: Serge Tichkiewitch, Burno Radulescu, George Dragoi, and Kusol Pimapunsri

Abstract: Every five years, the French Ministry of Industry launches a study about the key technologies for the next five years. Knowledge capitalization was one of the mentioned technologies in 2000. This paper starts with the description of some problems forecasted at that time and the actual situation since. In this context, a definition for knowledge management is presented, and some related concepts are proposed.
Finally, it is shown how the expert system technology associated with a cooperative design modeler allows the implementation of the knowledge management concepts.

Knowledge may be universal, vehicular or vernacular. All people normally share universal knowledge. This is for example the case with geometrical knowledge. A specific actor who is only concerned with his or her own job only uses vernacular knowledge. It does not need to be shared. Vehicular knowledge is the type of knowledge which can be exchanged between two or more actors, allowing them for instance to perform collaborative design based on a common understanding. Therefore, the latter type of knowledge is very important for establishing a dialog between two partners.


Managing in a Complex Environment

There is also a paradox to be surmounted: the safeguarding of know-how in time, while avoiding the risk of obsolescence of any part of the data.

8.3 Knowledge Management

In order to define and to give characteristics of knowledge management (KM), let us have a look ta the proposition of Y. Malhotra:

“Knowledge management caters to the critical issues of organizational adaptation, survival and competence in face of increasing discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technology and the creative and innovative capacity of human beings.”

This is a strategic view of KM that considers the synergy between technological and behavioral issues as necessary for survival in “turbulent environments”. The need for synergy of technological and human capabilities is based on the distinction between the “old world of business” and the “new world of business.”

Within this view, Malhotra defines the old world of business as characterized by predictable environments in which focus is on prediction and optimization based efficiencies. This is the world of competence based on  “information” as the strategic asset, and the emphasis is on controlling the behavior of organizational agents towards fulfillment of per-specified organizational goals and objectives. Information and control systems are used in this world for achieving the alignment of the organizational actors with predefined “best practices.” the assumption is that such best practice retain their effectiveness over time.

In contrast, high levels of uncertainty and inability to predict the future characterize the new world of business. Use of the information and control systems and compliance with the predefined goals, objectives and best practices may not necessarily achieve long-term organizational competence. This is the world of “re-everything”, which challenges the assumptions underlying the “accepted way of doing things”. This world needs the capability to understand the problem afresh given the changing environmental conditions. The focus is not on finding the right answers but also on finding the right questions. This world is differentiated from the “old world” by its emphasis on “doing the right thing” rather than “doing things right”.

KM is a framework within which the organization views all its processes as knowledge processes. According to this view, all business processes involved creation, dissemination, renewal and application of knowledge toward organizational sustenance and survival.

This concept embodies a transition from the recently popular concept of “information value chain” to a “knowledge value chain”.  What is the difference? The information value chain, considers technological systems as key components guiding the organization’s business processes, while treating humans as relatively passive processors that implement “best practices” archived in information databases. In contrast, the knowledge value chain treats human systems as key component that engage in continuous assessment of information archived in the technological system. In this view, the human actors do not implement best practices without active inquiry. Human actors engage in an active process of sense making to continuously assess the effectiveness of best practices.  The underlying premise is that the best practices of yesterday may not be taken for granted as best practices of today or tomorrow. Hence double loop learning, unlearning and relearning processes need to be designed into the organizational business processes.

Artificial Intelligence was a new technique which permitted the computer not only to solve equations but also to reason as an intelligent actor in order to solve problems or to give diagnoses. Prolog, Frames, Production Rules, and Case-based Reasoning are the new language used for the description of Expert Systems.


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