The Knowledge Age is upon us. The short-lived Information Age was an essential step in the evolution of business processes – and their supporting culture – to the Knowledge Age. A growing body of literature is emerging to record how firms recognize this on-going transition, and prepare their processes to leverage knowledge, now both, resident in their human and system resources. The theory of the knowledge-creating firm explains the differences among firms not as a result of market failure, but as a result of the firm’s vision of future and strategy. These emphasize the fact that decision- making is a knowledge-intensive activity.

We have seen that the decision maker is very much concerned with handling knowledge. This is exactly where decision support systems can help. They automate various knowledge management tasks. As a field of study, knowledge management, it is concerned with the representation and processing of knowledge. It is related to investigations in the field known as cognitive science. Decision support systems are fundamentally concerned with improving the effectiveness and efficiency of knowledge management activities, which occur in the course of decision-making.

In this context, the paper examines a new vision of the decision support systems (DSS) a knowledge-based approach. Unlike traditional views of DSS which consider decision making as an activity culminating in the choice of a course of action, the knowledge-based approach treats of the decision process as a knowledge-creating process, means that he is viewed as a conversion process between tacit and explicit knowledge. A decision is treated as a new piece of knowledge committing us to a course of action, obtained by the transformations of the existing knowledge-stock divided into tacit part and a codified part.

The general framework of the decision process involves the environment observations and their interpretation, the knowledge assimilation who will modify the knowledge-stock, a decision opportunity identification, the use, if is necessary, of the additional knowledge from the extern source, the flows of problem solving set up, the decisions and intermediary results stock as a new piece of knowledge and the decision presentation. The six important types of knowledge that decision makers need to manage may be classified in two categories, principals and secondary. The principal knowledge category contains the descriptive, the procedural and reasoning knowledge and the secondary category contains linguistics, assimilations, presentations knowledge.

The paper has introduced the generic DSS framework which allows that a decision support system can be studied in terms of four interrelated elements: a language system, a presentation system, a knowledge system, and a problem processing system. The first three of these are systems of representation: the set of all requests a user can make, the set of all responses the DSS can present, and the knowledge representations presently stored in the DSS. The problem processor is a dynamic system that can accept any request in the LS and react with a corresponding response from the PS.

More generally this paper provides the authors own assessment that the knowledge-based approach reveals better the objectives and the functionalities of the DSS in concordance with the new economical and technological issue in which the knowledge became the new computing paradigm.