**1. Introduction**

#### **1.1. Introduction in the paradigm of intelligent decision making process**

In the aftermath of the recent global crisis, the modern firm should proactively respond to the disruptive changes in the dynamics of markets, new technologies and the new architec‐ ture of the competition. Shen, Norrie (1999) proposed the following set of capabilities for the next generation of production systems: the integration of the firm with all its management systems and their partners to better respond to the global competition and to the move‐ ments in the markets; distributed structure based on knowledge; heterogeneous environ‐ ments (software and hardware heterogeneous distributed in the production and operational environment); interoperability opened and capable to integrate new systems in an dynamic way; efficient cooperation with suppliers, partners and clients, integration human-machine; agility (the ability to adapt to a new environment in the case of rapid changing); scalability (additional resources could be easily incorporate) in every point location at every level with‐ out affection to the inter-organizational interdependencies; a good tolerance to different types of errors.

In real word it is very difficult to change, to adapt and to innovate in the context of a central‐ ized managed process. It is necessary a new paradigm of intelligent decision making, more generalized, more flexible, more adaptable to change. The classical decomposition in subsys‐ tems- elements is not effective and the distributed method, which defines the components and the interactions between components in order to analyze the effects of dynamic interac‐ tion, is better in this emerging context.

© 2012 Prelipcean and Boscoianu; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Prelipcean and Boscoianu; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

It is necessary to adopt new methods, new technics and new instruments for decision sup‐ port capable to:

methodologies play an important part within the quality of the chosen solution. The accu‐ rate and equilibrated admixture between those three technologies (computerized models,

Emerging Applications of the New Paradigm of Intelligent Decision Making Process: Hybrid Decision Support Systems

for Virtual Enterprise (DSS-VE) http://dx.doi.org/10.5772/3371 93

The typical constraints of a DSS are: a facile user's control adaptability to certain situations / user's characteristics, as well as the level of use during the process of decisions issuing. The controllability refers to the possibility of using the system at any moment and respectively, the possibility of changing the course of running in accordance to the own wish. DSS should be flexible enough to adapt to the deciding representative; by providing trust, the DSS leads towards a synergic evolution of use, in conditions of customization. Agility represents the capability to change and adapt quickly to changing circumstances. Using DSS is intended to

Building a DSS starts with the participants (issuers of instruments, building analysts and the end-users), as well as the context elements (the current situation and the changes estimated in the context of the ITC progress) that interact with the organization. In this process, the specialists and end-users participate (analysts, designers or issuers of DSS instruments), working together within a team assigned by DSS. The DSS team will know details about the own products, as well as the competition alternative products; as regards the success of con‐ ception, building and implementation of DSS, the following conditions should be met: a bet‐ ter knowledge of the application, access to the knowledge sources, the identification of challenges of decision making process in connection with the end-users particularities, re‐ straining the information instruments and accurate methods for designing an efficient DSS. The DSS team should provide as faster as possible an employable version and easy to adapt

DSS is formed of four essential subsystems: the language subsystem, LS, which emphasizes the set of expression forms, by which the user can transmit; the subsystem of presentation, PS, that signifies a set of forms or means by which messages are transferred (from out to DSS) towards the user or third parties (executants of decisions, data sources within organi‐ zation); the subsystem of the knowledge elements, KES, which includes elements of knowl‐ edge purchased or created internally; the subsystem of problem solving – PSS, signifying the set of software modules by which the KES knowledge elements are processed, as result of rendering the input messages. The amplitude and characteristics of these four subsystems and the adopted solutions of information transposing can make a difference between the ap‐

KES includes the knowledge, whichever the user hasn't any ability or time necessary to accumulate it. KES has the mission of simulating the general knowledge volume, specif‐ ic to the application and decisional situation that an ideal decisional assistant should

databases and friendly interfaces) signify the technological centre of the DSS.

support all phases of the decisional process.

on the technological changes.

**2.2. The DSS components**

plication systems.

have.

*2.2.1. The Knowledge Elements Subsystem – KES*

